764 results about "Personalized learning" patented technology

The invention discloses an online teaching interaction method based on a multi-modal knowledge graph, a medium and equipment, and the method comprises the steps: firstly collecting original teaching information, and carrying out the cross-modal semantic alignment processing, and obtaining structured teaching information; then constructing a multi-modal knowledge graph containing text concept entities, video key frame feature vectors and voice text transcription contents, and forming a concept-visual feature association edge, a concept-voice segment association edge and a cross-modal similarity association edge; converting the user interaction behavior data into a knowledge graph query vector, extracting a three-dimensional teaching situation sub-graph from the multi-modal knowledge graph, and generating progressive or comparative teaching content according to a user operation type; and finally, dynamically adjusting a teaching strategy by constructing a cognitive state tracking matrix, and generating a personalized learning navigation map. The problems of knowledge fragmentation and insufficient interaction intellectualization in virtual simulation teaching are solved, and the teaching effect of ideological and political education is improved.

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 discloses a collaborative personalized learning system and method based on a large model, and relates to the technical field of collaborative learning, and the system collects and fuses multi-modal data, generates a student state vector, plans a personalized learning path based on the state and a knowledge graph, and generates multi-modal explanation content according with the student style. The method comprises the following steps: extracting a student reasoning path, identifying thinking deviation, analyzing task performance, generating a path and content adjustment suggestion, identifying a motivation state, triggering an intervention strategy, receiving a teacher strategy, and realizing path and style co-construction. The multi-modal content of the matched style is generated, adaptive intervention is achieved in combination with inference analysis and emotion adjustment, and the learning efficiency and the teaching response intelligent level are improved.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

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 provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

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 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 a teaching display system and method based on a knowledge graph, and a medium, and relates to the technical field of artificial intelligence and education, and the method comprises the steps: regularly collecting multi-modal original teaching data, and constructing a teaching knowledge graph; dynamically updating the relation weight of the teaching knowledge graph; according to the potential capability coefficient of each learning terminal, the distinction degree coefficient and the difficulty coefficient of the plurality of questions corresponding to each knowledge point and the relationship weight between the knowledge points, obtaining a cognitive dependency coefficient between the knowledge points corresponding to each learning terminal; generating a personalized learning path of each learning terminal according to the cognitive dependency coefficient and the learning target; performing interaction attribute addition on nodes in the teaching knowledge graph according to the personalized learning path, and generating a VR virtual scene and visual data; and real-time learning monitoring is performed on the personalized learning path of each learning terminal, and real-time learning feedback operation is performed according to the real-time learning monitoring result, so that the overall teaching quality is remarkably improved.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention discloses an interactive processing method and device based on course management, and relates to the technical field of intelligent education, and the method comprises the steps: collecting student information, carrying out the preprocessing of the student information, and generating an initial learning path according to the preprocessed student information; in the process that the student learns according to the initial learning path, learning behavior data of the student is monitored, and a personalized learning path is generated according to the learning behavior data of the student; monitoring emotion change data, cognitive load data and attention level data of the trainee in the personalized learning path in real time, constructing a learning state evaluation model, and evaluating the current learning state of the trainee; and dynamically generating an immersive learning situation according to the current learning state of the student. According to the invention, the pertinence and utilization efficiency of educational resources are improved, and the enthusiasm and effect of learning are enhanced.

The invention discloses a preschool education field knowledge base construction method fusing a knowledge graph and a large language model, and relates to the technical field of preschool education method optimization. Multi-channel collection of preschool education data and cleaning preprocessing; utilizing a customized model to extract entities and relationships, and constructing a structured knowledge graph; performing fine tuning on the big language model in the preschool education field; fusing atlas and model knowledge through a gating mechanism, and performing dual verification; establishing a monthly updating mechanism to realize incremental learning; in addition, personalized knowledge recommendation is realized based on a user portrait, interactive questions and answers are optimized through combination of retrieval and a model, and data security is guaranteed by adopting anonymization and authority control. Through multi-source data integration and intelligent technology fusion, an accurate and dynamic preschool education knowledge base is constructed, requirements of children are more accurately understood, content fitting cognition is generated, personalized learning recommendation is realized, interaction experience and knowledge service quality are improved, data security is guaranteed, and digital upgrading of the preschool education field is promoted.

