285 results about "Cognitive load" patented technology

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 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 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 educational resource recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring audio data, interaction data and task data acquired by a user terminal; performing feature extraction on the audio data, the interaction data and the task data to obtain an emotion feature vector, a learning rhythm vector and a content feature vector; inputting the emotion feature vector and the learning rhythm vector into a pre-constructed emotion recognition model to obtain a psychological state vector; the cognitive load is calculated based on the learning rhythm vector and the content feature vector, and then the cognitive load is corrected through the psychological state vector; matching a state interval of the corrected cognitive load according to a preset threshold interval; and according to the state interval, adjusting a difficulty coefficient of the recommended course, rearranging a course content sequence and an auxiliary learning prompt, generating structured data, and outputting the structured data as an intelligent auxiliary learning recommendation result. According to the invention, online learning interactivity and teaching quality in rural and remote areas are effectively improved.

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 discloses a short video content accurate recommendation system based on artificial intelligence image recognition, particularly relates to the technical field of short video personalized recommendation, and is used for solving the problem of matching of user interest and visual bearing capacity. The method comprises the following steps: extracting short video key frame spatial-temporal characteristics through a multi-scale convolutional neural network, constructing a cognitive load threshold curve in combination with a user micro gesture sequence, representing the tolerance range of a user to visual complexity in different time periods, and generating a visual complexity vector; calculating a visual load matching index by using an adaptive dynamic warping algorithm, and adjusting a candidate video sequence through a load penalty factor to generate an initial recommendation probability; modeling a user dynamic interest vector based on a gated loop unit network in combination with an attention mechanism; and finally, user interests and video semantics are fused through multi-dimensional features, a final recommendation list is generated through a multi-objective optimization algorithm under the constraint of visual load, and personalized recommendation of interest matching degree maximization and visual comfort optimization is realized.

The invention discloses an intelligent context perception English adaptive learning system and method, and belongs to the technical field of acknowledgement science and language education. According to the system, physical scenes, social contexts, emotional physiological states and cognitive load data of a user are comprehensively collected and fused through a multi-dimensional context perception and cross-modal analysis module; constructing a real-time user model containing deep cognitive features by means of a user dynamic cognitive modeling module in combination with a meta-learning algorithm; through a dynamic knowledge graph and a generative content engine, real-time generation and compliance verification of scenarized learning content are realized. According to the method, the problems of one-sided context perception, static content stiffness, single algorithm adaptation and shallow cognitive modeling of an existing system are solved, deep adaptation of learning contents and real-time states of users is realized, the scene practicability of learning is remarkably improved, and an efficient and flexible English learning solution is provided for users with different requirements.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The invention belongs to the technical field of visual training, and particularly relates to a multi-dimensional visual training feedback method for improving binocular fusion visual and stereoscopic visual functions, and the method comprises the following steps: carrying out medical baseline detection, generating an initial ability portrait through visual function evaluation and cognitive ability testing, matching training difficulty parameters, and generating a personalized scheme; multi-modal game training is carried out, training types comprise gamification training, animation scene training and offline family task training, and the training difficulty is dynamically adjusted; a tutor performs remote one-to-one guidance, and dynamically adjusts a training scheme according to a change report by tracking the training progress in real time; and the system performs multi-dimensional effect evaluation and self-optimizes a difficulty prediction algorithm. According to the method, the problems of dull training and passive participation, scheme stiffness and execution deviation, single stimulation and cognitive load, scene limitation and continuous interruption are solved.

The invention relates to the technical field of artificial intelligence and man-machine interaction, and discloses a picture book co-reading method, system and device based on multi-modal emotion recognition and a medium, and the method comprises the following steps: obtaining multi-modal time sequence data of a user in picture book co-reading; establishing and updating a longitudinal emotion file for recording historical interaction data for the user; based on the data and the archive, personalized multi-dimensional internal state evaluation is carried out, and the evaluation comprises calculation of cognitive load indexes representing difficult understanding, recognition of instant emotional states and analysis of contextual emotional deviations of the emotional states and current plot expectations; and finally, based on the multi-dimensional evaluation result, determining and executing dynamic interaction strategies such as simplification, guidance or excitation. According to the method, deep personalization is achieved by constructing the multi-dimensional state model and combining the longitudinal archives, the user state can be more accurately judged, prospective and self-adaptive interaction is achieved, and the interaction fineness and effectiveness are improved.

