139 results about "Knowledge updating" patented technology

The invention discloses a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention provides a multi-modal knowledge graph construction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a traditional Chinese medicine text; according to the traditional Chinese medicine text, candidate triples are generated through a large language model; generating a structured definition according to entities and relationships in the candidate triple; according to the structured definition, performing standardization processing on the candidate triad to obtain a standardized triad; according to the standardized triple, constructing an initial multi-modal knowledge graph by integrating multi-modal data; according to the initial multi-modal knowledge graph, constructing a theoretical layer knowledge graph through a neighbor perception filtering strategy; updating the theoretical layer knowledge graph through a dynamic knowledge updating mechanism; acquiring medical record data; constructing a real-world medical record layer knowledge graph according to the medical record data; and fusing the updated theoretical layer knowledge graph and the real-world medical record layer knowledge graph through an entity alignment mechanism and confidence fusion to form a multi-modal knowledge graph.

The invention relates to the field of data retrieval, and particularly provides a dynamic retrieval enhancement generation method and system based on a knowledge graph, a terminal and a medium, and the method comprises the following steps: extracting a structured triple from multi-source heterogeneous data through a large language model, and constructing a global knowledge graph by means of an entity linking technology; integrating a real-time data stream interface, and dynamically updating graph nodes and attributes based on an event-driven mechanism; adopting a RotatE model to respectively encode the entity and the relationship to a complex number space, and fusing to generate a mixed vector to construct an efficient index; after user query is received, topic nodes are positioned through semantic analysis, related entities are retrieved through mixed indexes, and multi-hop reasoning is executed along a relation path to generate reasoning sub-graphs and extended contexts; and finally, generating structured text answers by using a large language model, and adaptively outputting multi-modal results such as texts, charts and the like according to user requirements. According to the method, the knowledge updating timeliness, the complex query reasoning capability and the retrieval precision are effectively improved.

The invention belongs to the technical field of nuclear power station file management, and particularly relates to an intelligent nuclear power station file classification and retrieval system based on artificial intelligence. The file acquisition module is responsible for uniformly acquiring and accessing multi-modal files in the nuclear power station; the preprocessing and natural language processing module is used for carrying out deep processing and semantic analysis on the collected multi-modal document; the dynamic classification module is used for carrying out multi-dimensional automatic classification on the multi-modal document based on a deep learning model and a nuclear power industry knowledge graph; the dynamic knowledge updating module is used for continuously monitoring new documents and user feedback and keeping adaptability to industry changes; the intelligent retrieval module is used for providing a query function for a user through semantic vectorization and a large-scale rapid retrieval technology; and the intelligent recommendation module is used for providing targeted document recommendation for the user through association analysis and collaborative filtering. According to the method, the problems of low classification efficiency, insufficient retrieval accuracy, weak dynamic adaptive capacity and limited multi-modal data processing capacity are solved.

The invention relates to the technical field of artificial intelligence and knowledge engineering, and provides a digital human knowledge graph dynamic iteration system and method based on user feedback. According to the method, the timeliness and automation level of knowledge updating are improved; the configuration efficiency of technical resources is optimized; a data-driven iterative verification closed loop is constructed; according to the knowledge service system, the maintainability and robustness of the system are enhanced, the system has the capabilities of tracking, querying and rollback any change, the risk of system service degradation caused by misoperation or invalid updating is greatly reduced, and the stability of the whole knowledge service system is improved.

The invention provides a meta-universe virtual-reality interaction method based on causal invariance, belongs to the field of meta-universe virtual-reality interaction technologies, and is used for solving the problems of poor cross-domain adaptation, insufficient long-tail scene coverage and low dynamic robustness in related technologies. According to the method, through sensing layer causal kernel quality evaluation and data enhancement, reasoning layer causal invariance learning and cross-domain parameter migration, decision layer causal attention intention alignment, execution layer causal reinforcement learning control and iteration layer double-threshold knowledge updating, full-link parameter collaborative circulation is realized in combination with a causal data interface; and finally, the accuracy and the stability of virtual-real interaction of the element universe are improved, and efficient adaptation of cross-domain, long-tail and dynamic scenes is realized.

