205 results about "Cognition.status" patented technology

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

The invention relates to the technical field of personalized learning. The method comprises the steps that a learning target and a cognitive state vector serve as joint input information to be sent into a large language model, and a knowledge point set and learning gap information corresponding to the learning target are generated; and aligning the knowledge point set with a pre-repair relationship and a dependency relationship in a preset educational knowledge graph to obtain an alignment result, and when a difference value between the updated cognitive state vector and the cognitive state vector exceeds a preset cognitive state change threshold value, determining a learning target based on the updated cognitive state vector and the learning target. And performing semantic analysis and reasoning of the large language model again to obtain a new knowledge point set, determining a new learning resource sequence according to the updated personalized learning path, and pushing the learning resource sequence to the user terminal. The method has the effect of realizing closed-loop optimization of the learning scheme.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention relates to the technical field of brain-computer interface and man-machine interaction safety, in particular to a portable multi-modal brain-computer interface intelligent recognition system, which comprises a multi-modal data acquisition and preprocessing unit, a multi-modal data processing unit and a multi-modal data processing unit, the dynamic cognitive state feature extraction unit is used for generating a dimensionless dynamic cognitive state vector; the cognition-situation fusion and risk modeling unit is used for calculating a continuous and quantitative cognition safety risk index; the risk index generation and decision suggestion unit is used for generating hierarchical and adaptive intervention or auxiliary instructions according to the cognitive security risk index and the standardized situation data stream; according to the method, the problem of frequent occurrence of invalid alarms caused by excessive sensitivity of a traditional method is solved, and the accuracy of risk assessment is remarkably improved.

The invention provides a user cognition detection method and device, electronic equipment and a storage medium, and the method comprises the steps: under the condition that a detected target behavior event of a target user is a cognition sensitive event, according to historical behavior data of the target user and target behavior data corresponding to the target behavior event, obtaining a cognition sensitive event of the target user; determining a behavior characteristic index of the target user; generating a target feature vector of the target user according to the behavior feature index; a current cognitive state matrix of the target user is generated according to the target feature vector, the current cognitive state matrix is used for determining the current cognitive state of the target user, and the cognitive state is used for representing cognitive changes of the target user. According to the embodiment of the invention, the accuracy of detecting the cognitive state of the user can be improved.

The invention discloses a knowledge graph teaching path dynamic recommendation method and system based on reinforcement learning. The method comprises the following steps: constructing a cognitive adaptive dynamic knowledge graph; obtaining an individual multi-dimensional state vector of a student to be recommended; obtaining a trained student state perception model and a trained teaching path reinforcement learning model; inputting the individual multi-dimensional state vector of the to-be-recommended student into a trained student state perception model to obtain cognitive state information of the to-be-recommended student; and calling the trained teaching path reinforcement learning model by taking the cognitive adaptive dynamic knowledge graph as an environment and combining cognitive state information of the to-be-recommended student, so as to obtain a current personalized teaching path recommendation result of the student individual. The teaching content sequence can be dynamically adjusted according to the knowledge state and the learning behavior of the student, and the teaching integrating degree is improved; the teaching path is continuously optimized through learning, and different types of students can be adapted.

The invention relates to the technical field of data processing, in particular to a lifelong learning platform based on a multi-modal large model, and the platform comprises a cognitive modal analysis module which analyzes the cognitive state of user learning in real time; according to the system, key physiological signal indexes in the learning process of the user are captured in real time, the attention level, cognitive load and psychological pressure states of the user are deduced, the user experience and natural interaction ability are improved, the real cognitive state of the user is perceived, and the user experience is improved. The content difficulty is automatically adjusted in real time, decompression suggestions are pushed or personalized auxiliary strategies are triggered, cognition-emotion-behavior three-in-one adjustment is achieved, the problem that during resource pushing, manual labels and simple behavior records are depended on is solved, and resource pushing efficiency is improved by constructing an interactive neuroplasticity knowledge network diagram. The cognitive activity and the psychological load state in the learning process are reflected in real time, and the psychological fatigue of the user can be recognized.

