118 results about "Web of knowledge" patented technology

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

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 large language model auxiliary optimization strategic decision-making system and a large language model auxiliary optimization strategic decision-making method. The system comprises six core modules. The dynamic knowledge fusion module constructs a three-layer distributed knowledge network, constructs an entity association weight matrix through a bidirectional Transform model based on an attention mechanism, and realizes knowledge dynamic association in combination with a time attenuation factor and a hybrid coding technology. The large language model module performs field fine tuning by adopting incremental pre-training and low-rank adaptation technologies, and introduces an exclusive word segmentation list to improve professional analysis precision. The full-process intelligent writing module covers submodules for report generation, revision and the like, and supports full-life-cycle management of reports. The strategic decision intelligent deduction module integrates scene impact factors, and realizes multi-scene deduction through reinforcement learning and Monte Carlo tree search. The interaction display module provides a visual interface, and the multi-mode interaction module realizes full task chain management. According to the invention, real-time knowledge support and intelligent deduction capability are provided for strategic decision making, and decision making efficiency and accuracy are improved.

The invention belongs to the technical field of artificial intelligence and educational informatization, and relates to a multi-mode knowledge graph enhanced intelligent teaching-assistant question-answering system. According to the method, the knowledge graph construction technology, the multi-modal content analysis technology and the large language model reasoning enhancement technology are comprehensively applied, and the semantic understanding, knowledge integration and reasoning generation capabilities of the intelligent teaching assisting system in an education and teaching scene are improved. The related technology comprises layout analysis of textbook documents, semantic description generation of image content, entity and relation extraction of text content, multi-modal knowledge graph construction and question and answer reasoning and natural language generation combined with the knowledge graph. Through cooperative application of the technologies, semantic interconnection can be carried out on various modal information such as texts, images and tables in the textbook, and a searchable and traceable textbook-level knowledge network is formed.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent proposition method, system and device for simulating expert propositions and a storage medium, and the method comprises the steps: constructing a structured knowledge network fusing knowledge points, cognitive levels and proposition specifications; establishing a material vector database associated with the knowledge point system; according to the target knowledge point, proposition constraint information is extracted from the knowledge network, related materials are retrieved from a vector library, and the related materials are combined into a generative prompt to be input into a large language model to generate a test question first draft; inputting the test question first draft into at least one large language model serving as a simulation answering agent, evaluating the quality of the test questions by analyzing the answering process and result, and outputting the test questions reaching the standard. According to the method, the proposition efficiency and consistency can be remarkably improved, absolute dependence on expert experience is reduced, and automatic output of high-quality test questions is achieved.

The invention provides an intelligent document understanding method and system combining natural language processing and deep learning, and relates to the technical field of natural language processing and information management. Fusing the semantic pre-annotation result and the original text feature of the document, inputting the fused semantic pre-annotation result and the original text feature of the document into a cross-modal semantic enhancement model to obtain a document enhanced semantic representation, performing bidirectional semantic interaction with a dynamic business knowledge network based on the document enhanced semantic representation, generating an associated interaction result, and constructing a structured semantic asset; finally, the structured semantic assets are input into an intelligent document application engine, semantic feedback data are collected to optimize model parameters, document understanding accuracy and practicability can be improved, and diversified business requirements are met.

The invention relates to the technical field of textbook construction and optimization, in particular to an automatic textbook construction and optimization method and device.The method comprises the following steps that original resources of multiple types of textbooks are collected, and core knowledge elements in the original resources are recognized; constructing a structured mapping relationship between the knowledge elements and the teaching resources based on the core knowledge elements; on the basis of the structured mapping relationship, constructing a knowledge graph covering multidisciplinary knowledge points and a preposition dependency relationship, and automatically identifying knowledge faults and complementing missing association so as to form a knowledge network with self-evolution ability; based on the knowledge network, utilizing a hybrid generation model to cooperatively work, and quickly generating a structured textbook framework containing knowledge veins, teaching cases and exercise resources; learning condition data including classroom behaviors, learning feedback and knowledge mastering conditions are obtained, and a quantity and chemical condition report is obtained after data processing; and updating data based on the quantitative chemical situation report and the knowledge graph.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention relates to a multi-source information knowledge fusion and intelligent retrieval method and system, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the unified knowledge modeling of the multi-source heterogeneous data, and obtaining a unified knowledge framework; and based on the unified knowledge framework, calling a large language model to perform structured preprocessing on the multi-source heterogeneous data to generate a structured knowledge triple. And performing structured processing on the structured knowledge triple through the attribute graph, and performing vectorization storage on each entity associated data through the embedded model to obtain a fused knowledge body. And receiving a user query request, and in response to the user query request, performing deep analysis on the user input data to obtain a user query intention and query elements. According to the method, the condition graph is constructed according to the query intention and the query elements of the user, and the condition graph is mapped into the knowledge network for entity positioning and condition graph matching to generate the structured retrieval result, so that the network overhead and scheduling delay are reduced, and the retrieval response speed and accuracy are improved.

