7043results about "Electrical appliances" patented technology

A ground visual question-answering method based on dynamic dual-level visual information fusion includes using a dual-level multiscale network, which is divided into language-guided pixel-level features and region-level features. These two scale branches are combined to predict the final textual answer and ground answer. Furthermore, a question-guided dynamic region-level feature localization network is proposed to locate visual information guided by the question and adaptively assign masks of different sizes to ground answers, thereby enhancing the accuracy of locating and segmenting small targets. Additionally, a cross-modal aggregation module is designed to fuse features from both levels, enhancing the fusion of pixel-level and region-level features to improve the segmentation effect of ground answer masks' edges. The ground visual question-answering system built by the language-guided adaptive dual-level feature fusion network in this invention can effectively improve the accuracy of the entire model while answering questions and generating answer ground masks simultaneously.

The present invention provides a deep learning-based natural language understanding method and an intelligent teaching assistant system. First, it involves constructing a knowledge database and a question database, where learning material documents are saved into the knowledge database and preprocessed natural language information is saved into the question database. The method then involves learning and understanding the natural language information in the question database, searching for related knowledge points in the knowledge database based on the understood content, selecting the best-matched learning materials corresponding to these knowledge points as samples to respond to the natural language information, and generating a record that includes the question, response, and evaluation, which is saved into the knowledge database. Finally, it generates multiple forms of responses and outputs them according to the corresponding requirements.

Provided is a system of generating multi-style learning tutorials based on learning preference evaluation. The system dynamically explores preferences of learners in a learning process from a time dimension through a proposed deep reinforcement learning model, so that a deep reinforcement learning agent can accurately express learning behavior features of learners and realize the personalized and accurate construction of course tutorials. At the same time, using an internal knowledge base and an external knowledge base as resource support, a multi-modal knowledge map with a plurality of definition relationships is designed to overcome the limitations of construction of a course tutorial method.

The invention relates to the technical field of online network teaching, and discloses an online network teaching practice method based on a virtual technology, and the method comprises the steps: obtaining virtual scene data, and carrying out the integration and building of a dynamic knowledge graph and a multi-modal teaching resource library; learning behavior features are extracted according to the cognitive features of the learner, knowledge difficulty levels are divided, and immersive learning is guided; constructing a cognitive evaluation model, optimizing a learning path through hierarchical reinforcement learning, and generating a self-adaptive teaching instruction; collecting interaction track data to generate an operation thermodynamic diagram, and evaluating the skill mastering degree; complexity parameters of the virtual scene are dynamically adjusted, a virtual-real linkage experiment simulation environment is constructed, and skill training closed-loop feedback is achieved. The invention further relates to specific implementation modes of resource integration, cognitive feature extraction, learning path optimization and the like. According to the method, the personalized and immersive experience and skill training effect of online teaching are improved, and the method is suitable for the field of online network teaching.

The invention provides a middle and primary school multi-person foreign language situational teaching method and system based on VR, and relates to the technical field of virtual reality, and the method comprises the steps: obtaining a teaching task, constructing an immersive virtual teaching scene, setting a situational dialogue task and a self-adaptive difficulty adjustment mechanism, carrying out the semantic description and association mapping to form a multi-level semantic network, and carrying out the virtual teaching. Each student enters an immersive virtual teaching scene, participates in a scene dialogue task, collects multi-modal interaction data and transmits the data back to the edge computing server to obtain mother language voice of the student, translates the mother language voice to obtain a high-quality dialogue text and generates personalized dialogue feedback; performing multi-dimensional quantitative evaluation based on personalized dialogue feedback, generating an ability level portrait, determining the knowledge point mastering degree of each student, generating an knowledge mastering graph, constructing a language learning path optimization model, solving to obtain an optimal learning path, and generating an optimized teaching scheme.

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

According to an aspect of the present invention, there is provided an electrical audio signal processing system and device, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

Technical solutions are directed to creation and deployment of multi-stage test problems. A processor can provide, to a client, a first data structure for a first stage of a multi-stage test problem including a description for generation of a client solution for the first stage and a test case to evaluate the client solution. The processor can receive, from the client, a client data structure comprising the client solution including a client computer code generated at the client for the first stage. The processor can determine, by evaluating the client solution using an input value and an output value of the test case, that the output of the client solution satisfies a validity condition of the output value. The processor can provide, to the client, based on satisfying the validity condition, a second data structures for a second stage of the multi-stage test problem.

