122 results about "Cognitive status" patented technology

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 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 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 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 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.

The invention discloses an intelligent feature cognition detection device for visual detection. The intelligent feature cognition detection device comprises a multi-modal sensing module, a dynamic stimulation generation unit, an intelligent feature analysis unit and a dynamic cognition evaluation unit, according to the method, eye movement, pupil and electroencephalogram three-mode data are integrated, the sampling rate and precision are better than those of the prior art, environment self-adaptive calibration is combined, data integrity is improved, and the nerve and behavior process of visual feature cognition can be comprehensively reflected. Based on a linkage mechanism of a cognitive state and stimulation parameters, four types of stimulants close to an actual detection scene are generated, real-time adjustment of complexity, duration and interval is supported, the method is adaptive to testees with different cognitive levels, and compared with a fixed stimulation scheme, the relevance between a detection result and actual post ability is improved.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

The invention provides a vehicle early warning method, a vehicle and electronic equipment, and the method comprises the steps: determining the cognitive state information of a driver according to a head image sequence of the driver of the vehicle; based on the environment image sequence of the environment where the vehicle is located, risk field information in the environment is determined, and the risk field information comprises dynamic blind area information and predicted trajectory information of a risk target corresponding to the dynamic blind area information; according to the cognitive state information and the risk field information, a collaborative risk value is determined through a preset space-time attention fusion model, and the collaborative risk value represents the collision risk of the vehicle affected by the cognitive state of the driver and the risk target; and executing a corresponding early warning operation according to the collaborative risk value. According to the method, the real risk existing due to the unknown driver can be accurately quantified, so that the driving safety can be remarkably improved.

A cognitive load management system may continuously monitor a cognitive state of a driver of a vehicle, via a driver monitoring system (DMS). When a detected or estimated cognitive load of the driver increases above a first threshold cognitive load, the cognitive load management system may adjust one or more controls of the vehicle to reduce a stress of the driver. When the detected or estimated cognitive load of the driver decreases below a second threshold cognitive load, at which drowsiness or disengagement may impair a performance of the driver, a cognitive stimulator component of the system may engage the driver in conversation using a generative AI model. In this way, different strategies may be employed to address both high and low cognitive loads.

This invention discloses a non-invasive method, system, device, and storage medium for monitoring student attention based on gas sensing, belonging to the field of non-invasive sensing technology. The method includes: calculating a net physiological gas signal based on environmental baseline data and initial gas concentration data of the target student; calculating physiological characteristic indicators of the target student based on the net physiological gas signal, and determining the target student's metabolic rate level based on the physiological characteristic indicators; obtaining a state time-series signal based on the net physiological gas signal according to a preset discrimination criterion; inputting the net physiological gas signal and the state time-series signal into a pre-trained attention monitoring model to obtain the probability distribution of the target student's attention state; and determining the target student's attention state based on the target student's metabolic rate level and the probability distribution of the attention state. This invention assesses the cognitive state by capturing the gas released by the student, achieving non-invasive monitoring.

The invention relates to the technical field of electrical digital data processing, in particular to a peasant bookstore personalized reading recommendation system based on user portray.The peasant bookstore personalized reading recommendation system comprises the steps that person-place-time-production quaternary data of a user is obtained, and dynamic behavior data comprises a cognitive state obtained by analyzing an implicit feedback sequence; performing weighted fusion on the human-place-time-production quaternary data to generate a multi-dimensional dynamic user portrait; performing cross-domain semantic association on the basis of the multi-dimensional dynamic user portraits in combination with an indigenous knowledge graph to generate a personalized recommendation list; and performing adaptive adjustment on the personalized recommendation list according to the cognitive state of the user, and generating and distributing a final personalized recommendation list. According to the method, accurate prediction and adaptive content recommendation of dynamic and multi-dimensional demands of the user are realized, the technical problems of low recommendation accuracy and disjunction with a real scene of the user in the prior art are solved, and the recommendation accuracy and practicability are remarkably improved.

The invention belongs to the technical field of artificial intelligence, and relates to a teaching sequence dynamic rearrangement method and system based on CoT reasoning and a knowledge graph, and the method comprises the steps: S1, obtaining learning feedback information; s2, performing CoT multi-step reasoning analysis based on the learning feedback information to obtain a student state reasoning result; s3, judging whether the teaching sequence needs to be adjusted or not based on the student state reasoning result, generating a sorting signal according to the student state reasoning result when the teaching sequence needs to be adjusted, and keeping the original teaching sequence unchanged when the teaching sequence does not need to be adjusted; and S4, rearranging the teaching sequence of the candidate explanation knowledge point set based on the sorting signal under the constraint of the teaching knowledge graph. In the teaching process, a student learning state analysis mechanism based on multi-step reasoning is introduced, a CoT reasoning result is used as a control signal for teaching sequence adjustment, and a teaching explanation sequence is dynamically rearranged under the constraint of a teaching knowledge graph, so that self-adaptive teaching control oriented to a student cognitive state is realized.

