43 results about "Knowledge application" patented technology

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 relates to the technical field of computers, and discloses a knowledge management method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a knowledge task initiated by a user, analyzing the task intention of the knowledge task, dynamically selecting and combining one or more special intelligent agents from a plurality of predefined special intelligent agents based on the analyzed task intention, and storing the selected special intelligent agents. Generating a workflow for the corresponding knowledge task; the workflow is scheduled and executed, so that the selected special intelligent agents cooperatively work in sequence or in parallel, a task result is generated, and generated derivative knowledge achievements are captured; and processing the derivative knowledge achievement, calling an information extraction class agent to perform entity and relationship extraction on the derivative knowledge achievement, and after entity disambiguation, updating a structured result into the mapping knowledge of the unified knowledge base to form a knowledge closed loop. According to the method, knowledge can be driven to be actively, accurately and efficiently converted into actual productivity in various scenes, and knowledge application ecology capable of being evolved continuously is formed.

The invention provides a multi-agent collaborative semantic transformation framework for a rare disease recognition problem, and belongs to the technical field of artificial intelligence and medical image analysis. According to the method, the problems of scarcity of marked data and difficulty in cross-domain knowledge migration in rare disease recognition are solved. The method comprises the steps that S1, a multi-agent parallel network architecture is constructed, each agent is provided with a special semantic focusing module, and diversified features are extracted from different attribute perspectives; s2, a cooperative gating mechanism with dynamic temperature parameters is realized, and the cooperative and competitive relationship between intelligent agents is balanced in a self-adaptive manner; s3, applying a double-constrained cross-domain semantic alignment strategy to ensure that the converted semantic features are consistent with the original features and keep diversity at the same time; s4, adopting a progressive training strategy and a semantic consistency loss function to reduce an overfitting phenomenon in cross-domain knowledge migration; and S5, classifying and identifying rare diseases through multi-agent cooperation, and applying source domain knowledge to a target domain. The method is prominent in medical image application, the accuracy rate of identifying rare skin diseases by using only common skin disease data reaches 52.13%, and the method is remarkably superior to an existing method. The framework has wide applicability in cross-domain zero sample learning tasks, is particularly suitable for application in the field with definite definition of semantic attributes, and provides a new solution for diagnosis of rare diseases in medical images.

According to the multi-culture medical AI supervision and auditing platform provided by the invention, layered review is carried out on medical AI decisions by introducing a semantic sovereignty framework, so that the accuracy, fairness and compliance of the AI in a cross-culture situation are ensured. A multi-culture knowledge rule base is arranged in the platform, and the whole process of AI from data extraction, knowledge application to decision output is monitored. For each layer of semantics, the auditing unit automatically detects whether the AI has the problems of cultural semantic misunderstanding (such as misunderstanding of traditional terms such as qi deficiency), knowledge bias (such as neglecting local medicine by only adopting western medicine knowledge) or decision-making bias (systematic unfairness exists in suggestions for people with different cultural backgrounds) and the like. Once found, the platform generates an audit report alert and gives adjustment suggestions. Meanwhile, the platform checks ethical suitability of AI suggestions, and the situation that believes of patients and cultural custom taboo are violated is avoided; and through intention layer auditing, the AI decision is ensured to conform to value orientation and medical strategies of the same region. According to the method, the medical AI decision is more transparent and controllable, and the semantic rights and interests of each culture group in the AI era are maintained. As an innovative AI treatment tool, the platform can be used for a supervision institution and a medical institution to continuously audit various intelligent diagnosis and treatment systems so as to realize'controlled autonomous' AI: each cultural context is fully and autonomously learned, human expectation and ethical constraints are followed in output, and a solid guarantee is provided for safe and fair AI medical fusion in a global range.

The application relates to a knowledge-driven multi-agent cooperative reactor scheme demonstration system and a design method, medium and equipment thereof, the system comprising: a knowledge internalization unit for converting unstructured and semi-structured documents into a dynamic knowledge source which can be queried and utilized by a model in real time; a knowledge structuring unit for extracting core entities, relationships and attributes from the knowledge source and constructing a professional knowledge graph of a reactor design field; and a knowledge application unit for constructing intelligent agents and a collaborative working mechanism of multi-professional intelligent agents based on the knowledge source and the knowledge graph, and constructing an intelligent question and answer interface. Through automatic knowledge management and intelligent agent collaborative work, the application significantly reduces the time and effort of manual intervention and improves the efficiency of reactor scheme demonstration.

