181 results about "Cognitive capability" patented technology

In a described embodiment, a system for information processing is provided including a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system further includes a cognitive reasoning module configured to evaluate the reasoning process of the language model, emulate cognitive functions including metacognitive processes, and generate an assessment based on an analysis of the received feedback, wherein the assessment includes classifying the one or more outputs into components, assigning quality scores for each component, and identifying an improvement corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive reasoning module for adjusting the reasoning process of the language model based on the assessment is provided. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated assessment until a performance threshold is met.

The invention discloses an exercise and cognition ability assessment method, system and device, and the method comprises the steps: obtaining real-time exercise information, including real-time joint angle information, real-time gait information, real-time attention information and real-time decision speed information, of a user; constructing a physiological-behavior coupling model based on a hidden Markov model, and generating a joint evaluation index by inputting real-time motion information; performing feature extraction on the real-time motion information by using a hierarchical feature extraction model to obtain a motion feature vector and a cognitive feature vector; performing fusion processing on the feature vector and the joint evaluation index, and outputting abnormal state information including motion abnormality and cognitive abnormality; behavior abnormity is detected through the dynamic threshold model, a real-time feedback instruction is generated in combination with a preset feedback rule, and finally user training task parameters are dynamically adjusted according to the instruction. According to the method, through multi-dimensional data fusion and a dynamic evaluation mechanism, cooperative monitoring and self-adaptive training regulation and control of motion and cognitive competence are realized.

A computer system for persistent cognitive neural architecture implementing sophisticated state preservation and sleep-state optimization capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, and implements architectural changes. A meta-supervisory system tracks behavior patterns and extracts generalizable principles. A cognitive neural orchestrator manages operational states and coordinates decision-making across the network. The system maintains persistent neural network state through mechanisms that store and retrieve neural activation patterns and architectural configurations across operational sessions. During designated sleep states, the system executes optimization operations including memory consolidation and insight generation. This innovative architecture enables neural networks to maintain knowledge continuity across system restarts while implementing sophisticated optimization during periods of reduced demand, enhancing long-term performance through persistent cognitive capabilities.

The invention discloses an interdisciplinary PBL teaching plan intelligent generation system based on knowledge graph and multi-Agent cooperation, and the system comprises an interdisciplinary knowledge base construction module which constructs knowledge graphs of mathematics, physics and computer subjects through semantic vectorization and entity alignment; the retrieval enhancement module is used for dynamically supplementing knowledge blind areas based on an RAG mechanism; the teaching demand analysis module is used for analyzing teacher input and generating a teaching target matrix; the structured modeling module is used for decomposing tasks and designing a teaching framework according to a three-dimensional target system (cognition, ability and subject literacy); the teaching plan generation module is used for generating subject contents through multi-Agent collaboration; and the teaching plan optimization module verifies interdisciplinary consistency and outputs a standardized teaching plan. According to the method, the knowledge graph and multi-Agent dynamic weight distribution are creatively fused, deep integration of interdisciplinary knowledge is realized, teaching plan generation efficiency and scientificity are remarkably improved, and intelligent lesson preparation support is provided for teachers.

The invention provides an eye movement normal form and index evaluation method and system for cognitive disorder detection, and relates to the technical field of eye movement data analys.The eye movement normal form and index evaluation method comprises the steps that eye movement parameters of a subject under eye movement normal forms graded at different difficulties are obtained, and the eye movement normal forms are evaluated on the basis of the eye movement parameters under the eye movement normal forms graded at different difficulties; extracting eye movement features under different difficulty grading eye movement paradigms; the cognitive competence of the participant is evaluated, and an MoCA score is obtained; a statistical analysis method is adopted to evaluate the normality of eye movement parameters, correlation analysis is carried out on eye movement characteristics under MoCA scores and different difficulty grading eye movement normal forms, a Spearman correlation analysis method is utilized to calculate the correlation coefficient and significance between the MoCA scores and the eye movement characteristics, rank correlation graphs are drawn respectively, and the eye movement parameters are obtained. And obtaining eye movement indexes with significant differences among the participants with different degrees of cognitive impairment under each normal form and the feasibility of the eye movement indexes in cognitive competence evaluation.