The application provides a kind of method, equipment and storage medium based on knowledge graph mathematical resource recommendation, and the application relates to the field of online learning and mathematical resource personalized recommendation, which comprises: extracting the application mode of mathematical resources;According to the application mode, a multi-layer knowledge graph is generated;Diagnose the cognitive state of the learner, and plan the learning path according to the weak knowledge attribute of the cognitive state of the learner;According to the generated knowledge graph, the mathematical resources corresponding to the key application mode are found, and the online learners are personalized recommended based on the recent development zone and support degree.The technical effect of the application is that the interrelationship of knowledge attributes in mathematical learning resources is highlighted while ensuring good recommendation, and the corresponding resources are recommended through the application mode, making the recommendation more targeted and effectively helping online learners to flexibly use associated knowledge attributes.

The present application relates to a kind of automatic driving vehicle driving style dynamic adjustment strategy construction method, equipment and medium, the method will traditional fixed rule-based automatic driving style adjustment process change into the self-adaptive adjustment process based on driver internal cognitive state and takeover behavior information, by semantic alignment's latent dynamics modeling and model reinforcement learning strategy optimization framework, realize the dynamic matching between automatic driving behavior and driver trust and cognitive workload.Compared with prior art, the present application realizes the dynamic matching between automatic driving behavior and driver trust and cognitive workload, effectively improves the safety and collaborative efficiency of man-machine co-driving.

The application discloses an electroencephalogram signal driven adaptive storage method and electronic equipment, and relates to the technical field of electronic equipment, in particular to an electroencephalogram signal driven adaptive storage method and electronic equipment. The method comprises the following steps: acquiring first electroencephalogram signal data and brain blood oxygen monitoring data of a user; identifying the cognitive state of the user based on the first electroencephalogram signal data by using a first electroencephalogram signal data identification model; when the cognitive state is a concentrated state, extracting a first feature vector of the first electroencephalogram signal data; extracting a second feature vector of the brain blood oxygen monitoring data; performing cross-modal fusion on the first feature vector and the second feature vector to obtain a fusion feature vector, and identifying the memory type of the user based on the fusion feature vector; and storing prompt data of the current first prompt device in a corresponding storage space based on the memory type and a preset corresponding relationship between memory types and storage spaces. The application can adaptively store data according to the memory type of the user, so that the prompt data storage is more in line with the cognitive characteristics and actual use requirements of the user, and the utilization rate of storage resources is improved.

Systems and methods of the present disclosure are directed to neural stimulation via audio and visual stimulations. The combination and / or sequence of audio and visual brain stimulations can adjust, control or otherwise manage the frequency of the neural oscillations to provide beneficial effects to one or more cognitive states or cognitive functions of the brain, while mitigating or preventing adverse consequences on a cognitive state or cognitive function that stems from sleep deprivation. In doing so, the present systems and methods can reduce sleep fragmentation, improve sleep quality, and slow the progression of cognitive decline in a subject with Alzheimer's disease and MCI.

The invention provides a cognitive state recognition method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: carrying out the recognition of a cognitive state according to multiple pieces of user voice information, multiple pieces of user behavior information, a preset user voice text conversion model and a preset user behavior text conversion model; generating multiple pieces of user voice text information and multiple pieces of user behavior text information; according to the multiple pieces of user voice text information, the multiple pieces of user behavior text information and a preset initial cognitive state recognition model, initial cognitive state recognition information is generated; and generating target cognitive state identification information according to the initial cognitive state identification information, the multiple pieces of historical cognitive state identification information, multiple pieces of preset user cognitive state division threshold information and preset user cognitive state switching sequence information. According to the method and the device, the cognitive state of the user after receiving the design stimulation is accurately identified, and the receiving effect of the user on the design stimulation is adaptively evaluated.

A system for determining pilot cognitive states, includes one or more processors coupled to a memory configured to receive physiological data and aircraft state data, determine mental workload and mental fatigue of a pilot based on the physiological data, determine available attention resources of the pilot based on the mental workload and mental fatigue, determine attention allocation of the pilot based on the available attention resources and gaze patterns derived from the physiological data, determine situational awareness of the pilot based on the attention allocation and the aircraft state data, and generate a visualization of the available attentional resources of the pilot, the attention allocation of the pilot, the situational awareness of the pilot, and aircraft state data.

The invention discloses an intelligent evaluation guidance system based on a user portrait and an answering behavior, and the system comprises a user portrait module which stores the static attributes of a user; the answering behavior acquisition module is used for acquiring answering behavior data of the user in real time; the behavior characteristic analysis engine is used for analyzing behavior data and extracting confidence indexes and hesitation degree characteristic vectors; the intelligent guide strategy engine generates a guide instruction based on the portrait and the feature vector; the evaluation content dynamic scheduling module is used for scheduling questions from a question bank according to the instruction; the evaluation path recording and evaluating module is used for recording an evaluation path and generating a multi-dimensional capability evaluation report; according to the method, the microscopic behavior data of the user in the answering process is collected and analyzed in real time and is quantified into eigenvectors such as the confidence index and the hesitation degree, so that the system can insight into the cognitive state and the decision-making process of the user in the answering process; therefore, the problem that an existing system only depends on a final answer and cannot distinguish real mastering from fluke guessing, knowledge blind areas and immediate negligence is solved.

