441 results about "Learning behavior" patented technology

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention provides a course reconstruction method and system based on a knowledge graph, and relates to the technical field of intelligent education. The method comprises the following steps: collecting course resource data and student learning behavior data; constructing a knowledge graph fusing multiple courses; generating a personalized learning path based on the atlas and the behavior data; arranging experiment training tasks according to a path and collecting feedback data to update a graph state; and converting the student training result into a technical manuscript in a structured manner and accessing the technical manuscript to a management platform. Closed-loop optimization of teaching resource organization, path recommendation and achievement management is realized.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention relates to a personalized scientific education system based on AI agent driving, and belongs to the technical field of informatics. The education system runs a main agent, a user twinborn agent, a learning planning platform and a graph structure database. The main agent is used for collecting knowledge states and learning behaviors of users, constructing a user model, and training one or more user twin agents based on the user model. And the user twinborn agent is used for simulating learning behaviors and results of the user under different learning paths. And the learning planning platform combines the user model and the knowledge graph stored in the graph structure database to generate a plurality of candidate learning paths for the twin intelligent agent of the user to perform analogue simulation. And the main agent selects an optimal path according to a simulation result and implements the optimal path to the personalized teaching process of the user. According to the technical scheme, high-adaptability learning path recommendation and dynamic adjustment are realized, and the intelligent level of individualized teaching is improved.

The invention discloses a student user portrait construction method based on multi-modal data fusion and a terminal. The method comprises the following steps: acquiring learning behavior data, social behavior data and hobby and interest data of a user, and respectively extracting corresponding feature information; calculating the fusion weight of the feature information of each mode and generating a comprehensive feature vector; and on the basis of the comprehensive feature vector, performing student user portrait construction by using a clustering algorithm, an analytic hierarchy process and knowledge graph analysis. According to the method, data features of learning behaviors, social behaviors and hobbies and interests are extracted, modal information is synthesized by using a fusion weight mechanism, and finally a multi-dimensional and fine-grained student portrait is formed. Compared with a traditional single data source modeling method, the method has the advantages that the comprehensiveness, the accuracy and the personalized adaptive capacity of portraits are remarkably improved, and various application scenes such as personalized teaching recommendation, academic early warning and interest expansion activity matching can be effectively supported.

The invention provides a recommendation method and system fusing big language model reasoning and multi-source trajectory information, and the method comprises the steps: firstly obtaining user historical learning behaviors and static attribute information after receiving a user recommendation request, and constructing a static interest vector; in combination with the initial feature vector of the learned knowledge point and the map enhancement vector of the first-order neighbor node of the knowledge map, generating explicit and map extension interest vectors, and fusing to obtain a user interest vector; screening N unlearned knowledge points to form a candidate set through similarity analysis of user interest vectors and unlearned knowledge point vectors and / or reasoning of a large language model on user association information; and screening the target knowledge points through mastery degree verification of the pre-modified knowledge points, and outputting a recommendation result after sorting. According to the method, recommendation accuracy and suitability are improved, and personalized learning requirements are met.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

The invention discloses an intelligent agent-based large model education risk problem generation method, which is characterized in that a dynamic student intelligent agent is constructed to simulate a real learning behavior, and a potential risk problem in an education scene is automatically identified and generated in combination with the generation capability of a large language model; the method specifically comprises the steps of constructing a multi-dimensional student agent, constructing a risk problem generation rule base, generating potential risk problems and risk grade classification, optimizing risk problem generation quality, establishing an educational risk problem database, continuously updating and iterating and the like. Compared with the prior art, the method has the advantages that the safety and reliability of an AI education product are remarkably improved, the risk identification accuracy is remarkably improved along with the use time, an original solution is provided for safe development of the education science and technology field, healthy development of artificial intelligence in education application is guaranteed from the technical source, and the method is worthy of popularization and application. The method is widely applied to the fields of education content security auditing, self-adaptive learning system risk prevention and control and the like, and has good application prospects.