The invention discloses a software supply chain security detection method and system based on a large model agent, and relates to the technical field of network security. According to the method, a large-scale software supply chain knowledge graph is constructed based on a preprocessed large-scale software supply chain data set, and meanwhile, a software supply chain field large model is obtained; and after receiving a software supply chain question, directly generating a corresponding answer in the software supply chain security detection field by the software supply chain field large model in real time, or retrieving the large-scale software supply chain knowledge graph based on scene classification, and generating a corresponding answer in the software supply chain security detection field in combination with a retrieval result. According to the method, the problems of information fragmentation, knowledge updating lag, high manual analysis cost, low automation degree, high false alarm rate and the like in software supply chain security management of a traditional method are solved, and intelligent support is provided for constructing a credible, safe and transparent software supply chain system.

The invention relates to an IFC model data and fire-fighting specification knowledge semantic mapping method and system. According to the method, on one hand, a vector database is constructed and called, labels are added to IFC model data to be analyzed, and an ontology knowledge base is obtained; on the other hand, performing standard semantic analysis on the fire-fighting standard text to construct a structured rule base; based on the ontology knowledge base and the structured rule base, semantic mapping and semantic reasoning are carried out by utilizing a large language model, and a mapping and reasoning result is obtained; mapping and reasoning results are fed back to the ontology knowledge base to complete knowledge updating; and finally, performing compliance verification based on a reasoning result, and generating and outputting a fire-fighting compliance check report of the current BIM model. Compared with the prior art, the method has the advantages that a computer can accurately understand the meaning of the IFC model in the context of the fire-fighting specification, and the accuracy and reliability of intelligent examination are improved.

The invention provides a steelmaking process intelligent agent method and system based on multi-model collaboration, and belongs to the technical field of industrial intelligence. The method comprises the following steps: constructing and vectorizing a domain knowledge system containing a knowledge graph and a special corpus; a large language model optimized by domain data is adopted as a core; when the task is received, information is retrieved from the knowledge system through a retrieval enhancement generation mechanism to generate an enhanced context; autonomously invoking an external dedicated model or algorithm based on the context, and integrating their outputs to generate a decision scheme; and finally, feeding back and updating the knowledge graph according to an actual execution result, and adaptively adjusting model parameters. The invention further provides a corresponding system. According to the method, the utilization of domain knowledge is improved, active decision making is supported, and a knowledge updating and model optimizing mechanism is provided to integrate an automatic process.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a cross-model knowledge editing and updating method, device, equipment and medium, and the method comprises the steps: receiving a knowledge updating instruction, and generating a knowledge editing vector; writing the knowledge editing vector into an intermediate layer parameter of the target model set to obtain a parameter-updated target model set; generating an input sequence with a regulation mark when the input request and the knowledge update content meet semantic matching conditions, and inputting the input sequence into the target model set to obtain a model output set; performing output alignment and fusion through a consistency fusion module to generate a fusion output result; and when the output conflict is not eliminated, performing a re-reasoning operation based on the knowledge editing vector, and outputting a final result. According to the method, knowledge sharing and dynamic triggering among multiple models are realized through a cross-model mapping and consistency fusion mechanism, and the efficiency, timeliness and credibility of knowledge updating are improved.

The invention discloses a knowledge pushing method and system based on a knowledge graph and intention prediction, a terminal and a storage medium. The method comprises the steps that historical document data and new document data of multiple systems are crawled regularly and preprocessed; a triple in the preprocessed data is recognized and stored in a knowledge graph database, updating is suspended and manual auditing is triggered when new and old data conflicts are found, and updating is executed and changes are recorded after auditing is passed; collecting user behavior data, generating a behavior sequence, combining post information of the user, and utilizing the intention prediction model to output a demand probability; extracting associated knowledge according to the demand probability, and sending a knowledge packet link and a brief description to the user through an agent pop-up window; and receiving and storing feedback information of the user on the pushed knowledge packet, and performing system optimization based on the feedback information. According to the method, multi-source heterogeneous knowledge can be automatically integrated, knowledge updating can be monitored and synchronized in real time, knowledge is actively perceived and pushed based on context, and the utilization efficiency of enterprise knowledge is improved.

