123 results about "Optimal learning" patented technology

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention relates to the technical field of learning path recommendation, and discloses a learning path intelligent recommendation system based on user behavior big data analysis, and the system comprises a behavior data collection module which is used for collecting behavior data of a learner on a learning platform; the behavior inertia modeling module is used for extracting behavior inertia parameters of the learner based on the behavior data; the task capability graph construction module is used for constructing a task capability graph; calculating a grasp score according to the test score, and extracting a sub-graph map; and the recommendation path generation module is used for generating an optimal learning recommendation path according to the path recommendation engine, and selecting the optimal learning recommendation path as a recommendation result based on the path adaptation degree score. According to the method, personalized, rhythm-adaptive and resource-optimized learning path recommendation is dynamically generated on the basis of user behavior big data analysis in combination with post capability requirements.

The invention discloses a language learning assistance application system based on speech recognition, and belongs to the technical field of learning assistance. The system comprises an intelligent voice acquisition and enhancement module for realizing acquisition and preprocessing of high-quality voice signals in a complex environment; the multi-modal feature extraction module dynamically extracts and fuses multi-dimensional learning feature information, completes feature analysis and boundary segmentation of a phoneme level, and is responsible for feature normalization and optimization; the deep speech recognition module is used for realizing high-precision speech recognition based on the normalized features; the intelligent evaluation engine module is used for realizing real-time and multi-dimensional evaluation on the language performance of the learner; the personalized learning management module integrates the evaluation results for dynamically planning an optimal learning path for the learner, manages the learning progress, and provides learning effect prediction and intervention suggestions at the same time; and the learning interaction experience module is responsible for visual presentation of learning feedback, multi-modal man-machine interaction design and user interface optimization.

The invention discloses an adaptive learning path recommendation method based on a hypergraph neural network and knowledge tracking, and relates to the field of learning path recommendation, and the method comprises the steps: determining an incidence relation between learning resources, and enabling the incidence relation to serve as an edge of a learning resource undirected graph; the features of the learning resources serve as embedded feature vectors of all nodes of the learning resource undirected graph; updating the embedded feature vector by using a graph neural network to obtain a resource embedded vector, and taking the resource embedded vector as a node feature of a learner hypergraph structure; performing iterative aggregation on the learner hypergraph structure by using a hypergraph neural network to obtain a dynamic resource embedding and learner behavior sequence, generating an initial recommendation list, further generating a candidate learning path set, and generating a Pareto frontier solution set by using a non-dominated sorting genetic algorithm II; and calculating a comprehensive score of each path in the solution set based on a dynamic weight distribution strategy and a comprehensive utility function, and determining an optimal learning path. According to the invention, the accuracy and effectiveness of learning path recommendation are improved.

The invention relates to the technical field of data processing, in particular to a learning behavior data analysis system and method based on an agent, and the method comprises the steps: determining knowledge points in learning content, generating a visual knowledge point graph, testing the mastering degree of a learner for the knowledge points, generating a visual knowledge point mastering graph, and analyzing the knowledge points. The method comprises the steps of generating an optimal learning task which is most adaptive to the state of a learner based on the mastery degree of the learner and a learning behavior library, collecting real-time learning feature data of the learner during task execution, sensing the emotion and attention state of the learner in real time, and generating a personalized learning path in combination with a knowledge point graph, learning behavior analysis and the state of the learner. Learning tasks are dynamically adjusted according to real-time data, and learning strategies are optimized according to feedback of learners. Through dynamic perception and adjustment capability, the problem that a learning behavior analysis system in the prior art lacks real-time perception and dynamic adjustment of the state of a learner can be effectively solved.

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 discloses an application (App) and method capable of intelligently memorizing words, which can generate a personalized memory scene by acquiring personal interests and hobbies of a user, dynamically optimize an individual forgetting curve based on machine learning, and further refine optimal learning or review time according to a personal emotion curve. The system supports multi-language and multi-device adaptation, and comprises a personal interest scene generation module for constructing a personalized learning scene and generating example sentences and multimedia contents associated with interests by using natural language processing (NLP); a dynamic forgetting curve module; a two-stage modeling mechanism is adopted, and a fine-grained word-level review strategy is generated in combination with a group forgetting rule and user real-time test data; an emotion curve and a forgetting curve are combined, and review time is optimized through a double-layer algorithm; the multi-mode reward module is linked with the psychological health service platform through a point exchange mechanism; and the psychological consultation service is linked for the users with poor word reciting scores.

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.

The invention discloses an English learning behavior intelligent prediction system based on big data driving, and particularly relates to the technical field of data analysis, which comprises a data acquisition module, a learning behavior analysis module, a personalized learning path prediction module and an optimization and iteration module, the data acquisition module is used for acquiring learning behavior data of students from a learning platform, cleaning and preprocessing the learning behavior data and storing the learning behavior data in a database, and the learning behavior analysis module is used for analyzing learning habits, learning effects and behavior modes of the students based on the acquired learning behavior data and establishing personalized learning models of the students. The personalized learning path prediction module is used for intelligently recommending an optimal learning path according to historical data and learning states of students and dynamically adjusting learning contents and sequences, and the optimization and iteration module is used for transmitting feedback data to the personalized learning path prediction module to carry out dynamic adjustment and outputting the optimal learning path according to the feedback data. And regularly updating and optimizing the personalized learning model.

