70 results about "Cognitive competence" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

Disclosed in the present invention is a lightweight defect detection method based on hybrid multi-scale knowledge distillation. The method comprises: constructing a dataset; constructing a teacher network model and a lightweight student network model; using the dataset to train the teacher network model, and saving a weight file of the trained teacher network model; and loading into the teacher network model the saved weight file of the teacher network model, inputting defect images in the dataset into the teacher network model and the student network model to respectively obtain first multi-scale features and second multi-scale features, respectively inputting the first multi-scale features and the second multi-scale features into a cascaded knowledge blending module to obtain final deeply fused first multi-scale features and final deeply fused second multi-scale features, then calculating a hybrid multi-scale knowledge loss, and in combination with the prediction loss of the student network model, using a backpropagation algorithm to update network parameters, so as to obtain a trained lightweight student network model for implementing defect detection of intelligent manufacturing products. The cognitive ability and recognition performance for defects of different scales are improved.

The invention provides a security code automatic generation method and system based on a full information theory and mechanism ambiguity, and the method comprises the steps: S1, carrying out the feature extraction of a grammar verification layer of an input demand, extracting the grammar features of a code through the topological features of an abstract syntax tree, and carrying out the formalized rule verification of the extracted grammar features; s2, constructing a semantic association model of a cross-code snippet through a semantic analysis layer, and forming a propagation path for tracking a data flow and a control flow; s3, in the pragmatic optimization layer, combining pragmatic constraints of the business scene, and generating a security policy through a dynamic utility function; and step S4, optimizing parameters and security policies of the dynamic utility function through knowledge federal evolution and a closed-loop feedback mechanism. According to the method, a deep collaboration mechanism of grammar, semantic and pragmatic three-dimensional information and a dynamic strategy generation model are reconstructed, security logic is converted from external attributes to endogenous cognitive competence, and the security, interpretability and adaptability of automatic code generation are improved.

The invention discloses a virtual teaching training method and system based on a large language model and a medium, and the method comprises the following steps: determining preset feature parameters of virtual students according to teaching levels and subject contents; constructing a large language model, generating virtual students based on the large language model according to preset feature parameters of the virtual students, and inputting multi-modal teaching information; the virtual students perform model training and learning on the multi-modal teaching information to generate learning state information and feedback information; the virtual teaching expert proposes a suggestion for dynamically adjusting and optimizing the teaching strategy based on the evaluation result; dynamic learning and interactive feedback are carried out by simulating virtual students with different priori knowledge levels and cognitive competence, and meanwhile, real-time evaluation and personalized improvement suggestions are carried out on the teaching process by utilizing the constructed virtual expert system, so that a user is helped to continuously optimize a teaching strategy, and the teaching quality of a specific subject is improved.

The invention discloses an Alzheimer's disease auxiliary screening system based on intelligent interaction, particularly relates to the field of computer-aided diagnosis, and is used for solving the problem that in an existing screening method, it is difficult to quantify cognitive performance differences of life knowledge and professional knowledge at the same time. The method comprises the following steps: constructing a task interaction group covering life knowledge and professional knowledge, collecting and coding multi-dimensional interaction operation of a testee in a task process, and generating an interaction vector matrix; further performing jump point identification, vector residual calculation and invalid operation density statistics on the interaction behavior through a preset task logic chain, and quantifying the operation deviation condition of the interaction behavior; respectively generating life and professional cognitive performance indexes based on the analysis result, and constructing a cognitive deviation evolution curve of a difference value of the life and professional cognitive performance indexes along with time change in a set period; and finally, through identifying a continuous rising trend in a cognitive deviation evolution process, auxiliary screening and early warning of asymmetric degradation of cognitive competence are realized.

