2results about How to "Realize self-learning" patented technology

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

This invention discloses an intelligent control method and system for motorcycle calipers. The method involves real-time acquisition and preprocessing of multi-modal data, including vehicle speed, wheel speed, piston displacement, brake disc temperature, tilt angle, and lateral acceleration, using multiple sensors. Based on the data, braking demand and adhesion coefficient are calculated, and tilt angle and lateral acceleration are used to identify straight-line, curved, or emergency braking scenarios. The data is input into a pre-trained braking control machine learning model to generate a target braking force variation curve. Based on this curve, the drive motor and caliper are controlled for braking, and the model is periodically optimized based on braking feedback and historical data. A component degradation model is used to predict the braking efficiency decay trend, and the braking force is compensated and the model is updated. Closed-loop control is used to adjust the motor current in real-time to follow the target braking force. The brake disc temperature is continuously monitored, and thermal fade compensation is triggered when the temperature exceeds a threshold. This invention achieves adaptive, precise, and reliable intelligent braking for motorcycles.