552 results about "Knowledge extraction" patented technology

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

The invention discloses an expert question and answer technical method, system and equipment based on local geological knowledge graph semantic reasoning, and the method comprises the following steps: collecting and integrating multi-source heterogeneous geological data, including texts, images and remote sensing, and constructing a unified geological knowledge base covering multi-modal data; on the basis of a pre-training language model, semantic analysis is performed on a geological text, and'entity-relationship-entity 'structured knowledge is automatically generated by utilizing a triple extraction module. The method has field breadth and multidisciplinary fusion, is different from a knowledge graph technology focusing on single fields of mineralogy, geophysics and the like in the prior art, innovatively constructs a large-scale comprehensive knowledge graph system covering the whole geological disciplinary, supports cross-field knowledge extraction and reasoning, and is high in practicability. And complex interdisciplinary geological problems can be handled.

The invention provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

The invention relates to a construction method and device of a network security knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining multi-source data of a network security application scene, and carrying out distributed processing to obtain a security data set; performing structured storage management and security knowledge extraction on the security data set to obtain an initial knowledge graph; performing zero-day attack prediction construction based on the initial knowledge graph to obtain potential attack data; performing path fusion association on the potential attack data and the initial knowledge graph to obtain attack chain fusion information; and obtaining real-time event data of the network security application scene, and carrying out dynamic iteration updating on the initial knowledge graph by using the real-time event data and the attack chain fusion information to obtain the network security knowledge graph. According to the invention, the timeliness and practicability of the network security knowledge graph can be ensured.

The invention discloses a bidding document intelligent analysis and structuring method and system based on a multi-modal knowledge graph. The method comprises the steps of S1, obtaining bidding document content and technical data related to constructional engineering; s2, performing knowledge extraction on the text in the bidding document content, and constructing a multi-modal knowledge graph; s3, performing semantic optimization on the multi-modal knowledge graph to obtain an updated multi-modal knowledge graph; s4, converting the multi-modal knowledge graph into corresponding feature vectors, and fusing the feature vectors; s5, inputting the multi-modal data into the constructed multi-modal deep learning model for processing, and outputting element information of bidding document structuring; s6, performing hierarchical division on bidding document contents according to the element information, and identifying contents of different hierarchies; and S7, constructing a rule base according to the obtained technical data, and performing matching verification on the standard knowledge in the knowledge graph through the rule base.

The invention relates to the technical field of artificial intelligence and material engineering, and discloses a formula optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the knowledge extraction of an obtained structured formula data set and unstructured technical literature data in response to a formula optimization target input by a user, and generating a table literature knowledge set; inputting the table literature knowledge set and the formula optimization target into a large language model to obtain a generated text corresponding to the formula optimization target; performing logic rule screening and risk assessment on the generated text through a knowledge fusion layer to obtain text output conforming to a confidence threshold, and generating a new formula scheme according to the text output; and performing multi-objective optimization on the new formula scheme based on a preset experimental cost constraint condition, and outputting an optimized recommended formula and a support evidence chain. By automatically fusing innovative components and process information in literatures, the output recommended formula has scientific basis and interpretability, and the practicability and innovativeness of an automatic formula are improved.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

A knowledge graph construction method based on active learning and incremental learning comprises the following steps: S1, preprocessing data from a plurality of heterogeneous data sources, and extracting entities, relationships and attributes to form an initial knowledge network; s2, vectorizing elements in the initial knowledge network by using a knowledge graph embedding model, and performing entity alignment based on vector similarity to obtain an initial knowledge graph; s3, screening out candidate knowledge triples with high uncertainty and / or high representativeness from the initial knowledge graph by adopting an active learning strategy, and obtaining user labeling information corresponding to the candidate knowledge triples; s4, performing iterative optimization on a knowledge extraction model and / or a knowledge graph embedding model according to the user labeling information; s5, new data are fused into the optimized knowledge graph in an incremental learning mode, and knowledge conflict detection and resolution are carried out in the fusion process; and S6, circularly executing the steps S3 to S5 until the knowledge graph meets a preset quality condition.

