78 results about "Optimal learning" patented technology

An object of a system according to an embodiment is to provide optimal learning support according to individual learning needs of learners.SOLUTION: A system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension degree monitoring unit, and an advice providing unit. A learning history analysis part analyzes the learning history and interest of the learner. The customized content generation unit generates learning content on the basis of the result analyzed by the learning history analysis unit. The comprehension degree monitoring unit provides the learning content generated by the customized content generation unit and monitors the comprehension degree and progress of the learner. The advice providing unit provides advice based on a result of monitoring by the comprehension degree monitoring unit.SELECTED DRAWING: Figure 1

The application relates to an intelligent education management method and system based on an Internet of Things. The method comprises the following steps: inputting a multi-channel physiological feature sequence into a hierarchical probability state space model to obtain a short-term cognitive state and a short-term cognitive state posterior probability; performing stability statistical testing on the short-term cognitive state posterior probability, and marking the short-term cognitive state as a phase-established state when the stability condition is met; constructing a cost function based on the phase-established state and a probability evolution track, and performing rolling time domain optimization based on the minimum cost function and a dynamic refractory period constraint to obtain an optimal learning action; calculating a decision oscillation index according to a current executed learning action sequence, and increasing the weight of an action change amplitude penalty and prolonging the locking time of the dynamic refractory period constraint by a set step length when the decision oscillation index exceeds a preset first oscillation threshold. The method can guarantee the continuity of a learning process and the stability of a cognitive state.

The invention discloses a personalized smart course recommendation system for oral medicine based on a knowledge graph, and belongs to the technical field of course recommendation, and the system comprises a collection module which collects and fuses dominant static features and recessive behavior features of a user to form preliminary capability evaluation data; the analysis module is used for matching the preliminary capability assessment data with a preset oral internal medicine knowledge graph to generate a knowledge state mapping graph; the generation module is used for generating an optimal learning path from the current knowledge state to the target knowledge state according to a preset teaching strategy and a learning target on the basis of the knowledge state mapping graph; and the recommendation explanation module is used for analyzing the optimal learning path, enabling a preset course unit to correspond to the knowledge node to be consolidated or the association relationship, generating natural language description and completing personalized course recommendation. According to the method, the dominant static characteristics and the recessive behavior characteristics of the user are collected and fused to form the preliminary capability evaluation data, so that the accuracy of the initial recommendation stage is remarkably improved.

An object of the system according to the embodiment is to provide an optimal learning plan based on a learning history and a goal of a user, and to continuously check a progress status and an understanding level of learning.SOLUTION: A system according to an embodiment comprises a learning history analyzer, a learning plan generator, a progress checker, and a feedback provider. The learning history analysis unit analyzes the learning history and goal of the user. The learning plan generation section generates an optimum learning plan based on the data analyzed by the learning history analysis section. The progress checking section checks the progress and understanding of learning based on the learning plan generated by the learning plan generating section. The feedback providing unit provides feedback based on a result checked by the progress checking unit.SELECTED DRAWING: Figure 1

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.

This application relates to a method, system, device, and medium for personalized vocational skills learning path planning based on artificial intelligence. The method includes: collecting a user's structured event dataset; constructing a dynamic causal skill graph with skill points as nodes and causal effect strength as directed edges using two-stage linear regression; extracting the user's dynamic behavior sequence and inputting it into a pre-trained model to obtain a skill mastery vector; searching for a personalized causal learning path with the current mastered skill points as the starting point, the career goal as the ending point, and maximizing the cumulative causal effect as the objective; performing counterfactual reasoning on the learning path based on the structured causal model to predict the counterfactual mastery, learning time, and path under different intervention conditions; and obtaining the optimal learning path adjustment scheme by weighted fusion of the counterfactual results. This method enables personalized learning path planning and dynamic optimization with causal logic.

A system is provided.SOLUTION: A system comprising: means for inputting basic information of a subject; means for determining cognitive traits of the subject; means for suggesting an optimal learning method based on the determination; means for providing learning content; means for tracking learning progress and conducting comprehension tests; means for providing personalized feedback to the subject; and means for solving questions during learning in real-time.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for acquiring learning history and progress data of a student; means for generating an optimal learning plan and teaching materials based on the acquired data; means for providing the generated learning plan and teaching materials to the student; means for analyzing learning activity data of the student in real time and providing feedback; means for updating a progress chart based on the analysis result; and means for automatically generating a custom report.SELECTED DRAWING: Figure 1

