56 results about "Individualized learning" patented technology

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path planning method fusing artificial intelligence and education big data, and the method comprises the steps: collecting multi-modal education data of learning behavior data, cognitive process data and emotional state data from a learning terminal; constructing a learner portrait feature vector based on the preprocessed multi-modal education data; establishing a knowledge graph, and dynamically updating a weight matrix based on group learning data; and taking the learner portrait as a state space, taking a learning path decision as an action space, taking a learning effect as a reward function, generating a personalized learning path by adopting near-end strategy optimization, optimizing a path planning model, and generating a recommendation result. Personalized learning path planning is realized by constructing a multi-modal data fusion framework, a dynamic knowledge graph and an intelligent planning method.

The invention provides an artistic course personalized learning path method and system based on deep learning. Firstly, an artistic course learning basic information set of a learner and a preset artistic course module library are acquired; calling a pre-trained deep learning course dynamic association model to generate association strength description of the learning basis of the learner and each course module; screening and sorting based on association strength description to form an initial learning module sequence; acquiring a real-time learning behavior data set of the first course module learned by the learner, and generating a module adjustment signal; and adjusting the initial learning module sequence according to the module adjustment signal to obtain a personalized learning path adaptive to the real-time learning state of the learner, and generating a personalized learning path document comprising a course module learning sequence and module connection guidance, thereby realizing accurate customization and dynamic optimization of the learning path, and improving the learning efficiency. And the learning effect of artistic courses is improved.

The invention provides an intelligent course matching and promotion path recommendation method and device based on post portraits. The method comprises the steps of obtaining internal multi-source heterogeneous data of an enterprise and performing preprocessing to obtain a standardized multi-dimensional feature data set; constructing a dynamic post capability portrait by adopting a semi-definite programming method of an asymmetric random block model; analyzing a causal relationship between capability dimensions by applying a linear time primitive algorithm to obtain an employee capability assessment report; a constraint solution algorithm meeting excessive dissipation is adopted to calculate the capability gap matrix to obtain a personalized capability improvement requirement; screening and combining the learning resources through a weighted matching algorithm to obtain a personalized learning recommendation plan; and adopting a promotion path planning algorithm to construct multi-dimensional promotion possibility evaluation to obtain personalized promotion path suggestions. According to the invention, a closed-loop feedback mechanism of learning, evaluation and promotion is realized, and a systematic solution is provided for enterprise talent development.

The invention relates to the technical field of artificial intelligence and intelligent teaching, and discloses a self-adaptive intelligent teaching path planning method and system for personalized learning, and the method comprises the following steps: synchronously collecting multi-modal data streams in intelligent glasses and an intelligent earphone worn by a learner; identifying an immediate learning disorder of the learner based on the data stream; selecting and executing a teaching intervention strategy according to the obstacle, and monitoring a feedback behavior after intervention; an effectiveness score representing the intervention effect is quantitatively generated, and a strategy preference model is dynamically optimized based on the score; and according to the obstacle frequency of long-term statistics, the macroscopic learning path is adjusted. According to the method, a sensing, decision-making, evaluation and optimization closed loop is constructed, so that microscopic adaptive optimization of a teaching intervention strategy is realized, and instant difficulties encountered by learners can be accurately solved in real time. Meanwhile, dynamic adjustment of a macroscopic learning path is realized through analysis of long-term learning data.

The invention discloses a closed-loop transcranial magnetic stimulation brain regulation and control method based on multi-model fusion and individualized learning. Dynamic optimization and accurate regulation and control of TMS stimulation parameters are achieved. According to the technical scheme, the method comprises the following steps: collecting and preprocessing a multi-guide EEG signal in real time, and extracting frequency domain and time domain features and brain network topology indexes; the EEG state in the future 0.5-2 seconds is predicted by using depth sequential networks such as LSTM and the like, and an individualized stimulation parameter strategy is generated in combination with reinforcement learning and Bayesian optimization; tMS stimulation is triggered based on a prediction result, brain network indexes and reward evaluation, and long-term adaptive adjustment is realized through an individual memory pool. The traditional passive feedback mode is broken through, an active closed-loop mechanism of prediction-learning-regulation is realized, the TMS regulation efficiency and the individual adaptability are remarkably improved, and the method can be widely applied to scenes such as mental disease intervention, cognitive regulation and nerve rehabilitation.