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 invention discloses a content optimization system based on adaptive wisdom English teaching, and particularly relates to the technical field of artificial intelligence, which comprises a learning data acquisition module, a content intelligent recommendation module, a teaching strategy adjustment module and a content optimization and generation module, the learning data acquisition module is used for acquiring learning data of students from learning behaviors of the students and processing and analyzing the acquired learning data so as to evaluate the English levels of the students, and the content intelligent recommendation module is used for constructing student portraits based on the English level evaluation results of the students and analyzing the learning contents so as to recommend the students. The learning recommendation module is used for providing personalized optimal learning recommendation for students, the teaching strategy adjustment module is used for analyzing learning data of the students, diagnosing learning problems and formulating corresponding adjustment strategies so as to improve the learning effect and mastering conditions of the students, and the content optimization and generation module is used for optimizing and generating content according to feedback of the students and performance of the students. The existing teaching content is optimized, and new content is generated.

The invention discloses a learning path optimization method and system based on machine learning, and the method comprises the steps: collecting the learning data of a learner, and enabling the learning data to comprise a learning object and the information of the learner; establishing a double-layer knowledge graph according to the knowledge type of the learning object; collecting behavior information data of the learner, evaluating a learning style of the learner user according to the behavior information data of the learner, and analyzing and dynamically calculating a learning ability value of the learner and a difficulty value of learning resources in real time according to the learning behavior information data; according to the ability value of the learner, the difficulty value of the learning resource and the information of the learner, generating an optimal learning path by utilizing a double-layer knowledge graph, monitoring the current learning duration, score and participation degree change of the learner in real time, and circularly feeding back and updating the learning path, so that the learning efficiency is improved. And higher-quality and more personalized learning path recommendation is provided for learners.

The invention discloses a short-term wind power prediction method, and the method comprises the steps: S1, collecting historical wind power data, and carrying out the preprocessing of the data, S2, improving a frost ice optimization algorithm, and extracting the multi-frequency-domain features of the wind power, S3, constructing an LSTM model, and improving a fishing algorithm, and S4, determining the optimal learning rate and penalty factor of the LSTM model based on the S3, and obtaining an ECFOA-LSTM network model, and S5, based on the IMF component set obtained in the S2, obtaining a wind power prediction value. According to the invention, the frost ice optimization algorithm and the fishing algorithm are improved, the optimal learning rate and penalty factor of the model are determined through the improved fishing algorithm, and finally the ECFOA-LSTM network model is obtained.

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 invention relates to the technical field of data processing, in particular to a database task configuration method, which comprises the following steps of: acquiring execution time, completion degree and execution frequency of a plurality of users for completing each learning task, and generating execution data of each learning task; constructing a plurality of learning classification models, and selecting an optimal learning classification model; dividing users into a single type and a mixed type; configuring a plurality of learning tasks for single learning of the user, generating a task forgetting curve, and executing a task completion model to generate a task execution log; adjusting the task forgetting curve to adjust the time ratio of the newly added learning task to the previous learning task; learning tasks are divided into special learning tasks and general learning tasks, the general learning tasks are divided into hot tasks and cold tasks, and a task completion model is generated in combination with the previous learning tasks and the hot tasks; according to the method and the system, the accuracy of database task configuration is improved while learning tasks are effectively distributed to different students in a targeted manner.

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.

The invention provides an English teaching method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the English learning condition of a user; based on artificial intelligence, according to an English learning condition, matching an optimal learning material for the user; and based on the virtual teacher, performing English teaching on the user according to the optimal learning material. According to the invention, the optimal learning material is matched for the user according to the English learning condition of the user based on artificial intelligence, and English teaching is carried out on the user according to the optimal learning material based on the virtual teacher. Personalized instant adjustment of teaching content based on the English language basis, the learning progress, the learning ability, the learning preference and the like of the learner is realized, the learning experience of the learner is prompted, and the learning effect is improved.

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

The invention, which relates to the technical field of the adaptive education system, discloses an artificial intelligence-based adaptive education system comprising a data collection module, a path generation module, a recommendation module, a monitoring feedback module, an adjustment module and an optimization module. The data collection module is used for collecting learning behaviors, hobbies and interests and score performance information of students by adopting a multi-modal data fusion technology to obtain student learning data; the path generation module is used for processing the obtained student learning data by adopting a machine learning algorithm and dynamically generating a personalized learning path; the recommendation module is used for recognizing demands and preferences of students by utilizing collaborative filtering, sentiment analysis and natural language processing technologies according to the personalized learning path and the current learning state so as to obtain optimal learning resources; and the monitoring feedback module is used for collecting the use condition of the optimal learning resource and generating an evaluation result by establishing an evaluation model.

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.