The invention relates to the technical field of traditional Chinese medicine acupuncture and moxibustion teaching, in particular to a multi-view fused traditional Chinese medicine acupuncture and moxibustion visual teaching system which comprises a basic data construction module, an immersive environment building module, a multi-view interactive teaching module, a collaborative annotation conflict processing module, an annotation result auditing module and a model and data optimization module. Compared with the prior art, the traditional Chinese medicine acupuncture and moxibustion visual teaching method has the advantages that a new traditional Chinese medicine acupuncture and moxibustion visual teaching paradigm of data-driven cognition, interactive verification logic and intelligent optimization iteration is constructed through a closed loop of'problem definition-technical breakthrough-effect guidance ', space and modal limitations of traditional teaching are broken through, and the traditional Chinese medicine acupuncture and moxibustion visual teaching method is suitable for popularization and application. In addition, deep fusion of traditional Chinese medicine inheritance and modern medical education is achieved, and a reproducible digital solution is provided for standardized and precise acupuncture and moxibustion teaching.

A system for implementing blended learning, including: • a central processing unit (CPU) configured to receive, process and store data from a range of user interfaces, learning management software modules, classroom interaction devices and cloud-based data storage; • an AI engine integrated into the CPU, configured to analyze students' interactions with educational content, classroom activities, and performance data in real time, with the AI ​​engine applying machine learning techniques to create personalized learning paths for each student based on their individual learning style, past performance, and engagement patterns; • A range of devices for classroom interaction, including at least one smartboard, tablet, or interactive projector, each configured to capture real-time data on classroom activities such as student participation, oral responses, and collaborative tasks. The captured data is then transferred to the CPU for analysis by the AI ​​engine to refine and dynamically adapt learning paths for each student; • a student user interface, wherein the student interface displays dynamically generated content tailored to the student's progress and performance, and wherein the content includes multimedia materials such as videos, tests and reading material adapted based on the student's interaction history and learning needs; • a CPU-connected teacher interface, wherein the teacher interface displays real-time analytics and insights from student data, including performance trends, engagement levels, and potential learning gaps, and wherein the AI ​​engine provides the teacher with actionable recommendations for adapting teaching strategies, resources, and interventions based on the individual needs of the students and their performance in class; • a cloud-based storage system, whereby the system securely stores student profiles, historical performance data and learning content, and whereby the CPU retrieves, updates and stores data from the cloud-based system in real time to ensure continuous adaptation of the learning experience.

The invention relates to the technical field of power system operation training, and discloses a virtual reality-based power grid fault simulation training system and method, and the system comprises a virtual scene modeling module which is used for constructing a three-dimensional model of power grid equipment and an operation environment thereof, and providing a vivid training environment for subsequent modules; and the interaction and operation module is connected with the virtual scene modeling module and realizes interaction between the trainee and the virtual environment by using a VR head-mounted display, a handle and a motion capture device. By adopting a high-precision three-dimensional modeling technology, PBR material rendering and an LOD optimization strategy, high simulation of power grid equipment and an operation environment is realized, students can experience a vivid visual effect and immersion in a virtual reality scene, and meanwhile, by combining a gesture recognition technology realized by motion capture equipment and a convolutional neural network, the real-time simulation of the power grid equipment and the operation environment is realized. Operation actions of trainees can be accurately captured, a natural and visual interaction mode is provided, and the authenticity and operation experience of the training system are greatly improved.

In accordance with the present disclosure, systems and methods are provided for providing personalized education to users. the systems and methods may collect, process, and analyze various user information including data from compatible IoT devices using a composite AI model. The systems and methods may generate a personalized education profile for the user using the composite AI model based on the analyzed user information. The systems and methods may generate and deliver personalized education content aided by the use of the composite AI model. the system may also monitor and receive user feedback in real-time for updating the composite AI model, the personalized education profile, and the personalized education content.

