307 results about "Learning resource" patented technology

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention discloses an artificial intelligence self-adaptive education system based on big data, and relates to the field of intelligent education, the artificial intelligence self-adaptive education system comprises an education resource management module, a student data management module, a self-adaptive learning recommendation module, an auxiliary teaching decision module and a security reinforcement module, and a cloud data management library is used for classifying and integrating education resources. The method comprises the steps of establishing a knowledge graph, performing knowledge point classification on resources by constructing the knowledge graph, performing accurate matching on personal portraits of students and the knowledge graph through an incremental self-adaptive matching model, dynamically adjusting recommended learning resources in real time, analyzing collective learning problems through a big data intelligent analysis model, and generating collective teaching suggestions. A three-layer security firewall is adopted to ensure the security of the system; the problem that an existing self-adaptive education system neglects the learning progress and weak links of classes or groups is solved, and intelligent management, personalized learning recommendation and collective problem analysis of education resources can be achieved.

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 discloses a teaching information processing system and method based on electrical automation control, and relates to the technical field of intelligent teaching, and the method comprises the steps: collecting multi-modal teaching information, and carrying out the preprocessing; extracting and fusing features by using the preprocessed multi-modal teaching information to generate learning state features; constructing and dynamically adjusting a knowledge graph by using the learning state features to obtain a dynamic knowledge graph; generating a personalized learning path based on the dynamic knowledge graph, recommending learning resources, and recording the utilization rate of the learning resources; generating a comprehensive learning state of the student based on the learning state feature and the learning resource utilization rate; and generating teaching feedback according to the comprehensive learning state. According to the invention, by constructing the dynamic knowledge graph and combining the learning state characteristics and the learning resource utilization rate of the students, the knowledge point mastering condition of the students is dynamically evaluated, the personalized learning path is generated, and the adaptive learning resources are pushed.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

The invention discloses a teaching management method and system based on electronic technology courses, and relates to the technical field of teaching management, and the method comprises the steps: collecting and preprocessing multi-source data, constructing an interactive network diagram of students in courses, and carrying out the classification of student groups; constructing a knowledge graph in combination with the course data and the learning behavior data, and initializing a personalized learning path based on the knowledge graph and student group features; and learning resources of different paths are recommended through a multi-view attention mechanism. The multi-level learning path constructed through the knowledge graph ensures that the students grasp knowledge step by step, can adapt to individual differences of the students, performs resource recommendation by using a multi-view attention mechanism, and can realize high-matching-degree resource pushing on the basis of capturing individual features of the students, thereby effectively improving learning efficiency and resource utilization rate, and improving learning efficiency. The dynamic optimization of the learning path is realized, and the adaptability and pertinence of teaching management are remarkably improved.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

The invention relates to the technical field of personalized learning. The method comprises the steps that a learning target and a cognitive state vector serve as joint input information to be sent into a large language model, and a knowledge point set and learning gap information corresponding to the learning target are generated; and aligning the knowledge point set with a pre-repair relationship and a dependency relationship in a preset educational knowledge graph to obtain an alignment result, and when a difference value between the updated cognitive state vector and the cognitive state vector exceeds a preset cognitive state change threshold value, determining a learning target based on the updated cognitive state vector and the learning target. And performing semantic analysis and reasoning of the large language model again to obtain a new knowledge point set, determining a new learning resource sequence according to the updated personalized learning path, and pushing the learning resource sequence to the user terminal. The method has the effect of realizing closed-loop optimization of the learning scheme.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a personalized learning resource recommendation method and system based on deep learning. The method comprises the following steps: acquiring a user learning scene and a real-time operation behavior to construct a heterogeneous interaction graph; in combination with a Transform meta-coding model of an MAML architecture, pre-training a meta-model based on a real-time operation behavior to adapt to fine tuning of scene parameters; embedding a domain knowledge graph entity to obtain a semantic association vector, and capturing an entity pre-repair relationship; performing multi-hop reasoning on the heterogeneous interaction map through a GAT map attention network, and iteratively generating a user preference vector and a learning resource feature vector; for interactive sparse users, real-time operation behaviors are input into the pre-training meta-model to generate exclusive recommendation parameters, new resource feature vectors are generated in combination with the knowledge graph, and user preference vectors are matched based on the exclusive parameters; and predicting the interaction probability according to the matching vector, and outputting the target recommendation information according to the interaction probability so as to improve the recommendation efficiency and accuracy of the learning resources and guarantee the adaptation degree of the learning resources and the user.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

