98 results about "Optimal learning" patented technology

The invention relates to the technical field of artificial intelligence education, and discloses a personalized online education system and method based on artificial intelligence, and the system comprises a data collection and perception module which is used for collecting the dynamic behavior data and multi-mode perception data of a learner; the knowledge processing and constructing module is used for processing teaching contents to construct a structured knowledge graph; the user modeling module is used for constructing a dynamic learner portrait comprising a knowledge state model and a cognitive-emotional state model for the learner based on the collected data and the constructed knowledge graph; and the intelligent decision and intervention module is used for generating and executing personalized learning intervention based on the dynamic learner portrait. According to the method, by fusing multi-mode cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism, accurate and timely personalized intervention for learners and intelligent guidance of optimal learning strategies are realized.

The invention relates to the technical field of dynamic optimization, and discloses an intelligent learning dynamic optimization system introducing a time sequence. The system comprises a multi-source data preprocessing module, an intelligent learning knowledge graph establishing module, a learning path optimizing module and a learning resource matching module. Firstly, a data detection model is constructed based on a neural network to perform invalid data filtering and abnormal data detection; secondly, knowledge node association strength is calculated based on a time sequence, and an intelligent learning knowledge graph is established; marking a learning path in the intelligent learning knowledge graph, and performing dynamic path planning by using an improved Harris eagle optimization algorithm to find an optimal learning path; and finally, generating learning resource configuration according to the optimal learning path, and establishing a learning resource feature library for resource matching to obtain an intelligent learning optimization strategy. According to the method, the learning behavior data are analyzed and processed by introducing the time sequence, and the purpose of intelligent learning dynamic optimization is achieved.

An object of a system according to an embodiment is to provide optimal learning support according to individual learning needs of learners.SOLUTION: A system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension degree monitoring unit, and an advice providing unit. A learning history analysis part analyzes the learning history and interest of the learner. The customized content generation unit generates learning content on the basis of the result analyzed by the learning history analysis unit. The comprehension degree monitoring unit provides the learning content generated by the customized content generation unit and monitors the comprehension degree and progress of the learner. The advice providing unit provides advice based on a result of monitoring by the comprehension degree monitoring unit.SELECTED DRAWING: Figure 1

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

The invention relates to a dynamic concept cognitive anomaly detection method and system under an unbalanced condition, and the method comprises the steps: collecting the related feature data of a sample set in a scene facing a specific extreme event, and dividing an initial data set and a test set; constructing an initial learning model; performing dynamic event type prediction based on the test set, and classifying the event as an extreme event or a non-extreme event; constructing an active three-way concept learning module to form a positive concept space and a negative concept space based on a three-way decision idea and prediction probability values of extreme events and non-extreme events; according to the positive concept space and the negative concept space, introducing a concept center domain strategy for dynamic updating; and adopting a loss function minimization strategy to obtain an optimal learning model. The effectiveness of dynamic detection is improved by utilizing an active three-way concept learning method, a concept center field and a concept dynamic optimization strategy, and the capability of identifying extreme events under the extremely unbalanced scene condition is realized.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

A method and system for building and implementing a meta-machine learning (meta-ML) optimization engine for a neural network (NN) or a machine learning (ML) connective model. A computer processor may iteratively simulate a backpropagation algorithm by executing a sequence of optimization steps. At each optimization step a position of a loss function may be determined that may be closer than a previously determined position of the loss function to a local minimum. A computer processor may compute and store after each iteration a detachment of the loss function, learning rate, and optimal learning rate. A computer processor may train a machine learning connective model to model the optimal learning rates of the simulated backpropagation algorithm. The meta-ML optimization engine may be implemented for a NN or ML connective model by generating a modified backpropagation algorithm in which algorithmic features of gradient descent may be replaced by the meta-ML optimization engine.

The application relates to an intelligent education management method and system based on an Internet of Things. The method comprises the following steps: inputting a multi-channel physiological feature sequence into a hierarchical probability state space model to obtain a short-term cognitive state and a short-term cognitive state posterior probability; performing stability statistical testing on the short-term cognitive state posterior probability, and marking the short-term cognitive state as a phase-established state when the stability condition is met; constructing a cost function based on the phase-established state and a probability evolution track, and performing rolling time domain optimization based on the minimum cost function and a dynamic refractory period constraint to obtain an optimal learning action; calculating a decision oscillation index according to a current executed learning action sequence, and increasing the weight of an action change amplitude penalty and prolonging the locking time of the dynamic refractory period constraint by a set step length when the decision oscillation index exceeds a preset first oscillation threshold. The method can guarantee the continuity of a learning process and the stability of a cognitive state.

