232 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 relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention relates to the technical field of intelligent customer service, and provides an AI intelligent customer service system based on a large model, and the system is characterized in that an unstructured text processed by a multi-source knowledge fusion subsystem is reconstructed into structured knowledge entries with a multi-dimensional label system, intention classification and context association, and the structured knowledge entries are dynamically integrated into an enterprise knowledge graph; the response generation subsystem is used for receiving the content queried by the user, analyzing the intention through a deep semantic understanding model in combination with an enterprise knowledge graph, and dynamically maintaining the context in combination with a multi-round dialogue state tracking technology; meanwhile, emotion dimension analysis is carried out on the content, and a personalized response is generated by fusing user service feedback; and the autonomous optimization subsystem dynamically adjusts the weight distribution of structured knowledge entries in the enterprise knowledge graph and the priority of process nodes in combination with an attribution analysis result, and triggers a knowledge updating and API process reconstruction mechanism. The method has self-learning and continuous optimization capabilities, and continuously improves the service quality and the user experience.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention belongs to the technical field of mine ventilation monitoring, and aims to solve the problems that a traditional knowledge system based on a rule base has bottlenecks in the aspects of dealing with sudden working conditions, knowledge updating and semantic reasoning, and is high in construction cost and complex to maintain. The invention provides a mine ventilation knowledge graph construction method based on a large model, and the method comprises the following steps: S100, obtaining and cleaning a multi-source heterogeneous text related to mine ventilation, and obtaining a JSON format knowledge fragment of a unified structure; s200, constructing an ontology model and defining a semantic structure; s300, constructing four types of knowledge extraction tasks, and extracting entities, attributes and relationships; s400, constructing an entity alignment module; and S500, constructing a graph database structure. According to the method, automatic structured expression, semantic consistent fusion and intelligent visual query of knowledge in the mine ventilation field can be realized, and an interpretable, extensible and reasonable intelligent support platform is provided for a mine ventilation system.

The invention relates to the technical field of safety monitoring of constructional engineering, in particular to an intelligent construction safety monitoring system and method based on multi-modal data fusion, and the system comprises a plurality of modules: a 5GMEC-based heterogeneous data space-time alignment module which completes coordinate system conversion between laser point cloud and a BIM model by using an improved Fast-ICP algorithm; the feature level fusion network comprises a geometric feature branch (improved unscented Kalman filter processing point cloud normal vector) and a time sequence feature branch (LSTM processing stress sensor data); the NeRFXL fusion engine is used for fusing the point cloud and the video data by adopting a multi-scale neural radiation field; the multi-task anomaly detector is used for constructing a hierarchical Transform architecture and introducing a modal gating mechanism; and the dynamic knowledge graph engine constructs a knowledge graph updating module based on a graph neural network, and all the modules are in communication connection with one another and work cooperatively, so that functions of data processing, fusion, anomaly detection, knowledge updating and the like are realized.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

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 discloses a medical knowledge question-answering system based on an RAG (Retrieval-Augmented Generation) architecture, relates to the field of artificial intelligence and natural language processing, and specifically comprises an input representation module, a knowledge retrieval module, a context construction module and a generation reasoning module. The input representation module is used for encoding user query and medical knowledge entries into high-dimensional dense vectors and comprises a semantic encoder; the knowledge retrieval module comprises a sparse retrieval unit, a dense retrieval unit and a fusion sequencing unit; the context construction module comprises a feature splicing unit and a code fusion unit; and the generation reasoning module calls a large language model based on the fused context to generate medical question and answer content in a natural language form. According to the system, by introducing the structured medical knowledge base and a mixed retrieval mechanism, the accuracy and specialty of questions and answers are effectively improved, the language model illusion phenomenon is reduced, the knowledge updating capacity is enhanced, and the system is suitable for application scenes such as clinical consultation and intelligent medical questions and answers.