The invention discloses an RAG and knowledge graph question-answering method and system for security classification and grading of cross-border data, and belongs to the crossing field of big data security management, artificial intelligence and compliance technologies. The method comprises the following steps: step S101, receiving and preprocessing user input; step S102, performing question analysis and semantic intention recognition; step S103, intelligent scheduling control decision making; step S104, reasoning and executing the knowledge graph; step S105, performing RAG generation reasoning execution; step S106, performing intelligent fusion and decision traceability; step S107, carrying out multi-dimensional visual display; step S108, audit record and report generation; and S109, updating the dynamic knowledge. The method has the advantages of intelligent question and answer, multi-source fusion, sustainable evolution, traceability of the whole process and the like, and can be widely applied to compliance management and intelligent decision-making scenes related to data cross-border flow, such as enterprises, governments, finance, medical treatment and the like.

This communication equipment can update knowledge for semantic communication in a wireless communication system. This communication equipment can update knowledge for semantic communication in a wireless communication system. For example, the communication equipment can: determine a first sub-graph from graph data representing the knowledge; determine a first potential vector on the basis of pooling the first sub-graph; receive a second potential vector, a second feature map, and a second node index from other communication equipment; determine, on the basis of the first potential vector and the second potential vector, whether to update the graph data; on the basis of determining to update the graph data, recover a second sub-graph through un-pooling the second potential vector on the basis of the second feature map and the second node index; and update the graph data on the basis of the second sub-graph.

The invention relates to the technical field of knowledge maps, in particular to a veterinary treatment big data knowledge map construction method, which comprises the following steps of: acquiring animal case symptom characteristics, physical indexes, medical history records and intervention stage data, normalizing the symptom characteristics and encoding medical history to generate a case characteristic vector set; mapping symptoms and medicine nodes to establish a semantic relationship to calculate association strength, embedding physique and medical history to update node confidence to generate a personalized knowledge graph model, dynamically correcting edge weights in combination with feedback and medicine response, and extracting an effective intervention path to construct an association index to generate a veterinary treatment big data knowledge graph. According to the method, through normalization and sequential processing of multi-source case data, feature quantification and tracking are achieved, dynamic association is established based on semantic mapping and graph attention, confidence attenuation and an attribute weighting mechanism are fused, a node relation is optimized and self-adaptive evolution is carried out, individual difference and drug response capture is enhanced, and the updating performance of diagnosis and treatment knowledge is improved; and accurate and intelligent diagnosis and treatment analysis is promoted.

The invention provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps of: registering a dependency relationship between a task context and a concerned event triggering condition to the collaboration center, updating a dynamic dependency view through the collaboration center, analyzing knowledge updating time through the collaboration center, and generating a task context identifier set of the intelligent agent; constructing a fact set based on the task context identifier set, and inputting the fact set into a rule inference engine to execute rules in a collaboration rule base to generate a strategy so as to obtain a collaboration strategy; according to the method, the problem of efficiency bottleneck of event routing in the distributed intelligent system is solved from the system architecture level, and the real-time response capability and expansibility of the system in a high-concurrency scene are greatly improved on the premise of ensuring decision consistency.

The invention discloses an electric power two-ticket intelligent question-answering system based on an RAG technology and a knowledge graph, and belongs to the technical field of application of artificial intelligence in an electric power system. Aiming at the problems of knowledge update lag, weak logical reasoning and poor result interpretability in the existing electric power two-ticket generation and question-answering technology, the system constructs an electric power knowledge graph of explicit modeling equipment, operation and rule relationships by dynamically collecting and fusing multi-source heterogeneous electric power data; real-time retrieval and multi-hop reasoning are carried out by utilizing a retrieval enhancement generation technology in cooperation with the knowledge graph, so that the information accuracy and the logic depth are ensured; and dynamically generating a precise instruction for guiding a large language model in combination with task-driven interaction, finally outputting a structured operation ticket or question and answer result, and providing a visual reasoning path and a full-link audit log. According to the invention, intelligentization, real-time performance and credibility of electric power two-ticket management are realized, and ticket generation efficiency, operation safety and compliance traceability are significantly improved.

