671 results about "Knowledge retrieval" patented technology

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 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 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 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 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 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.

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 discloses an optimization design method of a large-model-driven mechanical part, which comprises the following steps of: performing knowledge retrieval according to a design task and a constraint condition, and generating text description of a structure design scheme by fusing the retrieved knowledge through a large-scale language model (LLM); the LLM performs task decomposition and modeling code mapping on the structure design scheme described by the text, and generated modeling codes are directly executed by modeling software to generate a three-dimensional model; the LLM generates a reasoning track and calls a finite element analysis tool set to perform performance analysis on the generated three-dimensional model; and according to optimization requirements and design constraints, the LLM optimizes the three-dimensional model to generate an optimal three-dimensional model. The invention discloses an optimization design system of a large model driven mechanical part. The optimization design system comprises a design scheme generation module, a parametric modeling module, a design scheme verification module and a structure parameter optimization module. The method and the system provided by the invention can save code quantity, reduce manual intervention and improve design efficiency.

The invention belongs to the technical field of data processing, and particularly relates to a potential safety hazard real-time identification method and system based on a multi-modal large model, the system comprises the multi-modal large model, a field knowledge base and a multi-modal inference engine, the multi-modal large model is responsible for visual feature extraction and scene semantic understanding of an input field image, and the field knowledge base is responsible for field knowledge base analysis; processing a natural language query provided by a user; the domain knowledge base stores construction safety related laws and regulations, guidelines and historical cases, and factual basis is provided for the system through a structured storage and efficient retrieval mechanism; the multi-modal reasoning engine coordinates the whole process of visual understanding, task decomposition, knowledge retrieval and report generation, and is a core control module for realizing multi-modal reasoning and decision making. And refined understanding of entities and hidden dangers in a construction scene is realized. According to the system and the method thereof, the image content and the text specification can be dynamically fused, and missing detection or misjudgment caused by modal splitting in a traditional method is avoided.

The invention discloses a large-model knowledge retrieval method based on knowledge graph enhancement, which comprises the following steps: S1, collecting multi-source data, constructing an original knowledge triple set, and generating a knowledge graph; s2, inputting natural language query data, executing semantic analysis and intention recognition, and forming a query semantic vector and a structured query template; s3, performing combined convolution operation on the target entity node and the relation vector through an improved combined graph convolution network CompGCN to generate a candidate knowledge list and distribute confidence; s4, calculating the similarity between the query vector and the knowledge vector by utilizing a semantic embedding mechanism and DistilBERT acceleration to obtain a first retrieval result set and a credibility score; s5, fusing the two types of retrieval results to obtain a knowledge result set; and S6, generating an answer text according to the fused knowledge result set, and outputting a reasoning path, entity reference and a relation link. According to the method, the accuracy, the response speed and the knowledge reasoning transparency of complex query processing are improved.

