211 results about "Web of knowledge" patented technology

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses an intelligent nursing training system and method based on a large language model, and belongs to the cross technical field of artificial intelligence and nursing education. The system comprises a large language model, a dynamic knowledge graph, a virtual case generation module, a multi-modal evaluation module and a federal learning framework. A dynamic knowledge network is constructed by integrating a hospital information system, a high-simulation case containing 60% of error scenes is generated in combination with a generative adversarial network, and a personalized training scheme is optimized by utilizing reinforcement learning. The method covers multi-source data management, nurse ability grading, real-time decision support (four-level alarm system) and closed-loop effect evaluation. The innovation points comprise: (1) a professional nursing large model, wherein the medical term understanding accuracy is greater than or equal to 95%; (2) hour-level updating of the dynamic knowledge graph; (3) clinical-training two-way data linkage, wherein the critical response time is less than or equal to 6 seconds; and (4) realizing cross-department collaboration by federal learning. The system provides an intelligent and personalized solution for nursing talent cultivation, and has industrial popularization value.

Provided in the present invention are a radiology report generation method and apparatus, and a terminal and a storage medium. The method comprises: inputting a radiology image to be processed into a pre-trained dynamics priori network model, wherein the dynamics priori network model comprises a dynamic knowledge graph network, a prior knowledge network and a decoder; in the dynamic knowledge graph network, obtaining a dynamic knowledge graph on the basis of the radiology image, and obtaining a visual representation on the basis of the dynamic knowledge graph; acquiring prior knowledge information corresponding to the radiology image, and on the basis of the prior knowledge information and the visual representation, using the prior knowledge network to obtain a prior representation vector; and inputting the visual representation and the prior representation vector into the decoder to generate a radiology report corresponding to the radiology image. In the present invention, the radiology report is generated by combining the dynamic knowledge graph with the prior knowledge information, such that visual and textual biases caused by limited data availability can be better handled, thereby improving the report generation quality.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

According to the agent construction method and the multi-agent cooperation method provided by the invention, the agent parameter configuration file is acquired, and the agent basic information, the ECA rule, the behavior tree path index and the performance function parameter configuration are loaded from the agent parameter configuration file; the knowledge hypergraph is adopted to aggregate agent basic information, ECA rules, behavior tree path indexes and performance function parameter configuration, and a dynamic knowledge network with semantic association is obtained; and after a prompt word used for calling the large language model is dynamically generated based on the dynamic knowledge network, the large language model is driven to generate an executable code, and when the executable code passes verification, an agent corresponding to the agent parameter configuration file is obtained. And then, aiming at a received user instruction, performing task processing by adopting the constructed multi-agent. Therefore, the autonomous decision-making capability and the dynamic adaptability of the intelligent agent are enhanced, and higher flexibility and expansibility are achieved.

The invention discloses an embedded hydraulic engineering risk intelligent regulation and control system based on a knowledge graph, and belongs to the technical field of hydraulic engineering. The system comprises a multi-level knowledge graph construction module for constructing a multi-level knowledge graph and forming a dynamically updated knowledge network; the atlas embedding expression module is used for encoding entity attributes, relation characteristics and time sequence evolution characteristics thereof in real time; the risk identification and evaluation module is used for integrating atlas embedded features and expert knowledge rules and analyzing hydraulic engineering risk features and spatial propagation modes thereof so as to realize accurate identification and quantitative evaluation of risks; the reinforcement learning regulation and control decision module encodes the risk state and the regulation and control target into a state-action space based on a deep reinforcement learning framework; the dynamic feedback optimization module is used for automatically updating the entity state and the relationship strength so as to form a closed loop of continuous optimization and knowledge accumulation; and the visual interaction module is used for visually displaying the risk situation, the regulation and control effect and the propagation path of the water conservancy project.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

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.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

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 provides a system association recommendation method and system based on semantic and business feature fusion, and belongs to the technical field of text association analysis. Text segmentation is performed on a system file based on a regular expression, and term semantic features are extracted; constructing a system associated business knowledge graph based on the association requirements of the term content of the system file and the external business information, and storing graph data by using a Neo4j graph database; creating a clause-department, clause-post and clause-professional three-layer system business knowledge network, and calculating graph structure features of each layer of network clause entity based on a Node2Vec algorithm; fusing the semantic features of the clause entities and the graph structure feature vectors, and establishing an association relationship between the clause entities in the system association business knowledge graph based on a cosine similarity method; and realizing system clause association recommendation based on a Cypher query language of a Neo4j graph database. According to the method, on the premise that the calculation complexity is reduced and the external knowledge is integrated, the system text association recommendation is more accurate.

