196 results about "Cognitive status" patented technology

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 an attention state recognition neural network modeling and reasoning method based on an electroencephalogram sequence. The core is to construct and train a deep neural network model suitable for electroencephalogram signals so as to realize intelligent recognition and classification. Firstly, wavelet transformation and time-frequency feature extraction are carried out on electroencephalogram time sequence signals, and a multi-dimensional input tensor is generated in combination with channel position information; and inputting the feature into a deep network fusing spatial convolution, gating circulation and a residual connection structure, and extracting spatio-temporal joint features. A cross-time-step attention mechanism and a dynamic loss adjustment strategy are introduced in a training stage, so that the discrimination capability of the model on an alertness state transition region is improved. The final model can conduct reasoning on electroencephalogram data of any length, and a classification label and a confidence score are output and used for measuring classification reliability. The method focuses on construction and optimization of a specific calculation model, reflects application characteristics of an intelligent algorithm in cognitive state recognition, and belongs to an intelligent calculation method with a neural network as a core.

The invention relates to the technical field of cognitive disease risk assessment, and discloses a cognitive disease intelligent risk management method based on multi-modal voiceprint data analysis, the method comprises multi-modal feature fusion, dynamic risk assessment and real-time intervention linkage, by setting a multi-modal feature fusion module, when cognitive disease risk assessment is carried out, a multi-modal voiceprint data analysis module is set, the risk of the cognitive disease is evaluated, and the risk of the cognitive disease is evaluated. The recognition specificity of different cognitive impairment types is guaranteed by fusing acoustic characteristics, rhythm rules and multi-dimensional voiceprint characteristics of semantic logic, meanwhile, a comprehensive feature vector is constructed based on depth feature extraction, neural function degeneration signals implied in voiceprint data can be captured in real time, the misjudgment risk caused by traditional single-mode analysis is reduced, and the recognition accuracy of the cognitive impairment types is improved. According to the method, the reliability of early cognitive impairment screening is improved, a dynamic risk assessment mechanism is constructed, a cognitive state score and an adaptive threshold are compared in real time during individualized cognitive tracking, and abnormal fluctuation of a cognitive function is recognized in time.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The present disclosure describes systems, devices, and methods of benefiting a cognitive state or function of a brain of a subject. The systems can include a portable system comprising a single headset device to be worn by a subject, comprising a pair of glasses configured to provide a visual stimulus to the subject, and a pair of supra-aural headphones configured to provide an auditory stimulus to the subject.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention relates to the technical field of artificial intelligence and flight training, and particularly discloses an intelligent training method and system for consciousness of a pilot. The method comprises the following steps: constructing a multi-modal flight scene and an original task structure chart, and extracting a flight task target and a semantic tag set; guiding a pilot to carry out natural language expression, collecting a voice stream and combining a task stage to align a text; comparing the language expression with task semantics by using a language model, and identifying expression differences; generating a feedback statement based on the difference, and pushing the feedback statement to the pilot; updating a task flow and reconstructing an execution script; finally, training records are counted, the capacity label weight is updated, and a capacity growth trend graph is generated. According to the method, accurate identification of pilot task understanding deviation and language expression defects can be realized, a scene training process dynamically adaptive to a cognitive state is constructed, and scene awareness and language expression ability of a pilot are remarkably improved.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.

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 relates to the technical field of intelligent evaluation, in particular to a novel multi-modal Alzheimer's disease early cognitive function intelligent evaluation method, which comprises the steps of multi-modal data collection, data processing and feature extraction, and cognitive evaluation model construction. By collecting image data, facial recognition, linguistic and acoustic characteristics, exhaled gas components and eye movement and gait data under a double-task normal form, multi-aspect physiological and cognitive information of a patient is comprehensively collected by adopting a multi-modal data collection technology, and multi-modal information of the patient is acquired through multi-modal data. The evaluation method can more comprehensively reflect the cognitive state of a patient, predicts and automatically identifies abnormal cognitive performance through the cognitive evaluation model, can significantly improve the diagnosis sensitivity and specificity of the early cognitive impairment of the Alzheimer's disease, overcomes the limitation of a traditional single-mode evaluation method, and has a good application prospect in the field of diagnosis of early cognitive impairment of the Alzheimer's disease. And early discovery and early intervention of clinical diseases are effectively supported.

