147 results about "Coursework" patented technology

This application provides an AI-based method for playing information on electronic class signs. Based on the course type, it adaptively searches for the corresponding anomaly detection distance threshold from a pre-set mapping table. It receives the pose features of multiple students wearing electronic school badges associated with the target class, transmitted within the previous first time period. The received pose features are clustered to obtain cluster center features. If the distance between any student's pose feature and the cluster center feature is greater than the anomaly detection distance threshold, a short video image containing the student is obtained. The course type and the student's short video image are input into a pre-trained model for determining abnormal classroom behavior. If abnormal behavior is detected, the electronic class sign in the classroom area displays the student's personal information and anomaly alerts.

This invention discloses a method and system for application design transformation based on technological experience, relating to the field of digital technology. The method includes: collecting open-source technology product data via the internet to construct a digital technology experience center; providing online technology experiences for students to obtain experience monitoring data; designing online courses based on a technology application creation platform to obtain online teaching courses; teaching students to obtain technology application concept design proposals; conducting simulation verification and evaluation through a mechanism simulation system to obtain simulation evaluation results for the design proposals; presenting the technology application concept design proposals in a roadshow to obtain a design demonstration dataset; conducting online expert evaluation; and transforming the technology application concept design proposals into innovative achievements based on the evaluation results. This addresses the technical problems of limited teaching resources, limited practical scenarios, and restricted technological innovation capabilities in existing science and technology education, achieving the technical effect of enriching technological experiences and enhancing technological innovation capabilities.

This invention relates to the field of computer software technology, specifically to a deep learning-based employee performance analysis and training course matching system and method. The system uses a multi-source data acquisition module to uniformly collect and standardize internal and external data to construct a complete employee dataset. An employee competency profile construction module extracts and fuses multimodal features from the data to generate dynamically updated multidimensional competency profiles. A performance weakness analysis engine analyzes the relationship between competency and performance, identifying weaknesses and strengths. A dynamic learning path planning module adjusts subsequent courses based on learning outcomes for continuous optimization. A training effectiveness evaluation and feedback module quantifies the training improvement effect and provides feedback to the system. A high-potential talent discovery and experience graph module constructs a reusable experience knowledge graph. An adaptive performance-competency causal evolution modeling module analyzes the causal relationship between competency and performance, dynamically adjusting competency weights and recommendation strategies.

The present application relates to the technical field of mental health information monitoring, and discloses a method for dynamically monitoring the mental health state of college students based on side-writing data, which comprises the following steps: collecting multi-source side-writing data of college students within discrete time slices; obtaining calendar events, course arrangements and time period information and constructing a context vector; generating expected side-writing features through a context alignment counterfactual digital twin model based on historical summary states and the context vector; calculating counterfactual residuals according to actual side-writing features and expected side-writing features; dividing the feature domain and accumulating the time window to construct a drift evidence chain; combining uncertainty indicators to obtain risk intensity and risk level through risk inference; calculating the remaining stable period according to the risk intensity change trend; and executing active micro-measurement and updating the stable period sample set or risk inference parameters when the trigger condition is met. Through the joint modeling mechanism of constructing side-writing feature vectors and context vectors, semantic bias caused by independent analysis of single behavior features is avoided.

The invention belongs to the technical field of education artificial intelligence, and particularly relates to an adaptive learning path generation method based on a knowledge graph, which comprises a basic construction stage of constructing a double-layer knowledge graph and acquiring and standardizing multi-source dynamic data, and is used for constructing a course and cross-domain double-layer knowledge system and a data base, a three-dimensional learning condition analysis stage including knowledge dimension, dynamic learning condition dimension and cognitive development dimension; the cross-domain knowledge migration mechanism breaks course barriers, associates current learning with core course knowledge points of the same major, helps students establish a complete professional knowledge network, avoids knowledge fragmentation, embeds cross-domain knowledge points in combination with professional planning and competition requirements, realizes connection of current learning and future application, improves the practical value and migration ability of knowledge, and improves the learning efficiency. The knowledge association weight is dynamically optimized, so that path planning follows domain knowledge logic and fits individual learning feedback, students are assisted in understanding internal association among knowledge, and knowledge construction logicality is enhanced.

This invention relates to the field of intelligent education technology and discloses a tiered training method for editing skills based on deep learning. The method includes: constructing a training course library containing course units; using the course unit as the smallest learning unit, and based on the pre-dependencies of the course units, collecting real-time interactive operation data of learners to construct multi-dimensional feature vectors for learners; inputting the multi-dimensional feature vectors into a trained deep neural network model to output the learner's current assessed comprehensive course ability index; using a clustering method to divide all learners into three levels: basic, intermediate, and advanced; associating each level with a subset of course units, obtaining the matching degree between each learner's current assessed comprehensive course ability index and the difficulty coefficient, and generating a personalized course learning path recommendation for each learner. This invention achieves tiered training for learners' editing skills.

