885 results about "Relationship extraction" patented technology

The invention relates to a knowledge graph construction method and system based on a large language model, and the method and system achieve the automatic construction and dynamic maintenance of a knowledge graph through multi-modal data fusion, reinforcement learning and comparative learning joint optimization, teacher-student model knowledge migration, time sequence dynamic analysis and an incremental updating mechanism. Constructing a reinforcement learning framework, and taking accuracy and integrity as reward indexes to train a large model to extract an entity relationship; a large-scale knowledge graph is used as a teacher model, and conflict resolution and semantic alignment of newly added knowledge and an existing graph are realized through a graph attention network; a verification rule is dynamically generated based on historical data and domain knowledge, and relation periodicity and mutation points are detected in combination with Fourier transform and a CUSUM algorithm; and finally, generating a traceable knowledge graph through incremental updating and version control. And the multi-modal data processing precision, the entity relationship extraction dynamic adaptability and the knowledge graph maintenance efficiency are improved.

The invention relates to the technical field of data processing, in particular to a method and system for processing data based on a large language model.The method comprises the following steps that based on inter-sentence punctuation positioning and part-of-speech tagging, sentence blocks are divided to generate functional partitions, themes and relational words are extracted to judge semantic chain starting points, a trigger index sequence is constructed, and logic jump points and breakpoint positions are recognized; and mapping the label structure to the language model output analysis deviation, and generating a label mapping combination list. According to the method, semantic turning nodes can be captured by analyzing the semantic direction change trend, the relation between the semantic turning nodes and verb and noun combinations is judged, the position of a trigger point of an actual information transfer effect is extracted, and the break point area of a semantic path is recognized through the positioning of key word starting and stopping blocks and the logical judgment of noun group cross combination; the path integrity has a clear fracture identifier, a traceable semantic mapping structure path is established in a language model, and the accuracy, coherence and hierarchy clearness of semantic reconstruction are enhanced.

The invention discloses a contract risk assessment and compliance check method, system and device based on a large language model and a medium, belongs to the cross technical field of artificial intelligence and natural language processing, and aims to solve the technical problem of how to automatically, quickly and accurately identify potential risks and compliance in a contract. According to the technical scheme, the method comprises the steps of data preprocessing, wherein non-plain text format files of various sources are subjected to data preprocessing and converted into plain text format files; constructing a knowledge performing semantic understanding and relation extraction on industrial standards, internal regulations of enterprises and a plain text format file obtained after preprocessing by utilizing a big language, and constructing a structured and queriable knowledge base; training a large language model: optimizing parameters of the large language model in combination with pre-training and supervised training; and risk assessment and compliance check: based on the trained large language model and the compliance rule base, performing identification and analysis of potential risk points and compliance check on the input contract text.

The invention relates to the technical field of large models, and discloses a knowledge graph and retrieval enhancement-based government affair question-answering method and system, and the method comprises the steps: carrying out the information extraction of each government affair document, and obtaining text information, multi-modal information and metadata; fusing the information to obtain document information; performing entity relationship extraction based on the document information to obtain entity, relationship and text key value pairs; constructing a government affair knowledge graph based on the entity, the relationship and the text key value pair corresponding to each government affair document; based on the type of the user query statement and the government affair knowledge graph, adopting a retrieval enhancement technology to generate a query answer; and optimizing the query answer to generate a target answer. According to the method, through the multi-modal information and the knowledge graph, the document content and the query intention are comprehensively understood, the question and answer accuracy is improved, the knowledge graph and a retrieval enhancement technology are fused, the illusion phenomenon of a large language model is reduced, the answer accuracy is improved, the safety, accuracy and readability of the answer are ensured through optimization, and the user experience is improved.

