5 results about "Learning center" patented technology

The invention discloses an end-end self-control-based transformer area light storage and charging coordinated regulation and control system and an autonomous method. The system constructs an end-end-transformer three-level autonomous architecture: an end-level autonomous unit is responsible for local millisecond-level reflection control; the edge autonomous nodes realize intra-zone collaboration through federated learning, and physical security constraints are embedded into AI decisions by using digital twin bodies; the court-level federation learning center realizes knowledge sharing under cross-court privacy protection through secure multi-party computing, the method further integrates transfer learning to solve the cold start problem of a new court, and a three-time scale regulation and control mechanism is adopted to cope with disturbance of different rates; the autonomous capability, the response speed, the safety level and the intelligent degree of the system are improved.

This application relates to a federated learning method, apparatus, device, storage medium, and program product. The method includes: training a first artificial intelligence model locally on a client using a training dataset to obtain the classifier gradient of the first artificial intelligence model; the sample data for each category in the training dataset is imbalanced, and the sample data for the first category does not meet the data balance condition; determining the gradient adjustment value corresponding to the first category based on the global gradient issued by the federated learning center; adjusting the classifier gradient according to the gradient adjustment value to obtain the adjusted classifier gradient; updating the first artificial intelligence model based on the adjusted classifier gradient; and upon reaching a convergence condition, sending the classifier gradient obtained in each iteration to the federated learning center so that the federated learning center can update a second artificial intelligence model based on the received classifier gradient, resulting in a model for processing recommendation tasks. This method can improve model training performance.

The invention discloses a federated learning-based ship inspection method and system and a computer program product, and relates to the field of ship inspection, and the method comprises the steps: deploying nodes at each inspection point, and deploying a federated learning center server at the center; an initial face recognition model is constructed, and initial parameters are configured and distributed; each node uses the preprocessed image to update and generate a model parameter update quantity and uploads the model parameter update quantity; aggregating all model parameter update quantities to obtain new model parameters, distributing the new model parameters to update the model parameters of the nodes, and updating the federal learning center server and the nodes to obtain a new model; repeatedly updating until a final model of which the identification accuracy of each node reaches a preset threshold value is obtained, distributing final model parameters to all nodes, and deploying the final models of all nodes; and calling the deployed final model by each node, executing face recognition to generate a polling recognition result, and uploading the polling recognition result. The method has the effect of high inspection face recognition precision.

This invention discloses a collaborative defense method and system for multi-terminal federated learning security agents. The method includes: initializing and configuring security agents for different types of terminal nodes and registering federated learning participation permissions for each terminal node; each terminal node training a local threat detection sub-model through a local security agent and uploading model parameters to a federated learning center node; the federated learning center node aggregating and generating a global collaborative defense model based on the model parameters of each terminal node using a weighted average algorithm; and each terminal node using the local threat detection sub-model and the global collaborative defense model to perform dual verification of local risks. This invention adopts a federated learning model, scheduling multiple terminal agents to share threat features through the federated learning center node, achieving a two-layer defense of "local detection + global collaboration," improving the identification rate of advanced threats propagating across terminals, and eliminating the defense blind spots of traditional solutions.

1. Name of the product in this design: Graphical User Interface for AI Student Ability Assessment and Planning in Electronic Devices. 2. Purpose of this design: for display and interaction. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: This system is designed to provide college students with a one-stop service for "ability assessment and planning". Through AI assessment, diagnostic reports and learning improvement plans, it helps users quickly identify their strengths and weaknesses, form clear career directions and stage goals, and integrate the assessment results into course learning and ability improvement. The main view of Design 1 features a "Competency Assessment and Planning" workbench page, which integrates information such as the AI ​​full-dimensional competitiveness assessment entry (Start Assessment Now / XXXXXXX Assessments Completed / My Approximate Score), career path prediction and goal planning (current stage, 1-3 year goals, long-term vision), assessment history and trend analysis, AI industry insights and skills enhancement plans (high-salary skill tags, personalized improvement routes, completion rate XXXXXXX%), and provides quick access to the learning center, making it easy for users to review assessment results and quickly take the next step. Click "Start Assessment Now" in the main view of Design 1 to enter the assessment answer page in the Design 1 change state diagram. The page displays the assessment progress (stage XXX / XXX, progress bar, XXXX%), and the questions and A / B / C / D options are presented in the middle. Users can answer questions one by one and go back to modify through "previous question". The system also provides real-time analysis and prompts (AI is analyzing your answering tendencies in real time...). After completing the quiz, users will be directed to the Design 1 Change Status Diagram 2 Diagnostic Report page. The page displays comprehensive employment competitiveness (XXX), core competency model (AI competency radar chart XXXXXXX), personality trait analysis, and AI in-depth diagnostic report (strengths analysis / improvement suggestions). A list of recommended career paths (job XXXXXXX, matching degree XXX) is provided on the right for users to view and select. The bottom supports "Return to Homepage", "Restart Assessment", and "Export PDF Report" for easy review and retention. Click "Enter Learning Center" in the main view of Design 2 to enter the Learning Center page of the Design 2 Change State Diagram. Users can filter courses under tags such as "All Courses / Professional Skills / General Qualities / Industry Knowledge", view their learning progress (XX%) in "My Courses in Progress" and continue learning. At the same time, they can get personalized course suggestions and advancement guides based on assessment results in "AI Intelligent Recommended Courses", and continuously track improvement effects through learning achievements and cumulative learning time (XXXXXXX), realizing a complete closed loop from assessment to diagnosis to planning to learning improvement. The "X" in the interface represents a text, number, or symbol area and does not represent the actual design content.