256 results about "Cognition.status" patented technology

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 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 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 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 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 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 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 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.

The invention relates to a knowledge tracking model fusing dynamic edge weight and forgetting gating, and aims to solve the problems that an existing knowledge tracking model cannot effectively model dynamic association of knowledge points and neglects a memory recession effect. According to the method, by constructing a time sequence diagram neural network, based on a learnable time attenuation factor and an attention mechanism, the edge weight between knowledge points is dynamically adjusted, and self-adaptive modeling of the incidence relation evolving along with time and learning behaviors is achieved. Furthermore, according to the Ebbinghaus forgetting curve theory, personalized forgetting rate parameters are defined, and the generated memory intensity is embedded into the LSTM unit to serve as a gating signal, so that the influence of knowledge decline on the cognitive state of the student is explicitly simulated. The invention also provides a hierarchical fusion architecture, which combines the time sequence behavior sequence and the map structure information, and improves the prediction precision and generalization ability of the model. According to the method, chain state degradation caused by knowledge forgetting can be accurately identified, and a theoretical basis and decision support are provided for intelligent recommendation of personalized review paths in an adaptive learning system.

The invention discloses an educational cognitive engine adaptive optimization method based on a large model, which comprises the steps of S1, formulating an acquisition specification, acquiring teaching behavior data from an educational database, forming a uniform format through an ETL tool, and mapping the behavior data into a cognitive state vector through a cognitive signal encoder in combination with a preset cognitive dimension; s2, cognitive association is extracted from the cognitive state vector through BERT, and an evolutionary knowledge graph with evolutionary markers is obtained through an evolutionary marker mechanism optimized by cognitive association weight; s3, based on the evolutionary knowledge graph, introducing an improved graph neural network to carry out cognitive evolutionary link modeling to obtain a cognitive link evolutionary model, and generating an optimal candidate strategy list through the cognitive link evolutionary model; and S4, accessing a basic education cognitive engine based on the optimal candidate strategy list to automatically match teaching resources, thereby effectively satisfying learning requirements of different students, and improving pertinence and effectiveness of teaching.

The invention relates to a cognitive state classification method based on EEG-fNIRS spatio-temporal fusion features, which comprises the following steps of: setting an experiment according to a mental calculation experiment normal form, and synchronously acquiring EEG data and fNIRS data of a tested mental calculation task; preprocessing the two collected data to obtain electroencephalogram signal data and hemoglobin concentration change data; eEG 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 space-time fusion network, EEG features, oxyhemoglobin HBO features and deoxyhemoglobin HBR features are obtained, fusion features are obtained through fusion, and an EEG classification result, an fNIRS classification result and a fusion feature classification result are obtained. According to the method, the characteristics of the EEG signal and the fNIRS signal are effectively combined, the overfitting problem in modal deep learning is relieved, and cognitive state classification can be accurately carried out.

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 provides an old people health management detection method and system, and the method comprises the steps: obtaining behavior task data and physiological data of old people through a scale or a non-invasive device, extracting data features, carrying out the fusion, building a cognitive state evaluation model set through combining an integrated learning model, a deep learning model and a statistical learning model, and carrying out the detection of the health management of old people. Training a cognitive state evaluation model set by adopting a differentiated learning strategy, constructing a dynamic evaluation system based on cognitive competence scores, determining evaluation frequency, determining evaluation items by adopting a correlation priority strategy of data features, establishing a nonlinear mapping rule, setting training reference parameters according to the cognitive competence scores, and evaluating the cognitive competence scores according to the training reference parameters. And making a personalized cognitive training scheme based on the dynamic evaluation system and the individual data characteristics of the elderly. The method is high in individuation degree and simple to operate, has continuous and comprehensive data, and can capture dynamic changes of cognitive competence, know differences of old people in real time and provide a timely and effective feedback mechanism.

The invention discloses an interactive computer programming teaching intelligent practical training platform and a construction method, and the platform comprises a practical training platform terminal, a multi-modal data collection module, a dynamic knowledge graph module and a dual learning module, the multi-modal data collection module is used for synchronously capturing a code editing event stream, biological characteristic data and a cooperative interaction log; the dynamic knowledge graph module is used for establishing an association network through an incremental graph updating algorithm, and the bidirectional reinforcement learning module is used for collaboratively optimizing instant feedback quality and long-term learning path planning of programming teaching through a hierarchical decision-making mechanism; code operation, biological characteristics and collaborative data are captured in real time through a multi-modal data acquisition system, and collaborative optimization of instant feedback and long-term path planning in a programming teaching process is realized by combining incremental association network construction of a dynamic knowledge graph and a hierarchical decision mechanism of dual reinforcement learning; the cognitive state is accurately evaluated through eye movement tracking and keyboard behavior analysis.

The invention provides a cognitive function evaluation method based on neurovascular coupling analysis. The method comprises a signal preprocessing step, a signal processing step, a feature extraction step and a coupling coefficient calculation step. A new normal form of cognitive evaluation is constructed from nerve-blood vessel coupling and cross-system cooperation dimensions, and accurate description of a cognitive state is realized through real-time quantification of a dynamic cooperation effect of brain nerve activity (EEG) and blood flow regulation (CBP variability) and establishment of an association relationship between a dynamic cooperation index and cognitive ability (alertness, reaction speed and memory). And a new objective basis is provided for the fields of plateau medicine, intelligent health, neurological and mental disease diagnosis and treatment and the like.