17 results about "Individual knowledge" patented technology

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

The invention relates to the technical field of computer-aided teaching and medical image recognition, and particularly discloses an intelligent knee osteoarthritis image analysis teaching system based on computer vision. The system comprises an image data management module, a student interaction and behavior acquisition module, a multi-dimensional skill quantitative analysis module, a personalized knowledge graph construction and gap analysis module, a self-adaptive teaching path planning module and a teaching feedback and report generation module. By collecting and quantitatively analyzing operation behaviors and sight tracks of students, a personal knowledge graph is constructed to accurately position skill defects, and a personalized teaching path is planned according to the skill defects, so that refined evaluation and adaptive teaching guidance of image analysis practical skills are realized.

The application provides a method and system for dynamic modeling and closed-loop iteration of individual knowledge and cognitive patterns of scientific researchers, relating to the fields of computer technology, artificial intelligence and information processing technology. The method includes: obtaining explicit knowledge data and interaction behavior data of scientific researchers; performing enhancement and structuralization processing; constructing objective knowledge base information based on explicit knowledge data; constructing historical behavior graph based on interaction behavior data and performing metacognition analysis to extract cognitive pattern information; constructing a structured context assembler dominated by cognitive patterns; in response to a scientific task request, calling the assembler to generate a task-level scientific portrait structured context, and inputting it together with the task request into a large language model to generate personalized output; collecting multi-modal feedback data and updating the cognitive pattern to realize continuous iteration. Through deep twin modeling and closed-loop optimization of objective knowledge and subjective cognition, the application significantly improves the personalization and intelligence level of scientific research assistance.

The invention discloses an intelligent interactive answer data analysis and evaluation system, and relates to the technical field of data analysis, the system comprises a pre-examination preparation module, an after-examination arrangement module and an answerer file module, the pre-examination preparation module is used for marking knowledge points related to each option of a question when a questioner sets the question, and the questioner file module is used for recording the knowledge points related to each option of the question when the questioner sets the question; the after-examination arrangement module is used for recording and transverse comparative analysis after answers give answers and enabling the answers to give question solving thoughts for mastered questions, and the answers archive module is used for establishing personal knowledge point mastering degree archives of all the answers, carrying out probability calculation on which questions are mastered, and storing the probability calculation result in a database; the pre-examination preparation module comprises a question labeling module, an option labeling module and a knowledge point label establishing module, and the question labeling module and the option labeling module are electrically connected with the knowledge point label establishing module.

The application discloses a kind of multi-dimensional learning situation graph construction method and system based on graph neural network, and the construction method includes: obtaining the multi-modal learning behavior data of student;Extract the dynamic multi-modal interaction features of behavior data, and determine the cognitive dependence relationship between knowledge points;Based on student, test question, three kinds of nodes of knowledge point, and cognitive dependence relationship, construct multi-level heterogeneous graph, the multi-level heterogeneous graph includes bottom interaction graph and upper cognitive graph;Multi-level heterogeneous graph is input into layered graph neural network, and the node embedding is updated in bottom interaction graph by multi-scale attention fusion feature, meta-path heterogeneous graph convolution, and knowledge is realized in upper cognitive graph Knowledge forward and backward propagation, generate learning situation graph based on updated node embedding, and the learning situation graph includes individual knowledge mastery state and group knowledge mastery distribution.The application can capture learning interaction and knowledge association features, improve the comprehensiveness of learning situation analysis, and significantly improve the accuracy of learning situation graph construction.

The invention discloses an intelligent education analysis and execution system and method based on a multifunctional all-in-one machine, and belongs to the field of education technology and office automation. According to the system, on the basis of hardware of an existing printing, copying and scanning all-in-one machine, a hybrid computing architecture is innovatively adopted, and a local computing unit and a cloud collaboration unit are integrated. The core of the method is that dual-mode correction and dual-mode work are supported: AI intelligent correction and standard answer correction are supported in correction; in the working mode, seamless switching between offline and online is supported. The system can selectively and automatically correct the test papers in batches and accurately print marks on the original papers, meanwhile, a dynamic personal knowledge graph is constructed by analyzing answer data, and accordingly, a personalized learning path is generated and printed and practice is consolidated. According to the invention, general office equipment is upgraded into an intelligent education terminal which is applicable to all scenes and integrates automatic correction, process evaluation, learning condition analysis and personalized tutoring.