The invention relates to an anatomy intelligent tutoring system and method based on term alignment and multi-modal verification. The method comprises the following steps: acquiring multi-modal data of a learner and implementing time axis alignment and term alignment to generate a standardized time sequence data set for feature extraction; inputting the feature vector into a long short-term memory network model, and analyzing the time sequence relevance to generate a cognitive state vector; when the conceptual error probability in the cognitive state vector exceeds a threshold value, extracting a term set to be verified based on a three-dimensional anatomical model interaction log; the term pair with the highest confusion degree is accurately positioned as an error attribution result by performing knowledge graph topological correlation analysis on the term set and calculating the confusion degree between the terms; and finally, in combination with the historical ability portrait of the learner, performing dynamic matching from the multi-modal strategy library to generate personalized tutoring content, and embedding the personalized tutoring content into a three-dimensional model interface to execute intervention, so that the effects of automatically identifying anatomical concepts from the multi-modal behavior data to understand error roots and generating targeted tutoring strategies are realized.

The invention relates to the technical field of education and teaching. The invention provides a classroom teaching dynamic feedback and evaluation system integrated with multi-modal emotion calculation. The system comprises a multi-modal data acquisition module used for acquiring multi-source data in a classroom teaching environment and sending the multi-source data to an emotion-cognitive calculation center; the emotion-cognition calculation center is used for carrying out fusion analysis on the multi-source data, identifying the emotion state and the cognition state of the student, carrying out attribution analysis through an emotion-cognition coupling model, generating a coupling state label for describing the specific reason of the learning dilemma, and sending the coupling state label to the dynamic intervention engine; the dynamic intervention engine is used for matching and generating individual-level, group-level and system-level teaching intervention instructions from the teaching strategy knowledge base according to the received coupling state labels; and the visual feedback and evaluation module is connected with the emotion-cognition calculation center and the dynamic intervention engine, and is used for providing real-time classroom state visual display for the teacher and pushing personalized learning suggestions to the student terminal.

The invention discloses an intelligent teaching assisting method and system integrated with the whole process and total elements of education and teaching, and relates to the field of education and teaching. According to the method, misunderstanding concept classification models are integrated, and a dynamic knowledge graph containing target subject knowledge is established; clustering knowledge concepts in the dynamic knowledge graph by adopting a Mapper algorithm of topological data analysis to obtain a course map; when the user completes interaction of the selected theme cluster, a cognitive state matrix is obtained by adopting a graph knowledge tracking model, an emotion category probability distribution vector is obtained by adopting an emotion classification model, and a learner state vector is obtained; a large language model optimized through process supervision and reinforcement learning is adopted as an inference engine; and outputting a targeted teaching strategy and teaching content by using an inference engine according to the learner state vector. According to the method, a personalized teaching environment which can perform smooth and dynamic natural language interaction and can accurately diagnose and effectively correct the specific deep-level cognition mistake of students in a target subject can be created.

The invention relates to the technical field of education, and discloses an online learning environment-oriented education interaction behavior recognition system, which is characterized in that a standardized cognitive model is constructed offline based on interaction data of a high-performance learning group; in the online stage, interaction events of individual learners are obtained in real time, high-dimensional cognitive trajectories representing cognitive states of the learners are generated, a trajectory dynamics analysis module executes double reference analysis, and internal dynamics indexes representing stability of the trajectories are obtained by calculating the maximum Lyapunov index, recognizing phase change points and the like; the external deviation measure of the difference between the trajectory and the normalized cognitive model is obtained by calculating the state deviation degree, the path efficiency and the like, and finally, the cognitive mode diagnosis module integrates the indexes and generates a structured diagnosis description according to a rule base. According to the method, through combination of internal dynamics and an external reference system, deep quantification and fine identification of the cognitive process of the learner can be realized, and an objective basis is provided for adaptive intervention.

