37 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.

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 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 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 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 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 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 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 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.

The invention discloses a cognitive training method and system based on emotion monitoring and triggering. The method comprises the steps that an emotion correction cognitive competence index RA and an emotion interference cognitive competence index DI of a user are obtained through a test task based on triggering elements and emotion monitoring; obtaining a user personal condition index PI; and selecting emotion correction cognitive ability training or emotion interference cognitive ability training for the user by combining the emotion correction cognitive ability index RA, the emotion interference cognitive ability index DI and the user personal condition index PI. According to the scheme, the emotion correction cognitive competence index RA and the emotion interference cognitive competence index DI are obtained in the evaluation stage, and the emotion correction cognitive training task or the emotion interference cognitive training task is matched for the user in combination with the actual demand condition of the user; therefore, the formulation of the training task is more in line with the cognitive emotion influence characteristic of the user and the actual living and learning environment state, so that a better training effect which is more in line with the user demand is achieved, and substantial help is provided for the user.

This application proposes a health management and detection method and system for the elderly, which obtains behavioral task data and physiological data of the elderly through scales or non-invasive devices, extracts data features and fuses them, combines ensemble learning models, deep learning models and statistical learning models to build a cognitive state assessment model set, adopts a differentiated learning strategy to train the cognitive state assessment model set, builds a dynamic assessment system based on cognitive ability scores, and determines the assessment frequency. It adopts a correlation priority strategy of data features to determine the assessment items, establishes nonlinear mapping rules, sets training benchmark parameters according to cognitive ability scores, and formulates personalized cognitive training plans based on the dynamic assessment system and the individual data characteristics of the elderly. This application has a high degree of personalization, simple operation, and continuous and comprehensive data. It can capture the dynamic changes in cognitive ability, understand the differences between elderly groups in real time, and provide a timely and effective feedback mechanism.

The invention provides a personnel cognitive competence training evaluation method and device based on multi-modal interaction, edge computing equipment and a medium, and relates to the technical field of human factor intelligence. The multi-modal interaction-based personnel cognitive competence training evaluation method comprises the following steps: receiving configuration information, and generating a multi-modal stimulation scheme for performing personnel cognitive competence training according to the configuration information; based on the multi-modal stimulation scheme, cognitive training is carried out on the user, cognitive ability training results generated by interaction of the user according to the multi-modal stimulation scheme are determined, and the cognitive ability training results at least comprise the correct rate, the error rate and the missing report rate; and determining a cognitive ability evaluation result of the user based on the cognitive ability training result. Through the method, the training effect and the evaluation accuracy of cognitive training can be improved.

The invention discloses a fusion type training method and system for multiple cognitive competence. The fusion type training method comprises the following steps: acquiring cognitive evaluation results of a user for attention, suppression force and memory; based on the cognitive evaluation result, obtaining a first training parameter related to attention and suppression force; acquiring a second training parameter related to memory based on the first training parameter; based on the first training parameter and the second training parameter, jointly configuring a cognitive training task with a corresponding difficulty level and training duration for the user; pushing the cognitive training task to the user for cognitive training so as to train the attention, inhibition ability and memory of the user at the same time; and obtaining cognitive training data of the user, and feeding back a current cognitive training result to the user. According to the fusion type training method, multi-dimensional cognitive ability training can be fused into a whole, a common single cognitive ability training form is broken through, and comprehensive ability improvement and migration application of the user are facilitated.

The application relates to the technical field of intelligent education management, in particular to an information-based teaching test system with adaptive learning progress, which comprises the following contents: a knowledge node construction module, a learning state recognition module, a test task generation module, a path scheduling adjustment module and a feedback data backtracking module.In the application, the knowledge point fine numbering management is realized through the construction of a directed edge numbering set, the dependence direction is set in combination with a label propagation algorithm, the semantic expression capability of the node relationship is enhanced, the state weight sequence is formed based on the matching of the answer behavior matching weight and the stability coefficient, the stability of the classification recognition is improved, the cognitive ability coding and the time consumption median value linkage state weight are introduced, the questions are filtered and a priority queue is constructed, the precision of the task pushing is improved, the path chain is constructed by fusing the numbering and the sorting weight, the path branch is expanded by calculating the dependence strength, and the individual adaptability and the task rhythm coordination capability of the path scheduling are enhanced.

The invention discloses a military school student cognitive ability evaluation method based on multi-agent and Bloom education target classification, and aims to develop an effective method to evaluate the online learning cognitive ability of a student, which is more and more extensive in use of an online learning platform in military colleges and universities. In order to analyze the cognitive level and ability obtained by students in online learning, a multi-agent system is developed in combination with deep learning and a Bloom education target classification theory, the system is composed of a plurality of independent agents, each agent is responsible for evaluating the cognitive ability of the students on different levels of a Bloom classification method, and the cognitive ability of the students on different levels of the Bloom classification method is evaluated. The system processes and analyzes learning data, including homework, test, classroom participation and the like, of trainees by using a deep learning technology, and evaluates the cognitive competence of the trainees through good, poor and average according to six levels of a Bloom classification method. According to the experiment, text replies of 300 students including 12 subjective questions are selected, and cognitive evaluation results of different machine learning algorithms are explored.

