828 results about "Knowledge retrieval" patented technology

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The invention discloses a method for enhancing dynamic knowledge retrieval based on a large language model, belongs to the field of knowledge retrieval, and aims to solve the problems of knowledge solidification, insufficient timeliness and illusion of a traditional LLM (Logistics Language Model). A multi-granularity knowledge base is dynamically constructed, and a rule and semantic partitioning technology is combined, so that a text is converted into a normalized vector, and a hybrid index is established; a two-channel retrieval triggering mechanism is adopted, keyword matching scores and BERT semantic probability analysis are fused, and retrieval requirements are intelligently judged; vectorization retrieval is realized through a BGE-M3 model, and candidate results are reordered in combination with a cross encoder to improve the precision. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updating and version control. According to the method, the answer timeliness and accuracy are remarkably improved, the context coherence of multiple rounds of dialogues is optimized, the method can be widely applied to the fields of intelligent customer service, professional questions and answers and the like, the LLM illusion risk is effectively reduced, and the knowledge traceability is enhanced.

The invention discloses an agricultural pest question-answering method based on knowledge graph adaptive hybrid retrieval enhancement. The method comprises the following steps: 1) data acquisition and arrangement; 2) construction of a knowledge graph and a vector library; 3) constructing a question and answer pre-classification system; 4) building and training a question type automatic classification model: automatically identifying and classifying the questions input by the user by using a small classification model to form a self-adaptive retrieval classifier; 5) self-adaptive knowledge retrieval based on question types: according to the queried question types, self-adaptively and dynamically selecting different retrieval methods of the knowledge graph and the vector library; 6) selecting a corresponding thinking chain reasoning method according to retrieval results of the knowledge graph and the vector library, and constructing a Prompt cue word with high pertinence; according to the method, the knowledge graph and vector library retrieval are fused, so that the answering precision and accuracy are improved, and by integrating a thinking chain (CoT) reasoning framework, the decision-making process of disease and insect pest problem analysis and answering has a clear logic chain.

The invention discloses a wind power fault diagnosis operation and maintenance method based on multi-source data and knowledge retrieval enhancement. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, knowledge base construction: performing knowledge extraction, and constructing a corresponding wind turbine generator operation and maintenance knowledge graph database; s3, multi-granularity problem decomposition and retrieval optimization are carried out, and a sub-problem list is generated by deeply grading and splitting problems in combination with doubt degree and dependency analysis; s4, knowledge retrieval enhancement generation: combining knowledge graph sub-graph retrieval and SCADA real-time data dynamic weight adjustment to generate a final answer; and S5, result generation and feedback optimization are carried out, and the validity of the diagnosis result is verified through a real-time verification mechanism. According to the method, the accuracy, the reliability and the real-time performance of fault diagnosis can be remarkably improved in a limited fault data environment.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

The invention discloses a user query complexity adaptive knowledge graph retrieval enhancement generation method, which comprises the following steps of: firstly, quantifying user query complexity according to a complexity measurement index contained in user query, and analyzing and complementing hidden logic of the user query; then, establishing a corresponding relationship between the user query complexity and the knowledge graph retrieval range and between the user query complexity and the knowledge graph retrieval strategy, and adaptively adjusting the knowledge graph retrieval range in combination with the user query complexity; then, adaptively screening out an optimal group of knowledge reasoning paths in the determined knowledge graph retrieval range based on reinforcement learning; and finally, answering the user query based on the selected knowledge reasoning path by utilizing a large language model. According to the method, the static limitation of the existing knowledge retrieval strategy is broken, the knowledge retrieval range and the retrieval strategy can be flexibly determined according to the query complexity of the user, and the retrieval precision and the retrieval efficiency are both considered.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses a multi-modal large model interpretable diagnosis method and system for a wind turbine generator, and relates to the technical field of wind turbine generator fault diagnosis, comprising the step of combining multi-modal data (vibration, time sequence, image and text) and topological information to realize fault diagnosis through cross-modal contrast learning and topological modeling. The method comprises the steps of multi-modal feature extraction, standardization and alignment, and feature fusion through topology embedding optimization and a cross-modal attention mechanism. In the fault diagnosis process, dynamic correction and path reliability evaluation are introduced by using a regular Agent and a topology consistent Agent, weighted fusion is performed on each modal feature and a topology structure, and finally an accurate fault type and a component positioning result are output. Through combination of knowledge retrieval and a multi-Agent decision model, the adaptability and precision of fault diagnosis are improved, especially in a complex environment, the fault mode of the wind turbine generator can be effectively identified, and the system reliability is improved.