The invention discloses an intelligent policy question-answering method and system based on retrieval enhancement generation and a medium. The method comprises the following steps: constructing a universal knowledge base and a plurality of mutually independent domain knowledge bases; in response to user input, the following steps are executed: performing routing analysis on the user input through a first large language model to generate a structured routing decision; retrieving the general knowledge base to obtain related general knowledge text segments and vectorization expressions thereof; according to the routing decision, a domain knowledge base corresponding to the at least one policy domain identifier is retrieved in parallel, and related policy text fragments corresponding to the at least one domain knowledge base and vectorization expressions of the related policy text fragments are obtained; obtaining an initial answer set based on retrieval results of the general knowledge base and the domain knowledge base; and performing intelligent fusion processing on the initial answer set through a second large language model, and generating and outputting response content. According to the method, the problem of knowledge updating lag is effectively solved, and the accuracy, timeliness and cross-domain specialty of policy questions and answers are improved.

The invention provides a multi-modal big language model inference engine which comprises a pre-training big language model adaptation layer, a pollution remediation knowledge graph enhancement module, a multi-modal information understanding and fusion unit, a technology implementation optimization module and an inference chain and decision interpretation generator. The system adopts a hierarchical modular design, all components communicate through a standardized API interface, efficient and stable data circulation is ensured, and the problems of knowledge application limitation, opaque reasoning process, difficulty in professional knowledge fusion, knowledge updating lagging, limited multi-modal data processing capability and the like in the existing contaminated site remediation decision process are solved.

The invention belongs to the technical field of intelligent question-answering systems, and particularly relates to an intelligent question-answering inference system based on a knowledge graph, which is characterized in that firstly, a knowledge graph construction module fuses multi-source data to generate a structured graph, and after a user inputs a natural language question, a question-answering analysis module completes intention classification and entity disambiguation and converts the question into structured query; an inference engine module fuses symbol rules and graph neural network inference through a hybrid inference sub-module, and a dynamic weight adjustment sub-module optimizes weights according to errors and attenuation factors to generate an inference result; the knowledge updating module incrementally updates the atlas in real time and detects conflicts, the interactive interface module visually presents a result, and the evaluation optimization module iteratively optimizes parameters in combination with offline evaluation and online feedback. The whole process is from user question asking to result output, accurate reasoning and continuous performance improvement are achieved, and multi-field question and answer requirements are met.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention discloses a flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

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 discloses a controllable text generation method based on framework semantic knowledge, and belongs to the technical field of natural language processing. Aiming at the problem of how to improve the semantic distinguishing ability of a model and the consistency of knowledge updating in context editing, firstly, a semantic framework network is introduced to carry out semantic analysis on input and associated contexts, and an adaptive threshold value is set through comparative analysis so as to enhance the semantic distinguishing ability and effectively isolate interference of irrelevant knowledge; and secondly, proposing a comparison perception distribution decoding mechanism, capturing offset generated in a knowledge updating process by comparing original and fused probability distribution, and designing a double filtering mechanism to ensure that the injected knowledge can be stably applied in different scenes. Experimental results show that the knowledge editing method is remarkably improved in the aspects of improving the accuracy, generalization and robustness of knowledge editing.

The invention discloses an intelligent report generation system, method and device and a storage medium, and relates to the technical field of data processing, and the system comprises a main research agent which is used for multi-source data information collection and preliminary analysis; the report writing agent is used for converting the research result into a professional report; and the review agent is used for reviewing the professional report. A multi-agent collaborative architecture and a domain knowledge enhancement technology are deeply fused, a full-link quality assurance system including data credibility evaluation, multi-source cross validation and dynamic knowledge updating is established, and collaborative optimization of professional report generation in three dimensions of timeliness, economy and specialty is realized.