The invention relates to a multi-agent cooperation-based language model reasoning resource scheduling method and system, and the method achieves the intelligent simulation of multi-view iterative thinking in a complex reasoning task through the construction of a multi-agent system which is clear in division of labor and is provided with a special knowledge base. View limitation and decision deviation of a single model in long-sequence and multi-step reasoning are effectively overcome; depending on the fusion of the general capability of the large language model and the task special knowledge base, the professionality and accuracy of the reasoning result are improved. Meanwhile, the problems of resource waste, redundant calculation and unstable convergence caused by cognitive overload in the cooperation process are solved by combining multi-round dynamic cooperation with a convergence mechanism of cognitive load perception and a dynamic token number limitation and low-confidence branch pruning strategy; therefore, on the premise that the reasoning quality is guaranteed, the utilization efficiency of computing resources is remarkably optimized, peak value occupation is reduced, the overall reasoning time delay is shortened, and reliable technical support is provided for efficient and stable deployment of a large language model in a complex task.

The invention relates to an e-book content real-time simplification and enhancement method based on reading ability evaluation, which relates to the technical field of education science and technology, and comprises the following steps: step 1, collecting multi-modal behavior data in a user reading process in real time, the multi-modal behavior data comprises reading speed, page residence time, repeated reading behaviors aiming at specific vocabularies or sentences, new word click frequency and duration, and accuracy and reaction time of embedded understanding test. By sensing the reading behavior of the user in real time, the reading ability of the user can be dynamically evaluated and accurately matched with the text difficulty, so that the text is automatically simplified to reduce the cognitive load when the user encounters the difficulty, and the content is enhanced to stimulate the deep thinking when the user has the difficulty, thereby not only remarkably improving the reading fluency and the understanding depth, but also improving the user experience. And through a closed-loop optimization system and personalized recommendation, continuous adaptation between the reading material and the user capability level is realized.

The invention provides an intelligent cognitive movement rehabilitation task generation method based on knowledge graph dynamic adaptation, and the method comprises the steps: collecting the behavior response data of a patient in real time through a multi-modal sensor array, and inputting a pre-trained state evaluation neural network model after preprocessing; outputting a four-dimensional ability state vector representing cognitive load, motion coordination, attention and fatigue degree, taking the vector as a dynamic query condition, starting multi-hop semantic reasoning in the cognitive motion rehabilitation knowledge graph, screening a candidate task set matched with the current state of a patient and associated with a rehabilitation target, and performing task selection on the candidate task set; according to the matching relation between the capability state and the task difficulty, execution parameters such as task presentation duration and action amplitude are dynamically adjusted, task sequence sorting is optimized based on the path distance, and finally generated personalized rehabilitation tasks are presented in a three-dimensional virtual scene mode through an interaction terminal. A closed-loop control process of real-time data acquisition, state evaluation, task generation and execution is formed, and dynamic personalized adaptation of the rehabilitation training process is realized.

The invention relates to a cognitive load monitoring method based on multi-source physiological information fusion. The cognitive load monitoring method comprises the following steps that 1, physiological information data collection is conducted through a multi-source data collection module; 2, performing data processing through a data preprocessing module to obtain preprocessed electroencephalogram, eye movement and electrocardio data; 3, performing data layer fusion, feature extraction and feature layer fusion on the preprocessed data to obtain a feature layer fusion matrix; and step 4, constructing a CNN-BiLSTM-Transform hybrid neural network model, carrying out decision fusion, and carrying out iterative optimization on the model through evaluation. According to the method, the core basis problem of multi-source physiological information data fusion is solved, and a high-quality data basis is provided for subsequent feature extraction and model training; the accuracy and environmental adaptability of state evaluation are greatly improved; and the sensitivity and the recognition precision of the model to the dynamic change of the cognitive load are effectively improved.