To provide a system for enabling an examinee to efficiently advance learning under an optimum learning environment.SOLUTION: A means for inputting basic information and a learning history of a learner, a means for transmitting the basic information and the learning history to a server, a means for storing the received basic information and learning history in a database, a means for analyzing the basic information and the learning history from the database and generating an optimal virtual competitor for the learner, a means for transmitting data of the virtual competitor to a terminal of the learner, a means for interacting and competing with the virtual competitor on the terminal, a means for transmitting a response of the learner and a competition result to the server, and a means for generating feedback based on the response of the learner and the competition result on the server; A system comprising: means for transmitting to a terminal; means for monitoring stress levels and learning effectiveness of a learner and adjusting settings of a virtual competitor; and means for transmitting adjusted data of the virtual competitor to the terminal.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide optimal learning content based on the learner's level of understanding and interests. [Solution] The system according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. The generation unit generates virtual teachers and characters based on the content provided by the provision unit. The feedback unit analyzes the learner's progress in real time through the virtual teachers and characters generated by the generation unit and provides feedback.

The application provides a microseismic real-time monitoring method and device based on compressed sensing and 5G node collection, solves the problem that microseismic monitoring data is easy to be submerged in noise interference, and microseismic data collection equipment cannot transmit data in real time, or the transmitted data is uneven and incomplete. It comprises the following steps: collecting data, establishing a geological and geophysical model, simulating fracturing based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring observation system; establishing an adaptive optimal learning dictionary to form a compressed sensing collection scheme; conducting a due diligence investigation of field obstacles and interference sources to form a compressed sensing active obstacle avoidance scheme; using a 5G node collection system to collect field data; applying compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data; carrying out microseismic data processing and interpretation, and feeding back the microseismic analysis results to the fracturing site to realize real-time monitoring of the fracturing construction process.

The system according to this embodiment aims to provide individual learners with an optimal learning plan based on their MBTI diagnostic results, thereby supporting their learning motivation and self-management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives MBTI diagnostic results. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The monitoring unit monitors learning progress based on the reminders delivered by the reminder unit. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit.

The invention relates to a fault detection method and device based on fault feature enhancement and twinborn contrast learning. The method comprises the steps of obtaining sample data of key equipment of a power plant at different moments; constructing a learning network model based on twin spatial-temporal feature comparison; carrying out optimization training on the learning network model based on a tiny fault feature distinction degree intensified training strategy and an Adam optimization algorithm; and inputting the operation data of the power plant into the optimal learning network model to obtain a power plant fault detection result. According to the method, a detection model is constructed by combining a twin spatio-temporal feature contrast learning network and a tiny fault feature distinction degree enhanced training algorithm, the model performance is improved through a mixed contrast cross entropy loss function and an Adam optimization algorithm, the method has a high fault diagnosis rate and a low false alarm rate, early tiny faults can be accurately recognized, the real-time diagnosis time is extremely low, and the method is suitable for large-scale popularization and application. And the power plant field real-time detection requirement is met.

The invention relates to the technical field of computers, in particular to a cross-subject learning fusion method and device, computing equipment and a storage medium, and the method comprises the steps: extracting knowledge points of all subjects to construct an association relationship between the knowledge points of the subjects, calculating an optimal learning sequence according to the association strength between the knowledge points, and obtaining interaction behavior data. And calculating a cognitive state, detecting a learning bottleneck, and recommending associated subjects to assist learning at the corresponding learning bottleneck so as to evaluate a learning effect based on knowledge, thinking and application dimensions. According to the method, knowledge association between subjects is constructed, cognitive evaluation is realized through pure behavior data analysis, a dynamic interdisciplinary knowledge connection mechanism is established on the premise that biological feature collection is not needed, the cross-subject transfer learning ability of learners is improved, and a safer and more universal intelligent learning solution is provided for the education field.

A system is provided.SOLUTION: A system comprising: means for collecting video, audio, and text data of a technology from a craftsman; means for analyzing the collected data and performing video segmentation and image recognition; means for collecting information on user preferences and strengths; means for generating optimal learning content based on the analyzed data and user information; and means for providing the generated learning content to the user and checking the comprehension level.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent maintenance, machine learning and artificial intelligence crossing of civil aviation, and provides a civil aircraft health management model optimization verification method based on continuous operation data. According to the method, performance degradation is sensed in real time based on continuous operation data, on-demand iteration of a model is realized through an increment and continuous learning strategy, hysteresis of traditional offline retraining is avoided, and the adaptability to new working conditions and new faults is improved; attenuation diagnosis and strategy selection are autonomously completed, manual intervention cost is reduced, an optimal learning scheme is matched in combination with historical knowledge and data characteristics, the model optimization period is shortened, and core performance indexes are improved; through knowledge precipitation and updating in the optimization process, a self-learning optimization decision-making system is formed, and the accuracy and efficiency of model optimization are continuously improved along with operation data accumulation.