The invention provides a cognitive ability training method and system based on a virtual reality technology, and the method comprises the steps: obtaining multi-data-dimension feature data used for evaluating the cognitive ability of a subject in the training process of each round; wherein the feature data comprises objective data collected by a sensor during training and subjective data counted after training; scoring the cognitive competence of the subject in each data dimension according to a preset rule, and performing weighted fusion of scores according to preset weight parameters for different data dimensions to obtain a comprehensive score; and taking the score of each data dimension and / or the comprehensive score as input, and utilizing a pre-trained artificial intelligence model to adaptively adjust the difficulty level suitable for the tested training task. According to the invention, the difficulty level suitable for the tested training task can be adaptively adjusted by using the pre-trained artificial intelligence model.

The invention discloses an auxiliary decision-making system for cognitive competence assessment of old people, which relates to the technical field of cognitive auxiliary decision-making and comprises an environmental noise acquisition and feature extraction module, a time sequence fluctuation analysis module, a dynamic threshold reconstruction and judgment module, a cross-domain consistency calibration module, a multi-modal synchronous verification module and a dynamic threshold regulation and control module. According to the method, the environmental noise is dynamically analyzed, so that the defects of a fixed noise suppression threshold and a static feature extraction model in the traditional technology are overcome. A sound field energy distribution model is constructed in real time and time sequence fluctuation analysis is combined so that a noise fluctuation interval can be identified, a voice starting point detection threshold value is dynamically adjusted and evaluation precision is enhanced. Through multi-mode synchronous verification combining a reaction time curve and a facial movement track, a time offset error is corrected, a judgment standard is adjusted in real time according to noise changes, misjudgment and virtual high risk early warning are avoided, and therefore the accuracy and reliability of cognitive ability assessment of the old are improved.

The invention relates to the technical field of data processing, and discloses an intelligent agent architecture-based education large model autonomous teaching planning method and system. The method comprises the following steps: inputting a cognitive ability matrix, a knowledge mastery degree vector and a learning preference vector into an improved Bayesian knowledge tracking algorithm, and adding an agent parameter consistency constraint term and a teaching planning coordination factor to obtain a learning track state vector; based on the learning track state vector, multiple agents cooperatively generate a recommendation content identification sequence, a difficulty level sequence and a time distribution sequence; and monitoring the knowledge mastery degree vector variation, and reordering the recommended content sequence when the knowledge mastery degree vector variation exceeds a preset threshold value to generate a teaching sequence. According to the invention, the Bayesian knowledge tracking algorithm is improved, the agent parameter consistency constraint term and the teaching planning coordination factor are fused, and coordination and unification of multi-agent teaching decisions in a communication-free environment are realized.

The invention relates to the technical field of child cognitive function evaluation, and particularly discloses an intelligent child cognitive function evaluation method and device and a medium, and the method comprises the steps: collecting real-time behavior physiological coupling response data of a tested child under the stimulation of color and digital dual tasks through a multi-modal stimulation collection device, and obtaining a cognitive response chain dynamic model; according to the cognitive reaction chain dynamic model, performing performance quantification on the joint distribution of the reaction accuracy and the reaction time of the child under the stimulation of color and digital dual tasks to obtain a cognitive performance quantitative index; meanwhile, self-adaptive calibration of the individual cognitive baseline is carried out to obtain individual cognitive baseline data; and finally obtaining a personalized course recommendation matrix. The method not only is helpful for evaluating the current cognitive ability level of children, but also can provide reference for self-adaptive calibration of individual cognitive baselines. The method is crucial for recognizing cognitive deviation and formulating a personalized training plan. Adjustment can be carried out according to the specific cognitive requirements of children, and the effectiveness and pertinence of training can be improved.