The invention belongs to the technical field of intelligent education, and discloses a personalized learning path intelligent planning system, which comprises a data acquisition module used for acquiring learning activity data, user behavior data and learning result data; the data processing module is used for preprocessing the collected learning activity data, user behavior data and learning achievement data to obtain a learning activity feature data set, a user behavior feature data set and a learning achievement feature data set; the learning fitness prediction module is used for fusing the learning activity feature data set, the user behavior feature data set and the learning achievement feature data set to obtain a comprehensive feature data set; a learning fitness prediction model is trained and constructed according to the comprehensive feature data set, and the learning fitness of the user is obtained through prediction of the learning fitness prediction model; each learner provides a personalized learning path and recommendation, so that learning better meets personal requirements, and the learning effect and efficiency of the learner are improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an intelligent equipment online language teaching translation system based on image recognition, which overcomes the defect of isolated recognition of characters or objects in images in the prior art, fuses visual information and accurate geographic positions, queries a scene expression rule set associated with the visual information and the accurate geographic positions, and translates the visual information and the accurate geographic positions. A scene translation data stream is generated and returned to the terminal equipment, deep perception and semantic understanding of the translation system on a real physical environment are achieved for the first time, personalized correction operation is introduced before driving display of the terminal equipment, and it is ensured that the final translation result is more accurate while the environmental context is considered. Compared with a current user individual, the method is most suitable and understandable, the interactive behaviors of the user are synchronously recorded so as to dynamically adjust the decision logic of the scene culture context, a system is helped to realize a self-evolving intelligent closed loop, and long-term and continuously evolved personalized learning experience is provided for the user.

The application belongs to the technical field of artificial intelligence, and specifically relates to an intelligent device online language teaching translation system based on image recognition, which discards the disadvantages of isolated recognition of characters or objects in images in the prior art, fuses visual information with accurate geographical positions, queries a scene-based expression rule set associated therewith, generates scene-based translation data flow and returns to a terminal device, first realizes deep perception and semantic understanding of a real physical environment by a translation system, and introduces individualized correction operation before driving and displaying at the terminal device, so that the final translation result is most applicable and understandable to a current user while taking into account an environmental context, the interaction behavior of the user is recorded synchronously, the decision logic of a scene cultural context is dynamically adjusted, the system realizes an intelligent closed loop of self-evolution, and long-term and continuously evolving individualized learning experience is provided for the user.

We provide the system. [Solution] A means for analyzing learner information received by a data processing device and generating an individualized learning plan, A means for dynamically generating test questions based on the generated learning plan, A means of monitoring learners' learning progress and providing real-time feedback, A system that includes this.

The invention discloses a disabled elderly body position intelligent control system based on deep learning posture recognition, and the system comprises a multi-mode perception collection module which is used for generating a multi-mode input data set; the topology voltage field feature extraction module is used for carrying out topology preserving mapping and generating a topology voltage field feature sequence; the body position time sequence recognition module is used for outputting a body position category, a posture change trend and a body position risk index through an improved Mamba model; the body position safety potential energy calculation module is used for generating a potential energy distribution result; the body position micro-action control module is used for generating a micro-action instruction, executing small-angle displacement and updating risk indexes in real time; and the individualized learning optimization module is used for storing the identification result, the risk index and the control record and continuously optimizing the parameters of the Mama model. Through an improved Mama model and a potential energy control mechanism, precise recognition and safe self-adaptive regulation and control of the body position of the disabled old person are achieved.

We provide the system. [Solution] An artificial intelligence means for collecting user information and generating an individualized learning plan based on said information, A user interface means that provides the user with an interactive learning experience based on the aforementioned learning plan, A business plan development support tool that analyzes users' business ideas and provides feedback on improvement suggestions, A means of forming a network for collaborating with other users on projects, Resource management tools that provide financial support and mentoring for projects, A system that includes this.

The invention discloses a multi-agent-based classroom teaching scheme evaluation method and system, and relates to the technical field of intelligent education, the system comprises a management center, and the management center is connected with an agent construction module, a basic processing module, a training analysis module and a collaborative evaluation module; collecting a classroom teaching scheme and constructing a comprehensive intelligent space; carrying out butt-joint teaching in the comprehensive intelligent space and collecting teaching comprehensive data; performing teacher self-evaluation on the comprehensive teaching data to obtain a self-evaluation score, and performing external evaluation through the self-evaluation score based on the comprehensive intelligent space to obtain a comprehensive score value; performing curve graph conversion according to the comprehensive score value to obtain a score dynamic curve graph, performing quality evaluation on the score dynamic curve graph to obtain an evaluation result, and performing simulation upgrading in the comprehensive intelligent space according to the evaluation result to obtain a personalized upgrading teaching plan; real-time feedback and adjustment are realized, the teaching effect and the evaluation efficiency are improved, and personalized learning is promoted.