The application discloses a virtual anchor intelligent idle chat system based on a dynamic knowledge graph, relates to the technical field of artificial intelligence and virtual anchors, and comprises data preprocessing, cross-mode fusion, intention distribution, response generation and knowledge updating modules. Current and historical dialogue data of a user is acquired first; dialogue text features and entity relationship features of a knowledge graph are extracted and fused; the correlation degree of user intention and knowledge entities is analyzed by using a graph neural network; idle chat responses fused with knowledge are generated accordingly; and finally, knowledge graph updating strategies are generated according to response feedback to drive the dynamic evolution of the knowledge base. The system realizes the deep dynamic fusion of idle chat dialogue and structured knowledge, and can autonomously optimize knowledge according to interactive feedback, thereby improving the knowledge, accuracy and long-term adaptability of responses of the virtual anchor.

The application discloses a lithium battery failure analysis method and system based on a multi-channel RAG, and belongs to the technical field of lithium battery failure analysis. The method comprises the following steps: constructing a field knowledge vector library and a multi-level failure analysis knowledge graph; constructing a multi-channel retrieval enhancement generation framework, analyzing a user query intention, and obtaining related knowledge through a multi-channel cooperative mechanism of knowledge graph structure retrieval, knowledge graph semantic retrieval and field knowledge vector retrieval; calling a large language model to infer and generate a failure analysis result, and performing self-adaptive adjustment based on quality evaluation; and dynamically maintaining the knowledge base through internal and external knowledge updating. The application realizes unified modeling and structured expression of multi-source heterogeneous knowledge of failure analysis, improves the relevance and integrity of the retrieval result, reduces the dependence on artificial experience, and enhances the explainability and intelligent level of failure analysis.

The invention discloses a fault diagnosis method for a hydraulic turbine governor of an electric power system, and relates to the technical field of hydraulic turbine governor fault diagnosis. According to the method, the problems of knowledge isomerism, dynamic reasoning limitation and low man-machine interaction efficiency in the prior art are solved by constructing a collaborative diagnosis system of'dynamic knowledge graph + RAG professional small model '. The method is characterized by comprising the following steps: firstly, constructing a speed regulator dynamic knowledge graph containing seven types of entities and eight types of relationships through a Bi-LSTM-CRF algorithm and an attention mechanism; an RAG professional small model is constructed based on an M3E model and an HNSW index; and finally, realizing knowledge self-optimization by combining user feedback through collaborative logic of'preliminary map diagnosis + small model accurate reasoning '. According to the method, fault entity accurate identification, generation type intelligent question and answer and dynamic knowledge updating are realized, the diagnosis response is fast, the accuracy rate is high, the dependence on the experience of technicians is reduced, and the method can be widely applied to intelligent fault diagnosis of the water turbine governor of the power system.

The invention provides a measurement knowledge intensive retrieval enhancement generation method and system. The method comprises the following steps: constructing a multi-level measurement knowledge base structured index; constructing a vector database based on a structured knowledge base corresponding to the structured index; and encoding a user query, performing similarity matching on the encoded query and the vector database, and inputting a matching result into the large language model to obtain a target response result. According to the method and the device, when the new rule is metered to be released, the new rule is analyzed into the semantic unit, the structured index and the coding vector update database are supplemented, the large model can generate the response based on the new knowledge, the large model does not need to be retrained, and the problem that knowledge update is lagged in a traditional method is solved.