The invention relates to the technical field of knowledge social interaction, and discloses a method and system for multi-user knowledge interconnection and intelligent representative interaction, and the method comprises the steps: relying on a large language model and a retrieval enhancement generation technology, analyzing the multi-modal content of a user, extracting a structured knowledge unit, and generating a multi-dimensional knowledge representation, constructing a personal knowledge portrait and storing the personal knowledge portrait into a private knowledge base; a personal intelligent representative is constructed, and cross-user knowledge accurate retrieval and authority control are realized; dynamically calculating the credibility level of user knowledge, and driving the self-adaptive adjustment of the authority; social behaviors are converted into knowledge graph optimization signals based on reinforcement learning, a knowledge interaction closed loop is constructed, and high-value knowledge is precipitated; the right is confirmed and the knowledge value is quantified through a double-track recording mechanism, the income is distributed based on a Shapley value algorithm, and a credible settlement basis is generated. The problems that existing platform knowledge and identity binding is loose, credibility is difficult to guarantee, and interaction is low in efficiency can be solved.

The application discloses a multi-center image recognition method and medium based on data-free knowledge distillation federated learning, each imaging center corresponds to a client, and all the clients share a server end; each client utilizes a trained local autism recognition model to output an autism recognition result according to a feature vector corresponding to an image of the local imaging center; wherein the learning and training method of the local autism recognition model of each client is that, on one hand, the local autism recognition model is learned and trained by using a local image database, and on the other hand, the local autism recognition model is learned and trained by using a data-free knowledge distillation method, autism classification knowledge is learned from other clients through the shared server end, and is migrated to the local autism recognition model, while the individual knowledge learned by using the local image database in the local autism recognition model is reserved. The application can eliminate the dependence of the federated distillation method on a real data set, and effectively improve the accuracy of multi-center autism classification.

A method of providing a personal knowledge graph-based journal includes generating a journal using a personal knowledge graph based on user data having a co-relationship, displaying a first image and a text of the journal in a first area of a display screen, the first image being generated based on first user data used to generate the journal, obtaining a user input for modifying the first image displayed in the first area, and changing the text of the journal based on the first image being modified.

The application relates to the technical field of artificial intelligence, and discloses a question and answer method and device based on retrieval enhancement generation, equipment and a storage medium in the fields of financial technology and medical health. The method comprises the following steps: obtaining a target input question and a target user identifier; finding out a personal knowledge subgraph corresponding to the target user identifier from a preset knowledge graph, and determining a first prompt node set according to the target input question and the personal knowledge subgraph; finding out a community knowledge subgraph corresponding to the target user identifier from the preset knowledge graph, and determining a second prompt node set according to the target input question and the community knowledge subgraph; determining user preference data according to the personal knowledge subgraph, and determining target prompt information according to the user preference data, the first prompt node set and the second prompt node set; and inputting the target input question and the target prompt information into a preset large language model to obtain target answer information. The application improves the accuracy and interpretability of personalized answers.

Embodiments of the present application provide a knowledge graph processing method and device, wherein the knowledge graph processing method comprises: obtaining one or more videos watched by a user and feature information of the user; determining a viewing time of each video in the one or more videos watched by the user, and taking one or more videos with a viewing time greater than or equal to a preset time threshold as initial videos; determining one or more target videos corresponding to the user according to attribute information of the one or more initial videos and the feature information of the user; extracting one or more video tags of the one or more target videos, and establishing or updating a personal knowledge graph for the user based on the one or more video tags.

The application relates to a scientific and technological service field knowledge base construction method and system based on a knowledge graph and RAG. Through data collection and preprocessing, multi-source scientific and technological data (such as patents, papers and technical documents) are cleaned, labeled and standardized to construct a scientific and technological knowledge graph and a personal knowledge graph, wherein the scientific and technological knowledge graph adopts a four-tuple structure of entity-relation-entity-attribute, and supports efficient knowledge management and retrieval. A RAG model is designed and trained, a retrieval module and a generation module are combined, related knowledge is retrieved from the knowledge graph, a natural language reply is generated, a dynamically updated knowledge base is constructed, new data is accessed in real time, and the retrieval and generation efficiency is optimized, the scientific and technological achievements can be retrieved according to user queries in combination with the personal knowledge graph and a multi-modal reply is generated, the model and the knowledge base are iteratively optimized through user feedback, and the timeliness and accuracy of the knowledge are ensured. The application significantly improves the intelligent level and user experience of scientific and technological services.

