15 results about "Basic knowledge" patented technology

The application discloses a knowledge graph construction method and device of a promotion path, wherein the method comprises the following steps: step S1, combing core concepts related to the promotion path and semantic relations thereof, constructing a basic knowledge framework, and creating an ontology model based on the basic knowledge framework; step S2, setting mapping rules between ontology data and a graph database, and mapping the ontology model to the graph database; step S3, collecting multi-source information, performing knowledge extraction on the multi-source information to obtain knowledge triples and constructing entities, and constructing a knowledge graph by comprehensively combining the ontology model and the entities; and step S4, performing retrieval and correlation analysis based on the knowledge graph, forming promotion path instances, dynamically updating the promotion path instances to the knowledge graph, and obtaining a promotion path knowledge graph. The promotion path knowledge graph is obtained based on dynamic updating of the knowledge graph.

This invention provides a method and system for constructing a professional knowledge graph for the hot rolling process of strip steel, belonging to the field of industrial monitoring and intelligent manufacturing. The method first acquires multi-source heterogeneous data of the hot rolling process of strip steel, and transforms structured and semi-structured data into unstructured data before merging them into an unstructured dataset; constructs an ontology for the professional knowledge graph, including entity types and entity relationships; divides the unstructured data into training and testing sets; constructs a joint extraction model based on the CasRel framework and multi-hop attention mechanism, and performs training and testing; inputs all unstructured data into the model, identifies and extracts a set of structured triples, processes and stores them to obtain the basic knowledge graph; acquires hot rolling fault data and constructs a fault knowledge graph, and fuses them to obtain the professional knowledge graph for the hot rolling process of strip steel. This invention constructs a professional knowledge graph for the hot rolling process of strip steel, improving the systematic capture capability of hot rolling process data.

This invention relates to the field of intelligent model training technology, specifically a method and system for training and inferring a large-scale knowledge-enhanced model in the water industry. The method includes: confirming the large-scale model's operating environment based on water industry model training and inference instructions; classifying and extracting the water industry dataset based on a basic knowledge parsing unit; abstracting features from multimodal operating data and industry experience knowledge sets based on structured modeling rules; updating parameters of a general basic large-scale model based on model training constraint rules; inputting real-time water industry operating data into the water industry knowledge-enhanced large-scale model for joint inference calculation based on a water industry operating condition inference unit; using a target knowledge representation set to screen candidate control strategies for compliance; verifying the execution of the target strategy; and obtaining the optimal operating strategy based on the strategy deviation evaluation value. This invention can improve the security, real-time adaptability, and reliability of large-scale model training and inference in the water industry.

The application provides a software test case generation method and system based on domain knowledge constraints. A domain knowledge base covering domain basic knowledge and industry testing criteria is constructed; the original requirement text is normalized, the semantic understanding ability of the knowledge base and the large language model is combined, the requirement type is identified and the structured decomposition rule is dynamically matched, and a structured requirement model is constructed; the test focus features in the requirement are extracted, and the test item decomposition requirements are formulated; the large language model is driven to expand the scene and systematically disassemble the requirement, and the systematic test items are generated according to the test item decomposition requirements; the test cases are generated in multiple dimensions with the test items as the input, and the traceable relationship among the requirements, the test items and the test cases is established; the integrity check and consistency check are performed, and the test design result is output. The application enables the large language model to perform the test design task in a controlled and professional knowledge environment, and realizes the systematic generation from the natural language requirement to the executable test case.

The application provides a construction method of a knowledge distillation model suitable for power line fitting detection, comprising the following steps: inputting image data of power line fittings into a basic knowledge distillation model to extract multi-scale feature data; performing redundant compression processing and convolution kernel learning on the multi-scale feature data to obtain a light knowledge vector and fitting detection general knowledge; training the basic knowledge distillation model using the knowledge and teacher-student global knowledge prototypes, and performing differential distillation training on classification heads and regression heads based on objects and information effectiveness respectively in the process to obtain a preliminary knowledge distillation model; and combining a comprehensive loss function to perform end-to-end weighted optimization on a student model in the preliminary knowledge distillation model to obtain an optimized final knowledge distillation model. The application can solve the problems of high model complexity and insufficient adaptability to complex power fitting detection scenes in related art knowledge distillation-based detection methods.

