497 results about "Cognitive load" patented technology

The invention relates to a multi-modal dynamic optimization educational resource recommendation system and method, and the system comprises the following modules: a multi-modal data collection module integrates video behaviors, answer tracks, physiological signals and other data through edge calculation, and constructs a learning feature map; the student portrait module adopts an LSTM-Attention network in combination with a graph neural network to dynamically model knowledge mastery and learning styles; the resource matching engine realizes multi-objective optimization of knowledge gain, cognitive load and interest matching based on reinforcement learning and knowledge graph analysis; the tag adaptive module dynamically adjusts resource weights through causal inference and comparative learning, the personalized recommendation module generates a dynamic learning path and pushes adaptive resources based on student portraits and real-time behavior data, and the learning progress tracking module monitors a learning state in real time and feeds back the learning state to the resource matching engine to optimize a recommendation strategy in a closed loop mode. The technical defects that resource recommendation of a traditional education platform is rigid and personalized adaptation is lacked are overcome.

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

The invention relates to the technical field of artificial intelligence, and discloses an English teaching training system and method fusing semantic matching and cognitive assessment, and the method comprises the steps: synchronously capturing a text response, a voice intonation, an eye movement track, a facial micro-expression and a touch rhythm generated in a learning process; semantic deviation deconstruction and cognitive intention quantization processing are carried out on the learning interaction original sequence, and a bidirectional deep semantic matching network is adopted to carry out context alignment on student answers and target corpora; based on the word meaning divergence point set and the cognitive load multi-scale vector, extracting a nonlinear diffusion trajectory of a learning state by using a time gating multi-layer recursive trajectory evolution algorithm; the knowledge point nodes, the deviation type nodes and the emotion triggering nodes associated with the emotion instability candidate segments are fused to construct a local learning map; and forming an emotion cognition feedback result driven by learning interest based on the local learning map and the self-adaptive error correction intervention sequence. The method has the advantage of improving the learning interest of students.

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses an SQL statement structure verification system based on large model and knowledge graph fusion enhancement. The SQL statement structure verification system comprises a knowledge graph construction module, an input design module, a verifier design module and an output design module. According to the method, a knowledge graph is introduced as a structured semantic support, and a closed-loop enhancement processing flow of SQL statements from generation, verification to optimization is realized in combination with a structure perception cue word design, a rule verification mechanism and a Function Call feedback interface; the defects that an existing Text2SQL method is insufficient in structural understanding, lack of semantic reasoning, weak in generalization ability, dependent on computing power and the like are overcome. According to the method, triple guarantee of semantic perception, rule driving and model assistance is realized, the accuracy, robustness and interpretability of SQL generation are remarkably improved, and the cognitive load and operation risk of non-professional users in database interaction are effectively reduced.

The invention relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention discloses a self-adaptive dynamic interface generation method and system based on a computing power host operation and maintenance platform, and relates to the technical field of operation and maintenance interface dynamic optimization. Aiming at the problem of static stiffness of an operation and maintenance interface in a complex scene, the scheme is adopted as follows: collecting user behaviors, a context environment and equipment data, and fusing into a multi-dimensional state vector; inputting the multi-dimensional state vector into a deep reinforcement learning model, and generating a dynamic interface strategy including layout component dynamic arrangement and function button priority ranking; designing a hierarchical multi-target reward function fusing operation efficiency reward, accuracy reward and cognitive load penalty, and performing strategy optimization; analyzing the optimized dynamic interface strategy through the cross-terminal rendering capability of the WebXR engine, and generating a front-end page adaptive to the current terminal; and continuously feeding back the collected data to the deep reinforcement learning model, and updating the network weight through a strategy gradient algorithm to realize user preference migration. According to the method and the device, cross-terminal self-adaptive dynamic interface generation can be realized.

