329 results about "Cognition.status" patented technology

The invention belongs to the technical field of learner cognitive state interpretation, and particularly discloses a brain-language-vision large model-based cognitive state interpretable method, which comprises the following steps of performing multi-modal data collection for the cognitive state of a learner; performing learner cognitive state characterization based on a multi-scale map convolutional network; brain-vision-language mixed multi-modal time sequence feature fusion based on hierarchical mutual information maximization is carried out; embedding knowledge graph nodes for enhancing the complex task understanding ability of the multi-modal large model; training a brain-vision-language multi-modal large model guided by knowledge; recognizing a learner cognitive state based on a multi-modal large model; the cognitive state of the learner can be explained and analyzed under the support of multi-mode owned reasoning. According to the cognitive state interpretable method based on the brain-language-vision large model, the input multi-modal fusion features are converted into high-quality prompts so as to guide the large language model to extract implicit knowledge, and important support is provided for reasoning generation.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses an EEG-fNIRS deep fusion cognitive state classification method based on feature decoupling, and the method comprises the steps: firstly synchronously collecting EEG data and fNIRS data of a tested mental calculation task, and carrying out the preprocessing of the two collected data, so as to obtain EEG data and hemoglobin concentration change data; secondly, electroencephalogram signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a deep fusion network, and public features and private features of EEG and primary features of fNIRS are obtained; and an EEG classification result, an fNIRS classification result and an output fusion classification result are obtained. And finally, designing decoupling loss and cross entropy loss based on a label smoothing strategy, and carrying out training. According to the method, the overfitting problem in fNIRS modal deep learning is effectively relieved, and cognitive state classification can be accurately carried out.

The invention relates to the technical field of medical auxiliary evaluation and human-computer interaction, in particular to a cognitive level evaluation system and method applied to a caregiver of a cerebral infarction patient. Generating task parameter data based on the multi-modal feature data through a task generation module; the cognitive state decoding module decodes a comprehensive cognitive state by using a double-flow deep learning network to generate a cognitive load, an attention trend and a task response score; the feedback optimization module predicts the change trend of the cognitive ability in combination with a dynamic Bayesian network, and dynamically adjusts task parameters through a closed-loop feedback mechanism; according to the method, the dynamic adaptation of the evaluation task and the design of the intervention task are realized, and the accuracy, interestingness and applicability of evaluation are effectively improved.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

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 provides a personalized teaching method and system based on generative artificial intelligence. The method comprises the following steps: constructing a knowledge graph and a teaching strategy template library of a target course; constructing a feature enhancement matrix; calculating node activation intensity, generating a plurality of candidate learning paths through a beam search algorithm, and screening an optimal learning path in combination with a preset teaching strategy template; using the generative model to generate standardized teaching content adaptive to the teaching strategy; dynamically optimizing a teaching scheme through a neuroplasticity model; and carrying out teaching adjustment based on the optimized teaching scheme. Standardized teaching content is generated by utilizing the generative model, the teaching content quality is improved, the teaching content is closely matched with a learning path and a teaching strategy of a learner, meanwhile, a teaching scheme is optimized through the neuroplasticity model, teaching parameters and cognitive resource allocation are adjusted in time according to real-time feedback and cognitive states of the learner, and the teaching efficiency is improved. Limited cognitive resources are reasonably distributed to key parts of teaching contents, and the teaching effect is improved.

The invention relates to the technical field of brain-computer interfaces, in particular to a method, a device and equipment for detecting the cognitive state of attention glasses in real time. The method comprises the following steps: acquiring multi-channel electroencephalogram data of attention glasses, acquiring multi-dimensional features corresponding to the multi-channel electroencephalogram data, and generating a detection sequence; performing time sequence analysis on the detection sequence, extracting change characteristics to construct a cognitive sequence, and generating state description according to the cognitive sequence; performing trend analysis on the state description, performing feature extraction on an analysis result corresponding to the trend analysis, constructing a conversion rule according to the extracted features, and generating a prediction sequence according to the conversion rule; performing threshold division on the prediction sequence, marking state intervals, constructing a criterion sequence, and generating an evaluation result according to the criterion sequence for performing signal detection to obtain detection information; and performing data conversion on the detection information, constructing a display sequence to mark a state type, and generating cognitive state information. And data continuity and signal integrity in a feature extraction process are realized.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention discloses an epileptic seizure auxiliary prediction and cognitive function evaluation system and method based on multi-modal data fusion and intelligent adaptive optimization. All the modules work cooperatively, and intelligent optimization from multi-dimensional electroencephalogram signal, motion data and physiological data collection, intelligent preprocessing and feature deep extraction and fusion to epileptic seizure prediction and cognitive function evaluation is achieved. Through the combination of neural architecture search and self-supervised comparative learning, the network structure can be automatically optimized on the basis of multi-modal data, and the ability of learning and identifying an epileptic seizure mode and a cognitive state is effectively improved. The system realizes high-accuracy epileptic seizure prediction and cognitive state evaluation through adaptive optimization, and remarkably reduces the missing report rate and the false report rate. The system has the characteristics of high efficiency, intelligence and accuracy, can provide timely early warning for epilepsy patients, provides scientific cognitive function intervention suggestions for doctors, and has wide application prospects.