The invention discloses a safety control method based on a knowledge graph, and belongs to the field of safety control, and the method comprises the following steps: crawling various types of data according to keywords through a crawler program to construct a fireproof database; carrying out semantic analysis on data in the fireproof database by using a natural language processing technology, and carrying out data processing on the fireproof database after semantic analysis through word vector modeling and entity relationship extraction; constructing an internal large-scale language model by using the processed fireproof database, and performing hierarchical clustering on the internal large-scale language model by adopting a Leiden technology; establishing a multi-dimensional mapping model based on historic building spatial features, cultural relic features and disaster-inducing factors in a hierarchical clustering result, and constructing a multi-modal dynamic association fireproof knowledge graph by using a knowledge graph technology based on the multi-dimensional mapping model; and dynamically selecting a search mode based on a user query keyword, and generating a security control result according to the query keyword based on the search mode.

The invention provides a project management method, system and device and a medium, and belongs to the field of project management. The method comprises the following steps: collecting project related data, and sorting and cleaning the project related data to obtain a structured data set; adopting an entity identification and relation extraction algorithm to carry out entity labeling and relation extraction processing on the structured data set so as to construct a knowledge graph related to the project; the method comprises the following steps: performing task decomposition by adopting a graph traversal algorithm on the basis of a knowledge graph, generating a task list and a priority sequence thereof, and monitoring task progress and resource utilization rate in real time on the basis of a dynamic optimization algorithm so as to dynamically adjust task priority and a resource configuration strategy. The task module is automatically decomposed based on the knowledge graph, the task decomposition efficiency and accuracy are improved, intelligent resource allocation and task priority adjustment are achieved in combination with dynamic planning and an optimization algorithm, the team cooperation efficiency is improved, and the flexibility and adaptability of a project are improved.

The invention relates to a method for constructing a subject textbook knowledge graph by using a large language model, and the method is realized through six steps: firstly, introducing a self-prompt framework, generating relation synonyms, synthesizing samples and sentence variants through three rounds of dialogues, and providing rich semantic guidance for subsequent relation extraction; secondly, guiding a large language model to accurately extract core knowledge point entities from teaching materials, exercises and PPT texts by means of a professional field instruction template; then, respectively extracting an attribute triple and a relation triple of the knowledge points by applying a multi-round dialogue mode and combining with a synthetic sample prompt; then, inputting the extracted triad into a verification module, and ensuring the accuracy through iterative verification; and finally, generating an entity embedding vector by utilizing an MPNet model subjected to subject knowledge fine adjustment, calculating entity similarity through a dynamic weighted pooling mechanism, judging entity pairs with high similarity, performing knowledge fusion if the entity pairs represent the same concept, and otherwise, reasoning a potential missing relationship and complementing the knowledge graph. According to the method, the knowledge graph of the course of the specific subject can be automatically constructed from the unstructured text efficiently and accurately, and powerful support is provided for teaching and learning of related subjects.

The invention relates to the technical field of artificial intelligence, and discloses a rapid labeling method based on an artificial intelligence large model, which comprises the following steps of: 1, extracting an entity set and a relationship set of a specific field through a document set of a target field, identifying nouns, terms and keywords related to the field in a document by using an entity identification technology to form an entity set, and marking the entity set and the relationship set; the method comprises the following steps of: establishing an entity set, analyzing logic and semantic relevance among entities by utilizing a relation extraction technology, generating a relation set, combining the entity set with the relation set to construct a knowledge triad, and the knowledge triad comprises an association relation among the entities. By introducing the domain knowledge graph, the entity and relation information extracted from the target domain document is structured into the knowledge triad, and the knowledge triad is matched with the to-be-annotated data to optimize the annotated semantic information, so that the domain adaptability and semantic consistency of the annotation result are realized, and higher accuracy and consistency effects of the annotation data in the professional field are obtained.

The invention discloses a cue word-based multi-stage serial knowledge graph construction method, which comprises the following steps of: pre-configuration: initializing various parameters and resources required by a system through large language model configuration, cue word set configuration and example set configuration; knowledge extraction: realizing accurate knowledge extraction through dynamic text partitioning and a multi-stage serial extraction chain, extracting entities, attributes and relationships from a text, and generating a preliminary knowledge triple; and graph construction: organizing the knowledge triples into a structured knowledge graph through database configuration, knowledge storage and construction and interface and query, and realizing query and visual display. According to the method, the sizes of text blocks are dynamically calculated according to a dynamic text blocking strategy, the situation of too many or too few blocks is reduced, the knowledge extraction efficiency and accuracy are improved, a knowledge extraction task is decomposed into an entity recognition stage, an attribute completion stage and a relation extraction stage through a multi-stage serial extraction chain, and the accuracy and consistency of extraction results are ensured.