The invention relates to the technical field of knowledge maps, in particular to a power grid production operation plan intelligent generation and correction method and system based on artificial intelligence, and the method comprises the following steps: constructing an equipment entity, a personnel entity and a power grid production operation plan task entity, and building a power grid multi-dimensional constraint entity knowledge map. According to the method, equipment, personnel and tasks are explicitly defined as entities, meanwhile, abstract constraint conditions such as safety regulations, equipment intervals and personnel qualification are modeled as entity nodes in a knowledge network, a power grid operation knowledge system containing a multi-dimensional constraint relation is constructed, and after a preliminary plan is generated, by means of the activation and satisfaction relation between the entities in a graph, the power grid operation knowledge system with the multi-dimensional constraint relation is established. Triggered but unsatisfied constraint items can be retrieved and positioned, and rapid and accurate identification and traceability of plan conflicts are realized.

The invention provides an educational policy retrieval method and system fusing a policy corpus and a large model, and relates to the technical field of artificial intelligence. Firstly, through a dynamic knowledge network construction module, an educational policy knowledge graph capable of being dynamically updated is constructed from four dimensions of time, space, effectiveness and a subject, version evolution, regional application and effectiveness relationships among policies are accurately modeled, and a structured basis is provided. And secondly, based on the map, a collaborative inference engine analyzes the complex intention of natural language query of the user by using a generative artificial intelligence model subjected to field fine tuning, and ensures that interpretation answers are accurate and compliant through a controllable generation technology under knowledge constraints. And finally, the feedback optimization system enables the system to be continuously adaptive and evolved by collecting multi-dimensional user behaviors and dominant feedback, mining high-frequency contradictory points in policy execution and driving bidirectional optimization of a knowledge graph and a generative model, and dynamic reasoning and closed-loop optimization of educational policy interpretation are realized.

The invention belongs to the technical field of computers, particularly relates to an enterprise consultation service system based on big data, and aims to solve the problems of data islands, decision lagging and insufficient predictive ability in traditional enterprise consultation service. The system comprises a global data access module, a knowledge graph construction module, a dynamic simulation engine, an intelligent consultation generation module and an interactive feedback optimization module, a time sequence evolution knowledge network is constructed by fusing internal and external multi-source heterogeneous data, multi-scene parallel simulation and intelligent suggestion generation are realized, and closed-loop feedback learning is supported. According to the scheme, the accuracy, foresight and response efficiency of enterprise strategic decision can be improved, and large-scale concurrent service and real-time dynamic optimization are supported.

The invention relates to the technical field of knowledge maps, in particular to a personalized question and answer recommendation method based on a knowledge map and two-layer attention, and the method comprises the steps: constructing and storing a structured knowledge network containing a plurality of entities and association relationships thereof, analyzing an input natural language query, and recognizing query elements; based on semantic comprehension, calculating a semantic matching degree between the query elements and candidate answers extracted from the structured knowledge network, and further identifying a deep semantic intention of the natural language query; and based on the identified query elements and semantic intentions, in combination with the semantic matching degree, performing retrieval in the structured knowledge network to obtain an initial result, performing structured recombination and natural language generation on the initial result, and outputting a readable answer. The method has higher question and answer matching precision and query generation quality in a data sparse and relation complex scene, and is widely applied to the fields of education question and answer, intelligent recommendation, knowledge retrieval and the like.