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 provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention relates to the technical field of intelligent education, in particular to a teaching optimization method and device based on teaching courseware and cognitive data, and the method comprises the steps: compiling the teaching courseware including various forms of contents based on a teaching target and the demands of a learner; carrying out modularization processing on the written teaching courseware to obtain a plurality of teaching modules, and deploying a monitoring point in each teaching module; acquiring cognitive data of the learner at each monitoring point in the running process of the teaching courseware in real time, and comparing the acquired cognitive data with preset cognitive data to determine the cognitive state of the learner; and adjusting the form content of the teaching courseware according to the cognitive state, and re-determining the cognitive state of the learner based on the adjusted teaching courseware until the adjusted teaching courseware is matched with the cognitive state of the learner. Therefore, the cognitive data of the learner is collected and analyzed in real time, the teaching courseware is dynamically adjusted according to the analysis result to adapt to the cognitive state of the learner, and the learning effect is improved.

The invention discloses a cognitive state evaluation, regulation and control method, system and equipment for few-electrode electroencephalogram signals. The method comprises the following steps: selecting a blood oxygen level dependent signal feature set related to a preset cognitive state; taking the blood oxygen level dependent signal feature set as a label of the preprocessed scalp electroencephalogram signal, and determining a trained first prediction model based on the label and the preprocessed scalp electroencephalogram signal; inputting the target scalp electroencephalogram signal into a trained first prediction model to obtain a target few-electrode electroencephalogram signal; filtering the target few-electrode electroencephalogram signal, and inputting the filtered target few-electrode electroencephalogram signal into a constructed cognitive state evaluation model to obtain a cognitive state output by the cognitive state evaluation model; and according to the cognitive state output by the cognitive state evaluation model, regulating and controlling the current tested target cognitive state. According to the method, the accuracy of cognitive state evaluation is improved, and the spatial specificity of online cognitive regulation is improved.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The invention belongs to the field of advertisement analysis, and provides an advertisement putting effect evaluation and analysis system and method based on data analysis, and the method comprises the steps: constructing a cognitive state vector of a user based on multi-source behavior data before and after the contact between the user and an advertisement; modeling is conducted on user cognitive state vector changes caused by advertisement putting, cognitive intervention vectors of the advertisements are generated, and the cognitive intervention vectors represent the variable quantity of the cognitive states of the users after the users make contact with the advertisements; based on the language feedback, the visual behavior and the emotion track of the user, evaluating the consistency between the user behavior response and the cognitive intervention vector direction, and outputting a multi-modal behavior consistency score; tracking a behavior path of the user from advertisement click to final conversion, forming a conversion chain feature, and identifying a path length, a path node type and a path response time delay; and fusing the cognitive intervention vector, the multi-modal behavior consistency score and the conversion chain feature, and calculating a cognitive effect comprehensive score of advertisement putting for putting effect evaluation.

The invention belongs to the technical field of cognitive function training, and particularly relates to an intelligent pushing system and method for cognitive function training based on multi-dimensional ability values. According to the method, the cognitive state and the training effect of the user can be evaluated more accurately by collecting the real-time feedback of the user in the cognitive function training process, the training content and difficulty can be dynamically adjusted according to the actual demand and the ability level of the user through intelligent pushing, and therefore the training individuation and effectiveness are improved, and in addition, the user experience is improved. By continuously monitoring and updating the training list, it can be ensured that the user is trained in the optimal state all the time, the problem of excessive training or insufficient training is avoided, and the method aims at helping the user to improve the cognitive ability and the life quality by providing a comprehensive, dynamic and personalized cognitive function training scheme.

The invention discloses a learner cognitive state recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining electroencephalogram data of a learner in a plurality of learning behavior processes; dividing data of each electrode channel in the preprocessed electroencephalogram data into frequency band data of a plurality of different frequency bands, segmenting the plurality of frequency band data corresponding to each electrode channel according to a preset time window to obtain a plurality of time sequence frequency band data, and extracting a plurality of signal features from all the time sequence frequency band data; constructing four-dimensional time sequence feature data based on the extracted signal features; performing fusion feature representation on the four-dimensional time sequence feature data to obtain a fusion feature vector; and inputting the fusion feature vector into a pre-trained cognitive state recognition model to obtain a cognitive state recognition result of the learner. According to the learner cognitive state recognition method and device, the electronic equipment and the storage medium, the cognitive state of the learner can be accurately recognized.