The invention relates to the technical field of art education data analysis, and discloses an AI-enabled data-driven art education resource recommendation engine, which is characterized in that user behaviors, resource metadata and context environment data are acquired through a multi-source data acquisition module; the user portrait construction module constructs a multi-dimensional user portrait by using a deep clustering algorithm and an attention mechanism; the resource feature extraction module extracts resource features by means of a graph neural network; the privacy protection processing module uses differential privacy and federated learning to protect data privacy; and the dynamic recommendation generation module generates a personalized recommendation list based on reinforcement learning. The recommendation engine can accurately recommend art education resources meeting user requirements, data privacy is guaranteed, resource association is mined, recommendation strategies are optimized, the quality and effect of art education resource recommendation are effectively improved, and personalized learning requirements of users are met.

The invention provides a multi-modal education evaluation system and method based on super-automation and federal cognitive calculation, and the system integrates multi-source heterogeneous data collection, a federal cognitive calculation engine, an automatic evaluation workflow engine and a privacy enhanced data processing architecture. And comprehensive acquisition, efficient processing, accurate evaluation and safety protection of education data are realized. Through the LSTM-Bayesian hybrid model of dynamic parameter adjustment, intelligent feature engineering, interpretability analysis, reinforcement learning strategy optimization and other technologies, the system can generate personalized learning path planning and an adaptive teaching intervention scheme, and the teaching effect and learning efficiency are improved. And meanwhile, the security and privacy of the education data are ensured by adopting privacy protection technologies such as differential privacy and homomorphic encryption.

Systems and methods for developing personalized instructional content include a computing device using software modules which capture data about subjects, teachers, and learners, train artificial intelligence models based on the acquired data, use the artificial intelligence models to generate instructional content personalized to an instructor and / or learner based their input, compare the generated instructional content to vetted sources and correct errors, and cause the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner.

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 embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

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 provides a personalized learning content recommendation method and system based on neighborhood and hypergraph collaboration, and relates to the technical field of content recommendation. The method comprises the following steps: constructing a hypergraph view and a neighbor graph view by obtaining user session sequence data; extracting global high-order relation features based on the hypergraph view; extracting local co-occurrence relation characteristics based on the neighbor graph view, and generating enhanced co-occurrence relation characteristics; embedding and splicing a sequence position code of a user learning path and learning content semantics, fusing context information through a gating dynamic attention mechanism, and generating a dynamic learning interest embedding expression; based on a global-local contrast learning strategy, performing nonlinear projection on dynamic learning interest embedding and global semantic embedding, and optimizing semantic consistency of the two types of embedding by comparing loss; and combining the cross entropy loss and the contrast learning loss of the recommendation task, constructing a combined optimization objective function, generating a personalized recommendation result, and realizing the technical effect of capturing dominant and implicit relationships between learning contents.

The invention discloses an intelligent oral English training system and method based on Prompt engineering, and relates to the technical field of oral English training. According to the method, a user is allowed to freely describe training requirements by using a natural language, the system automatically constructs a training Prompt, and personalized customization is realized; content deviating from a training target is accurately recognized through a deviation question detection module, a user is guided to return to a theme through real-time voice or text, and effectiveness and pertinence of training are guaranteed; a clear template definition ensures systematicness and reproducibility of a training task, and clear control and evaluation of a teaching target are facilitated; in combination with voice recognition, voice synthesis and text generation, natural interaction in a real context is realized, and the training experience is improved; on the basis of multi-dimensional automatic evaluation of a keyword hit rate, pronunciation quality and the like, personalized learning suggestions are supported; teachers are supported to generate training tasks in batches, students are supported to customize scenes, and different education environment requirements are met.