The invention discloses a medical text-oriented intelligent disease diagnosis causal atlas construction method and system. The method comprises the steps of medical text related data acquisition, causal entity identification, causal relationship extraction and entity unification. The model and the method for automatically constructing the causal knowledge graph are realized, the functions of dynamic uncertain causal knowledge graph editing, clinical intelligent auxiliary diagnosis, automatic extraction of electronic cases and the like are provided, and multi-source fusion of medical text data and expert experience knowledge is realized, so that the accuracy and the reliability of the system are improved. And a disease causal knowledge graph with a certain clinical application value can be established.

The invention relates to the technical field of data processing, and discloses a financial report generation method and system based on artificial intelligence. The method comprises the steps of preprocessing multi-source financial data to obtain target data; performing semantic understanding through a capital extraction algorithm to obtain a knowledge base; intelligent matching template filling is carried out to generate a preliminary report; verifying and analyzing the logic relationship by applying a causal relationship; optimizing the content based on the verification result; and performing multi-level auditing and updating to obtain a final report. According to the method, intelligent processing, deep semantic understanding and logic verification of financial data are realized by introducing a knowledge-based capital extraction algorithm and a causal relationship extraction model, so that a high-quality financial report which is strict in logic and professional in content is generated.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

The invention discloses a document layout analysis method based on a hybrid method, and belongs to the technical field of digital image processing. Named body recognition and relation extraction are carried out on each text node. The invention provides a novel channel shuffling dynamic range convolution algorithm to ensure that global channel features can learn features of similar pixels rather than adjacent pixels, and an adapter is used to learn text features with less parameter quantity. And finally, taking the predicted text block category and relationship as a result. Finally, a challenging document data set is used as a verification index (FUNSD), and the result shows that the performance of the method can be obviously improved.

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 relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention discloses a gravity dam safety risk management and control method and system based on a knowledge graph and a large model. The method comprises the following steps: collecting accident case data of a water conservancy project; performing entity and relationship extraction on all case data in the case library by using the fine-tuned model with domain knowledge to obtain a case data knowledge graph; the obtained case data knowledge graph is converted into natural language description, a text is used for fine tuning of a large language model, and finally a large language model with domain knowledge is obtained; performing information extraction on the engineering general situation information by using an engineering model, forming a natural language description problem by using an extraction result, and inputting the natural language description problem into a large language model with domain knowledge; the big language model answers the questions, and the expert judgment module provides expert opinions according to replies of the big language model. The intelligent system integrating artificial intelligence, reasoning and decision making is formed by utilizing the powerful language and reasoning ability of a large model.

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The invention belongs to the field of language processing, particularly relates to a multi-causal relationship extraction method and system for intelligent evaluation, and aims to solve the problems of performance bottleneck, remarkable wooden barrel effect and insufficient adaptability and integration caused by dependence on multiple independent models. The method comprises the steps that a causal analysis prompt template used for generating step-by-step reasoning is constructed to serve as a thinking chain prompt word, a fine adjustment corpus is generated in combination with an original corpus and input into a large language model for training, a low-rank adaptation technology is adopted in the training process to conduct fine adjustment training on the large language model, and a causal relationship extraction model is obtained; inputting corpora of a to-be-extracted causal relationship into the causal relationship extraction model, and outputting a causal relationship extraction result; and generating a comprehensive evaluation result and a decision suggestion. According to the method, the efficiency and the quality of the multi-causal relationship extraction task are improved, the stability and the adaptability of the system are enhanced, meanwhile, the labor cost is reduced, and scientific and reliable evaluation results and decision suggestions are provided.