The invention provides an intelligent interaction system and method based on a multi-stage cognitive mode, and the system comprises a multi-modal data collection module which is used for collecting user interaction data through a multi-modal sensor, and the data comprise language input, non-language behaviors, interface operation data, expressions, eye movement tracks and the like; and the cognitive feature analysis module is used for calling a deep learning model to perform feature extraction on the interaction data. According to the method, language, behavior, interaction, physiology and other data are fused through the multi-modal sensor, the cognitive driving vector is generated by using the deep learning model, the real-time cognitive state of the user is effectively captured, then the probability distribution of the cognitive stage is constructed in combination with Bayesian reasoning, the problems that in the prior art, only the cognitive level can be statically judged, and real-time updating is difficult are solved, and the user experience is improved. The accuracy and timeliness of user state perception are remarkably improved, and dynamic accurate recognition and continuous modeling in the cognitive stage are achieved.

The invention discloses an intelligent perception analysis system for the real-time participation degree and cognitive state of a mathematics classroom. The intelligent perception analysis system comprises a multi-source data acquisition module, a fusion analysis engine module, a real-time feedback module and an offline optimization module. The method has the beneficial effects that a dynamic participation index is innovatively designed, and an attention attenuation resetting mechanism is introduced; the real-time feedback module generates a cognitive thermodynamic diagram of HSL color mapping to position group obstacle points, and triggers self-adaptive question pushing; and the off-line optimization module dynamically updates the edge weight of the knowledge graph through error co-occurrence analysis, and early warns a cognitive confusion relationship without textbook association. According to the method, the limitation of single-mode perception is broken through, the problem solving step quality visual diagnosis and the cross-cycle cognitive impairment prediction are realized, a teaching closed loop of'multi-source perception-hierarchical quantification-real-time intervention-knowledge evolution 'is formed, and the mathematical classroom cognitive state analysis precision and the teaching intervention timeliness are remarkably improved.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

Evaluation system for obtaining and analyzing user interaction and physiological data to provide adaptive feedback, comprising: a portable multi-sensor device comprising: an electroencephalography sensor designed to measure the user's brainwave signals; and a galvanic skin response sensor designed to measure skin conductivity, which indicates the user's stress level; an interactive assembly device designed to recognize the user's assembly actions and generate corresponding assembly data; a computing device in communication with the wearable multi-sensor device and the interactive montage device, the computing device configured to receive and combine the brainwave signals, the skin conductance, and the montage data. Analyze the combined data in real time to create an estimate of the user's cognitive state and provide adaptive feedback to the user based on the cognitive state estimate via at least one output module. wherein the computing device is further configured to store the brainwave signals, the skin conductance, the compilation data, and the cognitive state estimate in a database for later retrieval and review.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention belongs to the technical field of cognitive impairment intervention evaluation, and particularly relates to an evaluation system and method applied to cognitive impairment intervention. According to the method, the cognitive state is evaluated and the training tasks are allocated based on the real data of the user in the natural life scene, so that the one-sidedness of only depending on a laboratory or one-time evaluation is avoided, the training and intervention are closer to the real demand and life situation of the user, and the training efficiency is improved through multi-source data fusion. The influence of single signal noise or accidental fluctuation on judgment is reduced, the robustness and objectivity of evaluation are improved, indexes such as cognitive performance, intervention indexes and deviation degree are numeralized, long-term tracking and transverse comparison of the effects of different intervention measures are facilitated, interpretable quantitative evidences are provided for clinical personnel, and the accuracy of evaluation is improved. Intervention indexes are calculated in real time, intervention measures are automatically adjusted or reselected according to feedback results, and a quick-response closed-loop system is formed, so that the time from problem discovery to effective intervention is shortened, and the intervention conversion rate is increased.

