114 results about "Knowledge integration" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention discloses a closed-loop fault diagnosis method based on the combination of an AI intelligent agent and power equipment simulation, which is applied to the field of power system fault diagnosis, and comprises the following steps: on the basis of a preset knowledge graph basic data set and power system multi-source data, performing data cleaning, feature extraction and knowledge integration; constructing a unified power equipment fault diagnosis knowledge graph, and performing reasoning on the knowledge graph and time sequence characteristics in combination with large model fine tuning or a mixed reasoning engine of a graph neural network to generate M candidate fault hypotheses; calculating the similarity between simulation and actual measurement waveforms through a DTW algorithm and a frequency spectrum comparison method, and screening high-consistency hypotheses; based on the logic verification rule base, performing causal graph reasoning, constraint checking and anti-factual thinking on the high-consistency hypotheses, and eliminating non-logic hypotheses; feeding back the abnormality found in the verification link to the AI agent, dynamically adjusting the reasoning strategy through reinforcement learning, and generating a convergent diagnosis result; and outputting a target diagnosis conclusion based on the converged diagnosis result.

The invention discloses a database interaction method, device and equipment based on multi-agent cooperation and a medium, and relates to the technical field of database interaction. The method comprises the steps of obtaining a query request, performing semantic partitioning and vectorization, and obtaining a retrieval vector set; and selecting a search source and searching to obtain an information block. And obtaining candidate knowledge through knowledge base retrieval. And integrating the information blocks and the candidate knowledge to obtain a candidate information set. And constructing a dynamic demand understanding model according to the query request and the candidate information set, and obtaining a task state matrix and a tool calling task. And calling a corresponding auxiliary tool and operating, updating the task state matrix according to an operation result and returning the task state matrix to the task planning agent to update the tool calling task until the tool calling task is no longer output, and obtaining a task result. And when the database interaction module is called, generating a chart according to an output result of the database interaction module, and outputting the chart and a task result together. Otherwise, directly outputting a task result.

Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and / or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

The invention discloses an OVER-NAV vision-language combined navigation system and method, and belongs to the technical field of intelligent navigation.The system comprises a navigation server, a user handheld terminal and a navigation terminal.The navigation server comprises a vision module, a language obtaining module, a vision map module, a semantic map module and a multi-modal feature cross-modal interaction module; the core of the system is that a multi-modal feature cross-modal interaction module comprises a topology-aware heterogeneous modal representation unified framework, a differential geometry inspired dynamic attention guidance mechanism and a heterogeneous knowledge integration and collaborative optimization framework, and deep fusion of three modals of visual information, a language instruction and a semantic map is realized; according to the method, the navigation accuracy is remarkably improved, the natural language instruction understanding accuracy reaches 92%, the high recognition accuracy is still kept in illumination changing and noisy environments, and the method is particularly suitable for complex shopping mall environments, outdoor-indoor seamless transition navigation and other scenes.

The invention discloses an intelligent multidisciplinary clinical consultation system and method based on a large language model, and relates to the technical field of medical artificial intelligence. According to the structured consultation suggestion set, the association degree value of the possible disease causes and the consultation departments is calculated in combination with the medical knowledge graph, the departments participating in the consultation are automatically matched, and the consultation sub-targets are determined, so that the problem of lack of interdisciplinary knowledge integration and dynamic coordination ability in the prior art is solved, and the situation that diagnosis and treatment only depend on a single subject is avoided; meanwhile, a multi-disciplinary consultation set is formed by counting the disease cause matching number, calculating the treatment emergency value and the priority index, determining the consultation sequence of consultation departments, obtaining consultation content according to matching of consultation sub-targets and clinical guidelines, and determining the application treatment scheme in combination with patient replies and historical treatment records; according to the process, multidisciplinary knowledge is integrated, comprehensive diagnosis and treatment suggestions are provided for the patient, and the defects of the prior art on a multidisciplinary cooperation overall solution are overcome.