We provide the system. [Solution] Information processing means for receiving learner information and generating an individualized learning plan, A planning means for presenting learning items based on the learning plan, taking into consideration the appropriate timing for relearning; Information analysis means for monitoring learners' learning progress and emotional state, and for providing psychological support, A display means that uses a portable information terminal to display personalized learning information to the user in real time and allows for two-way information exchange, A synchronization mechanism for periodically collecting learner progress data and sending it to a dedicated server, A system that includes this.

To provide a system for reducing the labor of a protector, and for effectively supporting the middle school examination of a child.SOLUTION: The system includes means for receiving necessary information input by the user and registering data, means for receiving a small test or a result of a class and analyzing data to create a user profile, means for specifying a weak point or a strong point of the user to create an individual learning plan, means for evaluating a progress status from an input learning progress and generating a recovery plan by the server, means for proposing a school of choice based on the characteristics, results, and progress of the user, and means for displaying the progress status and feedback of the user in a dashboard format.SELECTED DRAWING: Figure 1

The invention discloses a dynamic explanation partner training method and system for learning emotion recognition, and relates to the technical field of man-machine interaction education application, and the method comprises the steps: collecting multi-modal data, including face video data, voice audio data and interactive text data, of a learner in a learning process in real time; performing emotional state recognition based on the multi-modal data, and generating an emotional state signal carrying a credibility mark; based on the emotional state signal and the credibility, in combination with the learning task context of the learner, a teaching intervention strategy is generated through a dual-core decision-making mechanism, and the dual-core decision-making mechanism comprises a white box decision based on a preset rule and a black box decision based on a time sequence prediction model. The technical problems that an existing learning partner training tool is one-sided in emotion capture, lack of individuation in intervention and incapable of pre-judging learning contusion are solved, and the technical effects that individualized learning partner training better meets the requirements of learners, and learning assistance accuracy and effectiveness are improved are achieved.

We provide the system. [Solution] A means of storing the analyzed educational information in a database, A means for generating new test questions using a generation device, A means of identifying areas of weakness based on the answer information of individual learners, A means of providing a learning plan based on identified areas of weakness, A system that includes this.

The application discloses a big data-based intelligent education guidance evaluation system and method, belongs to the technical field of intelligent education and big data analysis, and comprises a multi-source heterogeneous education data acquisition module, a data preprocessing and feature fusion module, a multi-dimensional capacity evaluation module, a learning behavior sequence analysis module, an individualized guidance recommendation module and a multi-role visual display module. The method integrates multi-source data through a time sequence self-adaptive alignment algorithm, realizes five-dimensional comprehensive capacity evaluation by adopting a subjective and objective fusion weighting algorithm and a double-flow attention knowledge tracking algorithm, performs learning behavior time sequence modeling by using a key behavior node identification algorithm, and generates an individualized learning path by a cognitive load perception path optimization algorithm. The application solves the problems of single evaluation dimension, fragmented data acquisition and lack of data support for individualized guidance in the existing education evaluation system.

We provide the system. [Solution] An information processing device that collects and analyzes student learning data, An information processing device that generates an individualized learning plan based on the above analysis, A presentation device that outputs learning content based on the aforementioned learning plan, A communication device that provides real-time feedback on the learning content, A voice dialogue device that uses speech recognition technology to analyze students' questions and provide learning support, A system that includes this.

Provide a system. 【Solution means】 Means for collecting learning information of individual learners, Means for analyzing the collected learning information to identify the learners' strong and weak areas, Means for generating an individualized learning plan suitable for the learner based on the identified strong and weak areas, Means for presenting the generated individualized learning plan to the learner, Means for providing an immediate response to inquiries from the learner, Means for notifying the learner's learning progress, Means for providing an individually optimized learning experience in the local community where the learner resides, Means for sharing the learner's educational progress with parents or educational institutions, A system including the above.

We provide the system. [Solution] Means for collecting learner activity data, A means for analyzing the aforementioned activity data to generate an individualized learning plan suitable for the learner, A means of delivering learning content based on the generated individual learning plan, A means of collecting learner feedback and dynamically adjusting the learning plan, A means of visualizing and providing learning progress to learners, A system that includes this.

The invention is suitable for the technical field of collection training, and provides a self-adaptive collection learning path planning method and system, and the method comprises the steps: constructing a three-dimensional procurement capability model containing compliance operation, a negotiation strategy and risk management and control, decomposing the procurement capability model into capability units, and setting a quantitative index; collecting operation, decision and assessment data in the simulation scene in real time, and generating a dynamic capability assessment matrix; constructing a three-dimensional space model based on the matrix, calculating the association strength and the lifting priority of the power unit, and outputting a personalized learning path; recombining the knowledge module according to the evaluation value change of the capability unit; dynamically adjusting model parameters in combination with policies and industries and re-planning a path; a cross-dimension learning path is generated when the plurality of capability units reach the standard. According to the method, dynamic evaluation, personalized path adjustment and cross-dimension capability integration can be realized, the problems of one-sided evaluation, fixed path and insufficient adaptability in the prior art are solved, and the accuracy and efficiency of auditory collection training are improved.