The invention discloses a real-time collaborative cross-scene visual assistance and environment perception system for visually impaired people based on head-mounted equipment. According to the invention, through fusion of a multi-mode perception technology and a neural feedback mechanism, all-around environmental cognition support is provided for visually impaired people. Key targets such as curbs, steps and traffic signals in a complex scene can be accurately recognized, and a safe navigation scheme is generated in real time in combination with a dynamic path planning and obstacle avoidance algorithm. Through personalized feedback modes such as voice and vibration, the system can adjust the interaction rhythm according to the behavior habit and cognitive state of the user, ensure efficient and natural information transmission, effectively reduce the cognitive load of the user, help the visually impaired people to travel autonomously in different environments, improve the independent living ability, and improve the user experience. Real-time response and calculation efficiency are balanced through a collaborative architecture, so that a high-performance auxiliary function is not limited by heavy hardware equipment any more. The universal design reduces the use threshold, and is helpful for more visually impaired people to enjoy the convenience brought by science and technology.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention relates to the technical field of knowledge tracking, in particular to a knowledge tracking method based on hybrid convolution, and the method comprises the steps: obtaining the interaction information of a learner and education content; the interaction information is input into a preset knowledge tracking model, the answer correct probability of a target question is predicted, the knowledge tracking model is used for constructing an input sequence based on the interaction information, behavior characteristics in different time ranges in the sequence are extracted through multi-scale causal convolution, and the answer correct probability of the target question is predicted; modeling is carried out in combination with an attention mechanism with a distance penalty term, and finally knowledge tracking is completed in combination with question answering correctness probability prediction of the target question. According to the invention, knowledge tracking is carried out in combination with multi-scale causal convolution and a distance decay attention mechanism, and the ability of modeling the cognitive state of a learner is improved.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention provides a self-adjusting dialogue type multi-agent system and interaction method based on dynamic topology, and the system comprises a scheduling module which is used for receiving user input and routing a task to a corresponding field task agent in a conventional state; the domain task intelligent agents are used for processing the received tasks, evaluating the cognitive states in real time and sending out assistance signals when the cognitive states meet preset conditions; the shared knowledge management module is used for maintaining a dynamically updated distributed semantic knowledge graph; wherein the distributed semantic knowledge graph is used for sharing, associating and persisting structured knowledge entities and context information generated in the session process among all modules in real time; the topology control module is used for dynamically switching the communication control topology of the system from a first topology structure to a second topology structure in response to the received assistance signal; and when the assistance signal is eliminated, the communication control topology is recovered from the second topology structure to the first topology structure.

The invention provides an injection molding machine man-machine cooperation intelligent control system comprising an acquisition device used for acquiring state data of an operator; the signal preprocessing module is used for eliminating motion artifacts in the state data by adopting an improved multivariate empirical mode decomposition algorithm; the cognitive state decoding module is used for inputting a concentration time sequence of state data and outputting cognitive state parameters of an operator based on a space-time diagram convolutional network model; the digital twin synchronization engine is used for realizing millisecond-level alignment of injection molding machine data and state data through an OPC UA protocol; and the collaborative optimization engine is used for dynamically adjusting the injection molding process of the injection molding machine and the complexity of a human-computer interface based on a depth deterministic strategy gradient algorithm. By integrating a functional operator state monitoring technology, a digital twinborn model and a dynamic optimization algorithm, real-time perception of an operator cognitive state and intelligent regulation and control of injection molding process parameters are realized.

The embodiment of the invention discloses a question bank generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining historical examination data of a target object, and determining an individual forgetting curve of the target object according to the historical examination data; wherein the individual forgetting curve is used for representing a forgetting rule of the knowledge point memory amount of the target object about time; determining a target test question difficulty corresponding to the target object according to the individual forgetting curve; and screening test questions in a preset question bank according to the target test question difficulty, and determining a target question bank corresponding to the target object. According to the technical scheme provided by the embodiment of the invention, the problem that the generated test questions are easy to deviate from the cognitive state of the learner in the prior art is solved, the individual forgetting curve about the target object can be constructed based on the historical examination data, and then the test questions with the corresponding difficulty are screened based on the individual forgetting curve; and the adaptation degree of the generated test question and the current cognitive state of the target object is improved, so that the learning efficiency is improved.

A system for assessing a cognitive state of a user, comprising: a hardware output user interface providing user progression information at performing a task; an input user interface controllable by the user for performing the task; wherein response dynamics of the input user interface to user instructions are changed by the system during the performance of the task, thereby resulting in highly fluctuational motion of a body portion; a processor and a memory module including computer program code, collectively configured to cause the system to: receive over at least one hardware communication channel sensor based kinematic data indicative of kinematic parameters of the body portion at different times during the performance of the task; process the sensor-based kinematic data to characterize motion patterns of the at least one body part during the highly fluctuational motion; and determine a cognitive state of the user based on the motion patterns.