The invention discloses an intelligent accompanying method and system fusing distributed learning and privacy multistage regulation, and the method comprises the steps: collecting the multi-modal interaction data of a user, constructing a local cognitive stage map and an emotion evaluation model, and carrying out the semantic sensitive modeling and differential privacy disturbance processing according to a privacy strategy set by the user. Dynamic transfer learning and asynchronous contrast training of desensitized data are completed at edge nodes, and the desensitized data are uploaded to a cloud for aggregation, so that a multi-branch elastic strategy is generated, and accompanying requirements under different cognitive states and task situations are met. Meanwhile, the accompanying behavior style is dynamically adjusted through strategy feedback and a context matching mechanism, and personalized adaptation of language preference and ethical sensitivity of the user is achieved. The system has cross-modal privacy protection, multi-level model generalization, cultural expression adaptation and long-term accompanying evolution capabilities, improves the safety, individuation and ethical consistency of intelligent accompanying, and is suitable for various scenes such as education, health and psychological support.

The invention discloses a multi-user emotion recognition and digital human feedback method based on behavior data, and belongs to the field of artificial intelligence emotion calculation. Sensing user approaching, identifying and confirming the identity of a registered user, distributing an identifier, distributing an identifier for a visitor, and establishing an independent session channel in a multi-user scene; capturing time sequence dialogue behavior data containing voice rhythm features, dialogue interaction modes and linguistic features in real time; inputting the data into a sequence information processing model based on a state space theory and provided with a data dependence selection mechanism and an emotion analysis model spliced by static context features, and outputting multi-dimensional emotion and cognitive state tags; generating an emotion support strategy through a decision-making model in combination with the personal file of the user; and generating a multi-mode control instruction, and driving the digital human to synchronously present special effects such as expressions and the like. According to the method, privacy concerns are eliminated through non-intrusive collection, the method is adaptive to multi-user scenes, deep cognitive states can be recognized, and interaction effectiveness is improved for a long time.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The invention relates to the field of intelligent course recommendation, and discloses an intelligent course recommendation system and method combined with cognitive level recognition, and the method comprises the steps: constructing a multi-dimensional operation task scene through an interactive game task, and generating an initial cognitive state sequence; mapping the initial cognitive state sequence, and guiding an adaptive feature screening mechanism to highlight key behavior features in a specific emotional state so as to construct a cognitive state map; based on the cognitive state map, capturing short-term cognitive fluctuation and extracting core cognitive indexes in different time periods; fusing the cognitive level evolution trajectory with the historical learning trajectory of the student, the knowledge point mastering condition, the individual preference and the emotional state to generate a personalized cognitive level judgment result which reflects the real ability of the student in the current task and the learning demand of emotion adaptation; and dynamically generating a course recommendation strategy according to a personalized cognitive level judgment result. The method has the advantage of improving the judgment precision of the current cognitive level of the student.

According to the intelligent auxiliary learning method and system based on the model context protocol and the cognitive state modeling, decoupling of a model end and a tool end is achieved through the model context protocol, an error type-tool dependency topological graph is constructed through the cognitive state modeling, tool dynamic screening and pruning based on deterministic rule constraints are achieved, and the method and the system have the advantages that the method and the system are easy to implement. The Token consumption is reduced, and the accuracy of model reasoning is improved at the same time. In the aspect of safety control, semantic firewall middleware is introduced into the system, output streams are monitored in real time, illegal behaviors directly giving code answers are intercepted through text semantic analysis and code abstract syntax tree comparison, and a model is forced to turn to a thought guide mode. The system also includes hierarchical context compression to maintain long term memory, adaptive difficulty knowledge retrieval based on user cognitive states, and a mechanism to utilize code sandbox to assist verification of model reasoning logic correctness. According to the method, the behaviors of the large language model can be effectively regulated and controlled, and safe, efficient and personalized intelligent auxiliary learning is realized.

The invention discloses an examination and research word efficient mnemonic system based on an AI agent, and belongs to the technical field of artificial intelligence and natural language processing, and the system comprises a multi-dimensional semantic knowledge graph construction module, a cognitive state self-adaptive evaluation module, a personalized memory strategy generation module and a closed-loop feedback optimization module. The multi-dimensional semantic knowledge graph construction module generates a vocabulary semantic association network based on root and affix structures, semantic similarity and context co-occurrence frequency; the cognitive state self-adaptive evaluation module determines cognitive forgetting probability distribution of the user in combination with the semantic difficulty reference value; the personalized memory strategy generation module generates a multi-dimensional mnemonic scheme through an AI agent and determines a personalized review interval; the closed-loop feedback optimization module collects learning effect data and generates optimization parameters to realize system parameter iteration, and the four modules form a deep coupling closed-loop collaborative architecture to realize mutual promotion and superposition synergy.