According to the artificial intelligence knowledge and skill education system and method provided by the invention, through a vivid virtual learning environment constructed by the virtual reality module, a learner can throughout the mind of practice operation of artificial intelligence knowledge and skill, just like being in a real working scene or experimental environment, and the knowledge and skill education efficiency is improved. The interestingness, the participation degree and the concentration degree of learning are greatly improved, knowledge and skills can be better understood and mastered, knowledge and skill information sharing of different learning resource subjects is achieved, the learning effect is improved, perceptual mastering of practical skills can be rapidly achieved at low cost, and the interestingness, the participation degree and the concentration degree of learning are improved. And reference is provided for connection of school education and social practice education. According to the method, individuation and self-adaptive learning deepening are realized, individual differences of learners in a virtual reality environment are fully considered, abundant learner features are analyzed by using a neural network, an individualized learning path which is more practical is generated, and a virtual scene and a learning task are dynamically optimized according to a learning condition.

Embodiments of a content recommendation or selection system are described. The system uses a pipeline of machine learning models to select content for a user. In embodiments, a first user model generates a first score of content categories for the user based on short-term user data. A second user model generates a second score of the categories for the user based on long-term user data. The two scores are combined to select the categories to include on the content user interface. In embodiments, new categories are added to the recommendations based on an exploration-exploitation algorithm. In embodiments, content categories are organized on the user interface in a manner to promote neighborhood diversity. Advantageously, the machine learning pipeline enables independent configurability of various objectives of the content recommendation or selection system and reduces the amount of machine learning resources needed to implement the system.

The invention provides a learning training method and system based on an AI large model, and relates to the technical field of intelligent education. A learner portrait and a knowledge point graph are constructed, a learning path planning module is adopted to plan and optimize a learning path, a target learning path is determined, and a learning material recommendation engine is utilized to perform analysis to obtain customized learning resources. The method comprises the steps of obtaining an AI analysis large model, displaying a target learning path and customized learning resources to a target user for analysis learning, obtaining learning feedback data through a real-time feedback and evaluation module, carrying out dynamic analysis on the feedback data based on a backtracking mechanism adjustment module to obtain a learning optimization strategy, and carrying out training backtracking adjustment. The problem of low learning efficiency caused by insufficient learning resource precision and poor learning feedback and individuation degree is solved, and the effects of improving the learning efficiency and effect and meeting different learning individuation requirements are achieved.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention relates to an automatic education resource generation and dynamic adjustment system and method based on artificial intelligence, electronic equipment and a computer readable storage medium, and the system comprises a resource site management module which is used for integrating an open learning platform, a third-party professional site and college learning resources; the data acquisition module is used for acquiring multi-modal resources related to knowledge points; the digital person making module is used for generating a digital person model and a sound model of the teacher; the resource generation module is used for automatically constructing a PPT and generating a teaching video according to the course large model output; the dynamic adjustment module is used for performing real-time adjustment and intervention on the generated teaching resources according to teaching requirements; and the user interface module is used for interaction operation between students and teachers and the system. According to the method, the generation efficiency of the educational resources is remarkably improved, the individuation and interactivity of the resources are enhanced, the dynamic adjustment and optimization of the resources are realized, and powerful technical support is provided for online education.

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.

The invention provides a junior high school history auxiliary learning method and system based on a knowledge graph, and belongs to the technical field of learning resource management and recommendation, and the method comprises the steps: obtaining junior high school history teaching data; the sources of the junior high school historical teaching data comprise a junior high school historical textbook, a test question book, an educational resource platform and a knowledge base; constructing a junior high school historical knowledge graph mode layer based on the junior high school historical teaching data; preprocessing the junior high school historical teaching data; constructing a junior high school historical knowledge graph data layer based on the preprocessed junior high school historical teaching data and the junior high school historical knowledge graph mode layer; and assisting students in learning based on the junior high school historical knowledge graph data layer. According to the invention, the informatization and intelligence level of history teaching in junior high schools can be improved.