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 provides a hybrid AI adaptive optimization system based on agent context engineering. The hybrid AI adaptive optimization system comprises a context knowledge base, a generation module, a feedback analysis module and a knowledge integration module. According to the method, the problems that in an existing context optimization technology based on a large language model (LLM), context Bias is simplified, Context Collapse is caused by overall rewriting, and the learning process is high in delay, high in cost, unstable and the like are solved.

The invention provides an outline content generation method and system combined with natural language processing, and the method comprises the steps: firstly carrying out the medical semantic analysis of a medical question text inputted by a user, obtaining a question feature set, calling a medical literature database interface for retrieval based on the question feature set, and obtaining a matched medical literature knowledge set; extracting medical knowledge units with independent semantics from the literature knowledge set to generate a structured knowledge unit set, performing association matching on the structured knowledge unit set and the question feature set, distributing weights according to question type identifiers and information demand strength parameters to generate a weighted knowledge integration result, and performing knowledge integration on the weighted knowledge integration result; and finally, generating a medical question answering text according to a weighted knowledge integration result, and feeding back the medical question answering text to a user interaction interface for display, so that accurate, comprehensive and personalized medical question answering can be provided for the user, and the question answering quality and the user experience of a medical academic community are improved.

The invention relates to the technical field of artificial influence weather, and discloses an artificial influence weather knowledge graph construction method and system based on a large model. The method comprises the steps that multi-source meteorological data are collected and preprocessed to generate a standardized data set; extracting a meteorological feature set by using the pre-trained large model; detecting a data dynamic change frequency, and starting a dynamic strategy to switch and mark an abnormal data segment when the data dynamic change frequency exceeds a threshold value; in combination with a similarity matching result of the historical knowledge graph and the current features, decomposing the feature set of the non-abnormal data segments to generate knowledge feature subsets; correcting the knowledge feature subset based on the coupling relationship between the meteorological kinetic parameters and the environmental parameters; and reversely backtracking the key influence factors along the weather influence propagation map, and constructing the knowledge map. According to the method, multi-source data are integrated through standardized processing, abnormal data are dynamically identified, historical knowledge and physical mechanism are combined to correct features, key factors are finally backtracked to complete graph construction, and the method is suitable for knowledge integration and analysis in the field of artificial influence weather.

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 relates to the technical field of medical information processing, and discloses an intelligent prescription auxiliary issuing system and method based on generative artificial intelligence and a storage medium, and the system comprises a multi-modal input module, a model generation module, a knowledge integration module, a prescription generation module, an auditing optimization module and a prescription output module. The method corresponds to the system, and the storage medium corresponds to the method. According to the application, the limitation of function fragmentation is avoided, diagnosis and treatment instructions, personalized habits of doctors, dynamic medical knowledge and risk control requirements are cooperatively and comprehensively planned through the modules, a medication safety base line is kept through risk grading verification, optimization suggestions are generated according to diagnosis and treatment habits of the doctors, meanwhile, the accuracy and efficiency of prescription making are improved, and the application prospect is wide. The comprehensive requirements for safety, personality and high efficiency of prescription assistance in clinical scenes are fully met, powerful support is provided for diagnosis and treatment work of doctors, and medication safety and treatment effects of patients are indirectly guaranteed.

The invention relates to an unmanned aerial vehicle cluster task planning system of a task space unified view. The unmanned aerial vehicle cluster task planning system comprises an LLM-based high-level task allocation decision module, an SAC-based low-level path planning control module, an RAG knowledge integration-based multi-modal representation learning module and a closed-loop self-adaption and continuous improvement module. An advanced task allocation decision module performs advanced reasoning and thinking chain task decomposition by using LLM, and dynamically optimizes an agent reward function; the low-level path planning control module optimizes continuous path planning of the unmanned aerial vehicle based on SAC reinforcement learning; the multi-modal representation learning module fuses the heterogeneous perception data and the historical experience of RAG retrieval, and constructs a unified potential state representation aided decision; and each module is tightly coupled with a closed-loop self-adaptive mechanism, and self-adaptive adjustment reward signals are fed back in real time. The system effectively solves the problems of path crossing, non-uniform resource allocation and the like, improves generalization ability and decision-making precision, and realizes faster convergence and better performance in a complex scene.