This invention belongs to the field of artificial intelligence technology. The embodiments of this invention provide a fully automated interview assistance method, system, and device based on a large language model. First, it acquires recruitment requirements, initial interview questions, and multimodal data of job seekers from the recruitment end to construct a multimodal dynamic knowledge base. This multimodal dynamic knowledge base includes content dynamic mechanisms, structure dynamic mechanisms, and iterative dynamic mechanisms, used to update the knowledge base content and sub-question sequences, update the structure of the knowledge graph in the multimodal dynamic knowledge base, and optimize sub-question generation and retrieval strategies, respectively. Then, based on the multimodal dynamic knowledge base, it determines the job seeker's structured question set, and subsequently determines the job seeker's interview assistance strategy. This invention, through three update mechanisms, makes knowledge application more closely aligned with actual interview scenarios, overcoming the shortcomings of traditional static and rigid knowledge bases.

A class-incremental learning method based on knowledge bridging and class anchoring, which alleviates the catastrophic forgetting problem and improves the overall performance of the model class-incremental learning through knowledge bridging and class anchoring respectively; knowledge bridging aims to establish the semantic correlation between old classes and new classes, and applies the learned knowledge to learn new information by using feature-level distillation; class anchoring focuses on learning class-specific feature centers that are crucial for distinguishing all classes. Through the combination of the class-incremental learning method based on knowledge bridging and class anchoring proposed in the application, the original model can achieve better performance in the incremental learning task and effectively alleviate the catastrophic forgetting phenomenon.

This invention discloses an intelligent knowledge extraction system based on natural language processing, comprising: a text data acquisition module that acquires unstructured text and forms raw data; a text data preprocessing module that removes noise and standardizes the format of the raw data, outputting standardized text data; a text feature encoding module that converts the standardized text data into feature vectors that satisfy the input of an improved W2NER model; a knowledge extraction module that processes the feature vectors based on the improved W2NER model to obtain entity extraction results; an entity normalization module that performs ontology mapping and normalization on the entity extraction results; a knowledge fusion module that performs deduplication and fusion on the entity knowledge set; a knowledge storage module that stores the fused knowledge data in a structured manner; and a knowledge application service module that provides user knowledge query and reasoning services. This invention improves the accuracy of knowledge extraction and the efficiency of data application.

The application discloses a kind of big language model enhanced emission trading knowledge graph construction method and system, it is related to machine learning field.The present application obtains the unstructured text data in the field of emission trading, carries out document analysis and text segmentation to text data, obtains standardized text fragment;Based on the field of emission trading adaptability prompt engineering guides big language model, carries out entity recognition and relationship extraction to the text fragment, obtains initial knowledge triple and processing, obtains high-quality knowledge triple;Text fragment is converted and constructs structured triple with high-quality knowledge triple and semantic vector fusion's bimodal knowledge representation;The bimodal knowledge representation is stored to graph database, and constructs the field of emission trading knowledge graph;Based on double-path retrieval, carry out retrieval and knowledge traceability to knowledge graph, realize the field of emission trading intelligent question and answer and knowledge application.The present application realizes the efficient conversion from unstructured data to structured knowledge.

The invention belongs to the technical field of medical knowledge maps, and discloses a semantic understanding system and method based on a knowledge map, and the method comprises the steps: obtaining traditional Chinese and western medicine medical data; constructing a medical semantic nebula atlas to obtain a key concept pedigree; constructing a symptom-syndrome type mapping network to obtain a semantic core element set; constructing a double-domain semantic bridging knowledge base; performing space-time context analysis to obtain a context embedding vector and a concept rheological function family; performing semantic alignment to obtain a cross-domain semantic pedigree; performing multi-modal fusion analysis to obtain a modal translation matrix; performing semantic conversion to obtain a semantic resonance model; carrying out concept integrity verification to obtain a target fusion knowledge graph; and monitoring and optimizing to realize dynamic evolution of a traditional Chinese and western medicine semantic comprehension mechanism. According to the method, deep semantic fusion and adaptive optimization of traditional Chinese and western medicine knowledge are realized, and the application efficiency of interdisciplinary medical knowledge is improved.