The invention discloses a retrieval enhancement generation method for a time-sensitive problem, which comprises the following steps of: mixed time perception retrieval: enhancing document retrieval by adding time constraint on the basis of semantic relevance, and guiding by a time card to ensure that the retrieved document not only conforms to the meaning of query, but also conforms to the semantic relevance; the time context is met; the progressive multi-step reflection comprises the following steps of: firstly, acquiring and evaluating an initial document set by applying mixed time perception retrieval; if a document is retrieved, generating a final answer by using a large language model; otherwise, entering a reflection stage, and summarizing useful time information in the retrieved document into a context; and merging document sets accumulated in all iterations to generate a final answer. According to the method, a new framework integrating dynamic knowledge updating and time reasoning into the retrieval and generation process is provided, and accurate and timely response can be made to time-related problems.

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an intelligent auxiliary diagnosis and treatment system for doctors. Comprising a system initialization and role service registration module, a user input analysis and intention recognition module, an intelligent routing and professional tool set selection module, an SSE streaming response initialization and workflow starting module, a knowledge graph retrieval and reasoning module, a diagnosis and treatment logic reasoning and scheme generation module and a streaming result output and node state updating module. A dynamic knowledge update triggering and verifying module; according to the invention, on the system integration level, efficient integration of multi-professional medical resources is realized based on role-as-a-service architecture, and complete coverage of multi-professional knowledge modules of internal medicine, surgery, oncology and the like is ensured through a role service registration validity evaluation mechanism.

The invention discloses a tendering and bidding supplier intelligent portraying and matching system based on deep learning, and the system comprises the steps: constructing a multi-dimensional tendering and bidding knowledge base, and collecting qualification certificates, operation data and tendering and bidding files; processing the qualification certificate image by using a CNN to extract a key field to obtain a first weight; analyzing the operation data by using NLP to obtain a second weight; analyzing the bidding and tendering file by using NLP to obtain a third weight; fusing the weights and outputting supplier portraits; demand vector similarity is calculated by using a cosine similarity algorithm based on portraits, suppliers with first weights reaching the standard and high comprehensive similarity are preferentially matched, and the weights are dynamically adjusted; dynamically maintaining the knowledge updating data in real time by an API, re-evaluating the weight, and carrying out incremental learning on a training model; the risk control module is used for abnormal detection and scanning of abnormal operation data to generate risk scores, incorporating the risk scores into portraits and outputting a list of suppliers with annotations; according to the method, portraits of suppliers can be output in multiple dimensions, supplier lists matched with bid invitation can be provided, and the weight proportion is dynamically adjusted according to different enterprises and time.

The invention relates to the technical field of digital employees, and discloses a digital employee creation method and system based on artificial intelligence, and the method comprises the steps: obtaining and analyzing post data, generating a digital employee portrait and a dynamic knowledge base for creating digital employees based on the post data, and creating the digital employees based on the digital employee portrait and the dynamic knowledge base. The system corresponds to the method. According to the method, the digital employee portrait including the skill map and the character feature model is generated through the post data, and the digital employee can dynamically adjust the skill weight based on the parameters in the character feature model by combining real-time updating of the dynamic knowledge base, so that deep cooperation of the skill and the character is realized; when a digital employee processes an instruction, the digital employee accurately calls a core skill and an associated skill, adapts to a personalized scene through character features, and meanwhile, depends on dynamic knowledge updating, the autonomous response and sustainable evolution ability of the digital employee to a complex business scene is enhanced, and the intelligent level and the actual business adaptation degree of the digital employee are effectively improved.

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 discloses a three-normal-form automatic modeling method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing a structured domain knowledge base, and injecting three-normal-form design experience for an RAG framework; business semantics are clarified in real time in combination with user demand input and multi-round dialogue interaction of model questions; a double-stage AI recommendation engine is adopted to match user requirements and knowledge base semantic vectors, a high-precision model change scheme is generated, automatic normal form optimization is conducted on a recommendation scheme, DDL scripts, ER diagrams, data migration schemes and the like conforming to the normal form are dynamically output, integration is conducted in a sandbox environment, and finally closed-loop automatic modeling of'requirement-design-verification 'is achieved. The method is used for solving the problems that traditional data modeling is low in efficiency and high in error rate and cost rate, the problem that model knowledge updating needs full-amount or increment fine tuning, and the cost is high, and the problem that business semantic understanding is insufficient in three-normal-form modeling is solved.