The invention provides a personalized lesson preparation method and system based on multi-modal data fusion and a cognitive map, and relates to the technical field of intelligent education. Extracting a student cognition state from the student cognition map, and performing time sequence evolution analysis on the student cognition map to obtain a change trend of the student cognition state; semantic difference quantization is carried out on the cognitive state and the mastery degree prototype of the student based on the change trend, and a cognitive state residual vector is obtained; performing collaborative reasoning on the cognitive state residual vector to obtain a personalized lesson preparation path and teaching strategy configuration, and performing teaching resource matching and teaching plan assembly based on the personalized lesson preparation path and teaching strategy configuration to obtain a personalized hierarchical teaching plan; the personalized hierarchical teaching plan is dynamically adjusted through the classroom feedback data, and then lesson preparation evaluation data is obtained, so that personalized teaching strategy generation based on dynamic cognitive diagnosis and closed-loop optimization can be realized, and the accuracy of teaching intervention is improved.

The invention relates to the technical field of data processing, and discloses a dynamic cognitive training method and system based on a large language model and multi-dimensional evaluation, which combines multi-dimensional input data such as standardized cognitive evaluation, demographic statistics, historical behaviors and real-time performance to construct a user cognitive portrait. The dynamic prompting engineering module and the content generation process of the large language model are integrated, so that the training content, the prompting mode, the interaction style and the like can deeply meet the individual differences and specific requirements of users, the fine-grained and deep personalized training process is provided, and through the dynamic prompting engineering module, the user experience is improved. According to the method, the fine cognitive state change of the user in each interaction and task can be captured in real time, the change is immediately converted into accurate control over large language model behaviors, training content and difficulty which are accurately matched with the current level of the user are generated, highly-dynamic and real-time adaptive cognitive training can be achieved, and the user experience is improved. The problems that a traditional cognitive training system is lagged in adjustment and insufficient in roughness are solved.

The invention discloses a generative agent cognitive diagnosis framework fusing teacher thinking, and the framework is used for modeling a cognitive judgment process of a teacher in a real teaching scene, systematically constructing task-aware thinking modeling, a behavior track-based observation reflection mechanism, and a causal-driven explanation generation module. Therefore, dynamic modeling and deviation diagnosis of the cognitive state of the student are realized. The method has the advantages that firstly, the method has good cold start capability, and reasonable judgment can be made even if complete historical behavior data is lacked; 2, the method has relatively high interpretability, can output a causal chain of'knowledge state-behavior process-task performance ', and supports accurate teaching intervention; and thirdly, the method has the ability of distinguishing the thinking levels of students, can assist in completing Bloom cognitive classification, and provides effective support for task layering and teaching design.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path generation method and system driven by a dynamic knowledge graph. The method comprises the steps of collecting and processing multi-modal learning behavior data of a learner to construct and update a dynamic knowledge graph reflecting a knowledge mastering state and knowledge point association in real time; a double-engine diagnosis mechanism combining large model deep reasoning and knowledge graph real-time verification is adopted, and cognitive weak points and knowledge structure defects of learners are accurately recognized; and on the basis of a diagnosis result, a personalized learning path adaptively matched with the cognitive state of the learner is generated through multi-agent collaborative decision, and closed-loop optimization is performed on a knowledge graph and a path planning strategy according to real-time feedback of a path execution effect. According to the invention, the defects of the traditional adaptive learning system in the aspects of diagnosis accuracy, individuation degree and dynamic adaptability are effectively overcome, and accurate, efficient and continuously optimized individualized learning experience can be provided for students. According to the method, through deep fusion of double-engine diagnosis and the dynamic knowledge graph, the accuracy and reliability of cognitive state diagnosis are remarkably improved, and the illusion problem of a large model in the STEM field is solved; through a multi-agent collaborative decision-making mechanism, high personalization and dynamic adaptability of a learning path are realized; finally, a teaching closed loop with a self-optimization capability is formed, and the intelligent level and the teaching efficiency of the self-adaptive learning system are essentially improved.