An apparatus for data ingestion and manipulation, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a resource data file from one or more data acquisition systems, classify the resource data file to one or more educational categorizations, generate an educational module as a function of the resource data file and the classification of the educational categorizations wherein the education module comprises one or more machine learning models, retrieve a user profile of a plurality of user profiles as a function of a user input, create user-specific outputs as a function of the educational module, the user profile, and a conversational input and generate a virtual avatar model as a function of the user-specific outputs.

A virtual reality (VR)-based system for improving students' reading comprehension and writing skills. The system includes: a VR headset with an integrated eye-tracking system, high-resolution stereoscopic displays and spatial audio output, where the eye-tracking system detects reading patterns, fixation points and saccadic movements to assess reading engagement; a pair of motion tracking gloves with embedded pressure-sensitive capacitive sensors and haptic feedback actuators, allowing students to work with virtual reading material, perform handwriting exercises, and receive tactile reinforcement of text-based interactions; an adaptive learning module based on artificial intelligence (AI) that is configured to dynamically adjust reading complexity and writing exercises, responding to real-time metrics for interaction with students, with the AI ​​module using deep learning-based natural language processing (NLP) models to analyze reading fluency, written coherence, and syntactic complexity; a speech recognition and synthesis subsystem comprising a neural network-based acoustic model and a phoneme segmentation processor, wherein the subsystem assesses pronunciation accuracy, fluency, and articulation deviations by analyzing phonetic waveforms of spoken responses; a multimodal interactive interface that enables students to interact with virtual reading environments via a combination of hand tracking, voice commands and eye-controlled text selection, with the interface allowing students to highlight, comment on and orally summarize text content; A real-time dashboard for teacher monitoring, which is communicatively connected to the VR system. The dashboard offers educators detailed analyses of student comprehension patterns, writing progress, and AI-generated adaptive learning recommendations; and a cloud-integrated content management system (CMS) configured to store AI-curated reading materials, personalized writing assignments, and NLP-processed insights into student performance, with the CMS enabling adaptive curriculum adjustments based on students' learning trajectories.

The invention discloses an acute and critical disease nursing training system and method based on vr, and relates to the technical field of medical systems. A learning mode and an assessment mode are selected through a handle trigger; in the learning mode, the nursing operation process is dynamically displayed through vr glasses and a scene generation engine, and the scene generation engine constructs a disease evolution path based on a knowledge graph and associates user historical data to match personalized teaching cases; receiving a selection instruction of a user for an assessment item in the assessment mode, receiving an operation instruction of the user at the same time, recording time sequence data of an operation path and selecting a behavior logic chain; generating a score according to a dynamic scoring algorithm including an operation track accuracy parameter and a decision timeliness parameter; the user is personally on the scene to carry out nursing training and examination on acute and critical diseases, and trial and error in a highly-simulated critical scene are carried out; a targeted training scene is intelligently pushed according to the specialty and ability of the user; and the speed and quality of acute and critical disease nursing are balanced through multi-dimensional scoring.

The invention discloses a digital intelligent teaching recording and broadcasting system and method, and relates to the technical field of data encryption, and the method comprises the steps: collecting teaching videos and audios of a teacher through a high-definition camera and an audio device, carrying out the preprocessing, obtaining the course content, recognizing the key information of the course content through a natural language processing NLP and computer vision technology, and carrying out the recording and broadcasting. The method comprises the following steps: segmenting a video into single knowledge point fragments, performing classified storage, forming a learning resource library, collecting basic information and learning habit data of students, constructing a user portrait, matching learning resources, extracting the learning habit data in the user portrait, performing feature extraction, and obtaining a comprehensive feature vector; constructing a learning state evaluation model by using a convolutional neural network to comprehensively analyze the comprehensive feature vector to obtain a learning state comprehensive score, formulating a playing strategy according to the learning state comprehensive score and matched learning resources, regularly testing knowledge points of students, evaluating the mastering degree of the students, and updating a user portrait according to a result.