The invention discloses a personalized smart course recommendation system for oral medicine based on a knowledge graph, and belongs to the technical field of course recommendation, and the system comprises a collection module which collects and fuses dominant static features and recessive behavior features of a user to form preliminary capability evaluation data; the analysis module is used for matching the preliminary capability assessment data with a preset oral internal medicine knowledge graph to generate a knowledge state mapping graph; the generation module is used for generating an optimal learning path from the current knowledge state to the target knowledge state according to a preset teaching strategy and a learning target on the basis of the knowledge state mapping graph; and the recommendation explanation module is used for analyzing the optimal learning path, enabling a preset course unit to correspond to the knowledge node to be consolidated or the association relationship, generating natural language description and completing personalized course recommendation. According to the method, the dominant static characteristics and the recessive behavior characteristics of the user are collected and fused to form the preliminary capability evaluation data, so that the accuracy of the initial recommendation stage is remarkably improved.

An object of the system according to the embodiment is to provide an optimal learning plan based on a learning history and a goal of a user, and to continuously check a progress status and an understanding level of learning.SOLUTION: A system according to an embodiment comprises a learning history analyzer, a learning plan generator, a progress checker, and a feedback provider. The learning history analysis unit analyzes the learning history and goal of the user. The learning plan generation section generates an optimum learning plan based on the data analyzed by the learning history analysis section. The progress checking section checks the progress and understanding of learning based on the learning plan generated by the learning plan generating section. The feedback providing unit provides feedback based on a result checked by the progress checking unit.SELECTED DRAWING: Figure 1

The present application relates to the technical field of intelligent management of teaching data, and particularly relates to a method and system for integrated teaching intelligent management suitable for courses, posts, competitions and certificates, wherein the method for integrated teaching intelligent management suitable for courses, posts, competitions and certificates comprises the following steps: S1: obtaining a student portrait set containing multi-dimensional individual information of students, and connecting a preset external information base; S2: calling the external information base, and generating an optimal learning path for the students according to the student portraits and preset time nodes; S3: obtaining multi-dimensional individual information of the students when the students learn according to the optimal learning path, and updating the student portraits. The present application can integrate course learning, competition practice, certificate taking and post employment in the same framework, thereby solving the problem of fragmentation of each link in traditional teaching management, and enabling further optimization of the optimal learning path at different time nodes, so that the generated optimal learning path is always matched with the actual progress and final goal of the students.

This application relates to a method, system, device, and medium for personalized vocational skills learning path planning based on artificial intelligence. The method includes: collecting a user's structured event dataset; constructing a dynamic causal skill graph with skill points as nodes and causal effect strength as directed edges using two-stage linear regression; extracting the user's dynamic behavior sequence and inputting it into a pre-trained model to obtain a skill mastery vector; searching for a personalized causal learning path with the current mastered skill points as the starting point, the career goal as the ending point, and maximizing the cumulative causal effect as the objective; performing counterfactual reasoning on the learning path based on the structured causal model to predict the counterfactual mastery, learning time, and path under different intervention conditions; and obtaining the optimal learning path adjustment scheme by weighted fusion of the counterfactual results. This method enables personalized learning path planning and dynamic optimization with causal logic.

A system is provided.SOLUTION: A system comprising: means for inputting basic information of a subject; means for determining cognitive traits of the subject; means for suggesting an optimal learning method based on the determination; means for providing learning content; means for tracking learning progress and conducting comprehension tests; means for providing personalized feedback to the subject; and means for solving questions during learning in real-time.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for acquiring learning history and progress data of a student; means for generating an optimal learning plan and teaching materials based on the acquired data; means for providing the generated learning plan and teaching materials to the student; means for analyzing learning activity data of the student in real time and providing feedback; means for updating a progress chart based on the analysis result; and means for automatically generating a custom report.SELECTED DRAWING: Figure 1

To provide a system for enabling an examinee to efficiently advance learning under an optimum learning environment.SOLUTION: A means for inputting basic information and a learning history of a learner, a means for transmitting the basic information and the learning history to a server, a means for storing the received basic information and learning history in a database, a means for analyzing the basic information and the learning history from the database and generating an optimal virtual competitor for the learner, a means for transmitting data of the virtual competitor to a terminal of the learner, a means for interacting and competing with the virtual competitor on the terminal, a means for transmitting a response of the learner and a competition result to the server, and a means for generating feedback based on the response of the learner and the competition result on the server; A system comprising: means for transmitting to a terminal; means for monitoring stress levels and learning effectiveness of a learner and adjusting settings of a virtual competitor; and means for transmitting adjusted data of the virtual competitor to the terminal.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide optimal learning content based on the learner's level of understanding and interests. [Solution] The system according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. The generation unit generates virtual teachers and characters based on the content provided by the provision unit. The feedback unit analyzes the learner's progress in real time through the virtual teachers and characters generated by the generation unit and provides feedback.