The invention provides a test question parameter consistency detection method and system for group knowledge diagnosis. The method comprises the steps that group, student, test question and answer result data are collected and preprocessed to obtain an interaction matrix; individual knowledge distribution is constructed, group knowledge generality characteristics are obtained, and group knowledge generality and dependency are captured based on an attention mechanism; then test question parameters are coded, and the answer correct probability is calculated based on a cognitive diagnosis model; by taking variation lower bound (ELBO) maximization as a target and knowledge distribution KL divergence minimization as a constraint, iteratively updating parameters of an encoder and the like; and finally, outputting a knowledge diagnosis result and completing DIF detection through layered likelihood ratio test. According to the group knowledge diagnosis and test question function difference (DIF) detection method based on the hierarchical variation model, the problem that knowledge dependence of students in a group is ignored in traditional detection is solved, the knowledge point prediction precision of group knowledge diagnosis is improved, objectivity and precision of test question DIF detection are achieved, knowledge state distribution KL divergence is reduced, and inference efficiency is improved.

The application relates to the technical field of knowledge social interaction, and discloses a method and system for multi-user knowledge interconnection and intelligent representative interaction. The method comprises the following steps: relying on a large language model and retrieval enhancement generation technology, analyzing user multi-modal content, extracting structured knowledge units and generating multi-dimensional knowledge representation, and constructing a personal knowledge portrait and storing it in a private knowledge base; constructing an intelligent representative, realizing cross-user knowledge accurate retrieval and permission control; dynamically calculating the knowledge credibility level of a user, and driving adaptive adjustment of permissions; converting social behavior into knowledge graph optimization signals based on reinforcement learning, constructing a knowledge interaction closed loop, and depositing high-value knowledge; and through a double-track recording mechanism, the knowledge value is verified and quantified, the Sharpe value algorithm is used to distribute the income, and a reliable settlement basis is generated. The application can solve the problems of loose binding of existing platform knowledge and identity, difficult guarantee of credibility, and inefficient interaction.

Examples of the present disclosure describe systems and methods for providing textual summaries based on personalized prior knowledge. In examples, a user request for a summary of document or an entity is received. The document or documents associated with the entity are separated into segments and semantic embeddings are created for each segment. The semantic embeddings, a context of the user request, and relevant information available in the requesting user's previous knowledge base are provided as input to a personal knowledge system. Based on the input, the personal knowledge system outputs an indication of the segments that should be summarized. The indicated segments are provided to a summarization system. The summarization system generates a document summary or an entity summary and provides the summary to the user.

The application relates to a method and device for constructing a personal knowledge graph in an Internet community, and the method comprises the following steps: obtaining a full set of attention words and dividing the full set of attention words into field word tables according to fields; obtaining post review texts published by each user in the Internet community according to a user ID, and performing word segmentation processing to obtain corresponding word segmentation results; determining a user knowledge triple of each user according to the word segmentation results of each user, the full set of attention words and the field word tables, wherein the user knowledge triple comprises a user ID, a field and a personal knowledge representative word; determining a topic knowledge triple corresponding to each topic in the Internet community, wherein the topic knowledge triple comprises a topic representative word, a number and a topic; and generating a community personal knowledge graph corresponding to each user according to the user knowledge triple of each user and the topic knowledge triple. Through the technical scheme, people are fully and finely digitized, and accurate recommendation of community content topics is realized.

The present application relates to the technical field of highway electromechanical operation and maintenance, and particularly relates to a method and system for improving service efficiency of operation and maintenance personnel under the constraint of cognitive science; individual characteristics of electromechanical operation and maintenance personnel are described in multiple dimensions through efficiency and label mapping; in view of the fact that the existing training system is designed homogeneously and ignores the individual knowledge blind spots or skill short boards of operation and maintenance personnel, it is difficult to improve the service efficiency of operation and maintenance personnel, and the individual knowledge gap quantification model is proposed; the individual knowledge gap or skill short board is mined based on the multi-dimensional portrait label, and a personalized training scheme for operation and maintenance personnel conforming to the laws of cognitive science is generated in combination with the knowledge graph and large model technology. The present application aims to optimize the whole-chain technical system closed loop from efficiency evaluation, knowledge gap diagnosis to operation and maintenance service efficiency improvement, and ensure the long-term solidification of knowledge.