The invention discloses a power grid knowledge graph construction method and system based on a large language model, and belongs to the technical field of artificial intelligence and power system crossing. The method comprises the following steps: firstly, carrying out standardized preprocessing and semantic segmentation on a multi-source heterogeneous power grid specification document to generate independent semantic fragments; extracting a power grid entity and a relation triple by using the large language model subjected to field fine tuning; constructing a multi-level knowledge graph taking the equipment as the center and supporting dynamic updating; and finally, performing multi-hop reasoning based on natural language query, and outputting decision support information. The system comprises a preprocessing module, a knowledge extraction module, a graph construction module and an interactive reasoning module. According to the method, the problems of difficulty in semantic analysis, low knowledge extraction efficiency and dynamic updating lagging of the unstructured power grid document are solved, and the intelligent level of power grid dispatching and the fault handling efficiency are remarkably improved.

The invention provides a whole-crop explainable disease and pest diagnosis method and system based on a multi-mode large model. The method comprises the following steps: constructing a knowledge extraction model of a two-way cross attention mechanism based on relation guidance, extracting structured knowledge from authoritative agricultural data, and constructing a pest and disease knowledge enhancement database to dynamically retrieve prior knowledge of a target crop; a hierarchical image processing strategy is adopted, and global, local and target area multi-level feature information is extracted from an input image; inputting and priori knowledge are integrated into a comprehensive diagnosis instruction, and a thinking chain guiding module is introduced to guide a large model to carry out multi-step reasoning according to a reasoning path; and performing unified reasoning by using the multi-modal large model, and outputting a disease and pest diagnosis result and a diagnosis basis thereof. The method does not need manual marking of multi-modal data or retraining, can realize efficient diagnosis of whole crop diseases and insect pests under the condition that the multi-modal data and computing resources are limited, has low cost, strong generalization ability and high interpretability, and is suitable for large-scale agricultural production practice.

The invention discloses a field large model geological survey report generation method based on a knowledge graph, and the method comprises the following steps: S1, building an engineering survey field data set through multi-source heterogeneous data collection and structured preprocessing, and the engineering survey field data set comprises five text dimension tags divided according to engineering survey specifications; s2, constructing an engineering investigation knowledge graph; and S2A, knowledge extraction, wherein a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is adopted. According to the method, a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is improved on knowledge modeling to perform high-precision entity-relation joint extraction, a geological knowledge map with consistent semantics and clear structure is constructed based on RDF, rule reasoning and graph neural network reasoning mechanisms are fused, and the method has the advantages of high-precision entity-relation joint extraction and high-precision entity-relation joint extraction. Deep mining and complementation of explicit and implicit knowledge are realized, and the ability of the prior art in knowledge expression granularity and reasoning breadth is improved.

The invention provides a multi-mode PDF (Portable Document Format) document analysis method, device and equipment and a medium, and the method comprises the following steps: loading a PDF document and carrying out preprocessing, including page splitting and content cleaning, to generate standardized document data; dynamically extracting contents in the standardized document data, wherein the contents comprise paragraphs, pictures and table elements; processing the dynamically extracted pictures, including shielding meaningless pictures based on a preset rule, and analyzing picture contents by using a multi-modal model to generate readable picture information; processing the dynamically extracted table, including optimizing and merging the table structure into a single element format to generate structured table data; combining paragraphs, readable picture information and structured table data, converting the paragraphs, the readable picture information and the structured table data into a complete structured document format, and inserting in key positions to enhance coherence context description; and outputting the complete structured document format as a final analysis result. According to the method, the analysis precision and the knowledge extraction efficiency of the complex PDF document can be remarkably improved.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge extraction method and system based on semantic consistency evaluation and hybrid verifiable reward, and the method comprises the steps: constructing a training data set with evidence labeling; based on the training data set, reinforcement learning training is carried out on a pre-trained large language model, a group strategy optimization GRPO algorithm is adopted, and model output is evaluated by using a mixed reward function; and on the basis of an evaluation result of the mixed reward function, updating parameters of a large language model so as to generate structured knowledge which is correct in format, accurate in content and provided with verifiable evidence. According to the method, in reinforcement learning training, effective decoupling format, content and credibility evaluation is realized, and refined feedback is provided for the model; when the model outputs knowledge, traceable original text evidence is provided for the model, so that the credibility and the interpretability of the model are enhanced, the accuracy of outputting the JSON format by the model is improved, and the accuracy and the integrity of the model in the aspect of content extraction are enhanced.