The invention belongs to the technical field of ecological data processing. The invention provides an ecological environment admission intelligent analysis risk assessment method and system based on a large model. An integrated scheme of multi-source data acquisition and targeted preprocessing, large model driven knowledge extraction, dynamic ecological environment access knowledge graph construction, project information intelligent analysis, multi-dimensional intelligent matching, risk inference engine iterative reasoning, structured report generation and knowledge and rule base continuous optimization is adopted. A full-process automatic ecological environment admission analysis and risk assessment system is constructed, the core problems that traditional ecological environment admission assessment highly depends on manpower, is low in efficiency and is non-uniform in standard are solved, and the technical defects that an existing system lacks deep text understanding and automatic reasoning ability, knowledge updating lags behind and complex correlation analysis is difficult are overcome. Through close connection and data circulation of all links, the automation degree and operation efficiency of the assessment process are greatly improved, the consistency and objectivity of assessment standards of similar projects are ensured, and the comprehensiveness and accuracy of risk identification are improved.

The method for realizing the air separation equipment production technology intelligent question-answering machine driven by the air separation field knowledge graph mainly includes three stages. Firstly, the HanLP-kf language processing package and the Neo4j database are used to realize the standardized representation and storage of the air separation equipment production technology field knowledge. Secondly, the production technology field knowledge graph is constructed by taking the production technology field literature as the data source. Thirdly, the Multi-Raspberry Pi network acquisition system is used to acquire real-time production data in the distributed air separation equipment production field, so as to construct the dynamic real-time knowledge graph of the air separation production technology field. Finally, the Naive Bayes classification method is used to enable the air separation equipment production technology intelligent question-answering machine to analyze and feed back various professional problems input by the user interface. The method can effectively overcome the shortcomings of the traditional knowledge base and expert system in knowledge updating and iteration, and through the self-learning ability of the question-answering machine, the knowledge graph is managed and more complex and dynamically updated knowledge is applied, so as to provide the technical management personnel participating in the air separation equipment production with the field knowledge advancing with the times.

This application provides a method and system for dynamic knowledge updating of a large-scale model for operation and maintenance (O&M) technical services. It relates to the field of O&M technology, and models the knowledge generation triggering factors, iteration impact range, and knowledge association strength in multi-source O&M knowledge update sources to obtain a knowledge full-link evolution set including knowledge generation trigger records, iteration impact assessment results, and cross-knowledge association networks. Then, dynamic coupling rules are generated based on the real-time scenario characteristics of O&M tasks and this knowledge full-link evolution set. These rules are used to filter and integrate the knowledge full-link evolution set to obtain a scenario-coupled knowledge set. This set is embedded into the knowledge architecture of the large-scale O&M technical service model, and storage node parameters and retrieval logic are adjusted to obtain the basic updated model. Finally, the basic updated model is dynamically adapted in multiple dimensions, and the knowledge call priority sequence and storage node parameters are adjusted in conjunction with changes in scenario characteristics to generate an adapted updated model, thereby improving the adaptability of the large-scale O&M technical service model.

The invention provides an intelligent monitoring video retrieval method and device based on RAG enhanced retrieval. According to the intelligent monitoring video retrieval method and device based on the RAG enhanced retrieval, the external knowledge base can be dynamically integrated through an RAG mechanism, the natural language complex query accurate response to the monitoring video is achieved, minute-level knowledge updating is supported, and the retrieval efficiency and the system adaptability are remarkably improved.

The invention discloses an orthosis adaptive question-answer database construction method based on machine learning, and relates to the technical field of data processing.The method comprises the steps that firstly, an application platform of an orthosis adaptive question-answer database is marked as an orthosis question-answer interaction platform, and the operation process of the orthosis question-answer interaction platform is analyzed; the method comprises the following steps: performing dynamic adjustment on operation parameters of an orthosis question and answer interaction platform, monitoring to obtain question and answer quality data of the orthosis question and answer interaction platform in a preset monitoring period after the operation parameters are adjusted, and analyzing to obtain knowledge update indexes and semantic matching deviation values of the orthosis question and answer interaction platform in each monitoring period; and finally, according to the knowledge updating index and the semantic matching deviation value of the orthosis question and answer interaction platform in each monitoring period, processing to obtain updating information of the orthosis question and answer interaction platform in each monitoring period, and performing parameter configuration management on the orthosis question and answer interaction platform. And the service quality and the operation efficiency of the orthosis adaptive question and answer platform are improved.