A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention provides a system and a method for evaluating the independent living ability of old people. The method comprises the following steps: acquiring multi-modal original data (including physiology, behavior and interaction data) of target old people; performing modal coding, query vector interactive fusion (including missing information completion) and feature compression on the multi-modal original data to obtain a standardized feature vector; a first-layer model evaluates behavior and cognitive ability indexes, and a second-layer model combines individual background information to predict a living ability grade and a degradation risk; optimizing the first-layer model through a feature feedback mechanism; and finally, outputting and visualizing a structured evaluation result. The method breaks through the limitation of traditional manual evaluation, achieves the deep fusion of multi-modal data, improves the objectivity, robustness and interpretability of evaluation, and can meet the precise evaluation demands of home care, community monitoring and smart medical scenes.

The invention provides a cognitive load evaluation method, system and equipment oriented to a man-machine system and a medium, and relates to the field of man-machine systems. The method comprises the following steps: performing a cognitive ability test on a first target group to divide the first target group into a high cognitive ability group and a low cognitive ability group, and constructing a first classifier and a second classifier; collecting first multi-modal data, carrying out preprocessing and feature extraction, and fusing obtained key features into a target feature vector; based on the target feature vector, introducing a balance factor to train a first classifier and a second classifier respectively to obtain a first target evaluation model and a second target evaluation model; and carrying out fusion to obtain a comprehensive evaluation model. The method has cross-participant and cross-task characteristics, the application range and practicability of evaluation are improved, and meanwhile, the universality and usability of the model are enhanced; man-machine system cognitive load evaluation based on multi-feature fusion is realized, and a comprehensive, accurate and easy-to-operate evaluation tool is provided for a user.

The invention belongs to the technical field of cognitive function training, and particularly relates to an intelligent pushing system and method for cognitive function training based on multi-dimensional ability values. According to the method, the cognitive state and the training effect of the user can be evaluated more accurately by collecting the real-time feedback of the user in the cognitive function training process, the training content and difficulty can be dynamically adjusted according to the actual demand and the ability level of the user through intelligent pushing, and therefore the training individuation and effectiveness are improved, and in addition, the user experience is improved. By continuously monitoring and updating the training list, it can be ensured that the user is trained in the optimal state all the time, the problem of excessive training or insufficient training is avoided, and the method aims at helping the user to improve the cognitive ability and the life quality by providing a comprehensive, dynamic and personalized cognitive function training scheme.

The invention relates to the technical field of intelligent education management, in particular to a self-adaptive learning progress informatization teaching test system, which comprises the following contents: a knowledge node construction module, a learning state identification module, a test task generation module, a path scheduling adjustment module and a feedback data backtracking module. According to the method, refined numbering management of knowledge points is realized by constructing a directed edge numbering set, a dependency direction is set in combination with a label propagation algorithm, the semantic expression ability of a node relationship is enhanced, mastering weights and stability coefficients are matched based on answering behaviors, a state weight sequence is formed, and the stability of classification and recognition is improved; cognitive ability coding and time-consuming median value linkage state weight are introduced, questions are screened, a priority queue is constructed, task pushing precision is improved, numbering and sorting weight are fused to construct a path chain, dependency intensity is calculated to expand path branches, and individual suitability and task rhythm coordination ability of path scheduling are enhanced.

The invention discloses a robot control method based on multi-behavior fusion intelligent perception, and the method comprises the following steps: A, respectively constructing a facial expression intelligent recognition model, a body movement intelligent recognition model and a voice recognition control protocol set, and respectively establishing control instruction mapping rules; b, tracking and acquiring a facial expression image, a limb action image and a voice audio of a service object in real time; c, outputting a facial expression prediction result, a body language of the state and a voice instruction of the service object; d, robot action control instructions based on facial expressions, limb actions and voice instructions are obtained and fused, and a robot action control instruction after multi-behavior fusion is obtained; and E, controlling the robot to execute a corresponding action according to the robot action control instruction after multi-behavior fusion. According to the method, the cognitive ability and the context awareness ability of the robot can be effectively improved.