The invention relates to the technical field of physical education, in particular to a physical education method and system based on multimedia and holographic AR fusion, and the method comprises the following steps: collecting the motion data of a trainee based on a multi-source sensor array, constructing a motion parameterized model, and generating a holographic projection generation module through the cooperation with a standard motion database; a holographic AR teaching image is generated in real time, an environment perception module is combined to carry out adaptive projection on illumination and spatial layout, a spatial positioning module is used to establish coordinate mapping between a trainee and a virtual image, and a multi-channel feedback device is started to output visual, tactile and auditory signals. And finally, comparing the action parameters with the standard action data to automatically generate a training report containing wrong action labels and correction suggestions. According to the method, the motion capture precision and the interactive immersion can be effectively improved, and efficient and extensible technical support is provided for modern physical education and training.

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

Disclosed herein are methods, systems, and computer-readable media for causing a machine learning model to generate improved answer data based on an extended reality environment. In an embodiment, a method may include receiving the query, identifying at least one extended reality component associated with the extended reality environment as relating to the query, the at least one extended reality component comprising at least one of an object, a recording, or transcript information, and generating a prompt based on the query and the at least one extended reality component. The method may further include transmitting the prompt to a machine learning model, in response to the transmitted prompt, receiving answer data from the machine learning model, and based on the received answer data, generating content in the extended reality environment.

The invention discloses an education experience system based on a virtual reality technology, which relates to the technical field of virtual reality and comprises a data acquisition module, a virtual reality content creation module, an interaction module, a data analysis module, an evaluation module and a central control module. The data acquisition module acquires and processes teaching materials; the virtual reality content creation module constructs a teaching scene according to materials; the interaction module generates a plot by means of a specific architecture, and interacts with a user by combining action, voice and touch units; the data analysis module collects multi-dimensional learning data, carries out deep analysis and then outputs a result; the evaluation module evaluates the learning effect and the teaching suitability by means of the analysis result and the user performance; the central control module coordinates data interaction and instruction transmission of the related modules as a whole and assists education development together.

The invention relates to the technical field of intelligent teaching, in particular to an intelligent teaching system for traditional Chinese medicine clinical skill cultivation. The system comprises a virtual reality simulation module, an operation interaction module, an operation data analysis module, an adaptive learning feedback module and a teaching evaluation module. The virtual reality simulation module constructs a vivid traditional Chinese medicine skill training scene through three-dimensional modeling, ray tracing and physical simulation technologies, and the vivid traditional Chinese medicine skill training scene covers pulse feeling, acupuncture and moxibustion, massage and other operations. The operation interaction module collects user operation data and extracts features in combination with a variational auto-encoder. The operation data analysis module analyzes user operation deviation by using a graph neural network and a generative adversarial network, the adaptive learning feedback module generates a personalized learning strategy, and the teaching evaluation module evaluates a learning effect through fuzzy evaluation and analytic hierarchy process. According to the invention, intelligence, individuation and high efficiency of traditional Chinese medicine clinical skill cultivation are realized, the fidelity of teaching and the accuracy of feedback are improved, and the skill level and the training effect of learners are also improved.

The invention discloses a large-model intelligent child interaction cloud system and a control method thereof, toys and audio and video players are connected with a cloud server through a wireless network, personalized role interaction content can be acquired and updated in real time, and interaction content conforming to identity characteristics of each toy role is generated for each toy role through a cloud agent. The cloud server has a long-term memory ability, and the interaction between the user and the toy role is completely memorized and a dynamically updated knowledge base is formed by constructing a digital world and a timeline, so that the immersive learning experience of children is enhanced, and the interestingness and educational effect of interaction are improved. The electronic tag technology, the cloud large model intelligent agent, the long-term memory mechanism and the audio, image and video playing technology are combined, the one-way interaction mode of a traditional child toy is broken through, knowledge can be gradually accumulated in interaction between children and toy roles, and the interestingness of the children is improved. And through the timeline of the digital world, the toy role continuously grows and evolves along with the passage of time, so that continuous and personalized education and accompanying are provided.