This invention discloses a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform, belonging to the field of resource sharing technology. By deploying multiple types of sensors in the classroom to collect teaching environment data and construct a classroom environment feature vector, a resource environment demand vector is established, and the environmental adaptability between the classroom environment and various aesthetic education resources is calculated. The network link status between the classroom terminal and the cloud platform is detected in real time, and parameters such as bandwidth, latency, and packet loss rate are obtained. Resource network matching coefficients are calculated, and the environmental adaptability and network matching coefficients are further integrated to construct a comprehensive resource availability. A resource scheduling priority sequence is generated by combining course matching degree and concurrent call status. This achieves intelligent sharing and dynamic optimization scheduling of aesthetic education resources in multi-classroom scenarios, improving the environmental adaptability and transmission stability of resource sharing, and enhancing the utilization efficiency and teaching implementation reliability of aesthetic education resources on the cloud platform.

This application relates to the field of flight control technology, specifically to a high-maneuverability flight control method for fixed-wing aircraft based on curriculum-based reinforcement learning. It constructs a closed-loop learning system comprising a curriculum scheduler, an agent, a simulation environment, and an experience buffer. The agent has a policy network. The state space of the aircraft and the normalized control surface and throttle action space output by the policy network are defined. A curriculum difficulty measurement model is established, quantifying task difficulty through weighted state deviation and envelope penalty terms. Based on the current curriculum level and this model, a safe and difficulty-matched training task set is dynamically generated. In the simulation environment, the policy network is iteratively updated through a two-layer loop training process. After completing all courses, a high-maneuverability flight control strategy is obtained. This strategy is deployed to the flight control system to achieve high-maneuverability flight control based on real-time state. This achieves efficient, safe, and adaptive flight control strategy training and deployment.

The application discloses a Bloom cognitive hierarchy constraint-based achievement-oriented education diagnosis method and system. The method takes course outline text and student evaluation data as input, and realizes the automatic diagnosis of course goal achievement through three structural modifications in the large language model Transformer architecture: the logarithmic value of the cognitive hierarchy transfer matrix is embedded as a learnable bias item in the attention score calculation path to realize feature coding of cognitive hierarchy perception; the continuous differentiable relaxation technique is used to establish an end-to-end differentiable inference path for the course goal achievement weight; and the Bloom partial order constraint is coded as a training loss, so that the diagnosis result meets the cognitive hierarchy progressive relationship. The three modifications are optimized by a joint loss function, which significantly improves the diagnosis accuracy and promotes the interpretability and scientificity of the large language model diagnosis in the achievement-oriented education scene.

The present application relates to the technical field of intelligent management of teaching data, and particularly relates to a method and system for integrated teaching intelligent management suitable for courses, posts, competitions and certificates, wherein the method for integrated teaching intelligent management suitable for courses, posts, competitions and certificates comprises the following steps: S1: obtaining a student portrait set containing multi-dimensional individual information of students, and connecting a preset external information base; S2: calling the external information base, and generating an optimal learning path for the students according to the student portraits and preset time nodes; S3: obtaining multi-dimensional individual information of the students when the students learn according to the optimal learning path, and updating the student portraits. The present application can integrate course learning, competition practice, certificate taking and post employment in the same framework, thereby solving the problem of fragmentation of each link in traditional teaching management, and enabling further optimization of the optimal learning path at different time nodes, so that the generated optimal learning path is always matched with the actual progress and final goal of the students.

The application relates to the technical field of teaching, and particularly discloses a nursing intelligent teaching auxiliary system and method, which comprises a cloud server, a plurality of teacher terminals and a plurality of student terminals; the cloud server comprises a knowledge base construction module, which is used for acquiring teaching documents of adult nursing science, and establishing structured course knowledge graph data based on the teaching documents; a model construction module, which is used for acquiring a general large language model, and performing field adaptability training on the general model by using the course knowledge graph data, and outputting a special model fused with adult nursing knowledge; a teaching assistant module, which is used for accepting a lesson preparation request of the teacher terminal, inputting the special model, and generating lesson preparation information; and a student assistant module, which is used for receiving an auxiliary request of the student terminal, calling the special model according to the content input by the student, and generating auxiliary teaching content. The technical scheme of the application can meet the learning needs of different students and effectively improve the teaching quality.