The invention discloses a transformer substation work ticket knowledge graph construction method based on a large language model, and belongs to the field of energy power and artificial intelligence. The method comprises the following steps: based on standardized electrical drawing annotation data, generating an independent knowledge graph through data recombination preprocessing, rough knowledge graph construction and attribute supplementary reasoning; all the independent knowledge maps are divided into a framework layer map and a detail layer map according to voltage grades, and full map fusion is completed by adopting a hierarchical fusion strategy; carrying out breakpoint detection by using an isolated point detection algorithm based on degree centrality, carrying out connectivity analysis through depth-first search, and re-fusing multiple non-connected sub-graphs; and screening abnormal nodes based on an electrical connection rule, inputting the abnormal nodes into a large language model to automatically supplement Agents to generate correction suggestions, and completing map optimization. Through cooperation of the large model Agent and multiple algorithms, the electrical data structuring efficiency and accuracy are improved, and knowledge network support is provided for automatic operation and fault diagnosis of the transformer substation.

The invention relates to the technical field of code analysis, in particular to a code business logic vulnerability static analysis method based on a large language model, which comprises the following steps: performing semantic feature extraction based on a source code file set and a business requirement document to obtain a code business semantic feature map; constructing a business logic constraint knowledge network based on the code business semantic feature map, and generating a business scene simulation test case set based on the business logic constraint knowledge network; and executing the service scene simulation test case set, obtaining corresponding function runtime state data, and carrying out state transition trajectory analysis based on the function runtime state data to obtain an actual service state circulation sequence. By executing the test case and capturing the state data when the function runs, the state change of the program can be monitored in real time, an objective basis is provided for subsequent state transition trajectory analysis, and the reliability of an analysis result is ensured.

The invention relates to the technical field of data intelligent matching, and discloses an industrial data matching method and system based on artificial intelligence. The method comprises the following steps: receiving multi-source industrial data, and dynamically constructing an associated knowledge network; analyzing a natural language query intention of a user, and injecting the natural language query intention into the knowledge network as a constraint condition to generate an intentional knowledge network; interactive exploration is carried out on the network, paths and weights are dynamically adjusted according to feedback, and an optimized path set is formed; decision optimization is carried out in combination with a service rule, and a multi-level matching scheme is generated; and directionally retrieving and fusing data according to the scheme, and outputting a matching result. According to the method, through intention-driven network focusing and interactive adaptive exploration, the accuracy and the intelligent degree of complex industry data matching are improved.

A simulation method and related equipment for machining process training are disclosed. The method includes: acquiring engineering data and process data of the target workpiece; extracting features from the engineering data and combining them with the process data to construct a case resource library; performing semantic association analysis between the pre-set knowledge resource library and the machining cases in the case resource library to generate a process knowledge network; constructing a virtual training environment based on the process knowledge network; collecting interaction data of learners in the virtual training environment and evaluating the interaction data to obtain evaluation results; and generating guidance animations based on the evaluation results and the process knowledge network. This application can improve the learning efficiency of simulation training.

The invention belongs to the technical field of education artificial intelligence, and particularly relates to an adaptive learning path generation method based on a knowledge graph, which comprises a basic construction stage of constructing a double-layer knowledge graph and acquiring and standardizing multi-source dynamic data, and is used for constructing a course and cross-domain double-layer knowledge system and a data base, a three-dimensional learning condition analysis stage including knowledge dimension, dynamic learning condition dimension and cognitive development dimension; the cross-domain knowledge migration mechanism breaks course barriers, associates current learning with core course knowledge points of the same major, helps students establish a complete professional knowledge network, avoids knowledge fragmentation, embeds cross-domain knowledge points in combination with professional planning and competition requirements, realizes connection of current learning and future application, improves the practical value and migration ability of knowledge, and improves the learning efficiency. The knowledge association weight is dynamically optimized, so that path planning follows domain knowledge logic and fits individual learning feedback, students are assisted in understanding internal association among knowledge, and knowledge construction logicality is enhanced.

A system and method for an artificially intelligent (AI) platform comprising a Knowledge Net (KN) Platform for automatically creating accessible, transparent knowledge stores which reflect a complete spectrum of semantic relationships between concepts. The system comprises a knowledge discovery engine that creates a machine representation of the semantic knowledge graph of a concept, namely a Concept Semantic Knowledge Graph (CSKG), for explainable, human-analogous understanding of natural language data for the full range of automated natural language understanding tasks—wherein the knowledge discovery engine, in discovering the related concepts, uses a variety of linguistic learning methods.