This invention provides a method and system for constructing a deep learning-based model of exam question setter behavior, relating to the fields of artificial intelligence and educational assessment. The method includes acquiring question-setting records to construct a knowledge application graph, analyzing question-setting preferences based on time windows, training a question-setting behavior recognition network to optimize transfer paths and difficulty distribution, and achieving anomaly detection in question-setting behavior. This invention can accurately characterize the knowledge preferences and behavioral patterns of question setters, improve the efficiency of question-setting quality monitoring, reduce question-setting risks, and ensure exam fairness.

This invention relates to the field of smart education technology and discloses a knowledge graph-based smart education system and method. The knowledge graph-based smart education system includes: a multi-source data acquisition module, a knowledge graph construction and updating module, a personalized teaching push module, a learning dynamic analysis module, an intelligent interaction module, a permission management module, and a data storage module. These modules work collaboratively to form a complete smart education ecosystem. This invention addresses the problem of fragmented knowledge by constructing a systematic knowledge system. Through the knowledge graph construction and updating module, scattered teaching knowledge points are structurally modeled, clearly presenting the pre-dependencies, parallel relationships, and extensions between knowledge points. This breaks down the knowledge barriers of traditional teaching, helps students grasp the overall logic of the knowledge system, improves knowledge absorption efficiency and application ability, and solves the problem of weak knowledge connections in existing education systems.

The application discloses a kind of information collaborative processing platform and information query method based on knowledge graph, including data acquisition processing layer, knowledge extraction intelligent analysis layer and knowledge application decision layer;The data acquisition processing layer includes data acquisition module, data format conversion module and knowledge base management module;The knowledge extraction intelligent analysis layer includes keyword extraction module, keyword hierarchical module, semantic fusion module and knowledge ontology dynamic evolution module;The knowledge application decision layer includes database module, entity and relationship extraction module, event topic library, organization library, evaluation result acquisition module and military strategy guidance information update module and military strategy graph generation module.The application can realize the effective cooperation between military strategy display, military strategy dynamic adjustment and intelligence data real-time processing, and can also provide more perfect military strategy related information query result.

The invention relates to the technical field of man-machine interaction, in particular to an rag-fused communication field question-answering system, which comprises the following steps: semantic analysis adopts hierarchical abstraction and non-iterative embedding technologies to convert multi-modal input into structured semantic representation and break through the limitation of traditional modal isolation; according to context adaptation, accurate matching of a query intention and a professional context is achieved through a dynamic alignment algorithm, and the correlation of retrieval results is ensured; knowledge fusion adopts an incremental updating mechanism, the latest industry knowledge is dynamically integrated into the question and answer process, and the information timeliness is kept; feedback is optimized, a continuous learning closed loop based on user interaction is established, and the content of the knowledge base is automatically optimized through intelligent diagnosis. According to the method, full-process intelligent processing from information acquisition to knowledge application is realized particularly aiming at the characteristics of fast knowledge updating, strong specialty and the like in the communication field, and key indexes such as accuracy, response speed and adaptive capacity are remarkably improved.

The application discloses a kind of knowledge mastery degree evaluation methods based on multi-modal learning behavior data, its method includes: the visual, interactive and answering behavior modal data of target user in learning process are collected;Real-time time series feature extraction is carried out to each modal data, and the primary feature sequence respectively representing cognitive concentration, behavior participation and knowledge application degree is obtained;Feature alignment and fusion are carried out by hierarchical attention mechanism, and a comprehensive dynamic learning state vector sequence is generated;The sequence is input into the pre-trained knowledge mastery degree evaluation model, and the mastery degree probability distribution of target user on each knowledge unit evolves with time is output.The application realizes continuous fine-grained characterization to learning process, comprehensively utilizes multi-modal complementary information, overcomes the one-sidedness of evaluation of single data source, so that the evaluation result is more comprehensive and objective, and the change process of knowledge mastery state can be dynamically reflected.