The invention provides a personnel cognitive competence assessment and recovery method based on big data, and the assessment method comprises the steps: collecting basic data of a to-be-assessed target personnel, and sequentially carrying out a plurality of assessment tests under each cognitive competence assessment classification on the target personnel based on a plurality of preset cognitive competence assessment classifications; obtaining test data, a single capability evaluation value and completion time of each evaluation test, inputting the basic data and each test data into a prediction model, and calculating a big data analysis index of each evaluation test based on a big data analysis capability evaluation value output by the prediction model; and calculating a final cognitive competence assessment result of the target person according to the single competence assessment value of each assessment test, the completion time and the big data index analysis index. According to the method, a computer technology and a big data analysis technology are utilized to carry out multi-dimensional comprehensive assessment on the cognitive competence of the operator, and the accuracy, intelligence and comprehensiveness of assessment are improved.

The invention discloses a multi-modal representation supported learner element cognitive ability evaluation method and system, and belongs to the field of model evaluation and prediction. The method comprises the following steps: firstly, constructing a learner meta-cognitive ability evaluation model consisting of a feature extraction module, an enhanced representation module, a multi-modal fusion module and a prediction module; and then, in combination with a fusion auxiliary task of multi-level cross-modal comparison predictive coding, training the learner meta-cognitive ability evaluation model according to a framework of combining semi-supervised learning and active learning, and performing learner meta-cognitive ability evaluation by using the trained learner meta-cognitive ability evaluation model. According to the method, multi-modal unified meta-cognitive quantitative representation can be effectively utilized, the dynamic dependence and long-term incidence relation among multiple modals can be accurately captured based on a cavity cross-modal attention mechanism, the model training efficiency is improved through a framework combining semi-supervised learning and active learning, and meta-cognitive ability evaluation is achieved.

The invention discloses a cognitive behavior training system, method and device and a program product, relates to the field of medical rehabilitation, and is used for solving at least one problem of single training target, single task difficulty and single task element. According to the method, main training tasks of five dimensions are carried out in stages, cognitive competence corresponding to task training of each dimension is progressively carried out step by step, and centralized, continuous, selective, alternate and dispersive attention cognitive competence intervention is respectively carried out. The main training task of each dimension sequentially comprises multiple levels of sub-training tasks with gradually increased difficulty, and each level of sub-training task comprises multiple groups of segmented tasks. And respectively evaluating the training effect under each dimension. According to the method, the difference and compliance of cognitive behavior training are improved, the intervention efficiency is improved, and the long-term intervention effect is ensured.

The invention relates to the technical field of computers, in particular to an intelligent error data management method and device, and the method comprises the steps: obtaining historical error data and historical answering behavior data of a student, and constructing a subject knowledge graph and a student cognitive ability portrait; the method comprises the following steps of: performing semantic coding on a wrong question text through a pre-trained Transform model; based on the encoded vector representation, associating the error questions to corresponding knowledge point nodes in the subject knowledge graph, and identifying error types corresponding to the error questions; according to the knowledge point node attributes of the subject knowledge graph associated with the error questions and the identified error types, the cognitive competence portrait of the student is updated, and the mastery degree weight of the associated knowledge point nodes in the subject knowledge graph is dynamically updated; and based on the updated student cognitive competence portrait and subject knowledge graph, optimizing a cognitive state through LEMMA type reflection training, and generating a personalized learning path. According to the invention, accurate and personalized learning guidance can be provided for students.

The invention discloses a career planning evaluation method and system based on an AI large model. According to the method, multi-dimensional data of a user is collected and preprocessed, and an occupational planning evaluation analysis model is constructed. And performing occupational interest and cognitive competence evaluation on the user by using the model, and generating a personalized occupational planning report. The user can select or upload a resume, participate in evaluation and explore different professions and industries to customize the own occupational development path. In addition, the method also comprises the step of regularly updating the job application influence factors to adapt to the change of the employment market and ensure the scientificity and practicability of the occupational planning suggestions. According to the invention, through intelligent evaluation and planning, the efficiency and accuracy of occupational planning are improved, and powerful support is provided for occupational development of users.