The invention relates to the technical field of government affair data analysis, and discloses a government affair SaaS architecture and method based on cloud edge collaboration, and the government affair data processing method based on cloud edge collaboration comprises the steps: obtaining government affair scene feature data, and generating scene semantic representation; the scene semantic similarity is calculated, and the knowledge migration potential is evaluated; analyzing semantic features of a target scene, and screening clients participating in training based on scene similarity; processing the client model, and dynamically adjusting the aggregation weight according to the scene similarity and the model contribution degree; global model knowledge is extracted, and a target scene local model is optimized; through organic combination of federated learning technologies of scene semantic understanding and scene perception, the limitation that data privacy protection and scene specialization model training cannot be realized at the same time in the traditional technology is overcome, and the technical problems of cross-scene knowledge integration and scene specialization service in government affair data analysis are effectively solved.

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 discloses a BGE embedding model fine tuning method for power grid engineering cost knowledge, and relates to the cross technical field of artificial intelligence and electric power engineering cost, and the method comprises the following steps: collecting text data, and constructing a semantic pairing set based on a graph structure of cross-format semantic mapping and quality detection; constructing an initial knowledge corpus according to the semantic pairing set, and performing fine adjustment on a BGE pre-training model; performing vectorization coding on the initial knowledge corpus according to the fine-tuned model, and constructing an efficient vector database by adopting an FAISS index method; searching the question input by the user based on the efficient vector database, performing knowledge enhancement on a search result, and outputting final decision support; through the method based on cross-format semantic graph modeling, BGE embedding model fine adjustment and multi-scale vector index retrieval, the problems that a general embedding model is insufficient in understanding of power grid cost technical terms, the cross-document knowledge integration efficiency is low, and semantic matching is not accurate are solved.

A platform for creating, managing, and deploying digital twins of human experts through automated behavioral capture and analysis. The platform employs a data collection system that monitors and processes digital interactions, communications, and work patterns to create AI-powered digital representations of subject matter experts. These digital twins maintain the knowledge, decision-making patterns, and communication style of the original subject while preserving privacy and confidentiality boundaries. The platform includes systems for managing multiple instances of digital twins across different organizations, with capabilities for instance-level learning and knowledge integration. A comprehensive licensing and rights management system enables controlled distribution of expert digital twins while ensuring appropriate privacy and security controls are in place. The platform maintains continuous compliance monitoring and privacy enforcement across all twin instances, allowing for scalable deployment of expert knowledge while maintaining security and confidentiality requirements.

The invention relates to a rule-based reinforcement learning and thinking chain retrieval enhancement generation reasoning method. The method comprises the following steps of: preprocessing data; model training; and performing actual reasoning by adopting the trained model. The method has the beneficial effects that the complex multi-step reasoning capability of a large model is remarkably improved; knowledge integration is carried out, and the efficiency of retrieval enhancement generation (RAG) is optimized; the training cost is reduced; the stability of the strategy is enhanced; dynamic decision support and logic specification constraints are enhanced; the method can perfectly accord with verification requirements for multi-step reasoning ability, and can better adapt to knowledge and language styles in specific fields; the problem that a traditional RAG retrieval result is disjointed with an inference chain is solved, and the capacity of the model for processing complex and multi-step inference tasks is improved.

The invention relates to the technical field of artificial intelligence, and discloses a research report generation method, device and equipment and a storage medium, and the method comprises the steps: responding to an input research type subject, carrying out research step planning through a planning agent based on the research type subject, and obtaining a multi-step research plan with a logic progressive relationship, gradually searching in a search engine through a research agent based on the multi-step research plan to obtain research related information corresponding to each research step, and generating a research report corresponding to the research type subject according to the research related information corresponding to each research step; due to the fact that the multi-step research plan with the logic progressive relation is generated through cooperative work of the multiple agents and step-by-step searching is carried out in the search engine to generate the research report, intelligent planning of research report generation is achieved, and then the research efficiency and the knowledge integration capacity are improved.