An ontologically driven method and a system for constructing a fish knowledge graph in a target region, including: combing a fish data source in the target region, constructing a fish knowledge ontology in the target region, and collecting text data and image data; according to the fish knowledge ontology of the target region, performing knowledge extraction on the text data, and measuring information content of knowledge extraction results to obtain text information weights; according to the fish knowledge ontology in the target region, performing the knowledge extraction on the image data, and measuring information content of the knowledge extraction results to obtain image information weights; according to the text information weights and the image information weights, matching the first knowledge extraction results with the fish knowledge ontology in the target region, and constructing a multi-modal knowledge graph. The processing accuracy and efficiency of the unstructured fish information can be improved.

The invention discloses a material synthesis data extraction method and system based on a knowledge enhancement large model, and relates to the related field of artificial intelligence, and the method comprises the steps: retrieving literature data, and constructing a material synthesis knowledge text by executing data denoising and OCR text conversion; performing LoRA fine tuning on the basic large model, introducing a field instruction data set to perform fine tuning learning, and determining an extraction large model; performing semantic partitioning and vectorization on the material synthesis knowledge text, constructing a multi-level retrieval framework, performing retrieval enhancement in combination with a material science knowledge base, and determining an enhanced knowledge text; and constructing a data extraction prompt, combining the enhanced knowledge text with a material to synthesize a knowledge text, inputting the knowledge text into an extraction large model, and executing knowledge extraction processing. The problem that the accuracy of data extraction is insufficient in the prior art is solved, and the effect of improving the accuracy of data extraction is achieved. Meanwhile, manual work can be replaced to complete literature analysis extraction and domain knowledge association, and support is provided for material synthesis process recommendation.

The invention discloses an avionics system evaluation index system construction method based on a large language model agent. The avionics system evaluation index system construction method comprises the steps that a knowledge extraction module performs knowledge extraction on a design document of an avionics system by using the large language model agent and preliminarily constructs a knowledge graph; the reflection verification module automatically checks and corrects entities and relationships in the knowledge graph based on a large language model agent in combination with a learning normal form, and finally outputs an optimized knowledge graph; and the index generation module automatically generates an evaluation index for each entity node in the optimized knowledge graph based on a large language model agent to form an avionics system evaluation index system. According to the method, extraction, induction and system construction of avionics system evaluation indexes are automatically completed, and the method has the advantages that manual decomposition is not needed, the process is traceable, the coverage is wide, and the efficiency is high.

The invention provides a knowledge structured extraction method based on a multi-modal large model, and relates to the technical field of data processing. Comprising the steps of determining to-be-extracted attribute information according to a data extraction requirement, and generating a structured data model according to the to-be-extracted attribute information; according to the structured data model and a preset extraction strategy, performing data extraction on a to-be-processed original multi-modal file to obtain a plurality of extraction results; merging the plurality of extraction results to obtain a merged extraction result; and verifying the combined extraction result to obtain a target extraction result. According to the method, in knowledge extraction through a multi-modal large model, through an extraction strategy of combining multi-round extraction with staged extraction, the data volume of one-time extraction of the model can be reduced, data extraction omission is avoided, meanwhile, staged extraction enables the model to be gradually progressive from coarse to fine and from global understanding to field-level fine extraction, context loss is avoided, and the knowledge extraction efficiency is improved. And the precision of the extraction result is improved.