The application provides a gastroscope large model based on course learning and reinforcement learning iteration and a training method. Applied to the technical field of data processing, the method comprises the steps of initializing a visual feature extraction submodel and a generative language submodel, and constructing a feature dimension alignment unit between the two; determining whether the image feature dimension and the word embedding dimension are consistent; determining the current training data type; after the model completes basic knowledge learning, performing concept anchoring based on medical term data; using clinical instruction data to perform instruction following training of response logic; obtaining the model after course learning as a base; constructing a reward evaluation system containing multiple dimensions of indexes; inputting difficult case samples into the model to generate a diagnosis result, and calculating the score of the diagnosis result under each evaluation index; and determining a comprehensive reward value based on the score corresponding to each evaluation index and the corresponding weight coefficient.

The application discloses a kind of progressive retrieval enhancement generation method, system, terminal and medium based on reasoning chain.The generation method includes: based on source text data Construction Heterogeneous Knowledge Graph, Including the document corpus in external knowledge source is blocked processing, utilize big model from each document block Entity and relationship are extracted to construct basic knowledge graph, and the bidirectional index mapping between graph element and document block is established;Progressive decomposition is carried out to the retrieval question, and the query is decomposed into multiple interdependent sub-problems by big model, and the sub-problems and their dependency relationship are organized into a graph-structured logical execution graph;Progressive retrieval solving is carried out to the decomposed sub-problem, and the reasoning chain is constructed and the final retrieval answer is generated.The method disclosed in the application bridges the semantic gap, blocks the propagation of errors and provides a multi-hop question and answer solution with explicit reasoning guidance, improving the accuracy of multi-hop problem retrieval.

This invention relates to the field of knowledge graph technology, and more particularly to a method for rapidly constructing knowledge graphs based on multi-source heterogeneous data. The method includes: acquiring real-time uploaded data and generating an anchor node set; acquiring several anchor-based basic knowledge graphs from a highway database based on the anchor node set, and generating the confidence score of each anchor-based basic knowledge graph; generating a central graph vector based on the confidence score deviation rate; constructing an associated knowledge graph; simulating the pre-trained knowledge graph or associated knowledge graph according to event types in the highway database to generate event simulation probabilities, and generating event accuracy coefficients by combining the event probabilities and event simulation probabilities; classifying the pre-trained knowledge graph or associated knowledge graph based on the event accuracy coefficients; and adjusting uploaded tags or updating the highway database based on the classification results. This invention effectively improves the accuracy of rapidly constructing knowledge graphs based on multi-source heterogeneous data.

This application discloses a method, apparatus, equipment, and medium for reconstructing pharmaceutical digital textbooks based on competency graphs, relating to the field of digital data processing technology. The method includes: first, constructing a pharmaceutical competency graph and a pharmaceutical basic knowledge graph based on job requirement data from industry-education integration enterprises and knowledge points from traditional pharmaceutical textbooks; second, performing deep feature fusion and alignment mapping on these two graphs to generate a target digital textbook knowledge graph; third, extracting matching digital media resources from an online course platform using this graph as a guide; fourth, combining these media resources with the knowledge graph for structured layout to generate a draft pharmaceutical digital textbook; and finally, configuring and reconstructing the draft for interactive functions to ultimately obtain the target pharmaceutical digital textbook. This application effectively solves the problems of outdated content, monotonous presentation methods, and inability to dynamically match the actual job competency requirements of enterprises under industry-education integration in traditional pharmaceutical textbooks.

This application discloses a content creation system and method based on multi-agent collaboration. The content creation system includes an interaction subsystem, an agent subsystem, a knowledge subsystem, and a model subsystem. The agent subsystem includes a search and recommendation agent cluster, a creation agent cluster, and a review agent cluster. The knowledge subsystem is used to construct a basic knowledge base including business knowledge. The interaction subsystem is used to receive user-input creation request information. The search and recommendation agent cluster is used to perform intent recognition on the creation request information to obtain task intent information, and to retrieve knowledge context information matching the task intent information from the basic knowledge base. The creation agent cluster is used to generate initial draft text information based on the knowledge context information and task intent information. The review agent cluster is used to perform compliance correction operations on the initial draft text information to obtain optimized content results. This system can improve the quality and efficiency of content creation.

