微波阻抗变换器教学交互式系统开发及验证OA

To address the limitations of traditional Coriolis-force experiments,such as coarse measurements and limited functionality,this study develops an intelligent platform for the demonstration and investigation of Coriolis dynamics.The platform adopts an end-cloud col-laborative distributed architecture:the front end consists of an STM32 control unit and a K230 vision-acquisition module for multi-source data collection,while the PC-based back end integrates Bluetooth and Wi-Fi data transmission.By incorporating a YOLOv8-based object-detection model,the system enables real-time trajectory tracking and synchronous comparison between theoretical predictions and experimental measurements.Performance evaluation shows that the platform achieves a spatial resolution of 0.667mm/pixel,an end-to-end latency of less than 1.320s,and an R2 value above 0.99 between YOLO-extracted experimental traj-ectories and theoretical trajectories over 50 repeated trials under identical conditions,demon-strating high precision,real-time performance,and strong reliability.Through two represent-ative experiments—ball-motion deflection and fluid erosion—the platform supports both intui-tive visualization of classical physical laws and in-depth exploration of complex nonlinear phe-nomena.By integrating the Mamba model with physics-informed neural networks(PINNs),together with semi-supervised learning and self-attention mechanisms,the platform can pre-dict the outcomes of fluid-erosion experiments and infer the dominant physical mechanisms governing the erosion process directly from experimental data.This work provides a complete and practical solution for the modernization and intelligent upgrading of traditional physics ex-perimental platforms.