28 results about "Individual knowledge" patented technology

The invention relates to an AI-based subject education auxiliary method and device and a storage medium, which are applied to the technical field of intelligent teaching, and comprise the steps of: introducing a teaching plan script as a macroscopic framework of teaching, ensuring that an AI teaching process is always carried out around teaching design and core targets of teachers, and improving the teaching efficiency. The problem of teaching content deviation possibly occurring in a pure response type system is avoided, meanwhile, microcosmic dynamic adjustment can be carried out in the framework according to real-time feedback of students, and organic combination of teaching structure and personalized self-adaption is achieved. By constructing and dynamically updating the individual knowledge graph for each student, the system can accurately diagnose the weak knowledge points of the students and provide personalized teaching guidance and after-class homework according to the weak knowledge points, thereby realizing accurate and efficient'apportionality teaching '. According to the invention, the whole process of'teaching-learning-evaluation-diagnosis-mending 'is integrated, a complete data-driven teaching closed loop is formed, and the overall teaching quality is systematically improved.

The invention is suitable for the field of education data analysis, and provides a learning cycle data acquisition and evaluation management method and system, and the method comprises the steps: automatically collecting and fusing the full life cycle data of students, and forming a comprehensive data set; dynamically constructing a student digital portrait based on the data set, and constructing a personal knowledge graph taking knowledge points as nodes; in combination with the portrait and the knowledge graph, analyzing relevance between browsing behaviors and course selection and practice, and evaluating a career behavior mode and track; calculating a quantitative career-academic fitness index, identifying an imbalance risk and generating an early warning; personalized resources are pushed to the student terminal based on early warning, early warning is sent to the teacher terminal, and the intervention effect is continuously tracked. The system correspondingly comprises five modules. According to the method, the problems of data islands, portrait statics, lack of fitness and the like in the prior art are solved, and the accuracy, systematicness and effectiveness of learning cycle evaluation and management are improved.

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 provides a talent portrait generation method and device and electronic equipment, and the method comprises the steps: obtaining the structural personal information of a candidate, constructing a personal knowledge graph of the candidate based on the structural personal information, and enabling the personal knowledge graph to comprise a to-be-evaluated feature information node of the candidate; obtaining an industry portrait of the industry where the candidate is located, wherein the industry portrait is used for representing an industry feature tag of the industry where the candidate is located; calling a pre-configured standard evaluation map, wherein the standard evaluation map comprises reference feature information nodes of standard candidates under a preset dimension; complementing the to-be-evaluated feature information nodes in the personal knowledge graph according to the reference feature information nodes in the preset dimension based on the industry feature tags to obtain a complemented personal knowledge graph; and obtaining a talent portrait of the candidate based on the complemented personal knowledge graph. The multi-source candidate information can be effectively fused, so that the candidate portrait is completely constructed, and the requirement of an enterprise for accurate recruitment decision making is effectively met.

The invention discloses a dynamic knowledge base collaborative improvement method based on multiple rounds of dialogues, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring information of an Nth round of dialogues; performing structured processing on the Nth round of dialogue information to obtain Nth round of structured dialogue information; extracting an Nth round of fingering word from the Nth round of structured dialogue information; performing multi-dimensional semantic annotation processing on the Nth-round structured dialogue information to obtain an Nth-round structured semantic unit; taking the Nth round of anaphora as an independent semantic object, establishing an anaphora association relationship with the Nth round of structured semantic unit, and generating an Nth round of structured anaphora; taking the Nth-round structured semantic unit as an entity node, taking the Nth-round structured anaphora as an entity attribute, and establishing an Nth-round anaphora relation edge; and generating an initial individual knowledge base containing the entity-attribute-relation according to the Nth-round structured semantic unit and the Nth-round anaphora relation edge. The dialogue reply quality after multiple rounds of dialogues is improved.

A method performed by an electronic device for managing a personal knowledge graph is provided. The method includes detecting, by the electronic device, an instance including an incomplete node having missing knowledge property information in a personal knowledge graph, identifying, by the electronic device, candidate knowledge property information corresponding to the missing knowledge property information of the incomplete node, and performing, by the electronic device, a task of completing the incomplete node based on the identified candidate knowledge property information.