The application discloses an education intelligent agent system and method based on multi-engine cooperation and dynamic knowledge evolution, and the system comprises a data acquisition layer, which is responsible for collecting original data from various data sources and providing data support for the upper layer; a data layer is used for storing various data; a model layer is used for providing different levels of large model capabilities; an intelligent analysis layer uses an artificial intelligence layered large model as the technology, analyzes and processes the collected data, and generates high-level abstract data models and knowledge; an application service layer encapsulates the results of the intelligent analysis layer into reusable services; an application layer directly faces the application interface of the end user and provides specific functions; the application has the advantages that: a real personalized learning path planning is realized, through the deep cooperation of the six intelligent engines, the education mode innovation from "one face for thousands of people" to "one person thousands of faces" is realized, and the intelligent education experience of precise adaptation, real-time response and combination of learning and use is provided for each learner.

The invention discloses a multi-modal teaching resource intelligent adaptation and immersive learning interaction system, and belongs to the technical field of intelligent education. The system comprises a multi-modal resource management module, a learner cognition and behavior perception module, an intelligent adaptation engine, an immersive interactive scene generation and rendering module and a learning effect closed-loop feedback module, and all the modules form a complete working closed loop through bidirectional data communication. The system integrates multi-modal resources such as texts, VR / AR, audios and videos and constructs a cross-modal semantic association map, and then real-time cognitive states and interaction data of learners are collected through biosensing and behavior capturing equipment. According to the method, the defects of rigid resource adaptation, weak immersion, lack of collaboration and closed loop and insufficient creative support in the prior art are overcome, precise adaptation, high immersion interaction, creative learning and cross-scene closed loop driven by cognition are realized, and the learning efficiency and knowledge application capability are remarkably improved.

The invention is oriented to the field of data resource construction, aims to provide a self-adaptive dynamic knowledge updating method and system combined with spatio-temporal data, and solves the problem of association and fusion of spatio-temporal data (millisecond-level updating) and static entities (minute / hour-level updating) in a knowledge graph. In practical application, the knowledge graph only having static data is lack of timeliness and is easily disjointed from the real environment, and the prediction reasoning ability is limited. The knowledge graph fused with the spatio-temporal data better meets actual scene requirements, the problem that information of the knowledge graph is outdated can be solved, and dynamic cognitive competence is provided. According to the method, a proper updating mechanism is adaptively selected according to the data updating frequency, the data importance degree, the environment resource state and the like, the updating task is subjected to full-process management, the spatio-temporal data and the atlas entity are associated and fused for use, and the knowledge application requirement under the high timeliness requirement is met.

The invention provides an intelligent knowledge management system and method and a storage medium. The system comprises a knowledge application module which obtains input information and pushes interested knowledge to a current user; the intelligent engine module obtains to-be-queried knowledge corresponding to the input information when the current user has the operation authority corresponding to the input information; the knowledge management module is used for carrying out classified management on knowledge entries in the knowledge graph and carrying out approval, correction and release on newly added knowledge entries; and the basic support module is used for performing authority distribution on the user and performing authority verification on the current user when receiving the query request. For scattered knowledge points in the field of power marketing, multi-source heterogeneous data are integrated, the knowledge graph is established, information input by the user is accurately understood and analyzed based on the intelligent search engine module, the user can conveniently obtain power marketing knowledge by himself / herself, and the requirement of the user for the power marketing knowledge is met.

The invention discloses a construction site potential safety hazard intelligent judgment system and method based on an AI large model. The system comprises an acquisition unit and an intelligent judgment unit. The acquisition unit is used for acquiring video data of a construction site in real time and transmitting the video data to the intelligent judgment unit; and the intelligent judgment unit is used for receiving the video data, analyzing the video data by using an AI large model so as to determine whether a potential safety hazard exists in the field or not, automatically generating a message for reminding and sending the message to a safety manager once the potential safety hazard is found, and receiving submitted rectification feedback information. According to the invention, accurate identification of more scenes, deeper safety knowledge application and continuous optimization learning can be realized, and the safety management level of a construction site is comprehensively improved.