The invention relates to the technical field of education management, in particular to a learning intelligent course arrangement method and system based on time parameters, and the method comprises the following steps: obtaining student task records, analyzing task concentration degree and behavior fluctuation, obtaining cognitive load distribution, counting teacher teaching frequency and time matching degree, screening preference time periods, and constructing an adaptation table. Extracting curriculum attributes and front nodes, calculating priorities, generating a score table, identifying conflicting curriculums, matching available time periods, obtaining curriculum distribution, comparing student loads with teacher preferences, and screening abnormal curriculum arrangement time periods. According to the method, cognitive load distribution is constructed by analyzing multi-cycle task behaviors of students, time period preference is identified in combination with teaching activeness of teachers, resource matching precision is improved, curriculum arrangement fusion priority weight and neutral position intensity cross scoring is performed, time periods are accurately screened, conflicts are avoided, and cognitive load and preference deviation curriculums are identified. High matching of courses and behavior rhythms is realized, and adaptability and course arrangement scientificity are enhanced.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The invention discloses a graphical user side demand response strategy dynamic evolution analysis method. The method comprises the steps of obtaining demand response resource original data and constructing an initial resource model; constructing a resource flow dynamic structure model, and generating a resource flow topological structure and a resource association intensity matrix; real-time monitoring and evaluation of cognitive loads are realized, and user cognitive load indexes are calculated; interface element self-adaptive adjustment is executed, and interface parameter configuration is generated; and carrying out resource flow dynamic layout optimization to generate a resource flow visual layout. Through cognitive load dynamic evaluation and interface adaptive adjustment, the problem of cognitive load during arrangement of a large number of resources is solved, the strategy arrangement efficiency is improved, and the operation error rate is reduced.

The invention provides a mobile terminal online learning optimization method and system, and belongs to the technical field of mobile terminal learning platform optimization, and the method comprises the steps: 1, collecting the original behavior data of a terminal user, and outputting a learner state packet containing a real-time knowledge attention vector, a cognitive load index and an equipment efficiency score; 2, outputting a knowledge blind area mark set, and meanwhile, generating learning state description containing blind area marks and load levels; 3, generating a primary content optimization strategy based on the blind area mark set, carrying out strategy feasibility verification in combination with a load grade and an equipment efficiency score, and outputting a final executable strategy set; 4, executing the executable strategy set and controlling interface rendering and resource allocation; and 5, collecting eye movement track change data and system performance indexes, and outputting a feedback data stream used for updating the knowledge attention vector. The learning content and the user experience are optimized, the cognitive load is reduced, and the high efficiency and individuation of the learning process are ensured.

The invention relates to the technical field of big data analysis, and discloses a personalized learning path recommendation method and system based on big data, and the system comprises a data collection processing module, a knowledge graph construction module, a path optimization engine and a dynamic adjustment module. The system can dynamically trigger learning path reconstruction, improve the path matching degree in the middle and later periods of the learning process, set cognitive load constraint terms in a path optimization objective function, avoid continuous arrangement of high-complexity knowledge points, ensure that the learning process conforms to the cognitive psychology law, dynamically update knowledge attribute parameters through a matrix transformation mechanism, and improve the learning efficiency. The learning path is kept in an optimal state all the time, learning feedback data is continuously collected, the system can dynamically trigger learning path reconstruction, the path matching degree in the middle and later periods of the learning process is improved, a cognitive load constraint item is set in a path optimization objective function, and it is guaranteed that the learning process conforms to the cognitive psychology law.

The invention belongs to the technical field of online learning, and discloses a real-time interactive online Chinese language learning platform. Comprising the steps of constructing a virtual culture interaction scene; in the dialogue interaction process, continuous dialogue fragments are collected in real time; performing multi-level language bias error analysis on the continuous dialogue segments, identifying language errors, making corresponding language correction suggestions according to the language errors, and feeding back the language correction suggestions to the user through a progressive feedback mechanism; collecting multi-modal data of a user, carrying out nonlinear feature mapping, generating a multi-dimensional state feature vector, and dynamically evaluating a cognitive load and a learning state; the cognitive load and the learning state are fused, interaction regulation and control strategy reasoning is carried out, and the interaction mode and the interaction difficulty in dialogue interaction are dynamically optimized according to an interaction regulation and control strategy; according to the method, accurate control and dynamic regulation and control of the whole Chinese language learning process of the user are realized, personalized online Chinese learning experience is provided, and comprehensive improvement of the language ability of the user is promoted.

The invention relates to the technical field of teaching systems, in particular to a teaching information processing method and system based on artificial intelligence, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-modal data, and carrying out the fusion of the data through a time warping dynamic alignment algorithm; s2, cognitive state modeling: introducing a dynamic forgetting gate mechanism on the basis of a traditional LSTM, quantifying the half-life effect of knowledge points, and obtaining a deviation degree delta gt between a problem solving path of a student and an expected optimal path; 0.4, an alarm is triggered; and S3, a dynamic knowledge topological component dynamically updates a relation weight between knowledge points based on a graph neural network, and an improved Sweller cognitive load index is used. The method solves the problem that when students have unexposed understanding deviation in an algebraic'factorization 'link (such as a'square difference formula' and a'complete square formula '), a system still mechanically advances to a'polynomial operation' module to cause chain accumulation of knowledge vulnerabilities and finally cause systematic learning collapse.