The invention provides a trauma knowledge graph construction method and system based on a three-level fault-tolerant mechanism. The method comprises the following steps: acquiring a trauma condition core knowledge framework; extracting trauma condition rules based on the trauma condition core knowledge framework, and establishing a trauma condition rule base; dynamically generating a target cue word based on the trauma condition rule base by utilizing a large language model; processing the target text based on the target cue word by using a large language model to generate a candidate file; verifying the candidate file to obtain a verification file; and constructing the trauma knowledge graph based on the verification file. According to the scheme, a mode of combining ontology construction from bottom to top and entity node construction from top to bottom is adopted, various methods such as manual construction, rule extraction and a large language model are fused, the knowledge extraction precision and the map coverage degree are improved, the information processing capacity and the treatment decision-making efficiency in a complex trauma condition scene are remarkably improved, and the method is suitable for popularization and application. And construction of an intelligent medical system is supported.

The invention discloses a geological mineralization causal knowledge extraction method, a storage medium, equipment and a product, and relates to the field of geological mineralization, and the method comprises the steps: constructing a causal hypothesis of a causal role from an unstructured geological text; constructing a causal data set based on causal hypothesis, and training the natural language inference model by using the causal data set; wherein the input vector of the causal data set and the vector of the causal role are fused to form the input vector of the model; a bias matrix of a causal role is introduced to calculate a self-attention weight; obtaining gating weights of the causal role and the global context based on [CLS] embedding, and performing weighted summation on embedding of the causal role and [CLS] embedding by using the gating weights to obtain a final fusion representation; and identifying a high-confidence causal chain from the geological text by using the trained model to obtain a typical causal mode related to the specific mineralization. According to the invention, the chain type identification of the complex mineralization mode can be realized.

The invention discloses a knowledge graph construction method which comprises the following steps: acquiring document data from different sources, extracting long content from the document data, and segmenting the long content into a plurality of semantic text blocks; for each semantic text block, extracting all entities from the semantic text block by using a large language model, and analyzing the relationship between the entities; constructing a global semantic association graph based on all entities and the relationship between the entities, and dividing the global semantic association graph by using a graph clustering algorithm to form a plurality of knowledge communities; and creating corresponding entity nodes for the entities, creating corresponding edges for relationships between the entities, creating corresponding community nodes for the knowledge communities, and establishing belonging relationships between the community nodes and the corresponding entity nodes to form a knowledge graph, and storing the knowledge graph in a graph database system. On the basis, the knowledge extraction precision and the cross-domain generalization ability can be improved, and a hierarchical knowledge system can be formed.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

The invention provides an electric power overhaul decision-making method and device based on knowledge domain mapping map retrieval generation. The method comprises the following steps: performing deep semantic analysis on an electric power overhaul file through a prompt project guide large language model LLM, and constructing an electric power overhaul knowledge domain mapping map; generating communities with hierarchical structures by adopting a community detection algorithm Leiden based on the electric power overhaul knowledge domain mapping map, and creating a community abstract in a report form for each community; and mapping an electric power overhaul query request initiated by a user to the embedding space of the electric power overhaul knowledge domain mapping map through a vectorization technology to realize semantic matching with the community abstract, and outputting an electric power overhaul decision answer result corresponding to the electric power overhaul query request. According to the method, an intelligent question-answering system in the field of electric power overhaul is constructed by fusing knowledge domain mapping map retrieval enhancement generation and LLM, automatic knowledge extraction and map construction of unstructured overhaul texts are realized, and high-reliability decision support is provided for fault diagnosis, overhaul plan optimization and risk early warning of electric power equipment.

The invention discloses a ship knowledge graph construction method based on a large model and a graph neural network, and the method comprises the steps: carrying out the format conversion and protection type partitioning processing of a professional document, and completing the recursive segmentation through a placeholder protection formula, a table and other structures according to the semantic hierarchy, and obtaining a text block suitable for the extraction of an entity and a relation; extracting domain entities by utilizing a large language model containing thinking chain cues, and positioning candidate entities in combination with an AC automaton so as to improve the coverage rate and accuracy of relation triple extraction; performing new entity backfilling and relation standardization on an extraction result, and constructing an initial knowledge graph with a consistent structure; and inputting the initial knowledge graph into a graph neural network model with directional expansion and a multi-scale decoder, and complementing a missing relationship through link prediction, so that node distribution and a relationship structure of the graph are more complete. According to the method, a continuous processing chain from text preprocessing, knowledge extraction to graph completion is formed.