This invention relates to the intersection of cognitive training intervention and artificial intelligence, specifically to a method for matching optimal parameters for cognitive training based on an LSTM model. The method includes: designing features and methods for collecting user cognitive training log data; automatically evaluating user cognitive abilities based on a cognitive unit scoring method; and constructing a dataset based on the above design. The dataset is preprocessed to reconstruct a sequence dataset of cognitive training interventions as training samples. These training samples are then input into a depth-optimal difficulty prediction model designed based on an LSTM framework for training. Finally, the trained prediction model is used to predict the difficulty coefficient of cognitive training and to evaluate model performance. This invention, by training an LSTM prediction model to determine the difficulty of the next training session based on historical data from user cognitive training, ensures that users consistently undergo cognitive training at a challenging level of difficulty and intensity.

The embodiment of the invention discloses an intelligent teaching processing method and system based on intention recognition and cognitive tracking, and the method comprises the steps: collecting multi-round dialogue data of a student, mapping each round of dialogue data to a preset Bloom cognitive verb quantity group for intention recognition, obtaining a corresponding intention label, and storing the intention label in a database; a plurality of intention labels of the student are connected in series according to a time sequence to generate an intention migration chain; according to the intention migration chain, calling a preset intention-ability mapping matrix to calculate the cognitive ability score of the student, and constructing a corresponding cognitive tracking chain; and according to the cognitive competence score, performing cognitive intervention on the student meeting a preset triggering condition. According to the method, the dynamic evolution of the cognitive competence of the students can be continuously quantified and differentiated intervention can be carried out on the dynamic evolution.

An apparatus and a method for providing a user-customized program for improving cognitive ability are provided. The method comprises the steps of: providing a first questionnaire for a cognitive test to a terminal of a user; upon receiving a first response to the first questionnaire from the user terminal, calculating dementia risk of the user on the basis of the first response; providing a first training program for a preset first period to the user terminal on the basis of the dementia risk; providing, to the user terminal at the end time point of the first period, a second questionnaire for a lifestyle assessment based on the first response; upon receiving a second response to the second questionnaire from the user terminal, calculating a first lifestyle score of the user on the basis of the second response; and providing a second training program for a preset second period to the user terminal on the basis of the first lifestyle score.

The invention provides a cognitive ability evaluation system and method suitable for a plateau hypoxia environment, and relates to the technical field of cognitive and psychological testing. Comprising a cloud server, an APP test unit, an external portable monitor and a user client. The cloud server is used for generating a cognitive competence assessment task and a questionnaire according to the personal information of each tested user, the altitude environment information and a seven-point scale subjective evaluation method; the APP test unit is used for receiving the cognitive competence assessment task and the questionnaire, obtaining a first test result and a second test result, and obtaining the heart rate and the blood oxygen saturation of each tested user by using an external portable monitor; the cloud server is further used for performing analysis according to the first test result, the second test result, the heart rate and the oxyhemoglobin saturation and generating a test report; and the user client is used for displaying the test report. The special requirements of the plateau anoxic condition can be effectively met, and the oxygen supply strategy in the plateau anoxic environment is further optimized.

The present invention discloses an interactive game apparatus and method that combines physical construction with strategic gameplay, designed to enhance both motor and cognitive skills. The game merges elements of traditional block-stacking with action-based card play and a wrecking device that adds competition and unpredictability. The wrecking device and an action-dictating element are adapted to various forms such as elements, dice, a wheel, or different impact devices. Players take turns building one tower with stacking blocks on a base block, following instructions dictated by action elements. The wrecking device, triggered by specific card actions, allows players to disrupt the tower, creating a dynamic and engaging experience. The game is adaptable, with components that can vary in size, material, and design, making it suitable for different age groups and skill levels.

The invention provides mixed structure grease for improving brain cognition as well as a preparation method and application thereof, and the mixed structure grease for improving brain cognition comprises the following components in percentage by mass: 2-20% of 1, 2-dipalmitic acid-3-oleic acid triglyceride (PPO). The method is beneficial for improving the cognitive competence of the brain.

This invention provides a method for testing personnel's basic cognitive abilities and operational skills, a portable device, an edge computing device, and a storage medium. The portable device for testing personnel's basic cognitive abilities and operational skills improves integration and deployment efficiency through modular integration of hardware components, customized design of the enclosure structure, and seamless integration of software and hardware. It enables rapid switching from transportation to working status and meets the stringent requirements of users in special industries for deployment speed and ease of operation.