According to the operator code generation method and system based on large model driving and a multi-agent cooperation mechanism, automatic high-performance code generation is achieved through cooperative work of four kinds of agents including strategy recognition, operator generation, compilation testing and function testing. A strategy identification agent accesses a high performance optimization knowledge base (HPOK) by using retrieval enhancement generation (RAG), analyzes operator characteristics by means of a hybrid expert model (MoE), and generates an optimization strategy protocol oriented to a specific optimization target. And the operator generation agent calls a large language model (LLM) to generate an initial code, and iteratively corrects the code according to feedback iteration of compiling and function testing. And the compilation test and function test agents execute code compilation verification and function correctness verification respectively to form a closed-loop error correction mechanism. According to the scheme, through a modularized cooperation framework and dynamic knowledge retrieval, the operator code generation efficiency and quality oriented to diversified hardware platforms such as a CPU and a GPU are remarkably improved.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The invention discloses a traditional Chinese medicine knowledge retrieval enhancement generation method and device, which can be applied to scenes such as intelligent health consultation service, primary medical auxiliary diagnosis, traditional Chinese medicine education training, traditional Chinese medicine knowledge popularization, remote medical support, traditional Chinese medicine scientific research assistance, health management platforms and the like. According to the traditional Chinese medicine knowledge retrieval enhancement generation method and device, after the original input of the user is optimized to obtain the optimized query text, a double-path retrieval strategy is adopted, information is retrieved from the knowledge graph and the clinical medical record vector database at the same time, the retrieved information is organized into a structured context, and the search efficiency is improved. As input of large model reasoning, the structured context effectively integrates traditional Chinese medicine theoretical knowledge and practical experience, comprehensive reference information is provided for the large model, the knowledge limitation and illusion problems existing when the LLM processes traditional Chinese medicine problems can be effectively solved, and the LLM can provide professional, accurate and interpretable traditional Chinese medicine diagnosis services.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention discloses a medical knowledge question-answering system based on an RAG (Retrieval-Augmented Generation) architecture, relates to the field of artificial intelligence and natural language processing, and specifically comprises an input representation module, a knowledge retrieval module, a context construction module and a generation reasoning module. The input representation module is used for encoding user query and medical knowledge entries into high-dimensional dense vectors and comprises a semantic encoder; the knowledge retrieval module comprises a sparse retrieval unit, a dense retrieval unit and a fusion sequencing unit; the context construction module comprises a feature splicing unit and a code fusion unit; and the generation reasoning module calls a large language model based on the fused context to generate medical question and answer content in a natural language form. According to the system, by introducing the structured medical knowledge base and a mixed retrieval mechanism, the accuracy and specialty of questions and answers are effectively improved, the language model illusion phenomenon is reduced, the knowledge updating capacity is enhanced, and the system is suitable for application scenes such as clinical consultation and intelligent medical questions and answers.

The invention belongs to the technical field of RAG questioning and answering, and particularly relates to an efficient and accurate RAG questioning and answering method and device based on a knowledge graph and a storage medium. The method comprises the following steps: reading contents in a text file, dividing the contents to obtain text blocks, and obtaining an initial triple by using a large language model; performing post-processing on the initial triad to obtain an optimized triad, and constructing a knowledge graph; building an index key value pair based on the optimized triad and the text block to which the triad belongs, and storing the index key value pair in a vector database; performing mixed knowledge retrieval based on the index key value pair to obtain mixed knowledge beneficial to question answering: unstructured knowledge from the text and structured knowledge from the knowledge graph; and taking the mixed knowledge as the context of question answering, and reasoning by using a large-scale language model. According to the method, the response time is remarkably shortened, the unstructured knowledge and the structured knowledge are effectively integrated, and the accuracy of low-level retrieval and high-level retrieval is remarkably improved.

The invention provides an intelligent paper marking system based on a large language model. The intelligent paper marking system comprises an examinee test paper character recognition module used for carrying out image processing and content recognition on scanned or shot student answer sheets or answer sheets; the subject knowledge base is used for performing systematic arrangement and representation modeling on multi-subject teaching contents; the subject knowledge retrieval module is used for performing semantic analysis and matching on test paper questions and examinee answering contents to obtain subject knowledge related to the test questions and a scoring basis; a scoring template generator module; a large language model scoring module; and the comment correction module is used for optimizing and adjusting the preliminary comments generated by the large language model and outputting final comments with more pertinence and teaching guidance significance. The technical scheme can be widely applied to automatic evaluation scenes of subjective questions in the education field.

The invention discloses a retrieval enhancement generation method for multi-source heterogeneous data fusion, and relates to the technical field of cross-modal retrieval generation, and the method comprises the following steps: collecting structured data and unstructured data, respectively carrying out standardization processing and semantic cleaning, and outputting a multi-source heterogeneous data set; constructing double-view knowledge block representation by using a multi-source heterogeneous data set, and respectively generating a retrieval view and a generation view; carrying out weight adjustment and fusion on the retrieval view and the generation view according to the current query content, and outputting a fusion semantic vector; performing approximate vector matching retrieval on the knowledge fragments by using the fused semantic vector, performing semantic reordering on a retrieval result according to semantic correlation, and outputting an ordered knowledge fragment set; according to the method, the context adaptability and semantic expression capability of the generated input are enhanced, so that the accuracy of knowledge retrieval and the quality and pertinence of text generation are remarkably improved.