The state association database construction method and device, the storage medium and the computer device provided by the application, when constructing the database, first start the time synchronization control and cumulative error correction function of the multi-source data acquisition system, and monitor the target user through the system to ensure the synchronization and accuracy of multi-source data acquisition; if a collection event that meets the event triggering rule occurs in the monitoring process, the system automatically collects multi-modal data in the preset collection window, and performs association feature mapping and cognitive label matching on the data to form a multi-element data group with a clear data framework, meeting the fine-grained modeling needs of dynamic mapping of music-induced emotions and cognitive states; after the system monitoring ends, the collected data groups are subjected to data quality verification and model adaptation verification, and after the verification is passed, they are stored in the preset multi-layer structure database in layers, and the state association database formed in this way has high reliability and usability.

The invention provides an aviation man-machine cooperation task distribution system and method based on cognitive load prediction. And by dynamically optimizing task allocation, the cognitive load of the pilot is reduced, and the task execution efficiency and the flight safety are improved. According to the system, through multi-dimensional data fusion, intelligent algorithm optimization and man-machine interaction innovation, accurate monitoring and dynamic response to the cognitive state of the pilot are achieved, and the intelligent level of aviation task execution is remarkably improved.

The invention discloses a safety helmet intelligent control method and system based on electroencephalogram characteristics, and the method comprises the steps: decomposing a real-time electroencephalogram signal frequency spectrum to generate cognitive load frequency domain characteristics, and building an electroencephalogram control space; an electroencephalogram control space is scanned to identify a frequency band coupling unbalance point, a cooperative intervention opportunity is reversely derived, and a multi-channel adjustment mode is formed; mapping the stimulation frequency range to generate a frequency matching domain, and performing resonance analysis to determine a resonance intervention window; monitoring a frequency band in a resonance window to obtain an energy migration track, constructing a risk propagation chain to identify a cascade failure node and forming a key blocking anchor point; analyzing risk acceleration of the key blocking anchor points to form a state deterioration rate, fusing frequency domain features to generate dynamic delay tolerance, and generating variable speed control parameters in a fast-slow grading manner; the variable speed control parameter is compared with a threshold value to generate a control deviation signal, reverse phase modulation is carried out to generate a hedging stimulation waveform, a phase compensation execution instruction is formed, and accurate monitoring and safe intervention of the cognitive state are achieved.

The invention relates to a learning state monitoring method, system and device based on artificial intelligence and a medium. The method comprises the following steps: collecting signals such as physiological and behavior images of a target object through a multi-source heterogeneous sensor, and performing normalization processing on aligned features to generate a standardized feature vector; calculating a comprehensive cognitive state score of the feature vector by adopting a dynamic weighted fusion algorithm, and judging and outputting a soft label according to a preset threshold value; applying constraint random disturbance based on statistical distribution characteristics of the feature vectors to generate synthetic data, and associating the synthetic data with the soft labels to construct a soft label data set; by taking the feature vector of the data set as input and the soft label as a supervision signal, iteratively calculating loss through a preset loss formula, updating model network parameters to convergence, and obtaining an optimized neural network model; during real-time monitoring, processed sensor signals are input into the model, and quantitative learning state scores are output through end-to-end reasoning. The method can break through the limitation that traditional monitoring is high in subjectivity and insufficient in real-time performance.

This invention discloses an intelligent student homework grading system based on handwritten trajectory analysis and knowledge tracking, belonging to the field of handwritten trajectory recognition. It includes a data flow module, a parameter coupling calibration module, a trajectory analysis module, and a knowledge tracking module. This invention eliminates system errors through a unified time reference, geometric correction, and zero-drift filtering, ensuring the spatial consistency and physical accuracy of multi-source signals. By performing corresponding calibrations on independent physical error sources, it quantitatively characterizes the synergistic relationship between the curvature of the handwritten trajectory and the rate of change of acceleration. Weight adjustments are made to the TroOCR visual encoder to effectively suppress the interference of invalid writing on semantic recognition results, improving recognition accuracy. Combining the grading judgment results with the writing behavior, it achieves accurate inference from the surface-level correctness of the answer to the deeper cognitive state of the writing behavior.

The invention discloses an online education intelligent question and answer method and system based on a knowledge graph, and the method comprises the steps: converting a user learning log and historical performance into a dynamic cognitive state feature vector, and introducing the dynamic cognitive state feature vector into sub-graph extraction and reasoning logic of the education knowledge graph as a core constraint. According to the method, the system and the system, by converting a first repair relation of a teaching outline into a graph topology constraint, the system realizes conversion from static knowledge retrieval to self-adaptive cognitive path optimization, generated answers not only fit questions semantically, but also are accurately aligned with the current cognitive level of a user in knowledge difficulty and granularity, so that the user experience is improved, and the user experience is improved. The defect that answers are too difficult or too easy in a traditional scheme is effectively overcome, and the accuracy of knowledge transmission and personalized tutoring efficiency in an online education scene are remarkably improved.