The application provides a microseismic real-time monitoring method and device based on compressed sensing and 5G node collection, solves the problem that microseismic monitoring data is easy to be submerged in noise interference, and microseismic data collection equipment cannot transmit data in real time, or the transmitted data is uneven and incomplete. It comprises the following steps: collecting data, establishing a geological and geophysical model, simulating fracturing based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring observation system; establishing an adaptive optimal learning dictionary to form a compressed sensing collection scheme; conducting a due diligence investigation of field obstacles and interference sources to form a compressed sensing active obstacle avoidance scheme; using a 5G node collection system to collect field data; applying compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data; carrying out microseismic data processing and interpretation, and feeding back the microseismic analysis results to the fracturing site to realize real-time monitoring of the fracturing construction process.

The system according to this embodiment aims to provide individual learners with an optimal learning plan based on their MBTI diagnostic results, thereby supporting their learning motivation and self-management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives MBTI diagnostic results. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The monitoring unit monitors learning progress based on the reminders delivered by the reminder unit. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit.

The application discloses an experience learning method and device for automatic driving of a vehicle, and comprises the following steps: obtaining sensor data through a vehicle perception system; obtaining surrounding vehicle motion data through the sensor data; analyzing the surrounding vehicle motion data to obtain an optimal learning object vehicle; taking target vehicle motion data corresponding to the optimal learning object vehicle as a training set to perform model training, and obtaining an abnormal driving behavior mode; updating a vehicle automatic driving model according to the abnormal driving behavior mode; and generating a real-time driving strategy based on the updated vehicle automatic driving model. The automatic driving vehicle can learn the behaviors of surrounding vehicles to improve its own behaviors, so that the automatic driving vehicle can learn abnormal events, and the adaptability and robustness of the automatic driving vehicle itself are improved.

The invention relates to a fault detection method and device based on fault feature enhancement and twinborn contrast learning. The method comprises the steps of obtaining sample data of key equipment of a power plant at different moments; constructing a learning network model based on twin spatial-temporal feature comparison; carrying out optimization training on the learning network model based on a tiny fault feature distinction degree intensified training strategy and an Adam optimization algorithm; and inputting the operation data of the power plant into the optimal learning network model to obtain a power plant fault detection result. According to the method, a detection model is constructed by combining a twin spatio-temporal feature contrast learning network and a tiny fault feature distinction degree enhanced training algorithm, the model performance is improved through a mixed contrast cross entropy loss function and an Adam optimization algorithm, the method has a high fault diagnosis rate and a low false alarm rate, early tiny faults can be accurately recognized, the real-time diagnosis time is extremely low, and the method is suitable for large-scale popularization and application. And the power plant field real-time detection requirement is met.

The invention relates to the technical field of computers, in particular to a cross-subject learning fusion method and device, computing equipment and a storage medium, and the method comprises the steps: extracting knowledge points of all subjects to construct an association relationship between the knowledge points of the subjects, calculating an optimal learning sequence according to the association strength between the knowledge points, and obtaining interaction behavior data. And calculating a cognitive state, detecting a learning bottleneck, and recommending associated subjects to assist learning at the corresponding learning bottleneck so as to evaluate a learning effect based on knowledge, thinking and application dimensions. According to the method, knowledge association between subjects is constructed, cognitive evaluation is realized through pure behavior data analysis, a dynamic interdisciplinary knowledge connection mechanism is established on the premise that biological feature collection is not needed, the cross-subject transfer learning ability of learners is improved, and a safer and more universal intelligent learning solution is provided for the education field.