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 the technical field of data processing, and provides an AI fool-proof algorithm and digital model optimization method and system, and the method comprises the steps: collecting multi-modal process data, carrying out the time alignment and event segmentation, obtaining a standard behavior sequence and an abnormal behavior sequence, inputting a preset deep learning behavior prediction model according to the standard behavior sequence, and carrying out the prediction of an abnormal behavior. And carrying out structure matching on the behavior prediction result and the abnormal behavior sequence to obtain an optimized instruction set and semantic prompt information, and then optimizing a preset deep learning behavior prediction model to obtain an optimized fool-proof intelligent prediction model. By optimizing an original deep learning behavior prediction model, the adaptive recognition capability and risk avoidance capability of the model for abnormal behaviors are improved, and the problem that in a complex practical application environment, high-frequency operation environment changes cannot be flexibly processed, so that part of high-risk behaviors cannot be recognized or corrected in time, and the risk of abnormal behaviors is reduced is solved. And the problems of low accuracy and poor adaptability exist.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

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 relates to the technical field of education intelligent analysis, and particularly discloses a learner cognition level fine-grained tracking method and system based on a state space model. The method aims at solving the problems that an existing knowledge tracking method is low in cognitive level modeling granularity, weak in long sequence processing capacity, poor in educational interpretation and the like. According to the method, a Bloom cognitive classification system and a state space modeling technology are combined, and fine-grained and multi-level dynamic modeling and future learning performance prediction of the knowledge mastering state of the learner are achieved. The core steps of the method comprise: constructing a semantic mapping relationship between knowledge points and cognitive hierarchies (S101); collecting and encoding multi-source learning behavior features (S102); learning a cognitive state evolution trajectory based on the state space model (S103); and outputting the cognitive hierarchy classification and the answer performance prediction (S104). According to the method, by fusing the multi-dimensional learning behavior data and the state space modeling capability, the accuracy and personalized analysis depth of cognitive tracking are improved, and technical support is provided for precise teaching and intelligent decision making.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path planning method fusing artificial intelligence and education big data, and the method comprises the steps: collecting multi-modal education data of learning behavior data, cognitive process data and emotional state data from a learning terminal; constructing a learner portrait feature vector based on the preprocessed multi-modal education data; establishing a knowledge graph, and dynamically updating a weight matrix based on group learning data; and taking the learner portrait as a state space, taking a learning path decision as an action space, taking a learning effect as a reward function, generating a personalized learning path by adopting near-end strategy optimization, optimizing a path planning model, and generating a recommendation result. Personalized learning path planning is realized by constructing a multi-modal data fusion framework, a dynamic knowledge graph and an intelligent planning method.

The invention discloses an intelligent personalized topic recommendation method based on a knowledge graph, and relates to the technical field of information retrieval, and the method comprises the steps: constructing a multi-dimensional subject knowledge graph which defines knowledge point entities and topic entities through the knowledge graph, and establishes structural relationships and capability dimension attributes between the entities; forming a structured knowledge basis for recommendation; collecting learning behavior data of the user, and generating a user knowledge state model for dynamically evaluating the knowledge state of the user in combination with the multi-dimensional subject knowledge graph; selecting a corresponding recommendation strategy according to an output result of the user knowledge state model, and performing multi-dimensional question matching based on capability dimension matching based on the multi-dimensional subject knowledge graph to generate a personalized question recommendation scheme; and dynamically optimizing the multi-dimensional subject knowledge graph, the recommendation strategy and the matching rule based on the feedback of the user to the recommendation scheme. The problems that a traditional recommendation system is shallow in knowledge association, rough in diagnosis and rigid in strategy are solved.

The invention relates to an AI-based teaching resource intelligent recommendation method and system. The method comprises the steps of obtaining and performing cleaning and standardization processing on a multi-source data set to obtain standardized data, and constructing a knowledge graph; based on the standardized data, learning behavior characteristics of the students are extracted, a clustering algorithm is adopted to generate dynamic tags associated with a preset scene, and student portraits are obtained; based on the knowledge graph, performing semantic annotation on preset teaching resources, and dividing resource difficulty levels in combination with a post competency model to form an annotated resource library; and based on the student portrait, identifying a student learning demand, and according to the student learning demand, matching the resources in the labeled resource library to generate a personalized resource recommendation list. According to the method, by integrating the multi-source data and constructing the knowledge graph, the learning requirements of students can be accurately identified, intelligent recommendation of personalized teaching resources is realized, and the utilization efficiency of the teaching resources is also improved.

