Deep Learning-Enhanced High-Precision Wind Field Concurrent Triboelectric Sensing.

Area of Science:

• Materials Science
• Sensor Technology
• Environmental Monitoring

Background:

• Conventional wind sensing methods lack adaptability in harsh environments.
• Real-time wind field data is crucial for meteorology and environmental studies.

Purpose of the Study:

• To develop a robust and intelligent wind sensing system.
• To achieve accurate, real-time wind speed and direction prediction.
• To enhance environmental resilience and long-term signal stability.

Main Methods:

• Coupling a triboelectric nanogenerator (TENG) with vortex-induced vibration (VIV) and magnetic elements.
• Utilizing a dual-channel frequency difference mechanism for 360° wind direction decoding.
• Employing a deep learning model (Regression Transformer - ReT) for signal analysis.

Main Results:

• Achieved high-accuracy wind speed prediction with errors below 5% and wind direction error within 1°.
• Demonstrated robust performance in -28°C and wind-sand conditions.
• Validated system resilience and adaptability in extreme outdoor scenarios.

Conclusions:

• The developed MVTS provides a resilient, intelligent, and integrated solution for autonomous wind monitoring.
• The system is suitable for data-scarce and infrastructure-limited environments.
• This technology advances capabilities for environmental monitoring and meteorological forecasting.