The embodiment of the invention provides a dynamic learning planning teaching system and method based on multi-model agent cooperation, and relates to the technical field of intelligent education. According to the system, description information input by a user is obtained through a dynamic test question searching module, and a test question set is retrieved in a test question bank. And identifying wrong question data through a test paper dynamic analysis module, generating a structured wrong question bank through a wrong question mastering degree analysis module, and dynamically adjusting review interval parameters according to an interval repetition algorithm based on memory cards and answer data in the structured wrong question bank, so that a learning planning module generates learning task planning data. According to the system, a complete closed-loop teaching mode of question finding, wrong question, mastery degree analysis and question exercising can be constructed through a large language model, retrieval enhancement generation, YOLO image segmentation, an interval repetition algorithm and the like, dynamic test question searching, wrong question reason analysis, mastery degree tracking and personalized learning planning are achieved, and the accuracy of a recommendation result is improved.

The invention discloses a self-adaptive learning path recommendation system and a head-mounted device, and belongs to the technical field of intelligent education, the system comprises a multi-modal data acquisition module, a signal feature fusion module, a knowledge graph management module and a self-adaptive path recommendation module, electroencephalogram signals, visual behaviors and learning content data are synchronously collected through a head-mounted device, a fusion feature vector is generated by adopting a signal quality adaptive weighted fusion strategy, the knowledge mastery degree is evaluated based on a deep learning model, and a dynamic knowledge graph is updated in real time. According to the method, the recommendation priority is calculated by integrating the mastery gap, the knowledge importance, the dependence emergency degree, the forgetting risk and the fatigue state, dynamic planning and closed-loop adjustment of a personalized learning path are realized, the mastery evaluation accuracy and the learning efficiency are remarkably improved, and meanwhile, the data privacy security is guaranteed.

The present invention provides a method for providing individualized and interactive remote education and a system thereof. The method includes the steps of: storing educational materials and links to remote databases and / or library resources; providing educational materials to the educational material database and updating the educational materials stored in the educational material database; maintaining a user profile for each user; generating an individualized learning program for each user based on the user profile; and autonomously producing content to the user and forwarding user feedback via a generative artificial intelligence (AI) module.

The invention relates to the technical field of multi-modal data processing, in particular to a course management method and system of an enabling system, and the method comprises the steps: responding to a teacher end instruction, and collecting the real-time learning behavior data of a target student; performing data cleaning preprocessing on the real-time learning behavior data, and outputting a preprocessed data set; extracting learning behavior features of the preprocessed data set, and outputting a student feature set of the target student; inputting the student feature set into a preset ability layering model, and updating a learning path association tree structure; and performing path node sequence optimization analysis on the updated learning path association tree, and outputting a teaching strategy suggestion for the target student. The method has the effect of realizing personalized learning path dynamic optimization.

We provide the system. [Solution] A means for generating an individualized learning plan, Means for tracking and recording learners' progress, An information processing device for presenting learning content based on a generated learning plan, A means of providing rewards according to learning progress, A means of communication for receiving and resolving questions during learning, An educational support system that includes this.

We provide the system. [Solution] A computation means for analyzing the learner's learning progress and level of understanding in real time, A distribution method that dynamically provides educational materials optimized for learners, A dialogue device that accepts inquiries in natural language and generates responses immediately, A visualization method for visualizing the setting and achievement of learners' learning goals, A display device that shows educational materials in various formats, A proposal device that suggests individualized learning content for a specific social group, A system that includes this.

The application discloses a dynamic explanation accompanying training method and system for learning emotion recognition, relates to the technical field of human-computer interaction education, and comprises the following steps: collecting multi-modal data of learners in a learning process in real time, including facial video data, voice audio data and interactive text data; performing emotion state recognition based on the multi-modal data, and generating an emotion state signal carrying a confidence mark; generating a teaching intervention strategy through a dual-core decision mechanism based on the emotion state signal and the confidence, in combination with a learning task context of the learner; and the dual-core decision mechanism comprises a white-box decision based on a preset rule and a black-box decision based on a time sequence prediction model. The application solves the technical problems of one-sided emotion capture, lack of individualized intervention and inability to predict learning setbacks of existing learning accompanying training tools, and achieves the technical effects of making individualized learning accompanying training more suitable for the needs of learners and improving the accuracy and effectiveness of learning assistance.