The invention discloses a student state visual analysis system for optimizing virtual human teaching. The student state visual analysis system comprises a front-end sensing layer, a multi-modal feature extraction module, a time sequence analysis layer and a teaching decision visualization layer, the front-end sensing layer collects facial expressions and eye movement data of students in real time, and locates facial areas of the students in real time by adopting a parameter-optimized SSD target detection algorithm; the multi-modal feature extraction module extracts seven types of expressions and four types of eye movement features based on a RepVGG model through a multi-modal fusion method, and constructs a 11-dimensional time series data stream; the time sequence analysis layer dynamically models a cognitive state based on an LSTM network model, and outputs the cognitive state through a continuous behavior rule; the teaching decision visualization layer generates an interactive instrument board, realizes accurate mapping of a cognitive state and a course time axis through a thermodynamic diagram and a trend curve, and automatically marks a high-doubt section. The method breaks through the limitation of a single mode, improves the feedback efficiency, and is suitable for a virtual human asynchronous teaching scene.

The invention relates to the technical field of artificial intelligence and big data, and discloses a learning disorder assessment and adaptive training system and method based on multi-dimensional behaviors, and the system comprises a multi-modal assessment module, a path generation module, a nerve regulation and control module and an adaptive engine module. By fusing multi-modal data features of language behaviors, electroneurographic signals and task response behaviors and combining a cross-modal attention mechanism to dynamically construct a cognitive state recognition model, learning disorder typing accuracy is improved, multi-dimensional ability defects such as attention, memory, language understanding and execution control are quantified based on a three-dimensional cognitive map, and learning disorder typing accuracy is improved. It is ensured that the assessment result comprehensively covers the core dimension of cognitive impairment; transcranial direct current stimulation is synchronously triggered in the cognitive task starting stage, space-time precise matching of task state nerve regulation and brain region activation is achieved, stimulation target point coordinates and current intensity are dynamically adjusted according to real-time nerve feedback, and the neuroplasticity reconstruction efficiency is improved.

The invention provides a teaching information processing method and device, and relates to the technical field of artificial intelligence and the like. According to the specific implementation scheme, information input by a user is received; performing semantic and intention recognition on the information input by the user to obtain a cognitive state of the user for related nodes in the teaching knowledge graph; on the basis of the cognitive state and the user input information, historical dialogue information in a user dialogue portrait of the user and mastering information of a target concept node are updated, and a current focusing node is obtained from the user dialogue portrait; based on the mastering information, selecting a target node related to the current focusing node in the teaching knowledge graph, and replacing the current focusing node with the target node; generating a content constraint set based on the target node and the teaching knowledge graph; and generating next teaching content information based on the content constraint set and the historical dialogue information, and taking the next teaching content information as current teaching content information and sending the current teaching content information.

The invention provides a data processing method and system based on intelligent teaching, and the method comprises the steps: obtaining a real-time interaction data flow uploaded by a multi-mode interaction terminal through a knowledge graph processing engine, carrying out the spatial-temporal feature decoupling of the real-time interaction data flow, and extracting an interaction behavior feature vector; mapping the interactive behavior feature vector to a preset teaching resource knowledge graph, positioning a target knowledge node, and constructing a dynamic semantic topology subnet based on an adjacency relationship; calculating the projection deviation of the interactive behavior feature vector in the dynamic semantic topology subnet, and generating a cognitive divergence vector representing the cognitive state deviation degree; and generating a data stream reconstruction instruction based on the cognitive divergence vector, calling the associated semantic slice from the teaching resource knowledge graph according to the instruction to synthesize and encode the basic teaching data packet in real time, generating a target compensation data stream, and issuing the target compensation data stream. According to the invention, the real-time accurate adaptation of the teaching data stream and the user cognitive state is realized.

The invention relates to the technical field of limb rehabilitation training, in particular to an artificial intelligence-based cognitive impairment assessment and intervention training system, which comprises a path construction module, an action acquisition module, a matching recognition module, a rhythm regulation and control module and a cognitive intervention module. According to the method, the standard path is constructed in the virtual task and combined with the three-dimensional action data of the user to form the path section response result, so that the spatial integrating degree of the action of the user and the target path can be identified, and quantitative description of the action execution stability and continuity is realized through coordinate difference analysis; a cooperative path record of task execution is obtained in combination with a trajectory coverage rate and a matching relationship, and a trend analysis and parameter regulation mechanism is introduced, so that a task pushing rhythm and a user response rhythm are dynamically matched, and tracking correction of an intervention effect in a training stage and fine evaluation of cognitive limb disorder performance are realized. The analysis granularity of cognitive state and action collaboration is improved, and the mapping precision between the intervention content and the user execution characteristics is enhanced.