The invention discloses a knowledge service method combining a vector database and a graph retrieval technology. Comprising the following steps; the method comprises the following steps: S1, collecting internal data and external data in a specific field; s2, performing fine adjustment on the open source large language model through industry corpora in a specific field or starting to train from zero; s3, storing the vectorized data information in a vector database, and storing the knowledge network in a graph database; s4, establishing a graph-rag system which is used for carrying out more comprehensive expansion on questions of the user; s5, automatically matching nodes and edges in the knowledge network according to the extracted keywords; s6, fusing a vector result from the vector database and the knowledge network in the graph database; s7, updating and optimizing at regular intervals; according to the method, the efficiency and accuracy of user information retrieval are improved, the problem of information blind areas is solved, and the requirement of the user for quickly and efficiently obtaining professional knowledge is met.

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 discloses a shield risk tracing method based on a knowledge graph, and belongs to the technical field of shield risk analysis. Determining an entity set and a relationship set, and constructing a mode layer of the knowledge graph; performing data preprocessing on the document data to obtain a processed text, extracting a knowledge triple meeting requirements, and outputting a structured knowledge triple according to a predetermined format; performing entity alignment to obtain a normalized knowledge triad to construct a knowledge graph; calculating the membership degree of the node on the basis of the knowledge graph, and obtaining the posterior probability of the node in combination with the posterior probability of the node; and inferring a potential risk propagation path and relation strength based on the posterior probability of the node in the knowledge graph, and realizing dynamic analysis and prediction of the knowledge network. According to the method, structural association of risk factors is realized through the knowledge graph, and the uncertainty is quantified by combining fuzzy Bayesian reasoning, so that the problem that a complex causal relationship and information fuzziness are difficult to process by a traditional method is solved.

The invention relates to the technical field of background investigation, and particularly discloses a talent background investigation method based on multi-source data evaluation, which comprises the steps of constructing a dynamic talent knowledge graph, performing semantic alignment processing on multi-source cross-modal data, evaluating data quality based on a multi-dimensional confidence model, and constructing a confidence closed-loop correction mechanism. Performing intelligent correction on the low-confidence data, and reversely updating the credibility weight of the data source according to a correction result; an event-driven incremental update architecture is adopted, and dynamic events of multi-source data are captured in real time through a distributed event awareness engine. According to the method, the dynamic knowledge graph is used as a unified carrier, and the multi-source data is upgraded from fragmented storage to a semantic association network, so that the talent background survey data is converted from static piling to a dynamically evolved knowledge network, and the accuracy and timeliness of evaluation are remarkably improved.

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 discloses a bidding document auditing method based on a large language model, belongs to the technical field of text auditing, and solves the problems that after text contents of bidding documents are classified according to risk levels, the continuity of the bidding document contents is broken, and the bidding document auditing efficiency is high. Therefore, the problem that resistance text vulnerabilities cannot be recognized among bidding document texts with different risk levels in the auditing process can occur. Comprising the following steps: constructing a risk factor graph, analyzing a bid invitation file, constructing a structured knowledge network containing hard terms and elastic terms, and defining dependency and conflict relationships between the terms; and dynamically grading bidding document chapter risks, and fusing four-dimensional indexes including commitment density, fuzziness, reference frequency and topology centrality. According to the method, the absolute commitment is extracted from the high-risk chapter to serve as the anchor point, the semantic association fragment of the non-high-risk area is reversely traced, the risk level of the association area is dynamically improved after the constraint effect of the semantic association fragment is verified, and the problem of antagonistic vulnerability identification fault caused by grading auditing is solved.

The invention provides a virtual-real fusion engineering training method and system, and relates to the field of educational informatization and engineering practical training. According to the method, training knowledge points are extracted from multi-course resources through natural language processing, and a cross-course associated knowledge network is constructed; learning behaviors of students are collected to generate learning state vectors, and a personalized training path is planned by using a reinforcement learning algorithm. Students complete simulation and predict performance indexes on the digital twin platform, and design is automatically deployed to entity hardware for entity verification after the performance indexes are qualified; during operation, multi-modal data such as sensors and images are synchronously collected, stability, accuracy and compliance scores are output through a fusion model, an evaluation result is fed back to a knowledge network to dynamically adjust nodes and edge weights, training path closed-loop optimization is achieved, and the individuation, effectiveness and intelligent level of engineering education are remarkably improved.

The invention discloses an IETM intelligent retrieval method based on multi-modal fusion and a knowledge graph, and the method comprises the following steps: data layer construction: integrating multi-dimensional data, and establishing a multi-modal database; knowledge layer modeling: defining an IETM domain ontology through an ontology modeling tool, constructing a knowledge network, cleaning data and unifying entity annotations by using a rule engine, and forming an RDF triple knowledge graph which can be understood by a machine; and application layer retrieval: based on hierarchical semantic fusion of multi-modal data and a three-level progressive retrieval mechanism, carrying out deep analysis and accurate retrieval on the query intention of the user. According to the method, semantic enhancement is carried out on the text, three-level feature extraction is carried out on the image, and spatio-temporal joint coding is carried out on the video, so that deep semantic alignment and cross-modal semantic consistency improvement of the multi-modal data are realized, and the quality and effect of multi-modal data fusion are effectively improved.