The invention provides an intelligent photoelectric pod multispectral image segmentation method based on a transfer learning auto-encoder, and belongs to the technical field of image processing. Comprising the following steps: S1, preprocessing an image of the intelligent photoelectric pod; s2, training an auto-encoder model; s3, transfer learning and model fine tuning; s4, evaluating the model; and S5, updating the model. According to the method, the model pre-trained on the remote sensing image segmentation task is utilized, and the features of the model are migrated to the intelligent photoelectric pod multispectral image segmentation task, so that the dependence on a large amount of annotation data is greatly reduced. The transfer learning applies the knowledge of the pre-training model to a new task, so that the model training time can be shortened, and the consumption of computing resources is reduced. A self-encoder is used for extracting deep features in a multispectral image, and the adaptability of the model is improved through transfer learning.

The invention relates to a DIKWP aware operating system (AI-OS) and a security framework. The invention provides an artificial consciousness operating system and a security framework based on a DIKWP (Data-Information-Knowledge-Intelligence-Intention) model, and aims to solve the problems that the decision-making process of an existing AI (Artificial Intelligence) system is black, the existing AI system does not have self-cognition, the security is uncontrollable and the like. According to the operation system, the cognitive process of AI is divided into five stages of data processing, information extraction, knowledge application, intelligent decision making and intention management, and through core components such as a concept-semantic fusion kernel, a white-box evaluation module, a semantic security protection module and an intention regulation and control interface, an intellectual property of the AI is evaluated. Semantic checking, whole-process monitoring and purpose constraint of an AI internal cognitive process are realized. Wherein the kernel adopts a concept space and semantic space double-layer verification mechanism to improve AI semantic understanding consistency, the white box module records AI full-link reasoning to achieve interpretable auditing, the safety protection module is embedded into ethical rules to intercept violation output in real time, and the intention interface directly acts human high-level intentions on the AI decision process. Through the architecture, the decision-making process of the AI system is transparent and controllable, the output behavior is consistent with the preset target and value criterion, and the interpretability, safety and reliability of the AI system are remarkably improved.

The application provides a cooperation argumentation state representation method and system based on an online scientific argumentation map. Through the automatic and structured analysis and calculation of the interactive text and behavior data of online cooperation argumentation by an educational large language model, a paradigm shift from "post-event manual evaluation" to "process intelligent diagnosis" is realized. The system can quantitatively represent the multi-dimensional state of learners in cooperation depth (such as participation and interaction) and argumentation quality (such as logical mode and knowledge application) in real time and dynamically, and synchronously generate a visual scientific argumentation map integrating individual and group perspectives. This not only greatly improves the objectivity, timeliness and precision of process evaluation, enabling teachers to accurately grasp the learning situation and intervene in time, but also provides learners with intuitive and concrete reflection scaffolds, thereby effectively promoting the development of their high-order thinking and scientific argumentation literacy.

The invention discloses a dynamic matrix control method and system for exosome extraction, and relates to the technical field of exosome extraction, the system and the method realize digital integration and dynamic analysis of execution elements, sensors and control logic by constructing and maintaining a uniform dynamic state matrix in real time, and the system and the method are suitable for large-scale industrial production. Flow path control is converted from a fixed mode to flexible self-adaptive regulation and control, the action of a pump valve is automatically adjusted according to parameters such as the liquid level, and the automation degree and the process stability are remarkably improved. The membrane package load is dynamically balanced through an intelligent algorithm, process parameters are finely adjusted, membrane package blockage is relieved while the extraction efficiency and the product consistency are improved, and the service life of consumables is prolonged. In the knowledge application level, the system has autonomous learning and evolution capabilities, and quantitative evaluation and iterative optimization of the historical formula are realized by converting operation data into a structured performance file and constructing a knowledge base.