The invention relates to a simulation digital human real-time intelligent voice interaction system and a simulation digital human real-time intelligent voice interaction method based on vision and a large model, and aims to solve the problems of inaccurate target speaker recognition, high response delay and the like in digital human voice interaction in a complex scene. The system circles an effective recognition range through a camera, triggers audio collection in combination with face detection, locks a target speaker and reduces noise by using lip movement recognition and sound image fusion technologies, converts the target speaker into a text through voice wake-up, generates an answer by means of a large language model (LLM) and knowledge retrieval enhancement (RAG) technologies, generates low-delay voice through a voice synthesis technology accelerated by the vLLM, and performs voice recognition on the target speaker. And driving the preloaded digital human image to synthesize a video stream and pushing the video stream to a front end for rendering in real time. Accurate pickup, low-delay interaction and rapid digital human image switching in a complex environment are realized, the accuracy and real-time performance of intelligent voice question answering are improved, and the method is suitable for government affair halls, exhibition halls and other scenes.

The embodiment of the invention provides a method for reducing model output illusion based on knowledge retrieval. The method comprises the following steps: constructing a knowledge base for storing knowledge entries; receiving a question text input by a user, converting the question text into vector representation, performing matching retrieval on the vector representation in the knowledge base, and retrieving and outputting candidate knowledge entries; obtaining an output answer text of the question text generated by the large model, comparing the candidate knowledge entries with the output answer text, and judging whether the candidate knowledge entries violate the output answer text or not; and performing illusion detection on the output answer text, correcting the output answer text according to an illusion detection result, and outputting an answer corresponding to the question input by the user. According to the method, real-time or near-real-time verification and intervention can be carried out on model output, and illusion is inhibited from the source or the early stage.

The invention discloses a knowledge base retrieval method fused with a natural language large model, and relates to the technical field of artificial intelligence. Comprising the following steps that 1, a knowledge representation structure is constructed, and the knowledge representation structure comprises a knowledge base hypergraph based on knowledge fragments, entity identifiers and theme tags and a dense vector index recording dense vectors of the knowledge fragments; 2, analyzing an original natural language query input by a user, and performing matching in the knowledge base hypergraph based on the original natural language query to obtain an initial node set; 3, on the knowledge base hypergraph, determining a candidate knowledge fragment set and a corresponding multi-view local semantic abstract; and 4, calling the natural language large model, and generating a retrieval answer based on the target knowledge fragment set. According to the method, the accuracy, coverage and interpretability of knowledge retrieval can be remarkably improved.

The invention provides a vertical field data construction method based on a large model, which belongs to the technical field of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text, and segmenting the intermediate format text into a plurality of text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model according to pre-designed cue words to generate a plurality of candidate questions according to the content of the text blocks, and performing preliminary screening and fine screening on each candidate question to obtain a question set; pre-defining a mode of a knowledge graph according to field characteristics of the vertical field, processing all text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval on each final question in the question set based on the text block of the question source, performing global knowledge retrieval based on the domain knowledge graph, and generating a final answer and a final thinking chain. The method is suitable for different vertical fields, the data quality can be effectively improved, and the problem generation accuracy is guaranteed.

The invention relates to a knowledge retrieval enhancement method and system based on a biochemical knowledge graph. The method comprises the following steps: collecting biological and medical literatures and constructing the biochemical knowledge graph based on the collected literatures; obtaining a query text input by a user, performing biological and medical literature matching through the query text and the biochemical knowledge graph, and screening feature context information; and inputting the query text and the feature context information into a large language model, and performing answer generation based on knowledge graph enhancement. According to the method and the device, the unstructured knowledge in the original biomedical literature is converted into the structured graph which is used as a semantic enhancement basis of the large language model before the answer is generated, so that the correlation of the retrieval content and the accuracy of the generation result are improved.