The invention discloses an intelligent production control system and method for a small household appliance assembly line, and relates to the technical field of intelligent control, and the control method comprises the steps: obtaining multi-source heterogeneous data of the small household appliance assembly line, and carrying out the preprocessing; constructing an assembly process knowledge graph and a digital twinborn model; collecting a production line state vector in real time through a digital twin model; when product switching is detected, executing sub-graph matching query, extracting a target assembly sub-graph and generating an assembly instruction sequence; calculating a cognitive load index; and performing adaptive adjustment on the assembly instruction sequence according to the cognitive load index, generating an adaptive assembly instruction, and pushing the adaptive assembly instruction to a station terminal. According to the method, accurate extraction of the assembly instruction is achieved through the knowledge graph and the digital twinning technology, self-adaptive presentation of the instruction is achieved through cognitive load evaluation, and the technical problems that in an existing small household appliance assembly line, assembly guidance information is insufficient in pertinence, the obtaining efficiency is low, and the cognitive load of workers is easily increased are solved.

The present application belongs to the field of artificial intelligence and health monitoring technology, and specifically relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The disclosed system integrates multi-modal synchronous acquisition, dynamic feature fusion decoding and individualized closed-loop intervention functions. Through the spatio-temporal alignment analysis of facial micro-expression and physiological signals, the emotion recognition accuracy is improved. Combined with cognitive load correlation modeling and wearable multi-channel regulation terminals, adaptive feedback is realized, cross-period emotion evolution prediction and group situation awareness are supported, a complete closed loop of "perception-decision-intervention" is constructed, and the accuracy and initiative of emotion management in complex environments are enhanced.

The invention discloses an online training course personalized recommendation method and system, and relates to the technical field of online education. The method comprises the following steps: acquiring multi-modal learning interaction data of a user, and constructing a course knowledge graph comprising courses, knowledge components and relationships between the courses and the knowledge components; processing the course knowledge graph by adopting a heterogeneous graph attention network so as to track the dynamic knowledge state of the user; estimating the cognitive load level of the user through a pre-trained cognitive load classification model based on the multi-modal learning interaction data; and based on the dynamic knowledge state and the cognitive load level, generating a personalized learning path in a deep reinforcement learning framework by maximizing a cumulative reward function combining knowledge gain reward and cognitive load balance reward.

The invention relates to the technical field of digital cabin intelligence, in particular to an automobile cabin man-machine interaction design and optimization method fusing cognitive load assessment, and the man-machine interaction design comprises an illumination sensing module, an eye movement data acquisition module, a data processing module, a data fusion module, a cognitive load assessment module and a self-adaptive interface control module. The optimization method comprises the steps of S1, multi-source data acquisition and preprocessing, S2, data processing and prediction, S3, multi-source data fusion and scene classification, S4, cognitive load grade evaluation and S5, adaptive interface dynamic adjustment. The problems of low eye movement tracking data quality and inaccurate cognitive load evaluation caused by illumination interference are solved.

The invention discloses a digital art exhibition space display interaction method and system based on a VR technology, and relates to the technical field of exhibition man-machine interaction, and the method comprises the steps: carrying out the time sequence alignment processing of a multi-source asynchronous data flow, and constructing an emotional state confidence curve, an interest concentration curve and a cognitive load curve; constructing a user emotion space vector based on the tangential direction of the curve and the instantaneous value; and calculating a difference value between the user emotion state and the exhibit semantic emotion confidence coefficient, and respectively adopting different strategies according to the difference value: when the difference value is large, directly generating display content, and when the difference value is small, judging whether to execute deepening interaction or correct an emotion space vector through an interest maintenance coefficient. Through multi-modal data fusion and dynamic sentiment analysis, the problem that the existing VR exhibition content is static and fixed and cannot adapt to the user state in real time is solved, intelligent content display based on user sentiment change is realized, and the immersion and individuation level of exhibition viewing experience are remarkably improved.