The application provides a low-resource multilingual large model training method and system for personalized course learning, and belongs to the technical field of large language models, and comprises the following steps: S1: collecting training samples in multiple languages; each sample is subjected to deduplication and noise removal processing, then the data format is unified, and a language attribute is added as a language label; S2: initializing parameters of an adaptive sampling scheduler and a dynamic loss scheduler; S3: using a pre-trained large language model as a base model, and adding the adaptive sampling scheduler and the dynamic loss scheduler in the training process. The method can reduce the dependence on low-resource language labeled data, can adaptively balance the training weights of different language samples, and dynamically matches the difficulty of multilingual tasks and the learning progress of the model.

This specification provides a course data processing method and apparatus. One course data processing method includes: firstly, obtaining a school identifier and a corresponding course access link during the course data processing process; accessing a course webpage through the course access link and obtaining structured course data; then determining the course system type of the course webpage based on the school identifier; if the course system type is a third-party system type, using a traversal method to call each course parser of the third-party system to parse and verify the structured course data; finally, if the verification fails, performing course recognition on the course image according to the course recognition method corresponding to the display adaptation state of the course webpage and obtaining the target course data, thereby realizing course data processing for different course system types and different display adaptation states.

The present application belongs to the field of teaching, and particularly relates to a double-label teaching system based on professional courses and psychological growth space, comprising a double-label course library module, an emotion card teaching aid module, a classroom process control module, a growth record and evaluation module, and a teacher training and standardization module; and further comprising a double-label teaching method based on professional courses and psychological growth space, comprising emotion introduction, double-label setting, double-space synchronous training, fusion display output, and double-dimension growth evaluation. By constructing a double-label course system, building a double-dimension growth record and evaluation system, forming a complete and traceable teaching closed loop, and realizing the systematization of professional and psychological teaching and the unification of the teaching process.

The application discloses a course teaching resource recommendation method, comprising the following steps: establishing a course teaching resource recommendation dataset, learning text and image features of course content and course teaching resources in a training set, and calculating a similarity matrix between the text and image features; based on the similarity matrix and existing scoring samples, a low-rank sparse matrix decomposition recommendation model of the course teaching resources is constructed, and a scoring matrix is decomposed into a low-rank latent matrix related to course content and a sparse latent matrix related to course teaching resources; the low-rank matrix and the sparse matrix are initialized by using random numbers, the low-rank matrix and the sparse matrix are iteratively optimized by using a gradient descent method, and the optimization is updated until a target function based on the similarity matrix, the low-rank matrix and the sparse matrix converges; course content features in test data are extracted, corresponding course teaching resources are found from approximate estimation of the scoring matrix, and a recommendation list is returned according to scoring item sorting.

This invention relates to the fields of intelligent teaching and trustworthy artificial intelligence, providing a "value alignment" dialogue guidance method and its trustworthy evaluation system for ideological and political education. The system collects multiple rounds of teacher-student dialogue and encapsulates them into data packets containing round identifiers, unified timestamps, and conversation context indexes; it structures course themes, teaching objectives, and normative constraints into a computable constraint set; it identifies dialogue states and generates control contexts, driving the selection of guidance actions and the generation of candidate responses; it performs value alignment and compliance checks on candidate responses, rewriting, downgrading, redirecting, or rejecting responses based on the results, and retains a handling log; it evaluates the alignment degree, guidance effectiveness, risk compliance, stability, and interpretability of the entire process, outputting scores, cause identification, and evidence chains, and writing them into audit storage, achieving controllable dialogue guidance, quantifiable evaluation, and traceable process.

This invention relates to the field of semantic understanding technology, specifically to a method for semantic understanding and intelligent question answering in online courses for mobile terminals. The method includes the following steps: parsing the question text to identify subject-verb-object semantic relationships and generate corresponding structural expressions; collecting concept and behavioral terms from a knowledge base for semantic comparison and generation of course semantic expressions; reading positional analysis to generate question relationship expressions based on concept associations; collecting subtitles and knowledge structures for comparative association and generation of knowledge correspondence expressions; and extracting subtitle fragments and integrating playback positions to generate a processing scheme. In this invention, deep semantic comparison and positional analysis are performed by linking object terms in the course knowledge base, overcoming the limitations of shallow character matching to achieve deep question intent restoration. Simultaneously, subtitle content and chapter structure are collected for contextual analysis, and subtitle fragments are deeply integrated with video playback progress to construct a response mechanism that highly fits the actual teaching context, completely changing the isolated retrieval mode and eliminating interaction breaks.