The invention provides a method and a device for processing cognitive competence data of operators and electronic equipment, relates to the technical field of data processing, and solves the technical problem of low accuracy of cognitive competence classification evaluation results of existing operators. The method comprises the following steps: determining a training sample set and an initial cognitive ability classification model; training the initial cognitive competence classification model based on the training sample set to obtain a trained cognitive competence classification model; obtaining a quantitative data set of a to-be-identified operator; and taking the quantized data set of the to-be-identified operating personnel as the input of the trained cognitive ability classification model, and outputting a cognitive ability classification evaluation result of the to-be-identified operating personnel.

The invention provides an old people health management detection method and system, and the method comprises the steps: obtaining behavior task data and physiological data of old people through a scale or a non-invasive device, extracting data features, carrying out the fusion, building a cognitive state evaluation model set through combining an integrated learning model, a deep learning model and a statistical learning model, and carrying out the detection of the health management of old people. Training a cognitive state evaluation model set by adopting a differentiated learning strategy, constructing a dynamic evaluation system based on cognitive competence scores, determining evaluation frequency, determining evaluation items by adopting a correlation priority strategy of data features, establishing a nonlinear mapping rule, setting training reference parameters according to the cognitive competence scores, and evaluating the cognitive competence scores according to the training reference parameters. And making a personalized cognitive training scheme based on the dynamic evaluation system and the individual data characteristics of the elderly. The method is high in individuation degree and simple to operate, has continuous and comprehensive data, and can capture dynamic changes of cognitive competence, know differences of old people in real time and provide a timely and effective feedback mechanism.

The invention provides a group multi-scene cognitive training method and device based on MR and a multi-modal brain-computer interface, and relates to the technical field of cognitive training. The method comprises the following steps: performing cognitive competence evaluation based on a cognitive competence evaluation scale to obtain a cognitive competence evaluation result; based on a cognitive ability database, a preset group number of people and a candidate group database, performing cognitive ability matching according to a cognitive ability evaluation result; based on a cognitive task library, performing task matching according to the group cognitive ability vector; under the MR scene, based on the cooperative training group, the competition training group and the matched cognition task, group cognition training is carried out, signal collection is carried out, and multi-modal physiological signals are obtained; and performing cognitive competence evaluation by using the cognitive competence evaluation model to obtain a trained cognitive competence evaluation result. The group cognition training method based on the BCI multiple scenes is high in training efficiency, large in generalization degree and high in applicability.

A system, method, and computer platform product for an adaptive cognitive training platform. In accordance with various aspects of the present disclosure, an adaptive cognitive training platform is configured to process user activity data from one or more instances of a computerized cognitive training program to determine a baseline cognitive assessment for one or more cognitive abilities / skills of a user. The cognitive assessment model may be configured to further process the user activity data to predict a relative value or measure of efficacy for one or more computerized stimuli or interactions within the computerized cognitive training program. The adaptive cognitive training platform may be configured to configure, modify and / or present one or more graphical user interface elements for one or more subsequent instances of the computerized cognitive training program according to the predicted value or measure of efficacy of one or more cognitive training tasks.

The invention provides a jigsaw and music combined interactive training system and method, relates to the field of education and entertainment, and optimizes the training experience of a user by dynamically adjusting the difficulty and shape of a jigsaw task and music accompaniment and combining multi-dimensional physiological and psychological information of brain wave data, emotional state and cognitive load. The system comprises a brain wave acquisition module, an emotion recognition module, a puzzle generation module, a music generation module and a sensory feedback module. The jigsaw puzzle task and the music content are adaptively adjusted according to the real-time state of the user, so that the task is not too simple and not too difficult, and emotion fluctuation or cognitive fatigue of the user is effectively avoided. Through the combination of music and tasks, the emotion state of the user can be adjusted, the concentration and cognitive competence can be improved, the learning motivation of the user can be enhanced, the method can be widely applied to the fields of mental health, cognitive training and entertainment, the cognitive competence, emotion adjustment and task completion of the user are improved, and therefore the comprehensive psychological and cognitive improvement effect is achieved.