The invention discloses a knowledge network construction and recommendation method for artificial intelligence teaching, and relates to the technical field of education informatization, and the method comprises the steps: collecting structured course database data, semi-structured teaching material data and unstructured audio and video resources; executing cross-modal knowledge analysis by adopting a bidirectional sequence labeling model, and extracting an artificial intelligence teaching knowledge subject object and a multi-dimensional feature description thereof; constructing a knowledge unit, and adopting a model of'knowledge subject-feature dimension-feature value 'and'knowledge subject-association relationship-association knowledge subject'; vectorized semantic calibration is realized through an embedded space mapping technology, and heterogeneous semantic unit fusion is completed in combination with a dynamic similarity threshold mechanism; and screening high-quality knowledge units based on a credibility verification engine to construct an artificial intelligence teaching knowledge network. According to the invention, the organization efficiency and personalized service accuracy of artificial intelligence education resources are effectively improved, and the problem of knowledge acquisition efficiency bottleneck caused by discretization of multi-source teaching resources is solved.

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 discloses an intelligent learning partner toy interaction method based on a large model and an intelligent toy. The method comprises the following steps: constructing a knowledge network system of a target domain; constructing a target large language model based on the knowledge network system; capturing multi-source data of the user in real time; the multi-source data comprises one or more of a user voice instruction and a user interaction action; analyzing the multi-source data based on the target large language model to generate an answer text; performing illusion detection on the answer texts based on a preset detection model, and generating an interaction strategy based on the answer texts meeting conditions; and performing multi-mode feedback of the intelligent learning partner toy based on the interaction strategy. According to the method, through deep knowledge network construction, efficient real-time data processing, accurate answer generation, rigorous illusion detection and rich multi-mode feedback, a more intelligent, personalized and reliable learning partner toy interaction method is provided.

The invention belongs to the technical field of intelligent electric charge questions and answers, and particularly relates to a hierarchical knowledge network construction and retrieval method for intelligent electric charge questions and answers. The invention provides an iterative adaptive enhanced RAG method, based on a hierarchical knowledge network, information retrieved by a first round of problems is used as a knowledge carrier, newly retrieved information is continuously updated into initialized information in subsequent multi-round iterations, information is adaptively collected from the perspective of knowledge increase, and the information retrieval efficiency is improved. New knowledge and collected knowledge are flexibly integrated, and the problem that information interaction among different retrieval steps is insufficient is solved; in addition, a self-adaptive exploration stopping strategy is formulated, uncertain active retrieval is replaced, time deviation of active retrieval prediction is effectively avoided, and continuous knowledge increase is ensured.

The invention discloses a dialogue type AI intelligent outbound service automatic control method combined with a knowledge graph, and the method comprises the following steps: S1, constructing a multi-dimensional knowledge graph: integrating a domain ontology, a user portrait and a dialogue context, and forming a structured knowledge network; s2, dynamically generating a dialogue strategy: fusing rule reasoning and a machine learning model, dynamically selecting a dialogue strategy path based on a user response probability, and screening an optimal strategy through conflict detection; s3, real-time interaction control: realizing multi-round management through voice recognition, intention classification and dialogue state tracking, and triggering an emergency strategy in combination with sentiment analysis and silence detection; and S4, dynamically optimizing the system: updating knowledge graph entities / relationships by utilizing incremental learning, and optimizing strategy parameters and business rules in real time through reinforcement learning and association analysis. According to the method, the knowledge integration efficiency is improved, and the CRM data association response time is from gt; the result is reduced to 1t in 2 seconds; 200 milliseconds; the high-net-value customer conversion rate is improved by 42%, and the manual intervention demand is reduced by 70%.

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.

Implementations include a computer-implemented method for reducing cyber-security risk, comprising: accessing a knowledge mesh including a plurality of modules, wherein each module is associated with a respective aspect and maintains a knowledge graph specific to the respective aspect, wherein each knowledge graph is generated using data from one or more cyber-security repositories and includes nodes and connections between the nodes; performing an information completion process to generate connections between nodes of knowledge graphs maintained by different modules of the knowledge mesh, including performing at least one of: inheritance-based inference; natural language processing classifier-based inference; or natural language processing-based object matching inference; and identifying, using the generated connections between the nodes of the knowledge graphs, one or more actions to reduce cyber-security risk.