The invention belongs to the technical field of artificial intelligence education, and relates to an intelligent teaching assistance method and system based on a large language model. The method comprises the steps that text data, voice data and eye movement track data of students are acquired, feature extraction is carried out, and a three-dimensional cognitive state tensor model is constructed; constructing a cognitive state tensor decomposition constraint optimization function, performing tensor decomposition, and outputting a core tensor and a factor matrix; generating a teaching strategy, and defining a cross-modal kernel function to realize feature association; tensor decomposition parameters are dynamically adjusted; and outputting the updated teaching strategy. According to the method, the cognitive change trajectory of a learner can be captured from multiple dimensions, and the accuracy and comprehensiveness of cognitive state characterization are remarkably improved through a cross-modal kernel function and a gradient coupling mechanism; constructing a cognitive state tensor decomposition constraint optimization function to describe an evolution law of a cognitive state, and capturing a continuous change characteristic of the cognitive state along with time through a dynamic constraint condition of a time factor matrix.

The invention provides a learning input degree evaluation method and system based on reaction behaviors of human-computer interaction, and relates to the technical field of input degree evaluation, and the method comprises the steps: data collection and synchronous alignment: achieving time sequence alignment through collecting physiological behavior data; feature engineering and scene adaptive modeling are carried out, and a deep learning network is constructed to extract scene sensitive features; performing multi-modal fusion and cognitive state decoding, fusing eye movement, electroencephalogram and behavior data, and outputting cognition, emotion and behavior input degree indexes; performing adaptive intervention decision and execution, and dynamically selecting content highlight or visual guidance based on reinforcement learning; personal baseline evolution and model online updating are carried out, and continuous optimization of the system is realized. The system comprises a multi-mode fusion module, a cognitive state decoding module and the like. According to the method, the cognitive response is actively stimulated through standardized interaction tasks, the problem of data splitting is solved, a multi-level evaluation system is constructed in combination with the education theory, the teaching effectiveness is improved, an evaluation intervention closed loop is formed, and the evaluation accuracy and the personalized level are improved.

The invention discloses a psychological experiment task-oriented subject response data modeling method and system, and belongs to the technical field of psychological experiment task data modeling, and the method comprises the following steps: collecting subject response data; performing reaction flow serialization, converting original data into a continuous time sequence, and recognizing a cognitive state transition point through a multi-scale recursive segmentation method based on dynamic entropy perception; dynamic cognitive model construction: adopting an improved drift diffusion model combining dynamic parameter decomposition and hierarchical Bayesian inference to obtain a dynamic cognitive analysis model reflecting cognitive strategy switching, fatigue accumulation and learning effect; and reaction data modeling: establishing a causal relationship among a state label, a cognitive parameter and a reaction behavior, and realizing quantitative modeling and prediction of a psychological experiment task. According to the scheme, fine modeling and dynamic analysis of the multi-dimensional psychological reaction process can be realized, and the interpretability and the application value of psychological experiment data are improved.

The invention discloses a foreign language learning potential assessment method and system based on multi-modal data fusion, and the specific implementation scheme is as follows: creating a task event, collecting the multi-modal data of a subject in real time, and carrying out the time alignment of the multi-modal data and the task event through a timestamp or a trigger signal; performing data preprocessing on the multi-modal data and extracting behavior features and neurophysiological features; performing deep fusion analysis on the behavior features and the nerve physiological features in time dimension and space dimension to generate optimal comprehensive features; and performing quantitative evaluation on foreign language learning potential by using a deep fusion analysis result, and generating a feedback report. According to the method, foreign language learning potential is expanded into a behavior and cognitive nerve double-dimensional structure, and the problems of single evaluation dimension and insufficient mechanism interpretation caused by the fact that a traditional method only depends on behavior scores are solved; through synchronous acquisition and real-time analysis of the multi-mode reaction of the learner in the task, the change of the cognitive state along with time can be dynamically monitored.