The invention discloses a teaching reflection method and system based on intelligent classroom teaching, and relates to the technical field of intelligent classroom teaching. The method comprises the following steps: acquiring teaching outline information and teaching material structure information, and constructing a teaching knowledge graph; testing the learning style of the student to generate a learning style label; obtaining teaching activity content, calculating the adaptation degree between the teaching activity content and the learning style of the student, and generating a first teaching reflection result; acquiring learning behavior data and physiological performance data of the student, associating the learning behavior data and the physiological performance data with teaching knowledge graph nodes, evaluating the mastering level of the student on each knowledge node in the teaching knowledge graph, and generating a second teaching reflection result; and constructing a cognitive load prediction model, predicting and evaluating the cognitive load of the student, and generating a third teaching reflection result. According to the invention, a multi-dimensional, personalized and real-time teaching reflection scheme is constructed, and the effect and response capability of intelligent classroom teaching are remarkably improved.

The invention discloses an ancient poetry multi-mode teaching system based on HSK classification, and belongs to the field of intelligent teaching. Comprising a learning evaluation portrait module, a learning path generation module, a physiological emotion monitoring module, a cognitive load analysis module, a vocabulary grammar enhancement module, a modal semantic analysis module, an artistic conception analogy generation module, a holographic scene generation module, a feedback interaction experience module, a content dynamic adjustment module, a recitation pronunciation correction module and an expansion knowledge generation module. A performance analysis feedback module and a review memory enhancement module; according to the method, learning tasks can be updated in real time along with the change of the ability of students, a one-step teaching mode is avoided, the learning efficiency and adaptability are improved, the learning experience and continuity are greatly improved, cultural faults can be effectively reduced, non-native Chinese language persons can understand the deep significance of ancient poetry more easily, and the learning efficiency is improved. The memory depth of the learner to the poetry artistic conception and vocabularies is greatly improved, and the learning motivation is also improved.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The invention discloses an intelligent evaluation system for advertisement putting effect monitoring, and relates to the technical field of advertisement putting, and the system comprises the steps: loading a bidirectional pre-training language model and a multi-modal fusion model, and synchronizing advertisement metadata to generate a structured feature matrix; collecting user behaviors, biological characteristics and environment data in real time, generating a four-channel environment map, splicing the four-channel environment map with the structured characteristic matrix, and inputting the spliced four-channel environment map and the structured characteristic matrix into a Transform encoder to form a joint characteristic vector; matching a high-density high-emotion region according to the candidate budget allocation scheme, and generating a cognitive load index in combination with the facial micro-expression of the user; user interaction data cognition load scenes are analyzed, core advertisement group weights are evaluated and optimized through a regret value mechanism and a Shapley value, and a final dynamic putting scheme is generated; the response difference of the advertisement group to the cognitive load is quantified through the sensitivity coefficient, and the marginal contribution of the advertisement group is evaluated in combination with the Shapley value, so that the accurate identification and budget inclination of the core advertisement group are realized.

The invention relates to the technical field of security management, and discloses a security leadership intelligent evaluation method and system based on a knowledge graph, and the method comprises the steps: constructing a security decision cognitive load knowledge graph, and building a structured cognitive load mapping network; multi-modal cognitive load monitoring is achieved, physiological and behavior data of a tested person are collected, and multi-modal data fusion analysis is carried out; performing an adaptive cognitive load test, generating an adversarial limit scene, dynamically adjusting task complexity, and determining a cognitive resource critical point; analyzing attention resource allocation efficiency, and identifying a resource allocation strategy mode; quantifying a cognitive efficiency index, and evaluating decision-making efficiency and multi-target balance capability in a high-pressure environment; safety leader evaluation is extended from surface behavior observation to deep cognitive process analysis, and the cognitive limit of a leader in an extreme safety situation is accurately identified.