The application provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps: registering the dependency relationship between the task context and the event trigger condition concerned by the agent to the coordination center, updating the dynamic dependency view through the coordination center, analyzing the knowledge update time through the coordination center, and generating a task context identifier set of the agent; constructing a fact set based on the task context identifier set, inputting the fact set into a rule reasoning engine, executing the rules in the collaborative rule base to generate a strategy, and obtaining a collaborative strategy; and distributing the collaborative strategy to the corresponding agent and executing a cleaning process at the end of the task generation period. The application solves the efficiency bottleneck of event routing in a distributed intelligent system from the system architecture level, greatly improves the real-time response capability and scalability of the system in a high-concurrency scenario while ensuring decision consistency.

The application relates to a language model knowledge updating method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: locating a target neuron for a first training sample from a pre-trained language model; inputting a second training sample into the pre-trained language model to obtain a corresponding first prediction label; inputting the first training sample and the second training sample into a language model in an iteration process to obtain a prediction label corresponding to the first training sample and a second prediction label corresponding to the second training sample; generating a new knowledge introduction loss based on the prediction label corresponding to the first training sample and a training label, generating an old knowledge forgetting loss based on the prediction label and a historical training label, and generating an other knowledge maintenance loss based on the first prediction label and the second prediction label; and adjusting neuron parameters of the target neuron based on the losses until a convergence condition is met, so that a target language model is obtained. In this way, the knowledge updating efficiency can be improved.

This invention presents a knowledge evolution poisoning attack method for graph-enhanced retrieval and generation systems, belonging to the field of retrieval enhancement and generation. Through knowledge evolution forgery attacks and multi-target cross-subgraph collaborative attacks, it can generate evolutionary corpora that satisfy temporal constraints and have stronger structural connectivity without compromising the overall consistency of the knowledge graph. This allows for a more thorough exposure of GraphRAG's vulnerabilities in knowledge extraction, community partitioning, subgraph retrieval, and evidence aggregation stages in authorized evaluation environments. Compared to direct splicing injection, the samples generated by this method more closely resemble the "real knowledge update" distribution and can be used for pre-deployment red team evaluation, post-deployment regression testing, and effectiveness verification and parameter selection for defense modules such as consistency detection, retrieval filtering, and fact verification.

The application discloses a kind of knowledge base management method, device, computer equipment and storage medium, the knowledge base management method includes steps: quality detection is carried out to knowledge data in knowledge database, obtains quality detection result;According to quality detection result, determine the target knowledge data needing to update;Get reference knowledge data, and calculate the knowledge difference degree between reference knowledge data and target knowledge data;According to the corresponding relationship of knowledge difference degree and data update strategy, determine target data update strategy, update target knowledge data.This method realizes the precision and automation of knowledge update by the dual mechanism of quality detection and difference degree analysis, effectively improves the reliability and efficiency of knowledge base management, is suitable for dynamic maintenance and intelligent optimization of knowledge base system in financial technology, medical health and other business fields, can provide effective knowledge support for decision support and intelligent service in complex scenarios.

The invention provides an intelligent question and answer and business process coupled rail transit system and a coupling method, and relates to the technical field of urban rail transit, the system comprises a knowledge base module, an authority vector filtering module, a hybrid retrieval engine module, a resequencer module, a business process engine module and an interface adapter module. According to the method, the problems of knowledge update lagging, extensive authority and question and answer and business process splitting of an existing rail transit system are solved, release is taken into effect, paragraph-level authority isolation is realized, automatic handling of the OA business process can be completed directly through natural language dialogue, and the real-time, safe and closed-loop business requirements of rail transit are met.