The invention relates to the technical field of multi-mode interpretable decision scheme design, in particular to a multi-mode interpretable decision method and system and electronic equipment. According to the method, the intelligent decision-making level in the industrial manufacturing environment can be effectively improved through dynamic knowledge path optimization, multi-modal data fusion, symbol reasoning and self-adaptive feedback. A reinforcement learning driven dynamic knowledge retrieval technology is introduced, so that efficient organization and retrieval of multi-source heterogeneous data are realized; reasoning is enhanced based on the knowledge graph, the relevance between cross-modal data is improved, and the reasoning logic between the data is clearer and more reliable; and in combination with an ontological reasoning mechanism, the interpretability and transparency of the system are enhanced, so that the system conforms to causal derivation rules in industrial production. According to the technical scheme, dynamic knowledge path optimization, ontology reasoning and multi-modal data fusion are combined, and an efficient, accurate and explainable industrial manufacturing decision-making scheme is provided.

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention relates to the technical field of intelligent agent interpretable retrieval path generation, in particular to an intelligent agent interpretable retrieval path generation system and a verification method. The method comprises the following steps: acquiring agent associated knowledge retrieval data through associated knowledge retrieval, and constructing a node multi-path initial structure based on the data; determining a path node causal reasoning chain and node semantic interpretation information, and generating structured interpretable path data; detecting explainable path deviation by using the structured path data, implementing path structure simulation correction based on a deviation identification result to obtain path structure simulation correction data, and combining the correction data; carrying out interpretable retrieval path strategy optimization to generate optimized path retrieval strategy data, and carrying out intelligent agent interpretable retrieval path updating processing on the structured path data to obtain an updated intelligent agent interpretable retrieval path; retrieval path generation can be explained through the intelligent agent, so that path retrieval is more accurate.

The invention provides a text sentiment analysis method based on dynamic semantic segmentation and feature perception, and belongs to the field of natural language processing. Inputting the semantic vector sequence into a knowledge retrieval and dynamic graph construction model for multi-path context enhancement through the knowledge retrieval and dynamic graph construction model to obtain semantic features, semantic knowledge and graph structure information; the semantic vector sequence is subjected to multi-path context enhancement, the complex relation between different parts in the text can be comprehensively considered, and more comprehensive and deep semantic features and knowledge can be mined. Unified representation of semantic features is combined with heterogeneous graph features, an antagonism training strategy is adopted to train a knowledge retrieval and dynamic graph construction model, an improved ATOSS + module is introduced to carry out hierarchical attention fusion, and multi-granularity semantic enhancement features are obtained; therefore, emotion clues and semantic association hidden in the text can be captured, and the accuracy and integrity of semantic understanding of the text are improved.

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 provides an intelligent marketing copywriting generation and effect evaluation method driven by a large language model, relates to the technical field of language models, and comprises the steps of constructing a hierarchical cross-modal knowledge graph and establishing a knowledge retrieval index. Semantic analysis is performed based on a bidirectional attention mechanism, related knowledge is retrieved by using query vectors, and an initial marketing copywriting is generated. Performing knowledge consistency verification to generate a knowledge-enhanced marketing copywriting, and generating a candidate copywriting set by adopting a diversity sampling strategy; and performing knowledge coverage, creativity and expected effect scoring on the candidate copywriting by using a multi-target evaluation network, screening an optimal copywriting through a Pareto optimization algorithm, and taking a generated path of the optimal copywriting as a positive sample to update a knowledge graph and decoder network parameters. Through knowledge graph enhancement and multi-target evaluation optimization, high-quality and high-matching-degree marketing copywriting can be generated, and the marketing effect is improved.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

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 discloses a multi-path thinking chain reasoning generation method based on fine-grained knowledge retrieval, which comprises the following steps of: performing deep semantic analysis on a user question by adopting a large language model, and extracting a relationship between entities contained in a question text to generate a triple set; the triple is dynamically divided into different confidence sets by setting high and low confidence thresholds; constructing a thinking chain framework by using a predefined path to obtain an initial reasoning path set; optimizing the initial reasoning path, and sampling an optimized reasoning path set by using Top-k scoring to obtain a reasoning path set with high semantic relevancy; dividing the reasoning path set into a plurality of reasoning path groups with complementary internal path information to obtain a plurality of candidate answers; and adopting a majority voting mechanism to select a plurality of candidate answers, and finally obtaining consistent answers. According to the method, the reasoning accuracy and interpretability can be remarkably improved, and the reliability and robustness of a final conclusion are greatly improved.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