The invention relates to the technical field of information, and discloses an intelligent adaptive education training system based on big data, and the system comprises a data collection and integration module which is used for collecting learning behavior data and physiological data of learners from a plurality of education platforms and physiological data sources; the dynamic student portrait generation module is used for automatically generating and updating a dynamic student portrait according to the learning behavior data and the physiological data collected by the data acquisition and integration module; the personalized feedback and learning path optimization module is used for generating personalized learning feedback according to the dynamic student portrait and automatically adjusting a learning path by utilizing a multi-objective optimization algorithm; the system implementation and optimization module is used for detecting operation of the data acquisition and integration module, the dynamic student portrait generation module and the personalized feedback and learning path optimization module for integration, testing and optimization; and the data acquisition and integration module comprises a multi-source data integration unit.

The invention provides an artistic course personalized learning path method and system based on deep learning. Firstly, an artistic course learning basic information set of a learner and a preset artistic course module library are acquired; calling a pre-trained deep learning course dynamic association model to generate association strength description of the learning basis of the learner and each course module; screening and sorting based on association strength description to form an initial learning module sequence; acquiring a real-time learning behavior data set of the first course module learned by the learner, and generating a module adjustment signal; and adjusting the initial learning module sequence according to the module adjustment signal to obtain a personalized learning path adaptive to the real-time learning state of the learner, and generating a personalized learning path document comprising a course module learning sequence and module connection guidance, thereby realizing accurate customization and dynamic optimization of the learning path, and improving the learning efficiency. And the learning effect of artistic courses is improved.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students 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 discloses a knowledge graph teaching path dynamic recommendation method and system based on reinforcement learning. The method comprises the following steps: constructing a cognitive adaptive dynamic knowledge graph; obtaining an individual multi-dimensional state vector of a student to be recommended; obtaining a trained student state perception model and a trained teaching path reinforcement learning model; inputting the individual multi-dimensional state vector of the to-be-recommended student into a trained student state perception model to obtain cognitive state information of the to-be-recommended student; and calling the trained teaching path reinforcement learning model by taking the cognitive adaptive dynamic knowledge graph as an environment and combining cognitive state information of the to-be-recommended student, so as to obtain a current personalized teaching path recommendation result of the student individual. The teaching content sequence can be dynamically adjusted according to the knowledge state and the learning behavior of the student, and the teaching integrating degree is improved; the teaching path is continuously optimized through learning, and different types of students can be adapted.

The invention relates to the technical field of data analysis, in particular to a cloud-based teaching platform student learning behavior trajectory analysis method, which comprises the following steps: acquiring a student interaction log, extracting task completion and control operation discontinuity points, identifying continuous learning behaviors and switching types, constructing a candidate period sequence, analyzing time and label changes, and generating a trajectory analysis result. According to the method, the non-interaction time period of the student in task switching can be identified by collecting the time interval between task completion and first control operation, so that the potential attention distraction or task interruption behavior of the student is captured, and the interaction characteristic and the duration of the continuous behavior segment are combined; and active and inactive learning period switching characteristics are further marked, and a task label difference degree and a time interval are introduced in the process of constructing a learning period sequence as a screening basis, so that the analyzed learning paragraph is ensured to have content continuity and behavior pattern difference, and the recognition capability of rule switching behaviors in trajectory analysis is enhanced.

The invention relates to the technical field of intelligent teaching, and provides a self-adaptive personalized teaching system based on AI and a knowledge graph, and the system comprises a knowledge graph construction module, a student portrait module, a self-adaptive recommendation module, a teaching interaction module, and a management module. The knowledge graph construction module comprises a data processing unit, a knowledge extraction unit and a graph storage unit; the data processing unit is used for carrying out preprocessing such as word segmentation and stop word removal on the teaching text; and the knowledge extraction unit is used for extracting knowledge point entities and relationships between the entities from the preprocessed text through a natural language processing technology. The knowledge points are subjected to fine-grained modeling through the knowledge graph, the AI algorithm is combined to analyze student learning behaviors and evaluation data, knowledge vulnerabilities and learning characteristics of students can be accurately captured, the error rate is lower than that of a traditional method, the self-adaptive recommendation module generates personalized learning paths based on the reinforcement learning algorithm, and the learning efficiency is improved. And repeated learning of students on invalid knowledge points is avoided.