The invention discloses a deep reinforcement learning resource allocation method for green mobile edge computing, which comprises the following steps of: establishing a communication network model, and initializing a communication environment, the number of base stations, the number of users and the number of subcarriers; determining an optimization target and a constraint condition; the optimization problem is converted into a Markov decision process, intelligent agents, a state space, an action space and a reward function are determined, a deep reinforcement learning algorithm is used for training, and an optimal strategy is distributed for each intelligent agent; the intelligent agent continuously interacts with the environment through PPO and D3QN algorithms, and network parameters are optimized and updated; an optimal resource allocation scheme is obtained; through a distributed multi-agent deep reinforcement learning-based resource allocation algorithm, the maximization of long-term average energy efficiency is realized, and strategy coordination among multiple agents is promoted, so that resource allocation is optimized, and an efficient and sustainable resource management solution is provided for the development of edge computing and communication networks in the future.

The invention relates to the technical field of multilingual training, in particular to a multilingual training management system and method, and the system comprises a user management module, a learning resource acquisition module, a training model processing module, a learning processing module, an evaluation module, a personalized recommendation module and a training model adjustment module. The method comprises the steps that a user registers and logs in and sets a learning target; the system recommends a personalized learning path based on historical data and an intelligent algorithm; the user improves the ability through online learning and multi-mode training; the system evaluates the training effect in real time and generates optimization suggestions; a personalized learning path is generated by fusing multi-dimensional data of a user, weak items are fed back in real time in combination with a multi-modal intelligent evaluation model, multi-language full-question resources are integrated, and diversified training modes such as real question simulation and wrong question reinforcement are provided; the problems of scattered training resources, lack of personalization and the like in the prior art are solved, efficient and accurate multilingual training is realized, and the learning efficiency and effect are improved.

The invention provides an educational resource sequence recommendation method and a related device, and relates to the technical field of resource recommendation. After student learning behavior data, learning resource attribute data and knowledge graph structure information are obtained, a regularization matrix decomposition technology is adopted to carry out potential factorization modeling on a student-resource interaction scoring matrix, and basic preference embedding features of students and resources are extracted. Aiming at diversity and complexity of student learning behaviors, a multi-view feature coding module is designed, learning sequence and short-term interest features are extracted from a behavior sequence view, static attribute features of students and courses are extracted from an attribute information view, and knowledge point pre-correction dependence and association relationship features between courses are extracted from a knowledge structure view. In order to improve the consistency and discrimination of feature representation, on the basis of multi-view fusion features, a comparative learning mechanism is introduced, and the discrimination and migration modeling ability of the model to student interests and preferences is enhanced by constructing a positive and negative sample comparison loss function.

The invention provides an auxiliary use method and device for learning electronic products, equipment and a medium. According to the method, under the condition that the use data of the user in the learning electronic product is obtained, the predicted learning time information of the user for each learning module is determined based on the obtained use data; the predicted learning time information can be used for indicating and predicting time required by the user to learn one to-be-learned content item in one learning module, so that time allocation recommendation information is determined based on the predicted learning time information of the user for each learning module; according to the method and the device, the learning time allocated on different learning modules by the user is recommended to the user through the time allocation recommendation information, so that the user can perform targeted learning on the content of each learning module according to the indication of the time allocation recommendation information, and the use efficiency of various learning resources in electronic learning products is improved.

The invention discloses a multi-modal learning resource intelligent recommendation method based on an AI large model, and relates to the technical field of educational information, and the method comprises the steps: collecting multi-modal learning resources, carrying out the semantic feature extraction and vectorization representation, and obtaining a structured resource set; constructing a knowledge mastering state vector based on the resource set, and determining a knowledge hole set; virtual nodes are generated according to the knowledge holes, and an enhanced knowledge graph is constructed; calculating semantic path correlation and a path comprehensive score in the enhanced knowledge graph, and screening a candidate resource set; calculating a personalized score based on the candidate resource set, and generating a final recommendation list; and acquiring feedback data of a learner to obtain a learning income index, performing node and edge level attribution analysis, and performing enhanced updating and weight normalization processing on the knowledge graph according to an increment contribution weight to form a new knowledge graph. According to the invention, through knowledge mastering state modeling and map enhancement, automatic identification and dynamic completion of knowledge holes of learners are realized.