The invention relates to an interaction method and device based on virtual reality, equipment and a medium. According to the method, firstly, state data of a user in a virtual reality environment is collected and preprocessed to generate a standardized data stream, and then the distance, orientation similarity and interaction frequency between the user and a virtual avatar are calculated based on the data so as to construct a situational social graph representing social relation strength. Meanwhile, an attention model representing attention weight is constructed by analyzing a fixation point and a head direction of a virtual avatar of the user, and then information priority is calculated through weighted summation of social relation strength and the attention weight by utilizing a situational social graph and the attention model; according to the method and the device, the priority of the user is obtained, the audio, visual and text information is dynamically filtered or enhanced according to the priority to generate the optimized information flow, and finally the information flow is presented in the virtual reality environment, so that the cognitive load of the user in a dense social scene is effectively reduced, and the social interaction efficiency and immersion are improved.

An emotion-aware computing operating system integrates multimodal sensory inputs, stratified memory, cyclehit scoring, and history score values to predict user tasks, orchestrate autonomous cognitive agents, and adapt interface outputs in real time. A prediction module applies cyclehit scoring derived from historical task cycles and weighted history score values to forecast workflows.An orchestration kernel selects and coordinates agents, redistributes subtasks, and negotiates dynamically under cognitive load. A stratified memory fabric maintains ephemeral, situational, and long-term user models for personalization. An adaptive interface layer adjusts informational density and tool availability based on inferred user state. A certification and licensing API enforces agent onboarding, compliance, and monetization policies, requiring registration of performance metrics prior to integration. Embodiments include software, cloud, edge, and robotic platforms, enabling monetizable deployment across healthcare, finance, education, enterprise, and ambient device ecosystems.

The invention discloses an MES-based industrial production data intelligent analysis and management system, and belongs to the field of industrial production data analysis. The system comprises a central module used for synchronously acquiring a time sequence of man-machine interaction event data and machine tool sensor data; the evaluation unit is used for calculating a cognitive load index and a processing load entropy; and the resonance risk assessment module is used for calculating a comprehensive risk level. Wherein the calculation of the comprehensive risk level depends on an instantaneous risk item based on a cognitive load index and a processing load entropy and a mode matching item based on form similarity between a time sequence and a predefined out-of-step mode template. According to the method, man-machine is regarded as a dynamic coupling system for analysis, predictive intervention on potential processing failure risks can be achieved, and the stability and robustness of the production process are 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.

The invention discloses a smart education guidance evaluation system and method based on big data, and belongs to the technical field of smart education and big data analysis. The system comprises a multi-source heterogeneous education data acquisition module, a data preprocessing and feature fusion module, a multi-dimensional capability evaluation module, a learning behavior sequence analysis module, a personalized guidance recommendation module and a multi-role visual display module. A subjective and objective fusion weighting algorithm and a double-flow attention knowledge tracking algorithm are adopted to realize five-dimensional capability comprehensive evaluation, a key behavior node identification algorithm is utilized to carry out learning behavior time sequence modeling, and a cognitive load perception path optimization algorithm is utilized to generate a personalized learning path. The problems that in an existing education evaluation system, the evaluation dimension is single, data collection fragmentation is achieved, and personalized guidance lacks data support are solved.

The invention relates to a cognitive exercise rehabilitation training integration method based on multi-dimensional self-adaptive VR. The method comprises the following steps: presenting a three-dimensional rehabilitation scene through a head-mounted virtual reality device, synchronously collecting features of a patient, analyzing joint exercise data by using an exercise ability evaluation model, and generating exercise execution precision, fluency and stability indexes; electroencephalogram spectrum features are extracted through the cognitive load evaluation model, and a multi-dimensional rehabilitation feature vector is constructed in combination with task attention and cognitive resource allocation efficiency indexes output by the visual cognitive participation evaluation model; the training difficulty is dynamically adjusted based on the feature vectors, parameters are optimized in real time in the virtual scene, and the patient is made to be in the optimal challenge interval all the time; directional vibration stimulation is applied through the tactile feedback vest, three-dimensional sound field prompt is provided in cooperation with the spatial audio system, and a multi-sensory fusion closed-loop rehabilitation training system is formed. Cooperative training of cognition and movement functions is achieved, and individuation and adaptability of rehabilitation training are effectively improved.