The invention provides a double-layer adaptive fusion method and system for scientific and technical literature knowledge, and relates to the technical field of language processing, and the method comprises the steps: constructing a double-layer adaptive fusion architecture; receiving a scientific and technical literature text and extracting a text basic feature vector; the general knowledge layer processing module performs general high-dimensional feature vector mapping, and the professional knowledge layer processing module performs professional high-dimensional feature vector mapping; and the adaptive fusion module performs field confidence calculation, configures an adaptive fusion weight parameter, performs weighted fusion on the general-text high-dimensional feature vector and the professional text high-dimensional feature vector, and applies an output module to perform processing of the scientific and technical literature by using the fused feature vector. According to the method and the device, the technical problem of inaccurate knowledge fusion caused by poor adaptability of a general model, limited generalization ability of a field-specific model and lack of adaptability of static knowledge integration in the prior art can be solved, and the accuracy of knowledge fusion is improved by constructing a double-layer adaptive fusion architecture.

This invention discloses a federated graph learning method and system based on feature space construction and sharing, designed to address the issues of feature space heterogeneity, harmful knowledge integration, and insufficient personalized performance in federated graph learning in a cross-domain Internet of Things environment. The method includes: constructing a globally consistent feature space and achieving node feature alignment through random walk feature generation and standardization; designing a client-side hybrid feature encoder (CHFE) to combine global features (MLP encoding) and local features (GCN encoding) to generate a hybrid representation; proposing an independent adaptive aggregation strategy (IAA) to dynamically calculate aggregation weights based on domain similarity, model similarity, and gradient similarity; and introducing a personalized regularization mechanism (PPT) to balance global collaboration and local optimization. This method significantly improves the model's convergence speed and generalization ability under heterogeneous data, reduces communication overhead, and is suitable for privacy-sensitive scenarios such as healthcare and industry.

The present disclosure provides a method and system for dynamic enhancement of complex skills in a production line having uncertainty. The method comprises: acquiring a multi-view video segment, comprising an assembly task, in a virtual scene; inputting the multi-view video segment into a video parsing model to perform multiple rounds of interactive questions and answers between large models so as to generate a task action sequence and action instructions obtained after parsing; inputting the task action sequence and action instructions obtained after parsing and all nodes of a multi-level and multi-grade knowledge graph into a data enhancement network model for assembly knowledge integration to generate an enhanced action sequence; and performing secondary enhancement on the enhanced action sequence by using reinforcement learning in a simulation environment, so as to obtain a final task action enhancement result. The present disclosure aims to solve multiple problems in existing 3C product assembly technology, especially how to dynamically cope with different assembly tasks on a production line, and improve the accuracy and efficiency of assembly operation.

The present invention relates to the technical field of robots and knowledge engineering, and in particular relates to a hierarchical knowledge representation method for skills in a dynamic task scenario, an apparatus, an electronic device, and a storage medium. The method comprises: on the basis of different semantic knowledge types, using a graph database to construct a knowledge hierarchical understanding framework; and, on the basis of a preset task, performing knowledge extraction on the dynamic task scenario and, on the basis of a triple, performing external knowledge integration and dynamically updating a knowledge base to obtain a dynamic operation skill knowledge base. The application further discloses: designing a multi-level knowledge expression system of operations, planning, tasks, and objects, establishing a dynamic action library, a skill library, and a task library, and forming a robot skill operation knowledge library in a dynamic scenario together with a static entity library and a scenario library, thereby achieving knowledge guidance and data-driven operation task reasoning. Thus, a model is designed according to different scenario characteristics and an operation skill knowledge library is constructed after collecting data, thereby improving the interpretability, operation task planning, and skill migration efficiency of robot operation skill learning.

The invention discloses a water conservancy flood control scene-oriented question and answer intention recognition and multistage classification model construction method and system and a computer readable storage medium. The method comprises the steps of data collection and knowledge fusion; data preprocessing and intention system construction; constructing training data; training an intention recognition model; an online application mechanism; and model optimization and feedback closed loop. By constructing a standardized data acquisition process, a specialized intention recognition model and a high-adaptability interaction mechanism, accurate understanding and structured expression of the natural language instruction of the user in the flood control scene are achieved, and then the intelligent level of key services such as scheduling and early warning is improved.