The invention discloses a large language model retrieval enhancement generation method oriented to cyberspace security emergency intelligent analysis. The retrieval and generation capability of a large language model in knowledge questions and answers in professional fields is enhanced through a knowledge graph. The method comprises a knowledge base construction module and a knowledge enhancement response generation module. The method mainly comprises the following steps: collecting multi-source data and preprocessing to construct a basic data set; defining an entity type and a relationship type, and performing entity recognition and relationship extraction based on a large language model to construct a knowledge graph; designing a graph-based hierarchical retrieval strategy to perform knowledge retrieval; and generating professional answers through a knowledge enhancement generation framework. According to the method, the accuracy of knowledge questions and answers in the professional field is remarkably improved, the accuracy of the professional field in experimental evaluation reaches 86.7%, the coverage rate of retrieval knowledge reaches 81.2%, and the interpretation quality score is 4.35 score. According to the method, the limitation of a traditional large language model in professional field application is overcome, and good expandability and adaptability are achieved.

The invention discloses a multi-agent-based programmable logic controller structured text generation method. The method comprises the following steps: constructing a programmable logic controller programming task solution knowledge base; five large model agents are constructed, including a knowledge retrieval agent, a task planning agent, a structured text generation agent, a verification agent and an error correction agent. According to the method, through mutual cooperation of multiple agents and flexible calling of various tools and models, the knowledge deficiency of a large language model in the programming aspect of the programmable logic controller is made up by utilizing a retrieval enhancement generation technology, the illusion problem of the large language model in the process of generating the structured text of the programmable logic controller is effectively reduced, and the reliability of the structured text of the programmable logic controller is improved. And the success rate and the expansibility of automatically generating the structured text of the programmable logic controller are improved.

The invention relates to a federal knowledge retrieval and large language model enhancement system and method. The system comprises a server and a hardware accelerator. The server preprocesses an input query and extracts a head word of the query; the server retrieves the head word in the caching process through a cache module of the server, and if the cache of the cache module is not hit, the server sends a retrieval instruction to the hardware accelerator so as to retrieve in a local cache of the hardware accelerator; if the local cache is still not hit, the hardware accelerator generates parallel subtasks associated with the head word in a mode of segmenting a local knowledge graph, so that deep search is carried out, and noise is added into knowledge items retrieved by the parallel subtasks to protect sensitive information; and the hardware accelerator integrates the knowledge items added with the noise, performs reasoning through a large language model and generates an enhanced answer. According to the method, a two-stage dynamic cache system is constructed, the cross-device communication frequency can be effectively reduced, the query request is responded preferentially through the local high-frequency cache, and the overall network load of the system is reduced.

The invention discloses a focused mode context retrieval enhancement method and system in a knowledge retrieval system, and the method comprises the steps: carrying out the format conversion and preprocessing of a knowledge document, segmenting a text through a focused overlapping partitioning algorithm, and extracting structural features through a natural language processing technology to generate a directory; the text blocks are converted into semantic vectors through a BERT-Context model, and the semantic vectors are stored in a vector library; during retrieval, text blocks are quickly positioned according to question similarity, a long context is formed through splicing, similarity ranking is calculated, cue words are generated by high-correlation text blocks and questions together, and answers are obtained by inputting the cue words into a generation model. According to the method, the problems of semantic deficiency, incomplete context information and undetailed user retrieval context in the existing knowledge retrieval system can be effectively solved, the accuracy and integrity of knowledge retrieval are improved, and particularly, the method is excellent in performance when processing problems in complex fields.

The invention discloses a multi-modal data automatic processing and information extraction method, and belongs to the technical field of data processing. Comprising the steps of establishing an original knowledge base according to original training data in a business scene; preprocessing the original multi-modal data in the original knowledge base to obtain a preprocessed knowledge base; inputting the preprocessing knowledge base into a knowledge retrieval unit; a text to be queried is converted into a query vector, the distance between the query vector and a knowledge fusion vector representation vector in the knowledge retrieval unit is calculated, and a retrieval result is obtained through an approximate nearest neighbor retrieval algorithm; and performing multi-modal data fusion on a retrieval result through a cross-modal Transform model, and combining fused semantics with user query to generate an answer. According to the method, data of multiple modes such as texts, images, audios and videos can be processed, multi-mode fusion and reasoning are carried out through the visual language model, accurate extraction and structured storage of information are achieved, and the efficiency and quality of data analysis and mining are improved.