The invention discloses an underground engineering on-demand ventilation knowledge graph retrieval enhancement generation method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps of data cleaning and ontology layer modeling, knowledge extraction based on prompt engineering, knowledge graph data filling and storage, storage of an extraction result in a Neo4j graph database, and retrieval enhancement generation of an inference engine. Developing an intelligent question and answer platform, and providing knowledge graph visualization and interactive question and answer functions; the system comprises a data acquisition and preprocessing layer, an ontology and knowledge extraction layer, a graph storage layer, a semantic index and entity link layer, a sub-graph arrangement and cue word generation layer, a generation and reasoning layer and an application and interaction layer. According to the invention, the acquisition and management efficiency of underground engineering ventilation knowledge is improved, the professionality and accuracy of the question-answering system are enhanced, the interface is friendly, knowledge tracing is supported, and convenient experience is provided for users.

The invention relates to the technical field of knowledge extraction, and discloses a multi-source road traffic accident knowledge extraction method and system, and the method comprises the steps: determining a joint feature vector based on an accident knowledge triple marking matrix, constructing a comprehensive function based on the joint feature vector, and distributing marks for the accident knowledge triple marking matrix through the comprehensive function, an accident knowledge triple optimization mark matrix is obtained, the accident knowledge triple optimization mark matrix is decoded, and an extraction result containing knowledge triads is obtained. The hidden error accumulation caused by semantic ambiguity in the model is solved, and knowledge extraction with higher robustness is realized. Complete and credible structured input is provided for constructing nodes according to types in a subsequent knowledge graph, and high-quality construction of the multi-source road traffic accident knowledge graph from an unstructured text to a semantic-definite and logic-consistent multi-source road traffic accident knowledge graph is truly realized.

The invention relates to the technical field of intelligent teaching, and provides a self-adaptive personalized teaching system based on AI and a knowledge graph, and the system comprises a knowledge graph construction module, a student portrait module, a self-adaptive recommendation module, a teaching interaction module, and a management module. The knowledge graph construction module comprises a data processing unit, a knowledge extraction unit and a graph storage unit; the data processing unit is used for carrying out preprocessing such as word segmentation and stop word removal on the teaching text; and the knowledge extraction unit is used for extracting knowledge point entities and relationships between the entities from the preprocessed text through a natural language processing technology. The knowledge points are subjected to fine-grained modeling through the knowledge graph, the AI algorithm is combined to analyze student learning behaviors and evaluation data, knowledge vulnerabilities and learning characteristics of students can be accurately captured, the error rate is lower than that of a traditional method, the self-adaptive recommendation module generates personalized learning paths based on the reinforcement learning algorithm, and the learning efficiency is improved. And repeated learning of students on invalid knowledge points is avoided.

The invention relates to an intelligent generation method for a subway emergency disposal scheme. The method comprises the following steps: S1, constructing an emergency plan knowledge graph; s2, performing automatic accident analysis and information extraction; and S3, generating an emergency disposal scheme. The invention further relates to a subway emergency disposal scheme intelligent generation system and a computer readable storage medium. The method has the advantages of high efficiency, accuracy, intelligence and the like, intelligent generation of the disposal scheme is realized by performing automatic analysis on accident data and combining a knowledge graph RAG technology, and the scheme generation speed is remarkably improved. The semantic reasoning ability of the knowledge graph and the deep analysis ability of the large model are utilized to ensure the high matching degree of the generation scheme and the actual event. The knowledge extraction and relation recognition technology of a large model is combined, automatic generation and logic relation reasoning of the emergency task unit are achieved, and the intelligent level is improved.