The invention discloses a medical intelligent question and answer method and system based on multi-modal data, a medium and a product, and relates to the field of medical intelligent question and answer. Performing knowledge retrieval in a preset vector database according to the query content to obtain a knowledge retrieval result; performing query intention recognition on the query content to obtain a query intention recognition result; generating a query answer according to the knowledge retrieval result and the query intention recognition result; wherein the query answer comprises one or more of text, image annotation, voice synthesis and video interpretation. The system supports various output forms such as text, image annotation, voice synthesis and video interpretation, explains and analyzes the illness state or medical diagnosis and treatment results of the patient from multiple dimensions, provides rich and visual diagnosis and treatment reports and interactive interfaces for doctors and patients, and effectively improves the user experience and decision-making efficiency.

The invention discloses a new energy equipment health management intelligent question-answering system based on a large model and a knowledge graph. The system integrates eight modules including a data acquisition and preprocessing module, a multi-source heterogeneous data deep fusion module, a knowledge graph construction module and the like. The data acquisition and preprocessing module is used for collecting and processing multi-source data and providing support for subsequent modules; a multi-source heterogeneous data deep fusion module eliminates data differences; the knowledge graph construction module stores domain knowledge; the large model training module endows the system with semantic understanding and reasoning capabilities. Through cooperation of the modules, the system can realize efficient integration and analysis of multi-source heterogeneous data, accurately diagnose and predict equipment faults, and quickly and accurately perform knowledge retrieval and question and answer. Meanwhile, based on fault prediction and health assessment results, visual interaction and scientific decision support are provided, and full-life-cycle management of the equipment is facilitated. The system improves the health management level of new energy equipment, reduces the operation and maintenance cost, and promotes the intelligent development of the new energy industry.

The invention discloses a document review method based on multi-agent collaboration and retrieval enhancement generation. The method comprises the following steps: firstly, analyzing a document review rule input by a user into a rule semantic intermediate representation through a natural language processing technology, and calling a retrieval enhancement generation (RAG) module to expand related knowledge to form a structured rule library; secondly, multi-modal analysis and chapter segmentation are carried out on a to-be-examined document, and elements such as texts, tables, pictures and formulas are expressed in a unified mode; tasks such as rule analysis, document segmentation, knowledge retrieval, matching comparison and report generation are completed through a multi-agent cooperation mechanism; finally, when ambiguity exists in rule and document matching, an RAG module is introduced to retrieve supplementary evidences from an external knowledge base, explanatory comparison is conducted in combination with the generative model, and the accuracy and authority of judgment are improved.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention relates to the technical field of information retrieval, in particular to a financial knowledge retrieval method and system based on a vector database. The method comprises the following steps of obtaining financial text data, and performing association processing according to the financial text data to obtain financial index association data; performing numerical association reasoning on the financial index association data to obtain numerical association data; semantic dependency reasoning is carried out on the financial index associated data to obtain semantic dependency data; performing multi-head attention calculation according to the numerical association data and the semantic dependency data to obtain financial text feature data; and performing vector index generation according to the financial text feature data to obtain financial vector index data so as to perform financial knowledge base construction auxiliary operation. According to the method, the deep semantic dependency and index logic relationship between financial entities can be accurately captured, the integration and feature expression ability of cross-sentence information is enhanced, and the accuracy of financial knowledge retrieval is improved.

The invention discloses a method for constructing a large mineralization potential evaluation model based on LLM, and relates to the field of models.The method for constructing the large mineralization potential evaluation model based on LLM comprises the following steps that 1, a mineral geological map file is converted into a structured JSON format data set; step 2, constructing cue words, wherein the cue words at least comprise geological information extraction cue words, function task cue words and entity, relation and attribute extraction cue words; compared with the prior art, the method has the beneficial effects that the large mineralization potential evaluation model constructed by the method realizes text and map multi-mode mineral product geological map information identification and analysis, automatic mineralization geological feature analysis, mineralization feature information extraction and mineralization potential evaluation; the problems that in the prior art, man-made subjective influence exists, professional knowledge dependence is high, the geological information recognition accuracy rate in the recognition process is low, and information is lost during knowledge retrieval are solved.