The invention provides a neural network model co-evolution method based on model modularization and model merging, and belongs to the field of artificial intelligence. Comprising the following steps: (1) carrying out model modular decomposition based on gradient search; and through common optimization of a weight retention rate index and a performance index on a field pre-training set, extracting a function-related sparse module from the pre-training model. And (2) the module only updates the specific weight on the downstream task, so that knowledge updating is carried out on the specific field. And (3) knowledge fusion based on model merging. A sparse task vector is directly obtained by subtracting the weights of the fine-tuned modules from the weights of the pre-training models, and then a sparse weight updating matrix among the multiple modules is added back to the pre-training weights, so that a multi-task global model is obtained. According to the method, the mapping relation between neural network parameters and functions is defined, the model co-evolution effect is improved, and parameter conflicts in multi-task learning are relieved.

The invention discloses a cement manufacturing equipment knowledge graph construction method and system based on a large language model, relates to the technical field of artificial intelligence, and solves the problems that the prior art is lack of dynamic knowledge updating capability, cannot integrate new knowledge generated by equipment transformation and process optimization in time, and improves the construction efficiency. And the accuracy of the knowledge graph of the cement manufacturing equipment is relatively low. A field data set is generated based on historical multi-source heterogeneous data; generating a tuple generation model and a cement manufacturing equipment knowledge graph based on the field data set; dynamically updating and complementing the knowledge graph of the cement manufacturing equipment based on the multi-source heterogeneous data to obtain a newest knowledge graph of the cement manufacturing equipment; according to the method, the updating and complementing evaluation result is generated based on the newest cement manufacturing equipment knowledge graph, and the existing cement manufacturing equipment knowledge graph is dynamically updated and complemented after new knowledge is obtained each time, so that the accuracy and timeliness of the knowledge graph are improved, and the completeness and decision support capability of the knowledge graph are improved at the same time.

The invention relates to the technical field of intelligent education, in particular to an intelligent Agent heuristic question and answer teaching system based on knowledge graph driving, and the system comprises a four-layer progressive architecture including a data layer, a knowledge layer, an Agent layer and an interaction layer: the data layer is responsible for the collection, preprocessing and privacy protection of multi-dimensional teaching data, and provides high-quality input for the construction of a knowledge graph; the knowledge layer is used for converting data into a structured knowledge graph and storing a multidisciplinary knowledge network in an entity-relationship-attribute triple form; in the scheme, a'data-knowledge-Agent-interaction 'four-layer architecture is constructed, knowledge structured association and dynamic updating are realized based on a knowledge graph, weak points of students are accurately positioned by means of sensing, reasoning and decision-making modules of an intelligent Agent, a personalized problem chain is generated, and a multi-modal interaction and privacy protection mechanism is combined, so that the knowledge knowledge association and dynamic updating are realized. Systematization of a knowledge system, personalization of question and answer guidance, precision of data utilization and dynamic knowledge updating are achieved, and the defects of a traditional teaching system are effectively overcome.

The present invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on large language model technology. The method includes receiving multi-source heterogeneous data streams, completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; incrementally optimizing the skeleton, performing entity relationship disambiguation and conflict detection; iteratively updating the knowledge representation, and outputting a target knowledge graph that meets semantic consistency. The system includes data reception, semantic fusion, skeleton construction, optimization and knowledge update modules. The present invention effectively processes multi-source heterogeneous data, improves the accuracy, dynamic update capability and semantic consistency of the knowledge graph, and has broad application prospects in the fields of intelligent question answering, information retrieval, etc.

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.