The application relates to the technical field of computer information processing, and provides an education resource semantic retrieval method based on a knowledge graph. The method constructs a science education knowledge graph by structurally processing course textbooks, courseware, multimedia resources and experimental videos; in the knowledge graph construction process, sequential hypothesis testing and e-process evidence accumulation mechanisms are introduced to statistically control the significance of entity recognition results, thereby improving the stability and reliability of knowledge modeling; meanwhile, a subgraph graph neural network model with relationship strength perception is constructed, a resource structure semantic representation conforming to teaching logic is obtained, and the resource structure semantic representation is fused with a text semantic representation, so that semantic retrieval and sorting of the knowledge graph are enhanced. The method can output retrieval results that are coherent in teaching logic and adapt to different teaching scenes, and improves the accuracy of education resource retrieval and the teaching auxiliary effect.

The application discloses an education course demonstration education device, which comprises an innovation and entrepreneurship education demonstration device, the innovation and entrepreneurship education demonstration device comprises a demonstration table, a dynamic demonstration mechanism, a weighing mechanism and a display board, the dynamic demonstration mechanism is installed on the demonstration table and comprises a storage container, a main pump body, a guide pipe, a guide device and a main pipe, the storage container is installed on the back of the demonstration table and is connected with the water inlet end of the main pump body at the upper end, the water outlet end of the main pump body is connected with the guide pipe, the guide pipe is horizontally arranged on the top of the demonstration table, the guide device is divided into a plurality of groups and is connected with the guide pipe through a pipeline, the lower ends of the plurality of groups of guide devices are connected with the main pipe, a drain valve is installed at the lower right corner of the main pipe, the weighing mechanism is installed below the main pipe and is used in cooperation with the dynamic demonstration mechanism, and the display board is installed on the side of the demonstration table and is connected with the plurality of groups of guide devices through a line. The innovation and entrepreneurship education demonstration device designed in the application can dynamically demonstrate innovation and entrepreneurship education.

The application discloses a kind of education resource sequence recommendation method, device and equipment based on three-view structure distillation and storage medium, the method includes: based on student-course interaction log, the learning sequence of time sequence course index is constructed, three types of view hypergraph are constructed according to semantic, path, concept structure multidimensional feature, three-view course representation matrix is generated by structure coding fusion, and the recommended list is output by inputting education resource recommendation model;Education resource recommendation model generates student state vector by the coding of Transformer, and the total course score is obtained by fusing three-view branch score and predicted;The present application predicts the education resource recommendation by three-view collaborative scoring from multiple perspectives, accurately captures course representation, effectively depicts learning path high-order law, improves the accuracy, explainability and generalization ability of education resource recommendation in sparse interaction and cold start scene.

This invention belongs to the field of virtual assisted teaching technology, and particularly relates to an intelligent virtual assisted teaching knowledge needs analysis method. It includes the following steps: extracting teaching keywords to establish a keyword database; extracting teaching corpora; defining element features, completing feature annotation, and completing element identification; extracting element pairs, establishing element classification features, extracting element relationships, and completing classification; establishing a historical assisted teaching knowledge graph; acquiring historical information, extracting historical teaching data, and outputting an element attention output matrix based on a multi-head self-attention module; element path and element extraction; this invention achieves intelligent and efficient chemical knowledge needs analysis based on historical data of training subjects, and is used to provide training subjects with recommendations for teaching content and teaching needs, as well as an automatic planning method for teaching courses.

This invention discloses a question-answering method, device, and medium for a computer organization principles course, relating to the field of natural language processing technology. The method includes: First, integrating multi-source data, cleaning and format conversion to construct a question-answering dataset for training. Next, supervising fine-tuning of the ChatGLM3-6B model based on the LoRa method, while simultaneously vectorizing course documents using a semantic vector model to construct a text vector knowledge base containing a vector database and a custom code library, supporting document updates and code retrieval. Finally, a WebUI interface is built based on the LangChain-Chatchat framework, integrating the fine-tuned large model and vector knowledge base, providing users with two question-answering modes. This invention enables efficient fine-tuning of the model based on domain knowledge and constructs a corresponding question-answering assistance mechanism to provide professional, accurate, and tailored intelligent support for teaching needs.

The present application provides a course learning model training method, a course learning method and related devices, the course learning model training method comprises: using the problem data set corresponding to the target course to perform reinforcement learning training on the large language model, in each training process, the current problem data set is filtered using a question classification threshold, and the filtered problem data set is used as the training sample of the next round of iteration training of the large language model, until the course learning model is obtained under the condition that the large language model meets the target iteration condition; the question classification threshold is determined based on at least one of the correctness of the reply data corresponding to different questions and the course category. The method and device of the present application can provide an adaptive course learning scheme with single sample granularity, based on real-time capability feedback and without additional strategy network training, thereby improving the reliability of the course learning result.