The invention belongs to the field of multi-modal data processing, and particularly relates to a multi-modal fusion analysis method and system for test data, and the method achieves the intelligent analysis of real-time multi-dimensional demand data through constructing a bidirectional multi-modal constraint generation model and fusing multi-modal reasoning generation, forward logic constraint and reverse causal constraint sub-models. Wherein the forward logic constraint sub-model constructs a forward constraint hierarchical reasoning association node network through entity-relation extraction and a graph algorithm based on a test standard criterion and a historical constraint sequence, and generates a forward logic constraint set in combination with a depth index algorithm; the reverse causal constraint sub-model constructs a combined conflict value through modal reasoning accuracy, performs conflict tracing and adjustment in combination with a cross-modal conflict threshold, and generates a reverse causal constraint set; the two constraint sets are fused through a multi-modal reasoning generation sub-model, a target generation demand text meeting the confidence coefficient requirement is finally generated, and intelligent analysis of clinical test data is achieved.

The invention relates to the technical field of data processing, and discloses a multi-modal relation extraction method based on large model generation and knowledge graph integration, which comprises the following steps of: firstly, generating description for an image by using a large language model, constructing an image graph, analyzing a text, constructing a text graph, linking a knowledge graph based on a tagged entity, and extracting a multi-modal relation between the tagged entity and the knowledge graph; and constructing a knowledge graph sub-graph. And then, taking the labeled entity as an anchor point, splicing the three through similarity calculation to form a unified combined graph, and extracting and fusing multi-modal features by utilizing a graph information bottleneck technology and a GCN optimization graph structure, so that the accuracy and the processing efficiency of relation extraction are improved. And through multi-modal data fusion, the accuracy and the processing speed of relation extraction are remarkably improved. A graph structure is optimized by using a graph information bottleneck technology and a graph convolutional network, and redundant information is eliminated, so that semantic features in multi-modal data are effectively extracted and fused.

The embodiment of the invention provides a hydropower station knowledge graph construction method and system, and belongs to the technical field of knowledge graphs. The method comprises the following steps: collecting multi-source heterogeneous data related to a hydropower station, and executing format standardization processing and semantic normalization processing to obtain basic data; performing data classification on the basic data to obtain a plurality of data sets; matching a corresponding extraction model based on each data set to respectively execute entity extraction, relation extraction and attribute extraction to obtain an extraction result set; screening out repeated extraction results to obtain an atlas construction data set; and executing knowledge graph construction of the ternary array based on the graph construction data set, and obtaining and outputting a hydropower station knowledge graph. According to the scheme, the problem of heterogeneous data integration is solved, the practicability and expandability of the atlas in application scenes such as intelligent question answering and operation monitoring are remarkably improved, and reliable data support and technical guarantee are provided for intelligent application in the field of hydropower stations.

The invention provides a knowledge graph enhanced RAG intelligent question-answering method based on a complex process, which comprises the following steps: uploading a heterogeneous data file, creating an embedded vector and a vector index, and obtaining a private database; performing entity relationship extraction on the private database to obtain a knowledge graph, and combining the knowledge graph with the large model to obtain a query model; using the query model to decompose user questions, identify question types, question intentions and entity relationships, then judging whether related entities exist in the knowledge graph, if yes, outputting answers by using the query model, and if not, calling the large language model to perform question and answer reasoning; and constructing a historical question and answer abstract, and optimizing answers. According to the method, the accuracy and the credibility of answering are improved, meanwhile, the visual representation of the knowledge graph also enables the user to more intuitively understand how the system obtains a specific answer, and the interactivity and the credibility between the user and the system are enhanced.

The invention discloses an electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The invention relates to the technical field of vocabulary data, in particular to a construction method of a bilingual vocabulary data knowledge graph. The method comprises the following steps: acquiring a vocabulary database; performing data vocabulary preprocessing on the vocabulary database to obtain unstructured vocabulary data; performing nested relationship extraction according to the unstructured vocabulary data to obtain semi-structured vocabulary data; performing label analysis on the semi-structured vocabulary data to obtain initial vocabulary data; performing context coding on the initial vocabulary data by using a preset BERT model to obtain a vocabulary embedding vector; therefore, by introducing a multi-step collaborative mechanism of context coding, cross-language alignment and graph semantic mapping, the technical problems of incomplete structure, semantic disjunction, poor language transformation ability and the like of a traditional vocabulary construction method are solved, and the automatic construction ability and expression depth of a multi-language semantic knowledge system are improved.