The invention discloses a neurology patient body index monitoring system, and particularly relates to the technical field of body monitoring. The environment interaction and adaptation module is used for monitoring and adjusting the surrounding environment of a patient to optimize the health state of the nervous system; the emotion and psychological state monitoring module is used for evaluating the emotion and psychological state of the patient in real time by utilizing an advanced sensing technology and data analysis; and the cognitive function and learning support module is used for monitoring and promoting the cognitive function of the patient to improve neuroplasticity and cognitive ability through interaction and training. By integrating the environment interaction and adaptation module, the emotion and psychological state monitoring module, the cognitive function and learning support module and the comprehensive health ecosystem module, intelligent adjustment of the surrounding environment of a patient, real-time monitoring of the emotion and psychological state, improvement of the cognitive function and comprehensive integration of health resources are achieved; the system optimizes the environmental comfort through intelligent illumination, noise monitoring and air quality adjustment.

The invention provides a multi-mode content review method and device, a medium and a computer program, and relates to the technical field of content review. The multi-modal content review method comprises the following steps: calling a special recognition model to recognize subtitles and voice in target review content to obtain a character recognition result and a voice recognition result; the special identification model is a model obtained based on learning of a content sample and a subtitle label and a voice label of the content sample; and calling an artificial intelligence large model, performing fusion analysis and reasoning on the character recognition result, the voice recognition result and the video picture of the target review content according to the violation review rule, and outputting a violation behavior recognition result. The universality of the content review method is improved based on the good cognitive ability of the artificial intelligence large model; the artificial intelligence large model and the special small model are organically combined, the understanding cognitive ability of the artificial intelligence large model and the perception ability of the special small model are fully exerted, and accurate and effective examination of the content is realized.

The invention belongs to the technical field of artificial intelligence, and discloses a novel large model autonomous reasoning system based on fast and slow thinking, which comprises a system 1 fast integration information module, a system 2 logical reasoning and knowledge extension module and a knowledge induction module, wherein the system takes meta-cognitive competence as a core, and realizes autonomous reasoning in combination with a multi-level reasoning and knowledge induction module of a language model. According to the novel large model autonomous reasoning system based on fast and slow thinking, the fast and slow thinking theory is used for reference, the fast intuition of the system 1 and the logical reasoning of the system 2 are combined, the meta-cognitive ability is introduced, the novel autonomous reasoning system is constructed, the reasoning efficiency and accuracy of the model can be improved, and the reasoning efficiency and accuracy of the model can be improved. And the capability of processing complex tasks can be enhanced, and a new possibility is provided for application of artificial intelligence in a complex open environment.

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 discloses a data government method and system based on a large government affair model, and belongs to the technical field of deep learning, and the method comprises the steps: government affair data collection: obtaining basic data from a data source for implementing data government, the basic data comprising historical data and current data; government affair data treatment: preprocessing the collected basic data, and integrating the data to obtain a data sequence; a platform adaptively selects a preset deep learning framework according to the government affair scene features, and a staged parameter optimization strategy is combined to train and generate a government affair decision model with a domain cognitive ability; and intelligent analysis: deploying a semantic understanding engine, converting the unstructured government affair text into data vector representation which can be analyzed by a machine, and outputting a data analysis result through a multi-modal feature fusion technology. According to the invention, the data management difficulty in a government affair scene can be reduced, and deep transformation of intelligent government affairs and establishment of an intelligent decision support system are promoted.

The embodiment of the invention provides a method and a system for predicting cognitive competence of old people based on voice tasks. The method is applied to the technical field of data processing, and comprises the following steps: collecting voice data and cognitive ability data of old people, performing data perception, and constructing to obtain a voice cognitive ability feature sample of the old people; an integrated robust depth random configuration network model is constructed in a node increment mode, the network model is composed of a plurality of base learners, and each learner comprises a plurality of hidden layers; and performing integrated robust prediction on the speech cognitive ability feature sample according to the network model to obtain a network model integrated prediction result. In this way, the cognitive assessment scale and the voice data are adopted, the integrated robust depth random configuration network is constructed to achieve cognitive prediction of the old people, and the technical problems that in the prior art, cognition of patients cannot be effectively and accurately diagnosed, and the prediction effect is poor due to the fact that the number of influence factors of the voice data of the patients is large are solved.