The invention provides an AI intelligent education method and system based on a knowledge graph, and the method comprises the steps: extracting a knowledge entity and teaching semantic relation from multi-source education data, and constructing a multi-dimensional education knowledge graph containing a dynamic weight feature vector; learning behavior data of students are collected, knowledge point mastery degree is calculated based on a reaction theory, dynamic cognitive state vectors including cognitive loads and forgetting laws are generated in combination with time sequence data, and a personalized cognitive state model of the students is constructed according to the dynamic cognitive state vectors; a self-adaptive learning path is generated based on the map and a cognitive model, intervention is triggered when learning deviation is monitored, and a compensation learning sub-path is generated; and executing the compensation path, and carrying out synchronous iteration updating on the entity weight, the relationship and the cognitive model parameters in the knowledge graph based on a result. According to the system, precise personalized teaching and dynamic path adjustment are realized, and the teaching suitability and the learning effect are continuously improved through closed-loop optimization.

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention provides an intelligent teaching assisting system, a learning path optimization method, equipment and a medium, and the method comprises the steps: collecting the multi-channel learning behavior data of a student during the personalized learning interaction with an intelligent teaching assisting terminal through a multi-modal sensor, and obtaining a sequence dependency relationship between learning content units based on a preset teaching knowledge graph; for each teaching stage, learning behavior features are extracted from the multi-channel learning behavior data, structured feature vectors are constructed based on inherent attributes of learning content units, and joint representation vectors are generated through multi-modal fusion; aggregating the joint characterization vector to generate a teaching stage characterization vector, and evaluating a teaching effect based on the teaching stage characterization vector to obtain a learning state deviation degree; and screening teaching stages with substandard teaching effects by using the learning state deviation degree, and optimizing and adjusting a learning path based on a sequence dependency relationship. By adopting the scheme of the invention, closed-loop dynamic optimization of the intelligent learning path can be realized.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention discloses a multi-agent collaborative dynamic target interception decision-making method based on reinforcement learning. The method comprises the following steps: initializing an environment and agents; predicting the state of the intercepted target; distributing a global optimal task based on the predictive interception cost of the multi-agent; constructing an autonomous control model based on a near-end strategy optimization algorithm PPO for each agent, and designing a state representation vector of future collision perception; inputting the state representation vector into a neural network of the intelligent agent, and outputting an action instruction; designing a multi-objective reward function for guiding the intelligent agent to perform behavior learning in a training process; the intelligent agent provided by the invention has a higher-level strategy. Through finer state representation and more complex reward function design, the intelligent agent trained through PPO not only can accurately execute a pursuit task on a target, but also can learn advanced strategies such as autonomous obstacle avoidance, energy consumption reduction, efficiency improvement and the like.

The invention discloses an education robot teaching strategy generation system based on learning behavior big data analysis, and particularly relates to the technical field of intelligent education. The method comprises the following steps: acquiring learning behavior data of students in an education robot teaching process; identifying the active adaptation behavior of the student, and generating an active adaptation behavior identification result; based on the active adaptation behavior recognition result, analyzing a response relationship between the existing teaching strategy and the student learning behavior, and outputting a teaching strategy adaptability analysis index; according to the teaching strategy adaptability analysis index, the deviation degree of the existing teaching strategy deviating from the real learning requirement is evaluated, and a teaching strategy deviation evaluation result is obtained; an adaptive feedback suppression constraint and a strategy depolarization weight are constructed to generate a teaching strategy candidate set, screening and confirmation are carried out according to a consistency verification rule and a robustness threshold, an education robot teaching strategy is updated, and an education robot teaching execution instruction is formed. And the scientificity and individuation level of the teaching strategy are improved.

The invention provides a knowledge graph implementation method, device and equipment and a readable storage medium, and the method comprises the steps: automatically mining an actual use path between knowledge points from the analysis of a real topic by utilizing the appearance mode and the appearance sequence of the knowledge points in the analysis step of the real topic and combining the joint modeling of bidirectional co-occurrence and a unidirectional time sequence relationship, and obtaining the actual use path between the knowledge points; the implicit dependency and cognitive path between knowledge points are accurately captured, a dynamic knowledge graph with cognitive rationality is generated, the accuracy and rationality of knowledge association are improved, the knowledge graph is closer to real learning behaviors, and the learning efficiency is improved. And the knowledge graph is further converted into a transfer matrix capable of predicting student mastering evolution to simulate a process of knowledge diffusion along a reasonable path, so that dynamic tracking and accurate prediction of a student knowledge point mastering state are realized, and a basis is provided for personalized learning path recommendation.