The invention discloses a transformer fault diagnosis method and system based on a fusion knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured text and a semi-structured text; constructing a joint entity relationship extraction model and a question and answer module, wherein the joint entity relationship extraction model comprises a coding layer, a head entity recognition layer and a tail entity and relationship joint recognition layer; the question and answer module comprises a LangChain model and a large language model, and the output of the LangChain is used as the input of the large language model; obtaining a fault problem text, inputting the fault problem text into the LangChain model, outputting professional knowledge, inputting the professional knowledge and the fault problem text into the large language model, and outputting a professional fault diagnosis answer; and interactively combining the professional fault diagnosis answer with the high-quality knowledge triple text, and outputting a final fault diagnosis answer. According to the method, the problems of weak information relevance, low decision generation efficiency and the like in the operation and maintenance process of the transformer can be solved in a targeted manner, and the health management level of the power transformer is greatly improved.

The invention belongs to the technical field of new-generation information, and particularly relates to a power plant metal supervision entity relationship extraction method based on dual coding, which comprises the following steps: multi-modal data preprocessing: collecting and cleaning data, and carrying out data labeling; constructing a dual coding joint learning model: designing a network layer architecture, and training the dual coding joint learning model; constructing and querying a dynamic knowledge graph: extracting a model to generate a triple, and performing time sequence evolution analysis, causal reasoning interface and dynamic updating; and incremental knowledge updating and dynamic model optimization: an incremental learning and feedback module forms a bidirectional closed loop between the knowledge graph and the relationship extraction model, and continuous evolution of the system is ensured through dynamic knowledge updating and adaptive model optimization. According to the method, through bidirectional feature modeling of dual paths, collaborative representation optimization between entities and relationships is realized while context information is captured, so that the requirements of complicated data types and diversified semantic associations in an engineering scene are met.

The invention relates to the technical field of natural language processing, in particular to a hypergraph neural network-based multi-modal entity relationship extraction method, which comprises the following steps of: acquiring a text-image pair and inputting the text-image pair into a preset hypergraph construction model, acquiring node characteristics and a multi-modal hypergraph structure of the text-image pair, the hypergraph structure comprises a group of hyperedge sets, each hyperedge in the hyperedge sets can be connected with a plurality of nodes at the same time, and the number of the nodes connected with the hyperedges is not limited; the node features and the multi-modal hypergraph structure are input into a preset hypergraph neural network model, the multi-modal entity relation in the text-image pair is output, and the hypergraph neural network model is used for analyzing hypergraph node information propagation from the semantic and contextual relation and giving corresponding weights to semantic information between modals in combination with an attention mechanism. According to the method, the multi-modal information can be effectively fused, and the entity category and the entity relationship of the multi-modal information can be identified.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

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 discloses a major language model-based professional research and judgment report generation method and system. The method comprises the following steps of inputting a research and judgment report generation task instruction into a major language model; inputting the task instruction and the data text into an embedding layer of the large language model to obtain text embedding; combining the obtained prompt with text embedding, inputting the combined prompt and text embedding into a large language model, and performing relation extraction on personnel and cases in a research and judgment task; outputting a relation extraction result of the model to meet a target specified in the task instruction; a current task instruction is disassembled, and analysis and query are performed through an F query function according to instructions oriented to various scenes in an instruction library; and according to the data set processed in the step S400, finally realizing the output of the research and judgment report in a standard format. The method has the advantages that automatic and efficient professional research and judgment report generation is realized, and the labor cost is saved; the report content is combined with domain knowledge, and high specialty and logicality are achieved; and the applicability of the multi-scene improvement report is flexibly supported.