The invention relates to the technical field of computer processing, in particular to a learning strategy generation method, electronic equipment and a storage medium, and the method comprises the steps: obtaining an evaluation result after a target user completes individual ability evaluation; performing multi-modal interpretation processing on the evaluation result, and determining an interpretation video of the evaluation result; the interpretation content of the interpretation video comprises knowledge point mastering condition information of the target user, capability condition information of each evaluation dimension, subject potential prediction information and occupational prediction information; and matching the ability condition information of the target user under each evaluation dimension with the content label of the learning content in the learning resource library to generate a learning strategy. According to the method, the interpretation video with the multi-dimensional interpretation content is provided, the understandability of the user to the evaluation result can be improved, meanwhile, the personalized learning strategy is given, the evaluation result is converted into the action scheme, and the quality of learning development guidance can be improved.

The invention relates to the technical field of artificial intelligence and education technology cross fusion, and particularly discloses a new media course intelligent learning resource generation system based on generative AI, and the system obtains the multi-dimensional state data of a learner through the means of eye movement tracking, electroencephalogram collection, interaction behavior analysis and the like, and constructs a high-dimensional state space through fusion; differential induction logic programming and a depth feature extraction network are cooperatively compressed into a low-dimensional semantic meta-state space, and the model interpretability and decision-making efficiency are improved; dynamic matching of personalized resource types and difficulty levels is realized based on a graph neural network and reinforcement learning; calling a multi-modal generation model to generate texts, videos or interactive contents as required, and introducing a cross-modal consistency verification mechanism to guarantee the output quality; the system also has an online adaptive optimization capability, combines a symbol rule recombination mechanism and a differential privacy protection strategy, and takes account of user privacy security while ensuring long-term stable operation of the system.

The invention relates to the field of education, and discloses an interactive feedback system for modern education, which comprises a state monitoring module, a feedback optimization module, a learning recommendation module, a teaching analysis module and a long-term tracking module, the state monitoring module is used for collecting expression, voice and eye movement data of the student in real time to judge the emotional state and understanding condition of the student; the feedback optimization module dynamically adjusts a feedback strategy according to the learning state and presents the feedback strategy to the teacher; the learning recommendation module generates a learning resource list according to individual demands of students and pushes the learning resource list to the student terminals; the teaching analysis module analyzes the data of the student group and provides teaching strategy optimization suggestions for teachers; and the long-term tracking module records learning data of the students and performs time sequence analysis to form a long-term feedback report. According to the invention, real-time feedback, personalized learning support and data-based teaching strategy optimization for students can be realized, and the teaching efficiency and the learning effect of the students are improved.

The invention provides an intelligent course matching and promotion path recommendation method and device based on post portraits. The method comprises the steps of obtaining internal multi-source heterogeneous data of an enterprise and performing preprocessing to obtain a standardized multi-dimensional feature data set; constructing a dynamic post capability portrait by adopting a semi-definite programming method of an asymmetric random block model; analyzing a causal relationship between capability dimensions by applying a linear time primitive algorithm to obtain an employee capability assessment report; a constraint solution algorithm meeting excessive dissipation is adopted to calculate the capability gap matrix to obtain a personalized capability improvement requirement; screening and combining the learning resources through a weighted matching algorithm to obtain a personalized learning recommendation plan; and adopting a promotion path planning algorithm to construct multi-dimensional promotion possibility evaluation to obtain personalized promotion path suggestions. According to the invention, a closed-loop feedback mechanism of learning, evaluation and promotion is realized, and a systematic solution is provided for enterprise talent development.

The invention discloses a personalized learning scheme recommendation method and system based on artificial intelligence, and relates to the technical field of intelligent recommendation, and the method comprises the steps: collecting a learning behavior track and a knowledge mastering progress, carrying out the normalization processing, and generating structural feature input; performing multi-dimensional feature mapping and weight training on the structured feature input by using a reinforcement learning model to generate a learning preference vector; matching the learning preference vector with the weighted feature interaction matrix, calculating an adaptive score, and generating a candidate resource set; and performing iterative fusion on the candidate resource set and the real-time interaction feedback vector to generate a feedback fusion adaptive score matrix, updating adaptive scores by adopting a dynamic adjustment method, forming an optimized resource sequence, converting the optimized resource sequence into a task execution path, and generating personalized learning scheme recommendation. According to the invention, the accuracy of learning resource matching and the timeliness of personalized recommendation are improved.