The invention relates to a brain-computer interface control method and device based on cognitive enhancement, a medium and a product, and belongs to the technical field of brain-computer interfaces, the method comprises the following steps: detecting a first electroencephalogram signal through an electroencephalogram detection device of a brain-computer interface in response to an operation task input by a user to an interaction device of the brain-computer interface; determining a first parameter value reflecting a cognitive state parameter in the first electroencephalogram signal; when the first parameter value exceeds a set state expectation interval, controlling each stimulation device of the brain-computer interface to operate according to a set stimulation intensity sequence; when the second parameter value is in the set state recovery interval, controlling the running target stimulation device in the stimulation devices to stop running; wherein the second parameter value is determined to be a second electroencephalogram signal detected by the electroencephalogram detection device.

The invention discloses an active multi-mode education agent system and method, and belongs to the technical field of artificial intelligence education. The system adopts an end-cloud collaborative architecture and comprises two parts, namely end-side equipment and a cloud micro-service system. The end side device is responsible for collecting multi-modal data (emotion data, behavior data and content data) of a learner in real time and carrying out preprocessing and feature extraction. The cloud micro-service system comprises an API gateway service, a state reasoning service, an active response generation service, a multi-modal content generation service and a TTS service, and is used for processing and analyzing the multi-modal data and generating a personalized active response. According to the method, the confusion state of a learner can be actively perceived, the cognitive state is inferred based on a multi-modal data fusion algorithm, personalized education content is generated according to the cognitive state, a perfect service degradation strategy is provided, and the reliability and learning effect of the system are improved.

The invention discloses an interdisciplinary knowledge semantic fusion platform, which is oriented to STEAM and general knowledge education, and realizes association, migration and expression adaptation of multidisciplinary knowledge by utilizing a DIKWP (data-information-knowledge-intelligence-intention) model. The platform comprises an interdisciplinary semantic ontology library used for unifying different disciplinary concepts; the knowledge extraction and mapping module is used for mapping the course text into a DIKWP mesh semantic map and automatically identifying knowledge intersection points; the interdisciplinary content reconstruction engine is used for semantically embedding source subject knowledge in a target subject context to generate fusion resources; the learner intention tension model is used for generating and dynamically adjusting a personalized interdisciplinary learning path according to a learning target and a cognitive state; the AI teaching design and evaluation tool analyzes and feeds back content universality, creative challenges and teaching effects. The platform gets through a subject semantic barrier, reduces the development cost of a fusion course, improves the pertinence and interpretability of a learning path, and is suitable for basic education, higher education and training.

The application relates to an intelligent education management method and system based on an Internet of Things. The method comprises the following steps: inputting a multi-channel physiological feature sequence into a hierarchical probability state space model to obtain a short-term cognitive state and a short-term cognitive state posterior probability; performing stability statistical testing on the short-term cognitive state posterior probability, and marking the short-term cognitive state as a phase-established state when the stability condition is met; constructing a cost function based on the phase-established state and a probability evolution track, and performing rolling time domain optimization based on the minimum cost function and a dynamic refractory period constraint to obtain an optimal learning action; calculating a decision oscillation index according to a current executed learning action sequence, and increasing the weight of an action change amplitude penalty and prolonging the locking time of the dynamic refractory period constraint by a set step length when the decision oscillation index exceeds a preset first oscillation threshold. The method can guarantee the continuity of a learning process and the stability of a cognitive state.

The invention provides a cognitive behavior intervention system assisted by multi-modal artificial intelligence recognition, which can acquire multi-modal information, realize deep feature fusion and analyze a cognitive behavior association mechanism, and comprises the following modules: a multi-modal data acquisition module for acquiring and preprocessing multi-modal data of a depression patient; the feature fusion module is used for extracting features of the preprocessed multi-modal data for feature fusion to obtain a cognitive state feature vector and a behavior mode feature vector; the cognitive behavior association module is used for constructing a cognitive and behavior association model to generate a cognitive behavior analysis report according to the cognitive state feature vector and the behavior mode feature vector in combination with a preset cognitive deviation type library and a behavior trigger factor library; and the personalized intervention module is used for generating a personalized cognitive behavior intervention strategy through a decision optimization algorithm based on multi-feature weighting according to the cognitive behavior analysis report in combination with a preset user basic information library and a cognitive behavior intervention scheme library.