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

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

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

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

The invention 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 relates to the technical field of data processing, and discloses an IPTV interface element differentiation display method and system. The method comprises the following steps: generating interaction prediction data through voice intention recognition and time sequence correlation modeling, constructing an intention-element probability matrix according to education scene and cognitive load weighted coding, calculating element importance based on the probability matrix and gradually adjusting visibility, dynamically reconstructing layout and predicting a scanning path according to spatial constraint, and obtaining an intent-element probability matrix. And detecting the attention state, selecting a guide mode, and outputting personalized display. According to the method and the device, the problems of inaccurate voice intention perception and insufficient interface adaptation intelligence degree in IPTV interface element differentiation display are solved. According to the invention, the dynamic adjustment precision of the interface elements in the multi-mode voice interaction environment and the personalized experience quality of the user are improved.

The invention belongs to the technical field of education course recommendation, and discloses a personalized learning path recommendation method based on a knowledge graph, which comprises the following steps: acquiring a knowledge point set according to a learning target, and constructing a basic knowledge graph; matching the first similar group, and obtaining learning records of the first similar group to form a first reference record set; marking difficulty values for the knowledge points according to the mastery degree in the first reference record set, and generating a knowledge point learning sequence; taking the first reference record set as a target reference set, and matching an optimal learning track for each knowledge point to obtain a comprehensive learning path; after learning records of the learner are obtained, matching a second similar group, and obtaining a second reference record set; and optimizing the comprehensive learning path according to the second reference record set, and adding review nodes for the knowledge points with low mastery degree. According to the method, knowledge point difficulty values are marked based on actual learning data, a learning sequence of cognitive load balance is generated, a dynamic recommendation process from general to individuation is realized, and learning efficiency and experience are improved.

The invention discloses a coevolution driver cognitive load personalized quantification method, which relates to the technical field of traffic safety, and comprises the following steps: collecting a multi-modal physiological signal and processing the multi-modal physiological signal into structured feature data; constructing a labeled sample set D1 and an unlabeled sample set Du based on the structured feature data to perform semi-supervised collaborative pseudo labeling training, generating pseudo labels for unlabeled samples, and forming a self-labeled data set S; and constructing a training set and an enhanced training set by using S and D1, carrying out supervised contrast learning feature extraction, and carrying out model tuning in combination with an MAML algorithm. And finally, inputting any sample into the optimized model to generate continuous cognitive load value prediction. Wherein only a small amount of manual labeling is needed, the label quality can be improved, and the personalized quantification efficiency of the cognitive load can be improved; multi-modal information is fused, so that the robustness and the accuracy are improved; a continuous and fine-grained quantification mode is provided, and the accurate automatic driving decision-making requirement is met.

The invention relates to the technical field of digital twinning and intelligent text travel, and provides a text travel comprehensive service method and system based on digital twinning. According to the method, historical tourist data are collected to construct tourist type identifiers, multi-source culture resource data are integrated to construct a culture resource map, and then a cognitive load evaluation model is established; screening an initial culture entity set based on the tourist interest association degree, generating alternative narrative paths through path exploration, and performing comprehensive evaluation in combination with the cognitive load score and the interest association degree to select a target narrative path; and finally, according to the target path, synthesizing an integrated navigation content including navigation guidance, culture explanation and multimedia materials. According to the method, digital representation and personalized path planning of cultural resources are realized through a digital twinning technology, and the accuracy of text travel service, the quality of experience of tourists and the cultural transmission efficiency are effectively improved.

The invention discloses an advertisement information flow management method and platform applied to multivariate visual design, and belongs to the technical field of advertisement design, and the method specifically comprises the steps: dividing a screen interface into dynamic grid regions, and carrying out the comprehensive calculation based on user interaction behaviors and content semantic features, mapping the dynamic grid region into a semantic gravitational field representing user attention distribution; generating an initial advertisement container in a low-gravitational area at the edge of the screen, driving the container to migrate to a high-gravitational area according to the intensity gradient distribution of the semantic gravitational field, performing path optimization in the migration process, and dynamically adjusting the form of the container according to the visual density of a matched target area; dynamically adjusting the parameters of the advertisement container according to the semantic features of the native content of the target area and the real-time interaction behavior of the user; the cognitive load of the user is evaluated in real time according to the interaction behavior of the user on the surface of the advertisement container, information level folding is triggered when cognitive overload is detected, and secondary information or all information of the advertisement is dynamically hidden.

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 relates to the technical field of data processing, in particular to a lifelong learning platform based on a multi-modal large model, and the platform comprises a cognitive modal analysis module which analyzes the cognitive state of user learning in real time; according to the system, key physiological signal indexes in the learning process of the user are captured in real time, the attention level, cognitive load and psychological pressure states of the user are deduced, the user experience and natural interaction ability are improved, the real cognitive state of the user is perceived, and the user experience is improved. The content difficulty is automatically adjusted in real time, decompression suggestions are pushed or personalized auxiliary strategies are triggered, cognition-emotion-behavior three-in-one adjustment is achieved, the problem that during resource pushing, manual labels and simple behavior records are depended on is solved, and resource pushing efficiency is improved by constructing an interactive neuroplasticity knowledge network diagram. The cognitive activity and the psychological load state in the learning process are reflected in real time, and the psychological fatigue of the user can be recognized.

