13 results about "Knowledge application" patented technology

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.

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.

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 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 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 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.

A computer-implemented digital knowledge management system for institutional learning and decision support, comprising the following: A user interaction interface configured to receive structured institutional information from a variety of users connected to a university. This structured institutional information includes strategic issues, institutional goals, performance indicators, academic activity data, and decision-relevant input. an input processing unit that is operationally connected to the user interaction interface and is configured to capture, classify, and convert the received structured institutional information into machine-readable data sets; a collaborative culture assessment unit consisting of at least one processor and a memory in which executable instructions are stored, wherein the collaborative culture assessment unit is configured to collect and process organizational behavior data representing loyalty, engagement, cohesion, well-being and sense of community among members of the institutional leadership; a knowledge generation unit that is operationally linked to the collaborative culture assessment unit, wherein the knowledge generation unit is configured to analyze received knowledge records and generate new institutional knowledge by exploring alternative ideas, identifying new insights, and combining individual and collective institutional contributions; a knowledge exchange unit that is communicatively linked to the knowledge generation unit, wherein the knowledge exchange unit is configured to distribute the generated institutional knowledge to authorized users via controlled communication channels and structured knowledge dissemination procedures; a knowledge application unit designed to transform distributed institutional knowledge into decision support tools, including proposed strategic actions, operational recommendations, and measurable performance targets; an institutional learning repository consisting of a permanent digital storage system configured to store knowledge records, decision outcomes, institutional experiences, and insights gained from implemented decisions; a decision quality assessment processor that is operationally linked to the institutional learning repository and the knowledge application unit and is configured to evaluate generated decisions against predefined institutional criteria, including problem-solving effectiveness, strategic value contribution, achievement of institutional goals, and contribution to organizational learning; and a coordination processor configured to coordinate the operational interaction between the collaborative culture assessment unit, the knowledge generation unit, the knowledge sharing unit, the knowledge application unit, the institutional learning repository, and the decision quality assessment processor, such that the collaborative organizational conditions influence knowledge generation, knowledge distribution, knowledge utilization, and the subsequent evaluation of strategic decisions.

This invention belongs to the field of artificial intelligence communication technology and discloses a method and system for adaptive evolution and cross-domain collaboration driven by meta-learning of communication agents. Addressing the specific technical problems of communication agents, such as weak rapid adaptation to new scenarios, insufficient cross-domain collaborative knowledge transfer, and lack of autonomous optimization mechanisms for dynamic evolution, this invention constructs a meta-learning adaptive modeling framework for communication agents, designs cross-domain collaborative knowledge distillation and transfer algorithms, and proposes an autonomous optimization mechanism for the dynamic evolution of agents. This achieves a technical upgrade for communication agents from fixed model adaptation, isolated knowledge application, and passive iterative upgrades to rapid scenario adaptation, cross-domain knowledge reuse, and proactive evolution optimization.

The application discloses an operation and maintenance strategy determination method and device based on a step log and a storage medium, and relates to the technical field of data processing. The method is used to solve the problem that traditional operation and maintenance is separated from business and cannot quantitatively perceive business effects. The method is used to solve the problem of complex unknown business performance that has no matching preset rules. The method is used to break through the limitations of fixed rules and artificial experience. The method is used to improve the operation and maintenance capability of an intelligent operation and maintenance system when dealing with complex business problems.