A system is provided.SOLUTION: A system comprising: means for collecting video, audio, and text data of a technology from a craftsman; means for analyzing the collected data and performing video segmentation and image recognition; means for collecting information on user preferences and strengths; means for generating optimal learning content based on the analyzed data and user information; and means for providing the generated learning content to the user and checking the comprehension level.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent maintenance, machine learning and artificial intelligence crossing of civil aviation, and provides a civil aircraft health management model optimization verification method based on continuous operation data. According to the method, performance degradation is sensed in real time based on continuous operation data, on-demand iteration of a model is realized through an increment and continuous learning strategy, hysteresis of traditional offline retraining is avoided, and the adaptability to new working conditions and new faults is improved; attenuation diagnosis and strategy selection are autonomously completed, manual intervention cost is reduced, an optimal learning scheme is matched in combination with historical knowledge and data characteristics, the model optimization period is shortened, and core performance indexes are improved; through knowledge precipitation and updating in the optimization process, a self-learning optimization decision-making system is formed, and the accuracy and efficiency of model optimization are continuously improved along with operation data accumulation.

The system according to this embodiment aims to provide learning plans and emotional support tailored to the individual needs of each student. [Solution] The system according to the embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The community provision unit provides an online community where students and parents can interact with each other. The sponsorship unit partners with companies and educational institutions to obtain sponsorships. The course sales unit sells courses specializing in specific subjects or skills.

The invention belongs to the technical field of power system employee training, and provides a learning path planning and learning effect evaluation method and system based on a neural network, electronic equipment and a storage medium. The method comprises the steps of data acquisition, coding feature matrix construction, knowledge graph entity embedding generation, vector splicing, coding processing, matching score matrix calculation, curriculum personalized screening, knowledge level evaluation, learning demand analysis, optimal learning path screening and learning effect evaluation. According to the invention, through a two-stage learning path planning process combining actual demands and employee historical data, a learning demand list and a recommended course sequence are enabled to accurately fit employee knowledge shortages and business demands, and the path can be adjusted in real time according to the employee's own learning progress; in addition, due to the integration of the power grid business data, the training content hysteresis is avoided, and the training quality is improved.

The application discloses a centralized dynamic decision system of a dual-channel supply chain based on artificial intelligence, relates to the technical field of deep reinforcement learning, and has the following core modules: a strategy library module pre-stores multiple strategy models; a parameter setting module manages environment parameters; an agent management module constructs a joint agent fusing a production party and a first sales party based on parameters, and constructs an ordinary agent for each second sales party; a strategy matching and scheduling module matches an optimal strategy for each agent, that is, the agent respectively performs single-time trial operation based on each strategy model, scores according to the result, and selects the highest-scored model as the optimal strategy; and a simulation module makes each agent perform simulation iterative training of resource allocation decision-making according to the optimal strategy. Through the combined architecture of the joint agent and the independent agent, multiple reinforcement learning strategies are fused, the system can dynamically adapt to the optimal learning paradigm in the heterogeneous role collaborative task, and the automation degree of strategy configuration and the deployment efficiency are significantly improved.

The invention discloses a multi-modal preference-driven graph convolution combinatorial optimization learning path generation method, which comprises the following steps: acquiring multi-modal learning feature data, and carrying out multi-modal feature data fusion processing to obtain a learning resource initial feature matrix; a multi-relation adjacent matrix is constructed, normalization processing is carried out, and a normalized propagation matrix is constructed; updating the convolution features of the graph, and obtaining a learning gain score; calculating a comprehensive utility based on the learning gain score; integer programming is established under the condition of considering multiple constraints, and optimized candidate resources are obtained through screening; an edge cost function and a learning path objective function are adopted to generate an optimal learning path in the optimized candidate resources; in the optimal learning path generation process, dynamic re-planning is adopted; the dynamic re-planning comprises grasp updating, coverage demand decreasing and duration budget updating. By means of the scheme, the method has the advantages of being simple in logic, reliable in multi-mode fusion and the like.

A system is provided.SOLUTION: A schedule optimizing means for dynamically adjusting the learning plan based on the progress of the learner, an automatic answering means for immediately answering a question from the learner through a messenger application such as LINE, a detailed explanation means for providing a detailed explanation for an answer to a past question, and a real-time monitoring means for monitoring the learning progress of the learner in real time and providing appropriate feedback. AI.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide an optimal learning program tailored to the skill level and learning objectives of employees. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The monitoring unit monitors the progress of learning carried out by the learning unit and adjusts the learning plan as necessary.

The system according to this embodiment aims to efficiently create an educational system for reskilling and promote human resource development. [Solution] The system according to the embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials using internal company content. The learning unit proceeds with learning based on the roadmap generated by the generation unit. The evaluation unit evaluates the progress of learning carried out by the learning unit. The proposal unit proposes an optimal learning plan based on the progress evaluated by the evaluation unit.