This invention relates to a method and system for multi-source information knowledge fusion and intelligent retrieval. The method includes: acquiring multi-source heterogeneous data and performing unified knowledge modeling on the multi-source heterogeneous data to obtain a unified knowledge framework. Based on the unified knowledge framework, a large language model is invoked to perform structured preprocessing on the multi-source heterogeneous data to generate structured knowledge triples. The structured knowledge triples are then structured using attribute graphs, and the associated data of each entity is vectorized and stored using an embedding model to obtain a fused knowledge body. User query requests are received and responded to by performing deep analysis on the user input data to obtain the user's query intent and query elements. A condition graph is constructed based on the user's query intent and query elements, and the condition graph is mapped to a knowledge network for entity localization and condition graph matching to generate structured retrieval results. This reduces network overhead and scheduling latency, and improves retrieval response speed and accuracy.

The invention provides a knowledge network-oriented adaptive learning path planning method and system, and relates to the technical field of data processing, and the method comprises the steps: building a knowledge network by taking knowledge points as nodes and taking a first-to-second relationship of the knowledge points as a directed edge; setting a target node, and extracting a plurality of sub-graphs which can reach the target node from the knowledge network; performing breadth traversal on the plurality of sub-graphs from the path length and the path blocking degree to obtain an initial learning path; and performing mastery degree evaluation on the initial learning path, performing adaptive optimization on the initial learning path according to a mastery degree evaluation result, and determining a target learning path. The technical problem that learning efficiency and effect are low due to the fact that learning path planning cannot meet the individuation level of learners in the prior art is solved. The technical effects that the learning path is dynamically adjusted according to the personalized requirements of the learner, and the learning efficiency and effect are improved are achieved.

The invention provides a method and system for improving RAG recall rate and accuracy based on block global relevance, and the method comprises the steps: detecting a newly-added document through timing scheduling, carrying out block processing, and fusing an explicit structure relation and an implicit semantic relation to construct a weighted adjacency matrix; block global correlation scores are calculated through an iterative algorithm; the query similarity and the global correlation score are dynamically fused, and Top-K blocks are returned to generate answers; the system comprises a knowledge block graph construction server, a global correlation calculation server, a fusion retrieval server and a data storage server. According to the method, the limitation that traditional RAG only depends on local similarity is broken through, the core knowledge recall rate is remarkably increased, redundant noise is restrained, and the accuracy and logicality of generated answers are improved by quantifying the global importance of the blocks in the knowledge network.

The application discloses a crop disease and pest knowledge base self-learning and incremental updating method and system, belongs to the technical field of crop disease and pest diagnosis and knowledge base management, and has the technical scheme as follows: a first knowledge network is constructed through three-level credibility weighted fusion and graph node matching; the quality score and the compensated use frequency of a knowledge item are obtained through frequency statistics and multi-condition threshold value; a second knowledge network containing a new node and an associated edge is obtained through attention heat map extraction and attribute node generation; a third knowledge network and a rollback event record are obtained through semantic version control and execution rollback condition judgment; a fourth knowledge network and a migration record are obtained through subgraph clone adaptation for cross-crop knowledge migration and graph network fusion; and the application is based on knowledge network evolution and feedback driving, realizes knowledge base self-learning and incremental updating through exposure compensation, version control, cross-crop migration and other technologies, and improves knowledge coverage, updating efficiency and application reliability.

The application provides a dynamic AI knowledge graph system, and relates to the technical field of artificial intelligence and knowledge graph. The system aims to solve the problems of static information, missing relationship and context break in the existing AI memory scheme, and provides an efficient, dynamic and long-term evolving "memory system" for AI agents through automatic knowledge construction, dynamic time perception, real-time response change and mixed retrieval reasoning ability. Through the core processes of data encapsulation, knowledge pipeline processing, time sequence addition and fusion, multi-modal retrieval and context empowerment, the application realizes the real-time conversion of unstructured and structured information into a dynamic knowledge network with time perception ability, significantly improving the performance of AI agents in multi-round interaction, state reasoning and task planning.