The system according to this embodiment aims to provide learning plans and emotional support tailored to the individual needs of each student. [Solution] The system according to the embodiment comprises a learning plan provision unit, a progress management unit, a community provision unit, a sponsorship unit, and a course sales unit. The learning plan provision unit provides an optimal learning plan for each individual student. The progress management unit provides feedback and progress management based on the learning plan provided by the learning plan provision unit. The community provision unit provides an online community where students and parents can interact with each other. The sponsorship unit partners with companies and educational institutions to obtain sponsorships. The course sales unit sells courses specializing in specific subjects or skills.

The invention belongs to the technical field of power system employee training, and provides a learning path planning and learning effect evaluation method and system based on a neural network, electronic equipment and a storage medium. The method comprises the steps of data acquisition, coding feature matrix construction, knowledge graph entity embedding generation, vector splicing, coding processing, matching score matrix calculation, curriculum personalized screening, knowledge level evaluation, learning demand analysis, optimal learning path screening and learning effect evaluation. According to the invention, through a two-stage learning path planning process combining actual demands and employee historical data, a learning demand list and a recommended course sequence are enabled to accurately fit employee knowledge shortages and business demands, and the path can be adjusted in real time according to the employee's own learning progress; in addition, due to the integration of the power grid business data, the training content hysteresis is avoided, and the training quality is improved.

A system is provided.SOLUTION: A schedule optimizing means for dynamically adjusting the learning plan based on the progress of the learner, an automatic answering means for immediately answering a question from the learner through a messenger application such as LINE, a detailed explanation means for providing a detailed explanation for an answer to a past question, and a real-time monitoring means for monitoring the learning progress of the learner in real time and providing appropriate feedback. AI.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide an optimal learning program tailored to the skill level and learning objectives of employees. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a learning unit, and a monitoring unit. The reception unit inputs the employee's skill level and learning objectives. The generation unit generates a learning program based on the information input by the reception unit. The learning unit proceeds with learning based on the learning program generated by the generation unit. The monitoring unit monitors the progress of learning carried out by the learning unit and adjusts the learning plan as necessary.

The system according to this embodiment aims to efficiently create an educational system for reskilling and promote human resource development. [Solution] The system according to the embodiment comprises a generation unit, a learning unit, an evaluation unit, and a proposal unit. The generation unit automatically generates roadmaps and materials using internal company content. The learning unit proceeds with learning based on the roadmap generated by the generation unit. The evaluation unit evaluates the progress of learning carried out by the learning unit. The proposal unit proposes an optimal learning plan based on the progress evaluated by the evaluation unit.

The invention relates to a power grid operation situation deduction method based on artificial intelligence, and the method specifically comprises the steps: deeply revealing the health degree and fault consequences of an evaluated power distribution network based on processed historical maintenance data, and carrying out the deduction of the power grid operation situation based on a health degree evaluation result and a fault consequence degree evaluation result of power grid operation; generating a dynamic risk two-dimensional measurement space-time distribution diagram of historical faults of the intelligent power distribution network in severe weather; afterwards, a dynamic condition correlation recognition model and a saliency fuzzy risk inference model are combined, differential parallel solving is carried out on the correlation risk index of the discrete feature unit and the fuzzy risk index of the continuous feature unit, and on the basis of the solving result, through a multi-model integrated optimal learning structure, the fuzzy risk index of the continuous feature unit is obtained. And generating an operation situation dynamic distribution diagram in a future specific severe weather evolution process, and finally completing short-term prediction of the operation situation of the power grid based on the operation situation dynamic distribution diagram. The method has the advantages of convenience, accuracy and high efficiency.

The application discloses a decision tree-based uncertainty remanufacturing scheduling method, which aims at maximizing total remanufacturing profit and minimizing total time required for remanufacturing all waste products, sets the quality grade of the waste products as a fuzzy variable, evaluates the remanufacturability of the waste products by using a decision tree, and selects a remanufacturing line with the maximum expected profit for remanufacturing the waste products. Then, an improved teach-and-learn optimization algorithm is used, in which, in the teacher stage, the learner uses an adaptive inertia weight to update the reprocessing operation sequence, and when falling into a local optimum, the learner simultaneously learns from the teacher, the best learner and the learner with the maximum adaptive value in the last iteration, so as to relieve the algorithm from falling into a local optimum and improve the diversity of the population. The application realizes efficient solution of the model and can obtain a more reasonable scheduling scheme.