The embodiment of the invention provides an AI-based vehicle intelligent interaction method and system, and belongs to the field of artificial intelligence interaction. The method comprises the following steps: dynamically judging a cognitive load level in a current driving scene through collaborative perception of a driver state and an environment state; determining a target man-machine interaction strategy of the vehicle based on the cognitive load level; wherein the target man-machine interaction strategy comprises a voice output mode, information density of screen display content and a feedback prompt mode; determining a corresponding trigger rule based on a switching relationship between the current man-machine interaction strategy and the target man-machine interaction strategy; and responding to a trigger signal of the determined trigger rule, executing man-machine interaction strategy switching, and executing vehicle interaction based on the switched target man-machine interaction strategy. According to the scheme of the invention, closed-loop adaptive control of a human-vehicle interaction strategy on a cognitive state is realized.

The invention discloses a computer multimedia teaching system and device, relates to the technical field of digital education, and aims at high-dimensional behavior data collected by a multi-source sensor, environment modeling and time alignment are firstly utilized to generate an initial data set, and then noise segments are screened out through comparative learning and clustering. Then, time sequence and similarity correlation is established for the features of the students in different time periods by utilizing a multi-modal interaction graph, compact representation is obtained by utilizing graph neural network multi-layer iteration, and cross-time-period and cross-individual potential correlation is revealed; then, self-adaptive weight adjustment is carried out on the feature components by means of a reinforcement learning agent, the distraction degree is evaluated in real time, a strategy is iteratively optimized, and environmental fluctuation and individual differences are dealt with; and finally, fusing emotion analysis, further splicing emotion vectors such as language emotion and the like with cross-modal features, distinguishing attention changes caused by cognitive load and emotion fluctuation, and facilitating real-time detection and intervention in ideological and political teaching and professional teaching.

The invention discloses a robot gait training method and system based on reinforcement learning, and relates to the technical field of electric vehicle charging, and the method comprises the steps: carrying out the simulation learning initialization of a strategy based on a reference video; selecting a plurality of evaluation indexes to design a dynamic reward function so as to guide the initial strategy network to optimize the gait performance; through an environment difficulty scheduling mechanism, the training environment difficulty is automatically adjusted according to strategy performance; performing optimization training on the strategy network by using a PPO algorithm; and judging the optimized gait strategy by constructing a cognitive load index, and outputting a final gait strategy. According to the method, imitation learning, self-adaptive reward modeling, multi-stage scheduling, reinforcement learning optimization and cognitive feedback are organically fused, and a robot gait training framework with generalization ability, stability and social adaptability is constructed. According to the method, the naturalness, the stability and the man-machine friendliness of the gait of the robot can be remarkably improved under complex terrains and interaction scenes, and the method has wide application prospects and popularization value.

The invention relates to the field of curriculum personalized adjustment, in particular to a curriculum learning personalized adjustment method and system of an enabling system, and the method comprises the steps: obtaining user information and historical learning data of a user when a learning instruction of the user is received, and recognizing target curriculum data of the user according to the user information and the historical learning data; acquiring historical behavior data of the user, identifying a concentration attenuation trend of the user according to the historical behavior data, and segmenting the target course data into a plurality of course segments according to the concentration attenuation trend; when the user learns the curriculum segment, behavior data of the user is acquired in real time; judging the concentration feedback state of the user according to the behavior data; when the concentration feedback state of the user is positive feedback, generating a learning efficiency enhancement strategy and a composite excitation strategy; and when the concentration feedback state of the user is negative feedback, generating a cognitive load regulation strategy and a pressure balance strategy. The learning state of the user is judged according to the behavior data, and the system can flexibly adjust the learning strategy.

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 education, in particular to a learning condition analysis system and method for programming online learning, and the system comprises a programming behavior collection module, a code semantic quantification module, a cognitive load evaluation module, a learning stage discrimination module and a cognitive adaptation resource library. Wherein the programming behavior acquisition module is used for operating behavior data; the code semantic quantification module is used for analyzing an abstract syntax tree of codes submitted by a user; the cognitive load evaluation module is used for outputting a three-dimensional load index; the learning stage judgment module is used for judging the current period of the user; and the cognitive adaptive resource module is used for matching resources based on the judgment result of the learning stage judgment module. According to the method, the programming behavior data of the learner is collected in real time and the cognitive load evaluation is combined, so that the learning stage of the learner is accurately judged, personalized teaching resources are further provided, the programming learning efficiency is improved, and the cognitive load is reduced.