The invention relates to the technical field of artificial intelligence and communication engineering auditing, in particular to a multi-task allocation and decision optimization system for a communication engineering auditing agent, which comprises a data perception analysis module, a task feature vector generation module and a task feature extraction module, and the data perception analysis module is used for acquiring communication engineering settlement documents and unstructured field data and extracting features to generate task feature vectors; the task entropy evaluation module calculates a task information entropy, and divides an audit task into a low-cognitive load task or a high-cognitive load task; the cognitive resource adaptation module constructs an intelligent agent capability model, dynamically constructs an intelligent agent cooperation topological structure based on a cognitive adaptation degree coefficient and allocates tasks; the decision confidence monitoring module tracks an auditing process in real time, calculates a decision confidence fluctuation rate, and executes automatic auditing termination or triggers manual intervention operation; the efficiency feedback adjustment module calculates a dynamic resource efficiency ratio; according to the method, the problem that a fixed rule process cannot cope with high dynamic performance of unstructured data is solved, and dynamic balance between auditing efficiency and computing power cost is realized.

The application discloses a multi-modal-based digital human interaction control method and device, and relates to the technical field of artificial intelligence digital human, and specifically comprises the following steps: collecting multi-source heterogeneous multi-modal data of user interaction with a digital human in real time; constructing a dynamic adaptive multi-modal space-time hypergraph Transformer network, deeply fusing the multi-source heterogeneous multi-modal data, and obtaining a fused interaction feature vector; generating a multi-dimensional interaction situation tensor based on the fused interaction feature vector; generating a digital human response action sequence, voice content, expression posture and interaction strategy according to the multi-dimensional interaction situation tensor, and driving the digital human to execute; acquiring feedback data of the digital human after executing the response in real time, and optimizing parameters of the dynamic adaptive multi-modal space-time hypergraph Transformer network based on the feedback data; and effectively improving the naturalness and response accuracy of digital human interaction, and reducing the interaction misunderstanding rate and user cognitive load.

The application discloses a hearing aid health analysis method based on physiological signal monitoring function, comprising the following steps: collecting physiological signal data of a user in real time through a multi-modal biological sensor array integrated in a hearing aid body; the hearing aid body comprises a digital signal processor and a wireless communication module; the physiological signal data at least comprises an electroencephalogram signal, a galvanic skin response signal and a temporal artery pulse signal; the physiological signal data is subjected to motion artifact elimination processing through an adaptive filtering algorithm; a preprocessed physiological signal is subjected to feature extraction through an embedded health analysis engine, and a composite health feature vector comprising a sympathetic nerve activity index, a cognitive load metric and a cardiovascular function index is generated; a health risk assessment report is dynamically generated based on the association between the composite health feature vector and a preset hearing scene; and a graded health alarm signal is output through a bone conduction vibration unit and an audio synthesis module of the hearing aid body.

The application discloses a display control method, an electronic device and a vehicle, and relates to the technical field of intelligent cockpits. The method comprises the following steps: acquiring a plurality of gaze point coordinates of a driver of the vehicle, determining a region label corresponding to each frame of the gaze point coordinates, monitoring the plurality of gaze point coordinates of the driver, and determining gaze information of the driver according to a monitoring result, wherein the gaze information of the driver comprises a gaze switching frequency used for indicating that the driver switches gaze between a road region label and a head-up display region label of the vehicle; determining a cognitive load of the driver according to the gaze information of the driver, and controlling display content of the head-up display of the vehicle according to the cognitive load of the driver, so that the display content of the head-up display of the vehicle is matched with the cognitive load of the driver. The application can objectively determine the cognitive load of the driver in real time, accurately match the display information with the cognitive ability of the driver, and fully exert the display function of the head-up display of the vehicle.