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 auxiliary method for prognosis prediction of renal clear cell carcinoma, which comprises the following steps of: extracting four encoders by multi-modal representation and acquiring multi-modal knowledge; extracting three types of knowledge components from the CT image and the pathological image: individual knowledge only existing in a single mode, common knowledge existing among multiple modes and derivative knowledge obtained through interaction among the modes; optimal weights are dynamically allocated to different types of knowledge through a self-adaptive fusion strategy, so that optimal fusion features are obtained; and the extreme learning machine is used to predict each center feature so as to obtain a prediction result of the ccRCC patient. In the training process, through the guiding loss of the knowledge level and the sample level, different knowledge is more discriminative and distinctive. The ccRCC prognosis evaluation performance is effectively improved, so that the auxiliary diagnosis effect is achieved.

The invention relates to the technical field of highway electromechanical operation and maintenance, in particular to an operation and maintenance personnel service efficiency improvement method and system under acknowledgement science constraints. Individual features of electromechanical operation and maintenance personnel are described in multiple dimensions through efficiency and label mapping; in view of the pain point that the existing training system homogenization design neglects the individual knowledge blind area or skill weakness of the operation and maintenance personnel and the service efficiency of the operation and maintenance personnel is difficult to improve in a targeted manner, an individual knowledge gap quantification model is provided; and mining individual knowledge gaps or skill shortages based on multi-dimensional portrait labels, and generating an operation and maintenance personnel personalized training scheme conforming to the acknowledged scientific law in combination with a knowledge graph and a large model technology. The invention aims to ensure long-acting solidification of knowledge through closed-loop optimization of a full-chain technology system from efficiency evaluation, knowledge gap diagnosis to operation and maintenance service efficiency improvement.

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.

The present application relates to the field of information technology, in particular to a sleep health dynamic evaluation method and system based on user feedback driving and a medium. The method comprises: constructing an individual knowledge graph reflecting individual health causal relationship by using time series causal analysis method; constructing a group knowledge graph reflecting group health causal relationship by combining agglomerative hierarchical clustering method and hierarchical federated learning method; triggering individual knowledge graph update and triggering group knowledge graph delayed incremental reconstruction based on real-time acquired user feedback information and health factor data; dynamically fusing the weight of the updated double graph based on the cumulative number of user feedback information to obtain the comprehensive weight of the user health factor, and then performing weighted calculation on the newly acquired health factor data to obtain the sleep health evaluation score. Thus, the real-time feedback data is deeply utilized to realize dynamic, accurate and interpretable sleep health evaluation, and the technical prejudice that "group and individual are irreconcilable" is broken through.

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.

Disclosed is a method performed by an electronic device in order to manage a personal knowledge graph, the method comprising: the electronic device detecting an instance including an incomplete node having missing knowledge attribute information in a personal knowledge graph; the electronic device identifying candidate knowledge attribute information corresponding to the missing knowledge attribute information of the incomplete node; and the electronic device performing the task of completing the incomplete node on the basis of the identified candidate knowledge attribute information.

A pregnancy nutrition knowledge graph construction and application method based on a large language model relates to the technical field of knowledge graphs, and comprises the following steps: S1, collecting non-structural medical data to obtain an identifiable knowledge text; s2, determining a relationship between a target triad entity and entities in a triad; s3, the recognizable non-structural knowledge text is screened through a large language model, and a triple instance set conforming to the target triple type is obtained; s4, constructing a pregnancy nutrition knowledge graph based on the triple set; s5, constructing a pregnant woman individual knowledge graph according to the personalized information of the pregnant woman; and S6, recommending a pregnant woman individual pregnancy nutrition scheme in combination with the pregnancy nutrition mapping knowledge domain. The invention aims to utilize a large language model to construct a question and answer task, realize zero sample extraction of pregnancy nutrition triple knowledge pairs aiming at an unstructured text, form a knowledge graph by using the extracted triple knowledge pairs, store the knowledge graph in a graph database Neo4j, realize visual display of the knowledge graph and support subsequent reasoning and analysis.

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.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information. Responsive to receiving an augmented user query, query-record mapping operations may be performed based at least in part on a comparison of information associated with the augmented user query to the project name, sentence embedding, word embedding, subgraph information, or a combination thereof.

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.