The application discloses a database interaction method and device based on multi-agent cooperation, equipment and medium, and relates to the technical field of database interaction. The method comprises the following steps: obtaining a query request, performing semantic segmentation, and then vectorization, and obtaining a search vector set. A search source is selected and searched, and an information block is obtained. A candidate knowledge is obtained through knowledge base retrieval. The information block and the candidate knowledge are integrated, and a candidate information set is obtained. A dynamic demand understanding model is constructed according to the query request and the candidate information set, a task state matrix and a tool calling task are obtained. The corresponding auxiliary tool is called and operated, the task state matrix is updated according to the operation result, and the task planning agent is returned to update the tool calling task, until no tool calling task is output, and a task result is obtained. When the database interaction module is called, a chart is generated according to the output result of the database interaction module, and the chart is output together with the task result. Otherwise, the task result is directly output.

The invention discloses a document-level relation extraction method and system based on knowledge enhancement and evidence guidance, and belongs to the technical field of natural language processing and information extraction. According to the invention, three core modules are mainly used for cooperative work: a document graph enhancement module is used for constructing a hierarchical heterogeneous graph and fusing co-reference analysis to enhance semantic representation; the knowledge enhancement module introduces an external knowledge base and adopts a confidence coefficient filtering mechanism to reduce knowledge noise; the evidence guidance reasoning module realizes multi-hop reasoning through axial attention and evidence supervision, and solves the technical problems of decentralized modeling of reasoning capability, large knowledge integration noise, insufficient evidence guidance and limited long-range dependence capture capability in the existing method. Experiments show that the method can effectively capture inter-sentence dependence, suppress knowledge noise and improve multi-hop reasoning stability, and can be widely applied to scenes such as knowledge graph construction, intelligent question and answer and information retrieval.

The application relates to the technical field of artificial intelligence, and discloses a research report generation method, device, equipment and storage medium, the method comprising: in response to an input research topic, planning an agent to plan research steps based on the research topic, obtaining a multi-step research plan with a logical progression relationship, and based on the multi-step research plan, gradually searching in a search engine by a research agent to obtain research-related information corresponding to each research step, and generating a research report corresponding to the research topic according to the research-related information corresponding to each research step. In the application, multi-agents work together to generate a multi-step research plan with a logical progression relationship and gradually search in a search engine to generate a research report, thereby realizing intelligent planning of research report generation and improving research efficiency and knowledge integration capability.

The application discloses a method and system for simultaneously generating multiple 3D objects by using text and 2D bounding boxes, comprising a 3D content enhancement module, a layout control module, a multi-view consistency control module and a layout and multi-view knowledge integration module. The 3D content enhancement module is used to improve the quality of the generated 3D content, the layout control module is used to control the position between the multiple 3D objects simultaneously generated, the multi-view consistency control module is used to maintain the multi-view consistency of the multiple objects generated, and the layout and multi-view knowledge integration module is used to unify the multiple distilled knowledge and further enhance the quality of the generated objects. The application does not need to change the structure and parameters of the pre-trained model, and simultaneously solves the problems that the existing method cannot understand the complex logical relationship in the input text and can only generate a single object.

The invention provides a multi-modal video knowledge query system combined with agent task intention, and relates to the technical field of multi-modal fusion, and the system comprises a feature extraction module which is used for receiving video clips and text problems, and carrying out the processing of a video frame sequence through a lightweight three-dimensional convolutional network to generate a video time sequence feature vector; performing semantic analysis on the text question to generate a text semantic feature vector representing question semantics; and the mapping module is used for analyzing the corresponding relationship between the space coordinates of the video key frame and the text semantics according to the relevance between the video time sequence feature vector and the text semantic feature vector, constructing a dynamic space semantic mapping representation, and determining an initial space reference position based on the mapping representation. Through multi-modal feature fusion, dynamic space-time constraint and external knowledge integration, the task intention of the agent is captured, the comprehensive answer fitting the scene is generated and output in the form of voice, and the accuracy, efficiency and interaction convenience of multi-modal video knowledge query are improved.