The invention relates to the technical field of artificial intelligence, in particular to a construction method of an agent management platform supporting knowledge retrieval, generation and optimization. The method comprises the following steps: acquiring multi-modal data, and performing semantic fusion based on the multi-modal data to obtain semantic fusion data; performing knowledge retrieval according to the semantic fusion data to obtain knowledge retrieval data; establishing a three-level reflection system according to the knowledge retrieval data, including a result reflection layer, a process reflection layer and a strategy reflection layer, so as to obtain result reflection layer data, process reflection layer data and a strategy reflection layer, and performing hierarchical system fusion to obtain three-level reflection system data; and cue word optimization is carried out based on the three-level reflection system data to obtain cue word optimization data. The knowledge retrieval accuracy and the system operation efficiency of the agent management platform are improved based on the artificial intelligence technology.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention relates to a maintenance operation guiding method, system and equipment based on intelligent interaction, and the method comprises the steps: analyzing the operation intention of a user as at least one type of equipment dismounting, fault removal and knowledge retrieval, and generating a multi-mode guiding strategy; based on a multi-modal guiding strategy, coordinating and controlling a man-machine collaborative operation link; after one job is completed, multi-dimensional verification data is constructed, and when any one-dimensional verification data exceeds a corresponding knowledge graph threshold value, intervention work is activated; and generating a guiding efficiency file which is synchronously mapped with the operation link and comprises a multi-mode guiding strategy triggering log, user response behavior sensing data and a strategy adjustment utility evaluation graph, and binding the guiding efficiency file and the unique identification code of the maintenance object through an encrypted data link and storing the guiding efficiency file and the unique identification code in a preset database. According to the method, the talent cultivation period is remarkably shortened, meanwhile, the maintenance quality stability and the knowledge inheritance efficiency are synchronously improved, and reliable technical support is provided for equipment full-life-cycle management.

The invention discloses a running state monitoring method of an electric energy metering box, a storage medium and equipment, and relates to the technical field of intelligent power grid monitoring. The method comprises the following steps: performing multi-scale frequency domain decomposition on a multi-dimensional electrical signal to generate subsequences with different frequency characteristics; a dynamic window mechanism is adopted to adaptively segment signals and optimize a filtering strategy; extracting spatio-temporal joint features through multi-scale time sequence convolution and a physical constraint graph network; inputting the features into an attention prediction module integrated with a historical knowledge base for knowledge enhancement reasoning; and generating prediction uncertainty estimation based on Monte Carlo Dropout, calculating a composite abnormal index in combination with the distribution deviation degree, and triggering graded early warning. According to the method, accurate detection of transient abnormality and long-term trend is realized, the prediction accuracy is improved through spatio-temporal feature fusion and dynamic knowledge retrieval, the false alarm rate is reduced by using an adaptive threshold strategy, and a multi-dimensional and high-reliability state monitoring solution is provided for the electric energy metering box.

The invention relates to the technical field of model training, and discloses a knowledge manual question-answering system and question-answering method based on a large language model, and the system comprises a knowledge slicing module which is used for dividing a knowledge manual into a plurality of independent knowledge units according to a preset rule; the text vectorization and recall module is used for converting the knowledge units into text vectors through an embedded model and storing the text vectors, and is also used for retrieving the knowledge unit with the highest similarity in the knowledge base based on the question input by the user; the large language model initialization module is used for configuring a large language model special for the field and setting a cue word template; the workflow arrangement module is used for deploying knowledge retrieval nodes and big language model nodes through a workflow platform, inputting retrieved knowledge units as contexts into a big language model, generating final answers and feeding back the final answers to the user; the problems of low query efficiency and poor accuracy caused by insufficient training data in an existing query method based on a large language model are solved.

The invention discloses a retrieval generation method and system based on fusion of a large model and a knowledge graph, and relates to knowledge graph, knowledge query and large model technologies, and the method comprises the following steps: obtaining original document data, and preprocessing the original document data; extracting a triple structure from the preprocessed document data by using a large model to construct a vectorized knowledge base and a knowledge graph; performing GraphRAG retrieval and multi-hop path reasoning on the received user query through a pre-constructed vectorization knowledge base and a knowledge graph; according to the structured knowledge of GraphRAG retrieval and multi-hop path reasoning and the context of the document, a result meeting the query requirement is generated through the large model. According to the embodiment of the invention, the knowledge graph is automatically constructed and updated through the powerful semantic understanding capability of the large model, and the GraphRAG technology is combined, so that accurate retrieval and multi-step reasoning of complex multi-hop and high-relevance problems are realized, and the depth and accuracy of knowledge retrieval are realized.