The invention discloses an 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 modular content generation method and system for embedded teaching, and relates to the technical field of teaching semantic generation. The invention discloses a modularized content generation system for embedded teaching. The modularized content generation system comprises an embedded teaching module division module and an embedded teaching content generation module, according to the method, the problems of loose organization and poor reusability of the traditional embedded teaching content are solved through semantic analysis and structured processing of the original embedded teaching content; by introducing a skill dependence analysis mechanism, a clear preposed skill set and a clear output skill set are distributed for each initial teaching module, a teaching path network oriented to capability growth is constructed, and the definition of teaching logic and the controllability of a learning path are improved; learning fatigue evaluation is carried out by fusing multi-dimensional factors such as skill span, path complexity and cognitive load, and cognitive overload or content redundancy in the learning process of students is effectively avoided.

The invention belongs to the technical field of intelligent sports and computer vision crossing. The adaptive training method based on Tai Chi action recognition is provided, and space action features are extracted according to three-dimensional human skeleton key point coordinates of a human body; extracting multi-scale motion features of the human body in a time domain according to the spatial motion features; global attention aggregation is carried out according to the multi-scale motion features of the human body in the time domain, and a human body motion recognition result is obtained; according to a human body action recognition result, matching a corresponding Tai Chi style drawing theory; superposing multi-dimensional visual prompts into a Tai Chi motion video according to the Tai Chi motion style theory so as to intuitively prompt a user about action key points; calculating an action problem based on priori knowledge or a preset template, prompting an error position and an error reason by adopting graphs and / or characters, and giving an adjustment suggestion; according to the invention, more personalized training experience can be provided, cognitive load can be reduced, and training efficiency, training effect and cognitive learning fluency can be improved.

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 an online self-study system and method fusing user behavior analysis and immersive interaction, equipment and a medium. The system comprises a multi-source data acquisition module for acquiring behavior data and physiological data of a user in real time; the user behavior analysis module is used for analyzing the behavior data and the physiological data by utilizing a pre-trained user learning state model and generating learning state information containing fatigue characteristics and cognitive load characteristics; the scene parameter generation module is used for generating immersive learning scene parameters according to a preset environment generation rule on the basis of the learning state information; and the immersive presentation module constructs and presents an immersive learning scene dynamically adaptive to the user state according to the parameters. The technical problem that a traditional online self-study environment lacks personalized adaptation ability is solved, and immersive interaction experience of automatically optimizing the learning environment based on the real-time physiological and behavior state of the user is achieved.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

Methods and systems are herein providing for predicting driving performance based on mental state. In one example, a vehicle system comprises a vehicle computing system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the vehicle computing system to adjust one or more vehicle behaviors according to predicted driving performance, wherein the predicted driving performance is determined based on one or more mental states of a driver.

The invention belongs to the technical field of man-machine interaction, and particularly relates to an active triggering and terminal interaction method and system based on emotion recognition, and the method comprises the steps: collecting the multi-modal attention data of a user; quantizing the multi-modal attention data into a multi-channel attention state vector; predicting an attention state change trend in a period of time in the future based on the historical attention state sequence; introducing a channel interference matrix, quantifying an attention interference relationship among different sensory channels, and calculating an optimal interaction triggering opportunity and an optimal interaction triggering channel according to the predicted attention state and the channel interference matrix; interaction is carried out through the optimal interaction triggering channel at the optimal interaction triggering opportunity; according to the interaction method, through a coordination and predictive interaction triggering mechanism, the interaction efficiency is improved, the attention conflict and cognitive load in a multi-task environment are effectively reduced, and the interaction opportunity is accurately optimized.

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 discloses a cognitive load assessment method based on fusion of behavior characteristics and heart rate variability in a classroom video. According to the method, face and behavior videos of students in a real classroom are collected, behavior characteristics such as eye movement tracks, sitting postures and facial expressions of the students are extracted through a computer vision algorithm, heart rate variability indexes are predicted in combination with a remote photoplethysmography technology, and a time sequence characteristic sequence is formed. And then, a time sequence deep learning model is adopted to carry out joint modeling on the multi-modal time sequence characteristics, and the cognitive load scale level is taken as a supervision signal to construct a classification model to realize cognitive load level prediction. The method has the advantages of non-contact, automation, high adaptability and the like, and can be applied to personalized teaching monitoring and intelligent teaching feedback.

The invention relates to an Internet of Things data man-machine interaction visualization system based on artificial intelligence, which belongs to the field of man-machine interaction technology, industrial Internet of Things and artificial intelligence technology and comprises a multi-modal data acquisition unit, a cognitive load state real-time inference module, a man-machine interaction interface self-adaptive generation module and a closed-loop regulation control module. The multi-modal data acquisition unit is used for acquiring multi-modal data related to the state of an operator in real time; the cognitive load state real-time inference module is used for inferring and generating a probability vector representing the current cognitive load state of the operator based on the acquired multi-modal data; according to the scheme, a complete feedback control loop is constructed by arranging the multi-modal data acquisition unit, the cognitive load state real-time inference module, the human-computer interaction interface self-adaptive generation module and the closed-loop regulation control module.