The invention relates to the technical field of data processing, and discloses a teaching content integration method and system based on a knowledge graph, and a storage medium. The method comprises the following steps: performing cognitive difficulty labeling on multi-source teaching resources, and constructing a concept ontology library; fusing the semantic similarity and the cognitive span through a pre-dependency matrix algorithm to generate a pre-dependency graph; carrying out six-layer cognitive layer coding to obtain a multi-layer semantic matrix; the cognitive competence of the learner and the semantic matrix are dynamically planned and recombined, and a self-adaptive content sequence is generated; and constructing a timestamp fusion space, and obtaining a dynamic personalized learning path through a relationship attenuation model evolution graph. According to the method, the problems that in an existing teaching content integration method based on the knowledge graph, cognitive load quantification is missing, concept dependency relation mining is inaccurate, static knowledge representation is limited, and dynamic updating is lagged are solved.

The invention discloses a personalized virtual-real interaction cognitive game system based on AI adaptive brain cognitive enhancement, comprising: a cognitive game design module for designing a plurality of cognitive tasks corresponding to attention, memory, execution function and perception function based on a dynamic difficulty adjustment algorithm according to user characteristics; the dynamic difficulty adjustment algorithm is used for dynamically adjusting the difficulty of a specific level task according to the behavior state and the psychological state of the user; the cognitive task guiding module interacts with the user through a multi-mode guiding mechanism to guide the user to complete a cognitive task; the data tracking analysis module is used for recording the performance and growth trajectory of the user in the completion process of each cognitive task; the instant feedback module provides feedback based on an instant feedback generation algorithm; the cognitive competence evaluation report module is used for generating a corresponding cognitive competence evaluation report according to the performance and data tracking analysis algorithm; and the data storage module is used for storing behavior data, emotion records and cognitive competence evaluation reports of the user.

To provide users with effective training for spatial cognitive capacity that can be simplified for even the old and the young, the present invention comprises a content acquisition unit (299) that acquires content that is for receiving input of an active reaction by a user in response to an object, a difficulty selection unit (227) that selects a difficulty for the content, and a content reproduction unit (230) that reproduces content that corresponds to the difficulty selected by the difficulty selection unit.

The application discloses a kind of fusion training method and system for multiple cognitive abilities.The fusion training method includes: obtaining the cognitive evaluation result of user for attention, inhibition and memory;Based on the cognitive evaluation result, obtain the first training parameter related to attention and inhibition;Based on the first training parameter, obtain the second training parameter related to memory;Based on the first training parameter and the second training parameter, configure the cognitive training task of corresponding difficulty level and training duration for user;Push the cognitive training task to user for cognitive training, for simultaneously training the attention, inhibition and memory of user;Obtain the cognitive training data of user, and feedback the current cognitive training result to user.The fusion training method can integrate multidimensional cognitive ability training into one, break the common single cognitive ability training form, and help to improve and transfer the comprehensive ability of user.

This invention provides a method and system for training and evaluating pilots' cognitive abilities, relating to the fields of cognitive science and computer-aided training technology. Based on a training and evaluation system integrating personnel management, evaluation management, and training management, it completes a phased cycle of initial assessment, competency training, periodic assessment, and targeted training. The system collects operational data and adaptively adjusts training difficulty based on difficulty parameters, allowing assessment results to directly drive training program configuration. Furthermore, through the fusion analysis of multiple rounds of assessment and training data, it generates a digital profile of cognitive competence, achieving precise quantification and continuous improvement of pilots' cognitive abilities. The training effects of this invention can be transferred to real-world mission scenarios, making it suitable for large-scale application.