The invention belongs to the technical field of new energy automobile teaching, and particularly relates to a VR interactive new energy automobile high-voltage electrical principle teaching method, system and equipment, and the method comprises the steps: obtaining the physical parameter data and dynamic circuit simulation data of a new energy automobile high-voltage electrical system; constructing a high-precision 3D model according to the physical parameter data, inputting dynamic circuit simulation data into the model, and generating interactive principle annotations and particle effect animations; based on a gesture recognition model and a new energy automobile VR interaction technology, circuit state changes in the particle effect animation are obtained, a new energy automobile high-voltage system principle and a signal flow are displayed, a practical operation simulation training scene is constructed, student operation data are recorded according to the practical operation simulation training scene, an AI evaluation model is trained, and an evaluation report is obtained. And adjusting interaction feedback parameters of the training scene, and meanwhile, generating a personalized learning report of the trainee. Therefore, the problems of safety risk, teaching lag, abstraction obstacle and the like in the prior art are solved.

The invention discloses an error diagnosis classification and intelligent review planning method based on multi-dimensional analysis, which is suitable for a personalized learning support system. The method comprises six steps of error collection, automatic labeling, error attribution, capability evaluation, knowledge point correlation analysis and dynamic review planning, and integrates OCR recognition, TF-IDF feature extraction, convolutional neural network classification, knowledge graph modeling and Q-learning intelligent strategies. The system can realize accurate classification and reason diagnosis of wrong questions based on answering behaviors and cognitive features of students, optimizes a review plan in combination with a forgetting curve and habit error, supports an online and offline mixed review mode, and significantly improves learning efficiency and personalized adaptation ability.

The invention provides a learning task generation method based on historical answering and real-time evaluation data, and the method comprises the steps: firstly constructing an initial state vector of a student, extracting a knowledge point range, building an association network, and then evaluating a current mastering state of the student through a probability graph model; for weak knowledge points, a greedy strategy optimization algorithm is adopted to generate a task sequence, and grading is performed according to difficulty and content depth; dynamically adjusting task difficulty and a content presentation sequence in combination with learning preferences of students, and generating a customized task sequence; through a real-time feedback mechanism, student state vectors are continuously updated, the mastery degree is re-evaluated, and a task sequence is optimized; finally, a personalized task set is generated by considering time constraints. According to the invention, accurate evaluation of the learning state of the student and dynamic optimization of the task sequence are realized, and the effect of personalized learning is effectively 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.

According to the student personalized learning path generation method based on the neural network, a data acquisition module is deployed on a teaching platform, a learning terminal and an interactive interface, and multi-dimensional data such as student learning duration, interactive frequency and wrong question records are acquired in real time. A knowledge graph is constructed based on a course outline and a prior textbook structure, and a graph neural network GNN is utilized to dynamically adjust association weights of nodes and edges of the graph neural network GNN to reflect weak knowledge areas of students. A Bi-LSTM encoder is constructed for the collected data to generate a behavior feature vector, and the behavior feature vector and a knowledge graph node embedded vector are aligned and fused through an attention mechanism to obtain student personalized multi-modal representation. And taking the representation and the knowledge graph node weight as an environment state, selecting a next knowledge point to be learned as an action space, designing a reward function by integrating multiple indexes, updating a target by using a deep Q network, and training a strategy network to generate a personalized learning path. The trained strategy is deployed as micro-service, a path is displayed according to knowledge module sorting, and related parameters are iteratively updated according to student feedback to improve the recommendation effect.

The invention discloses a writing brush calligraphy practice correction system based on real-time handwriting analysis. The system comprises a sensing layer used for collecting handwriting tracks, physiological signals, environmental parameters and ink mark characteristics; the edge calculation layer is used for executing noise filtering, coordinate system normalization, multi-modal data alignment and feature primary extraction through an Apache Kafka data pipeline; and the algorithm analysis layer is used for carrying out super-long calligraphy stroke sequence modeling by adopting an S4 architecture time sequence model, introducing a Neural ODE module for modeling, capturing dynamic characteristics in a continuous pen wielding process, constructing a multi-physics-field coupled PINN framework, constraining neural network prediction through a physical loss function, deploying a dual-stage characteristic extractor to extract high-order characteristics, and extracting the high-order characteristics. Book style features are extracted, and the current practicing book of the user is classified in real time; the intelligent correction layer is used for generating a dynamic correction suggestion, constructing a personalized learning path, realizing self-adaptive scoring and providing aesthetic dimension feedback at the same time; and the user interaction layer is used for providing an AR correction interface.