This invention relates to the field of computer technology combining artificial intelligence and educational measurement. It provides an intelligent automated examination system for university courses, comprising an automated examination center unit, a hardware and network construction unit, a hardware and software operation coordination unit, a software interface unit, an anti-cheating unit, a question bank unit, a marking unit, and an intelligent question bank management and maintenance unit. The system achieves student information binding through two-factor authentication; it ensures fair and randomized question paper generation by controlling the overall difficulty of the exam paper with a target average difficulty and tolerance; and it generates question bank management results using point-to-two correlation coefficients and two-column correlation coefficients as core statistical indicators, combined with multi-dimensional indicators, and constructs a closed-loop update mechanism. This invention enables full traceability and auditability of the examination process, effectively solving industry pain points and is suitable for the construction of automated examination centers in universities and daily course assessments.

The application discloses a cross-domain insulator defect detection method and device, medium and product, relates to the field of power transmission line detection, and comprises the following steps: constructing a teacher-student collaborative optimization cross-domain detection model based on a structure-consistent teacher network and a student network; obtaining a trained student network based on the teacher-student collaborative optimization cross-domain detection model by using source domain data and target domain data; the student network is supervised learning by using the true value label of the source domain data, and is un-supervised training by using the pseudo label generated by the teacher and the unlabeled target domain data; a cross-view invariance representation module is introduced into the student network; a low-confidence prediction-driven self-optimization module is introduced into the teacher network; a course-guided difficult example alignment module is also introduced into the student network; and insulator defect detection is performed based on the trained student network. The application can improve the robustness and generalization ability of the insulator defect detection model in the cross-domain scene.

The present application relates to the technical field of new energy training, and particularly relates to a new energy intelligent training and examination system and method based on cloud rendering, which comprises a cloud rendering hub platform system that generates a computing resource allocation scheme according to virtualized and integrated graph computing resources based on course learning request data uploaded by a student terminal device and a student access terminal type; a post skill training and examination module that generates personalized learning evaluation data containing ability short board positioning according to course interaction data and student learning behavior data of the student in a virtual simulation environment; and a virtual-real fusion module that generates force feedback operation data that is synchronously updated with a real job training scene by fusing the virtual training environment and the real job training scene through a data coupling engine. The present application realizes a new energy intelligent training and examination system that seamlessly fuses virtual and real job scenes, and improves the utilization efficiency and precise allocation of cloud rendering platform resources.

This invention provides a power trading training system, belonging to the technical field of training systems. The system includes: a login module: upon detecting a user's login, it verifies the user's legitimacy based on the power trading training platform, grants the user login privileges, and stores the corresponding login information in a database; a course assistance module: after successful login, based on user privileges and a decision support mechanism, it performs a time-limited key verification and matching to decrypt the encrypted user-specific training courses; a learning module: based on the user's learning hours for decrypted courses during the training period, it obtains a first learning pass status and a second learning pass status based on the final exam results for all decrypted courses, and obtains a learning pass score for each decrypted course; a ranking module: based on an indicator evaluation model, it obtains the weight of each decrypted course for the user and, combined with the learning pass score, generates a corresponding ranking. This provides a convenient training method and improves training efficiency.

The application discloses an AI intelligent agent-based learning plan generation method and device, relates to the technical field of education, and comprises the following steps: acquiring a knowledge point set corresponding to course material information of a student in a target time period from a companion learning system, determining basic learning information of knowledge points in the knowledge point set and prerequisite knowledge information and correlation information between the knowledge points; generating attribute information of the knowledge points based on the basic learning information, the prerequisite knowledge information and the correlation information, and constructing a knowledge graph corresponding to the knowledge point set based on the attribute information; mapping the knowledge graph in a knowledge point learning grid corresponding to the course material information to obtain a target knowledge point learning grid; and generating a learning plan of the student in the target time period based on the target knowledge point learning grid, and displaying the learning plan to the student in the target knowledge point learning grid in the form of a learning path. The application can guide the student to understand knowledge logic and improve learning efficiency and knowledge transfer ability.

A system and method are provided for generating a course. The method includes obtaining answers to one or more questions pertaining to the course; using the answers to the one or more questions to generate a set of inputs; feeding the inputs to a large language model (LLM); receiving a response from the LLM providing a course outline; enabling content to be generated for at least one portion of the course outline; providing a second set of inputs to the LLM; and populating the at least one portion of the course outline using a response from the LLM.