This invention relates to the field of meteorological information service technology, and discloses a method and system for intelligent question answering in event meteorology based on a large language model. It aims to solve the technical problem that existing meteorological services cannot intelligently integrate multi-source heterogeneous information to respond to complex user needs. The method includes: constructing a multi-source knowledge base covering basic meteorological knowledge and event services; creating and encapsulating dedicated data interface tools; receiving user natural language questions, performing intent recognition based on a large language model, dynamically scheduling the interface tools and knowledge base to acquire multi-source data; and finally generating a natural language response that matches the user's intent through data fusion processing. This invention, through a combination of knowledge base construction, dedicated meteorological tools for events, intelligent scheduling, and multi-source fusion, realizes the transformation of event meteorological services from "fragmented" to "one-stop," improving the intelligence level of meteorological services and user experience.

This invention belongs to the field of server fault monitoring technology in electronic information science, and relates to a method and system for root cause mining of server operation faults based on knowledge graphs. The method includes: obtaining the state index and basic knowledge graph of each hardware entity based on sensor data and simulation task logs of the server cluster; obtaining the cross-domain anomaly propagation confidence based on the topological relationship between nodes in the basic knowledge graph, combined with the state index of the hardware entity and the latency of the task entity; obtaining the trigger probability of the hardware entity based on the cross-domain anomaly propagation confidence and the state index of the hardware entity; and determining the server operation fault based on the magnitude of the trigger probability of each hardware entity. It can mine the hardware nodes that cause cluster faults from complex task congestion phenomena through cross-domain anomaly propagation confidence and trigger probability.

The application provides a port intelligent water transportation knowledge graph construction and fusion method and system, and belongs to the technical field of intelligent ports. The method comprises the following steps: constructing a port water transportation basic knowledge graph; fusing constraint rules of port scheduling to the basic knowledge graph to form a hierarchical knowledge graph; performing feasibility screening on a to-be-scheduled ship based on the constraint rules to generate a feasible candidate set; generating an evaluation value for each candidate scheme; judging whether the evaluation value meets the requirements; if not, adjusting the constraint rules and returning to re-screening; if yes, selecting an optimal scheme, combining a general large model to generate an optimal scheduling scheme containing berth allocation, a time window and a natural language explanation. The application realizes global optimization through explicit expression of scheduling rules by the knowledge graph and a feedback iteration mechanism, and generates an interpretable scheduling scheme through the general large model, thereby improving the intelligent level of port scheduling.

The application discloses a business demand generation and verification method, device and equipment, and a computer program product. The method comprises the following steps: receiving a business demand writing request of a user; generating a preliminary demand document by using a large language model according to the business demand writing request, a pre-constructed basic knowledge base and a pre-defined demand template; performing duplicate checking on the preliminary demand document by using the basic knowledge base and a demand duplicate checking strategy to obtain a demand duplicate checking result; and performing content checking on the preliminary demand document by using the basic knowledge base, the large language model and a demand content checking strategy according to the demand duplicate checking result to obtain a final demand document. The application deeply integrates AI technology around the whole process of demand writing, constructs a complete link from demand intention generation, content intelligent creation, demand duplicate checking to quality automatic control, improves the quality and efficiency of demand writing, reduces the labor cost, and promotes the development of bank business demand management in the intelligent and automatic direction.

This invention proposes a method and system for constructing a knowledge base based on a tree structure. The method includes: document parsing and hierarchical tree initialization; semantic segmentation and leaf node generation, traversing nodes to divide long text content into a set of semantically independent text fragments, creating new leaf nodes for each text fragment, and using the corresponding nodes in the original document tree as their parent nodes to form a basic knowledge tree; bottom-up multi-granularity summary generation, starting from the leaf nodes of the basic knowledge tree, generating summaries for each internal node; cross-node relationship enhancement, forming an enhanced knowledge graph; structured indexing and persistence, constructing a searchable index for each node in the knowledge base. This invention maintains the logic and coherence of the input obtained by the large language model, thereby significantly improving the accuracy, professionalism, and logical consistency of the generated answers, effectively avoiding misinterpretation or logical contradictions caused by contextual fragmentation. It also improves the accuracy and efficiency of retrieval.