We provide the system. [Solution] Means for collecting educational information and evaluating it based on reliability and accuracy, A means for receiving user learning requests and generating an optimal learning plan based on them, A means of calculating rewards based on the frequency and conditions of use of educational data selected from a large amount of information, A means for presenting an optimal educational plan in response to user input in an information processing device and adjusting the plan via an artificial intelligence agent, A system that includes this.

The system according to this embodiment aims to understand the individual learning and behavioral status and provide an optimal learning plan and behavioral modification program based on that understanding. [Solution] The system according to the embodiment comprises a shooting unit, an analysis unit, a generation unit, and a provision unit. The shooting unit captures the subject's daily activities in photographs and videos. The analysis unit analyzes the information captured by the shooting unit to understand the subject's learning and behavioral status. The generation unit generates an optimal learning plan and behavioral modification program based on the situation understood by the analysis unit. The provision unit provides the program generated by the generation unit.

The application discloses a control method of an electric energy converter, belongs to the technical field of electric energy conversion, and combines a particle swarm algorithm with a single neuron PID algorithm, finds an optimal learning rate of the single neuron PID by virtue of the strong optimization capability of the particle swarm algorithm, and then inputs the optimal learning rate into a single neuron PID module to perform PID control. The control method of the electric energy converter can realize low overshoot, low steady-state error and high dynamic response performance.

An object of the system according to the embodiment is to provide an optimal learning curriculum based on the learning progress and understanding level of each student.SOLUTION: A system includes a learning history analysis unit, a curriculum generation unit, a question generation unit, and a difficulty level adjustment unit. The learning history analysis unit analyzes the learning history. The curriculum generation unit generates an individual learning curriculum based on the data analyzed by the learning history analysis unit. The question generation part generates a question for understanding level confirmation on the basis of the curriculum generated by the curriculum generation part. The difficulty level adjustment unit adjusts a difficulty level of the question generated by the question generation unit.SELECTED DRAWING: Figure 1

This invention proposes a personalized learning path recommendation method based on deep reinforcement learning. The method includes: acquiring an educational dataset and constructing a knowledge graph with triples as basic units based on the dataset; acquiring the user's learning sequence and inputting it into a question-answering model to obtain the probability of the user correctly answering the next question; filtering learning paths based on the knowledge graph and the probability of the user correctly answering the next question to obtain the optimal learning path; and recommending the optimal learning path to the user. This invention effectively eliminates the interference of browsing order by deeply analyzing the learner's actual performance in various tasks and inversely deducing the true logical preconditions between knowledge points, thus constructing a reliable underlying knowledge network. During recommendation calculation, the system adopts a pruning-then-filtering mechanism, compressing the massive computational dimensions by several orders of magnitude, ensuring recommendation quality, significantly reducing computational costs, and achieving extremely fast real-time response.

We provide the system. [Solution] A generative model that generates personalized educational information based on the user's ability level, learning strategy, and interests, A computing device that analyzes the user's past learning history and ability assessment and automatically suggests the optimal learning path, A presentation device that provides the aforementioned user with an educational experience involving interactive evaluation and immediate explanation, A control device that monitors the progress of learning in real time and adjusts the learning plan based on the user's results, A system that includes this.

The invention discloses a generative AI-assisted local merchant training content automatic generation and simulation drilling system, and relates to the technical field of generative artificial intelligence, and the system can dynamically adjust the training content and difficulty according to the actual response of an employee through monitoring the physiological state of the employee in real time and carrying out multi-modal data fusion in combination with the dialogue content, thereby improving the training efficiency. The staff is ensured to be always in the optimal learning state, the high-fidelity simulation scene generated by the system is highly vivid, the participation degree and immersion of the staff are greatly improved, and the training is closer to the actual working scene. Besides, a personalized subsequent training scheme generated by the system based on a physiological-cognitive two-dimensional evaluation report can accurately perform intensive training for weak links of employees, and at the same time, a training path is dynamically adjusted to form a closed-loop self-optimization training ecosystem, so that professional communication ability in a complex situation is enhanced, and training efficiency is improved. And an efficient and personalized training solution is provided for local merchants.