The invention belongs to the field of teaching application of information technology, and provides an interactive narrative education game intelligent generation method, system and device, a medium and a program product. The method comprises the following steps: (1) intelligent generation of interactive narration: constructing a lightweight intelligent generation model of interactive narration to generate game narration content, and constructing a multi-modal scene generation engine to generate corresponding multi-modal scene content based on the narration content; (2) user interaction feedback: a user responds to game content through voice or text, and narrative content is adjusted according to user feedback; and (3) personalized cognitive adaptation: dynamically adjusting the narrative content and the game difficulty by evaluating the element cognitive ability of the user. The dynamic narrative education game is generated through interaction with the user, the cognitive ability of the learner can be adapted, the personalized learning requirement of the learner can be met, and a new path is provided for constructing the interactive narrative education game.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The invention discloses a picture book live broadcast personalized book recommendation method and system based on artificial intelligence, and relates to the technical field of live broadcast book recommendation, and the method comprises the following steps: obtaining multi-modal interaction data of a user in picture book live broadcast microphone connection, and generating a three-dimensional ability portrait vector and a psychological portrait vector representing the cognitive ability and psychological tendency of the user based on the multi-modal interaction data; performing time sequence analysis, and calculating an evolution direction vector and an evolution rate scalar; when the evolution direction is positive, according to a comparison result of an evolution rate scalar and an adaptive threshold range, updating a preset cooperation coefficient in the threshold range, if the evolution rate scalar exceeds the threshold range, adjusting an ability integrating degree weight and an emotion resonance degree weight, and dynamically updating parameters based on weight adjustment counting; and calculating a matching total score through a matching decision formula by using the dynamically adjusted parameters, and outputting picture book recommendation. According to the method and the device, the user state is adapted in real time in live broadcast interaction, the recommendation strategy is adaptively optimized, and the accuracy of the instant individuation degree of recommendation is remarkably improved.

The invention provides a cognitive function evaluation method based on neurovascular coupling analysis. The method comprises a signal preprocessing step, a signal processing step, a feature extraction step and a coupling coefficient calculation step. A new normal form of cognitive evaluation is constructed from nerve-blood vessel coupling and cross-system cooperation dimensions, and accurate description of a cognitive state is realized through real-time quantification of a dynamic cooperation effect of brain nerve activity (EEG) and blood flow regulation (CBP variability) and establishment of an association relationship between a dynamic cooperation index and cognitive ability (alertness, reaction speed and memory). And a new objective basis is provided for the fields of plateau medicine, intelligent health, neurological and mental disease diagnosis and treatment and the like.

The invention discloses a delayed symptom recognition and intervention system which comprises a delayed symptom recognition system and a delayed symptom intervention system. The delay symptom identification system comprises a multi-modal data acquisition module used for collecting data; the data analysis and mining module is used for preprocessing the data and evaluating the characteristics and level of the delay behavior; and the delayed symptom recognition result module is used for obtaining a delayed symptom recognition result and the level of related core cognitive competence. The delay symptom intervention system comprises a personalized intervention module used for generating a personalized cognition and nerve regulation intervention scheme; the intervention guidance feedback module is used for performing delay symptom intervention training; and the intervention data management module is used for recording intervention training data. A multi-modal data acquisition module is designed in the delayed symptom identification system, and the delayed symptom of an individual is comprehensively evaluated through multiple dimensions, so that actual actions can be conveniently captured to evaluate the delayed symptom; by setting a personalized intervention module, a cognition and nerve regulation intervention scheme is customized for a specific individual.

The invention discloses a cognitive evaluation system and method based on enterprise training, and the system comprises a training unit which is used for displaying the content of a corresponding training course through a display screen, and enabling employees to carry out the interaction and feedback through an interaction interface; the data acquisition unit is used for acquiring physiological data and behavior data of the employees in the training process in real time through a plurality of sensor modules arranged in the working environment of the employees; the analysis unit is used for analyzing the physiological data and the behavior data to obtain a corresponding cognitive analysis report, and the cognitive analysis report comprises the cognitive ability state, the training effect and the health condition of the employee; and the feedback and adjustment unit is used for dynamically adjusting the training course content or progress based on the cognitive analysis report and assisting an enterprise in performing performance evaluation and management on the employees. The learning condition and the health condition of the employees are comprehensively known, data support is provided for performance evaluation and talent management, and enterprises are helped to carry out personnel management and development planning more scientifically.