The invention relates to the field of education, and discloses a teaching window control method which comprises the following steps: synchronously acquiring eye movement tracking data, electroencephalogram signals and teaching semantic flow intensity in real time through a multi-modal sensor to form a multi-modal perception data input source; inputting the multi-modal perception data into a quantum annealing optimization module, constructing a Hamiltonian model based on semantic coupling strength and physical display constraints, and generating a window layout candidate parameter set in parallel; and performing symplectic geometry manifold correction on the candidate parameter set, calculating a physical adaptability boundary of the compensation display device through a differential geometry contact coefficient, and screening out a feasible solution space meeting curvature constraint. According to the method, the eye movement track and the electroencephalogram characteristics are fused through the multi-mode sensing unit, real-time layout optimization driven by the cognitive state is achieved, the limitation of separation of physiological signals and interface design is broken through, and window arrangement is made to accurately accord with the instantaneous cognitive load level of a learner.

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 provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention provides a hierarchical attention cognition diagnosis method based on knowledge association and abnormal behavior mixing, which comprises the following steps of: aggregating heterogeneous test question attributes such as test question texts, concepts and difficulty by adopting a graph neural network technology, and updating neighbor nodes to obtain knowledge association; a short-term attention mechanism based on working memory capacity is adopted to aggregate preorder knowledge information associated in a time sequence, and first-order coupling of'knowledge-time sequence 'is achieved; key features reflecting student guessing and carelessness are extracted from click stream features, guessing and carelessness functions are set to calculate guessing indexes and carelessness indexes, and first-order coupling of'behavior-time sequence 'is achieved; based on the knowledge state and the abnormal behavior index, setting a guess gate and a carelessness gate, correcting the cognitive state of the student, obtaining the comprehensive ability state of the student, and realizing second-order coupling of'knowledge-time sequence-behavior '; and finally, adopting a multi-task learning method, and introducing an abnormal index prediction task so as to correct the answer performance prediction of the learner in the future.

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 discloses an EEG concentration degree-distraction degree two-dimensional classification method based on nonlinear mathematics. The method comprises the following steps that 1, high-precision electroencephalogram signals are collected; 2, performing complex number transformation on the electroencephalogram signals, and separating concentration degree and distraction degree related signals; 3, extracting a two-dimensional phase angle, and constructing a concentration-distraction phase space; 4, performing feature extraction by applying a nonlinear coupling dynamic model; 5, quantifying the complex dynamic state of the cognitive state by using a chaos and fractal analysis method; and step 6, performing state classification by using a support vector machine in combination with dynamic system characteristics. The invention relates to the technical field of EEG concentration degree-distraction degree two-dimensional classification methods. According to the EEG concentration degree-distraction degree two-dimensional classification method based on nonlinear mathematics, more comprehensive cognitive state description can be realized, a complex cognitive state is captured through a two-dimensional model, the classification precision is higher, and the nonlinear model can identify fine state differences.

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 discloses an English teaching method based on human-computer interaction. The method comprises the following steps: acquiring English pronunciation data of a user, and generating a pronunciation feature vector; capturing a text reading track, and constructing an attention hotspot map; recording a distribution matrix of error types from a grammar error correction interface; performing multi-dimensional feature fusion on the pronunciation feature vector, the attention hotspot map and the distribution matrix, and inputting into a long short-term memory neural network to generate a dynamically updated cognitive state vector; performing semantic matching on the cognitive state vector and a pre-constructed English knowledge point graph; when it is detected that the user has the same phoneme pronunciation errors three times continuously, activating a virtual teacher low-speed demonstration mode, and synchronously adjusting the exaggeration amplitude value of the virtual population animation; and carrying out clustering analysis on the weak knowledge point set, and according to a clustering analysis result, screening explanation demonstration fragments containing associated knowledge points from the resource library, and inserting the explanation demonstration fragments into the current teaching process.

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 belongs to the field of flight safety, and discloses a civil aviation unit cognitive state difference evaluation method and system based on an LSTM-MHSA algorithm and a hierarchical coupling framework, and the method comprises the steps: S1, respectively collecting the electroencephalogram and psychological cognitive data of a pilot before and after the execution of a flight task, and after preprocessing, the scaled electroencephalogram features and psychological cognitive features are reconstructed to form a unified individual cognitive ability feature multivariate time sequence sample set. S2, constructing a deep learning model LSTM-MHSA, and extracting pilot personal cognitive ability time sequence evolution characteristics and pilot and copilot cognitive interaction fusion characteristics; s3, using an LSTM-MHSA model to evaluate the overall ability scores of the pilot in different psychological cognition dimensions and the psychological cognition cooperation mode of the pilot and the copilot in the flight mission period, and achieving the difference evaluation of the cognitive state of the civil aviation unit. The method aims at comprehensively evaluating the psychological cognitive ability of the pilot from the aspects of individuals and units.