This invention discloses a three-color probability assessment method and system based on high-precision clause mapping, comprising the following steps: Step S1: Receive text input containing facts to be assessed, and perform structured preprocessing to extract core entities, time elements, and request types to generate a structured case representation; Step S2: Based on the structured case representation, plan a mapping path from the starting point of the case to potentially relevant clauses in a preset clause knowledge network. The mapping path consists of multiple sequentially connected nodes and directed edges, where each node corresponds to a clause to be assessed, and each directed edge corresponds to a verification operation based on logical rules. This invention, through high-precision clause mapping, probability calibration, versioned threshold management, and structured report generation, overcomes the shortcomings of existing technologies and achieves standardization, traceability, and efficiency throughout the entire assessment process.

The invention relates to the field of coal bed gas exploration, and particularly discloses a coal bed gas multi-source heterogeneous data fusion and management method based on a knowledge graph, which establishes a unified semantic framework by constructing a coal bed gas field ontology model and definitely defining entity types of geology, wells, engineering, data and the like as well as attributes and relationships thereof. Structured knowledge is extracted from multi-source heterogeneous data based on the framework, knowledge fusion is achieved through entity alignment and conflict resolution, and finally the processed knowledge is stored in a graph database to construct a coal bed gas knowledge graph. Based on the knowledge graph, intelligent query, visual correlation analysis, decision support and other services can be provided. According to the method, semantic-level deep fusion of coal bed gas multi-source heterogeneous data is realized through a knowledge graph technology, a data island is broken through, and a unified knowledge network is constructed; the analysis and mining capability of the complex incidence relation is enhanced through a graph structure expression mode, and multi-hop query and causal inference are supported.

The application discloses a substation work ticket knowledge graph construction method based on a large language model, and belongs to the field of energy power and artificial intelligence. The method comprises the following steps: based on standardized electrical drawing annotation data, independent knowledge graphs are generated through data reorganization preprocessing, rough knowledge graph construction and attribute supplement reasoning; all independent knowledge graphs are divided into framework layer graphs and detail layer graphs according to voltage levels, and hierarchical fusion strategies are adopted to complete full graph fusion; breakpoint detection is carried out by using an isolated point detection algorithm based on degree centrality, connectivity analysis is carried out through depth-first search, and non-connected subgraphs are re-fused; abnormal nodes are screened based on electrical connection rules, a large language model is input to automatically fill in Agent to generate correction suggestions, and graph optimization is completed. Through the cooperation of the large model Agent and multiple algorithms, the application improves the efficiency and accuracy of electrical data structuring, and provides knowledge network support for substation automation operation and fault diagnosis.

The invention discloses a map construction method and system based on traditional Chinese medicine knowledge. Semantic analysis is combined with a traditional Chinese medicine term dictionary to extract a core entity, excavate a traditional Chinese medicine major disease association and prescription compatibility relationship, construct a knowledge network and define entity attributes and a hierarchical structure, and form a traditional Chinese medicine knowledge model; basic theories and clinical schemes are further integrated and expanded to the acupoint conditioning relation, the consistency is judged, the map visual rendering and intelligent retrieval functions are achieved, and finally the treatment scheme is determined and optimized by fusing clinical requirements. According to the invention, systematicness and availability of traditional Chinese medicine knowledge are significantly improved, clinical diagnosis and treatment efficiency and accuracy are promoted, and intelligent auxiliary decision making is realized.

The invention provides a stomatology teaching system fusing a knowledge graph and multi-modal intelligent interaction, and the system comprises a stomatology knowledge graph construction module which is used for constructing a knowledge graph containing a stomatology teaching entity and a semantic relation based on obtained data; and the multi-modal teaching resource semantic association engine is used for carrying out semantic annotation on teaching resources and knowledge point nodes in the knowledge graph and establishing a closed-loop teaching link of knowledge point-explanation-case-practice. According to the technical scheme of the invention, after the knowledge graph is constructed and the nodes of the knowledge graph are associated with the teaching resources, the closed-loop teaching link is established, so that the associated knowledge points in the teaching resources are associated, and the purposes of associating teaching resource information and establishing a systematic knowledge network are achieved.