The invention discloses an MCI intervention system based on virtual reality cognitive motion training difficulty dynamic adjustment. The MCI intervention system comprises an upper limb motion task prototype module, an upper limb motion task dynamic difficulty adjustment module, a virtual cognitive task prototype module and a virtual cognitive task dynamic difficulty adjustment module. A dynamic difficulty adjustment algorithm based on Pareto frontier is introduced in the system, modeling is carried out on the relation between task intensity and participant performance, possible combinations of various task difficulties and corresponding tradeoffs are evaluated from the relation, and therefore dynamic optimization adjustment of the task difficulties is achieved. Finally, through random contrast research carried out in cooperative hospitals, the obvious effect of the system in the aspect of improving the cognitive ability and the upper limb movement function is verified. The invention provides an innovative system which combines cognition and exercise training and performs personalized intervention through a dynamic difficulty adjustment mechanism, and an effective path is provided for health management and cognitive function improvement of old people.

The invention discloses an application ability cognition and interaction method based on a large language model. The invention discloses an application ability cognition and interaction method based on a large language model, which realizes more intelligent and efficient application cognition and interaction experience by utilizing strong language understanding and generation ability of the large language model. According to the method disclosed by the invention, the cognitive ability of the user in various application scenes can be improved, a more personalized and natural interaction mode can be provided, and more convenient and intelligent application experience is brought to the user.

The invention particularly relates to a closed-loop neural feedback training method and device based on tactile-motion electroencephalogram signals. The method comprises the steps that the current finger somatosensory time discrimination threshold and electroencephalogram data of a tested individual are collected; performing neural feedback training on the tested individual by using a closed-loop neural feedback system and an electroencephalogram amplifier; and collecting the current finger somatosensory time discrimination threshold and the electroencephalogram data of the tested individual again, and processing the behavioral data of the finger somatosensory time discrimination threshold of the tested individual and the electroencephalogram characteristic data of the tested individual to obtain an effect after neural feedback training. According to the method, the primary somatosensory cortex damage is utilized, fewer index tactile electroencephalogram characteristic indexes constrained by cognitive competence are extracted, a patient can observe signals related to the electroencephalogram characteristic indexes of the brain in real time in the closed-loop neural feedback training process, self-adjustment is carried out, and the accuracy of the patient is improved. Therefore, the basic nerve mechanism of the human body is more effectively changed, and the early Alzheimer's disease is controlled and intervened.

The invention belongs to the technical field of cognitive power, and relates to a cognitive competence assessment method, training method, system, equipment and medium, the system comprises a data acquisition unit used for acquiring electrophysiological signal data and behavior data of a user; the evaluation unit is connected with the data acquisition unit and is used for executing a cognitive evaluation scale task and synchronously acquiring the electrophysiological signal data, the behavior data and the scale score of the user; the fusion analysis unit is connected with the data acquisition unit and the evaluation unit and is used for integrating the cognitive evaluation scale data, the electrophysiological signals and the behavior data and inputting the data into a multi-modal fusion evaluation model so as to output a cognitive load index and an emotional stability score; and the training unit is connected with the fusion analysis unit and is used for adjusting the VR training difficulty according to the cognitive load index and the emotion stability score. According to the scheme of the invention, the accuracy of cognitive ability evaluation and the applicability of cognitive training are effectively improved.

In described embodiments, a system for information processing is provided that includes a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system also includes a cognitive inference module configured to evaluate an inference process of the language model, simulate a cognitive function including a meta-cognitive process, and generate an evaluation based on an analysis of the received feedback, where the evaluation includes classifying one or more outputs into components, assigning a quality score to each component, and outputting the one or more components based on the quality score. And identifying improvements corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive inference module for adjusting an inference process of the language model based on the evaluation. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated evaluation until a performance threshold is met.