This invention relates to the field of cognitive health companionship technology, specifically to a multimodal companionship system for patients with cognitive impairment based on AI technology. The invention receives memory materials from a family member's terminal, constructs an interactive virtual scene, configures guiding questions, records the patient's answers to all guiding questions, sets the initial difficulty level of cognitive training based on an assessed memory recall integrity score, and collects performance data after the patient completes cognitive training tasks to determine if the initial difficulty level needs adjustment. This achieves precise matching of cognitive training difficulty to the patient's current cognitive level. Simultaneously, based on the performance data of cognitive training tasks within a subsequently set training period, a cognitive ability report is generated and compared with historical cognitive ability reports in multiple dimensions to assess the patient's cognitive ability improvement. This reflects the dynamic changes and improvement trajectory of the patient's cognitive ability, effectively improving the targeting and effectiveness of cognitive training.

The invention discloses a cognition improving system and method based on a drawing therapy. The cognition improving system comprises an evaluation module which is internally provided with a pre-training model and a comparison unit so as to interpret painting works and compare and analyze data; the cognitive learning module comprises a voice unit and a learning unit and is used for forming a personalized cognitive learning scheme according to user selection on the basis of preset cognitive painting works; the practical operation training module comprises a theme practical operation unit and is used for forming a personalized practical operation training scheme according to a preset practical operation painting work and user selection; and the feedback module is used for pushing the personalized cognitive learning scheme and the personalized practical operation training scheme to the user for cognitive learning and practical operation training, so that the cognitive ability of the user for psychological analysis of the cognitive painting works is improved.

The invention relates to the technical field of artificial intelligence, in particular to an extensible large language model driven cognitive competence assessment system and method.One or more large language models are adjusted and optimized by using an example data set with a label, and questions in a project question bank are automatically classified by using the adjusted and optimized large language models, so that the accuracy of cognitive competence assessment is improved. Obtaining a classification result; constructing a cognitive competence test based on the classification result, generating a structured evaluation result, and storing the evaluation result in a historical record of the user; the method comprises the steps of obtaining an evaluation result of a user, based on historical records of the user and the evaluation result, activating a dialogue agent driven by a large language model, converting recommended items into a structured query request, integrating retrieval results and generating a customized course scheme exclusive to the user, and according to the method, the large language model (LLM) is deeply integrated into an education evaluation and course generation closed loop; and full-process automation of automatic question classification, dynamic cognitive competence diagnosis, continuous model optimization and personalized course delivery is realized.

The invention discloses a teenager cognitive competence and psychological development evaluation method and system based on artificial intelligence, and relates to the technical field of teenager cognitive competence and psychological development evaluation. Progressive development and stepped transition of cognitive competence are effectively captured, and misjudgment of a traditional time sequence model on discontinuous changes is avoided. Based on a continuous coherence characteristic monitoring mechanism of topological data analysis, cognitive representation dimension mutation caused by strategy recombination and neuroplasticity is accurately recognized, and the delay bottleneck of a traditional method on burst mode recognition is broken through; and the incremental learning triggering module and the evaluation path optimization module cooperatively operate, so that online evolution of model parameters is realized while the calculation overhead is reduced, and continuity and consistency of long-term tracking are kept.

The application provides a collaborative learning intervention method and system under reinforcement learning, comprising the following steps: selecting an intervention action from an intervention action space based on a perceived state of a collaborative discussion environment; obtaining a reward and a next moment state fed back by the collaborative discussion environment after executing the intervention action, and then obtaining transition data; the state comprises collaborative sessions of all learners in the collaborative discussion environment; repeating the above steps until a preset number of transition data are obtained; training a Q network based on the preset number of transition data; in the training process, an improved Q learning method is used to update a Q value function; using the trained Q network to determine a current intervention action corresponding to a maximum Q value in a current state; and providing an intervention suggestion to the learner based on the current intervention action. The application can provide learning support for learners who do not fully master the meta-cognitive strategies of collaborative learning, and promote the collaborative activities among learners and the development of individual meta-cognitive ability.