The application provides an enterprise knowledge base construction method and system based on AI technology, and belongs to the technical field of computers. The method comprises the following steps: matching corresponding access interfaces for a plurality of data source tools used by each department of a target enterprise, and obtaining initial construction data; matching an adapter for the data source tools by using AI technology, and analyzing and converting the initial construction data of the data source tools to obtain intermediate construction data; identifying an implicit mapping relationship of the same business entity in different data sources by using a graph neural network, performing conflict detection and disambiguation processing on the intermediate construction data based on the mapping relationship, and obtaining final construction data; adjusting a minimum granularity unit of knowledge extraction according to feedback of a downstream knowledge application scene of the final construction data by using a reinforcement learning model, and generating a knowledge candidate set; performing knowledge extraction and standardization processing on the knowledge candidate set respectively to obtain standardized knowledge data; and constructing an enterprise knowledge base based on the standardized knowledge data.

The invention belongs to the technical field of artificial intelligence, and provides a full-process automatic interview assisting method, system and equipment based on a large language model.The method comprises the steps that firstly, recruitment requirements of a recruitment end, initial face test questions and multi-modal data of job seekers are obtained, and a multi-modal dynamic knowledge base is constructed; wherein the multi-modal dynamic knowledge base is provided with a content dynamic mechanism, a structure dynamic mechanism and an iteration dynamic mechanism which are respectively used for updating knowledge base content and a sub-problem sequence, updating the structure of a knowledge graph in the multi-modal dynamic knowledge base and optimizing a sub-problem generation and retrieval strategy; and then, according to the multi-modal dynamic knowledge base, determining a structured question set of the job seeker, and further determining an interview auxiliary strategy of the job seeker. Through three updating mechanisms, knowledge application is more suitable for an actual interview scene, and the defects that a traditional knowledge base is static and rigid in structure are overcome.

The invention discloses an intelligent content collection and knowledge storage system and method based on large model driving. The system comprises a configuration management module, a site detection and change monitoring module, a content extraction and processing module, an intelligent analysis and storage module and a knowledge retrieval and question and answer module. The method comprises the following steps: discovering an acquirable URL list by automatically analyzing input site information, and extracting core text content from HTML (Hypertext Markup Language) by utilizing network analysis to carry out correlation judgment and Markdown conversion; based on an LLM large language model and a pre-trained Embedding model, text information of the content is converted into high-dimensional vector representation, and knowledge storage is carried out; and configuring an RAG architecture, integrating a vector database and an LLM large language model, and generating high-quality and accurate answers. According to the scheme, the problems of low content collection efficiency, insufficient intelligence, poor robustness, knowledge updating lagging and knowledge application limitation in the prior art are solved.

The invention discloses a territorial space intelligent collaborative management system, and the system comprises a digital twin environment which is used for providing a learning and deduction simulation environment for a decision-making agent, and constructing a territorial system mirror image which is highly real and can achieve the efficient calculation; the strategy emergence engine is used for generating, evaluating and optimizing territorial space planning strategies and realizing autonomous'emergence 'of excellent strategies; and the man-machine collaborative cockpit is used for realizing interaction between a decision maker and the strategy emergence engine module and realizing efficient, visual and unbiased man-machine collaborative decision. Through the intelligent collaborative management system, the problems of systematic splitting, decision space limitation, static bottleneck of computing resources, knowledge application disjunction and the like in current territorial space management can be solved, and the intelligent management level of territorial space multi-target collaborative optimization and sustainable development decision is remarkably improved.

This invention relates to the field of knowledge-based brain data management technology, and discloses a knowledge-based brain data-driven intelligent manufacturing management method for the aviation manufacturing industry. It utilizes natural language processing technology to analyze internal data, constructs a large-scale knowledge base for the aviation manufacturing field, and builds various training algorithm models for the aviation manufacturing field on this basis. The purpose of this invention is to address the problems of knowledge acquisition, knowledge integration, and knowledge application in the aviation manufacturing field, as well as the personalized needs of aviation users, by providing a thinking mode that conforms to human users. This method can reduce production consumption, improve product testing efficiency, product qualification rate, production yield, quality inspection efficiency, and testing accuracy, and achieve a more advanced and efficient intelligent manufacturing management method for knowledge-based brain data in the aviation manufacturing industry.