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 provides a medical skill assessment and training method and device based on a virtual patient, and relates to the technical field of digital computation.The method comprises the steps that image data, pathological data and physiological signal data corresponding to historical patients are obtained, and a virtual patient three-dimensional model is generated; performing multi-scale feature coding on the operation track, the instrument contact pressure and the biological tissue deformation parameter corresponding to the historical operation data of the target user to generate an operation feature vector with time sequence relevance; inputting the operation feature vector into a virtual patient three-dimensional model, and obtaining a physiological parameter change sequence corresponding to the operation feature vector; performing conjoint analysis on the operation feature vector and the physiological parameter change sequence to obtain an evaluation index matrix; generating an adaptive training scene according to the evaluation index matrix; the adaptive training scene comprises complication simulation information, abnormal sign triggering information and emergency state intervention information, so as to train the target user according to the mixed reality interaction equipment.

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

A computer-implemented method is disclosed for transforming academic test performance into personalized feedback and learning recommendations. The method involves presenting an academic test to a user via a user interface of an online learning platform and receiving the user's submitted answers. The system accesses input parameters including historical user-performance data, correct answers, and coaching session data. The user's responses are compared with the correct answers to identify incorrect responses. A prompt generator creates a prompt to guide and constrain an AI engine in analyzing the test responses. The AI engine correlates the incorrect responses with historical performance data and coaching session information to detect learning patterns or recurring errors. Based on the identified patterns, the system generates personalized feedback and targeted learning recommendations to address specific learning gaps. The method enables adaptive, AI-assisted post-assessment guidance, improving learning outcomes through individualized support.

Systems and methods for providing a guidance function for use of large language models within educational environments are provided herein. In an example, a system includes instructions to receive, from a first client device, a request to generate content within an application and initiate, by the application, a guidance function. The guidance function observes interactions between the first client device and a content generator and determines a first interaction between the first client device and the content generator. The guidance function also determines a first text portion generated based on the first interaction between the first client device and the content generator and generates a relationship between the first text portion and the first interaction between the first client device and the content generator. The guidance function may also store the relationship between the first text portion and the first interaction.

The present disclosure describes a system and method for conducting continuous vocabulary matching assessments, distinguished by its dynamic, metadata-driven taxonomy that seamlessly adapts to diverse educational curricula. The system manages vocabulary associated with one or more courses and receives input from learners regarding vocabulary words and their corresponding definitions. The system dynamically generates assessment probes, enabling ongoing tracking of student progress and comprehension throughout the course. The system integrates a ChatBot offering real-time feedback and personalized instructional content to learners and collaborates with instructors. The ChatBot provides insights, recommendations, and strategic interventions based on continuous analysis of student performance, setting this system apart from traditional tools that lack such adaptive, interactive capabilities. By utilizing a dynamic taxonomy and real-time interactions, the system ensures that assessments are both relevant and responsive to the evolving educational context, supporting the acquisition and retention of essential vocabulary across a wide range of subjects.

Disclosed is a preference model that improves the alignment of large language model (LLM) responses. For a particular scenario, a set of principles are identified that, when adhered to by the LLM, improve the quality of LLM responses. In a long form question and answer scenario, better answers tend to be useful, relevant, grounded, thorough, and true, although more and different principles are similarly contemplated. In a scenario that generates a movie script, better responses may include a relatable protagonist, a character arc, and a satisfying denouement. The disclosed axiomatic preference model is trained to understand when a response does or does not adhere to these principles. Once trained, the preference model may be used as a drop-in replacement of existing preference models used to train an LLM.

The invention discloses an English word memorizing system based on a memory curve, and the system comprises a login module which is used for carrying out registration and login operation, obtaining personal information, and recommending learning contents and plans; the learning module is used for executing a learning and memorizing process and recording a learning progress and a memorizing condition; the memory curve updating module is used for generating an initial memory curve and dynamically updating memory curve parameters; the plan generation module is used for automatically generating a personalized review plan; the review module is used for regularly sending review reminding according to the review plan; and the feedback module is used for obtaining feedback and suggestions of the user and carrying out optimization and improvement. According to the method, the review time is intelligently arranged based on the memory curve, the natural law of human memory and forgetting is followed, the learning content and the review plan are customized according to the individual difference of learners, the learning motivation is enhanced, the learners are endowed with more autonomy, the self-management and self-driving capabilities are cultivated, and a foundation is laid for lifelong learning.