Deep Learning‐Enhanced High‐Precision Wind Field Concurrent Triboelectric Sensing

Abstract Reliable and real‐time wind field sensing is critical for environmental monitoring and distributed meteorological forecasting. However, conventional solutions often suffer from structural complexity and poor adaptability to harsh environments. In this work, a magneto‐vortex triboelectric sensing system (MVTS) is developed by coupling a triboelectric nanogenerator (TENG) with a vortex‐induced vibration (VIV) structure and magnetically reinforced elastic elements. The system converts wind‐induced oscillations into electrical signals and supports full 360° wind direction decoding through a dual‐channel frequency difference mechanism. Material‐level optimization using FEP, nylon, and rabbit‐fur electrostatic compensation enhances environmental resilience and long‐term signal stability. A deep learning model, Regression Transformer (ReT), is constructed to extract temporal and frequency domain features from multichannel TENG signals, enabling high‐accurate prediction of wind speed and direction. Controlled indoor experiments confirm a maximum wind speed error of 0.69 m s −1 , with prediction errors consistently below 5% and a wind direction error within 1°. Additional validations under −28 °C low‐temperature conditions and wind‐sand environments demonstrate the system's robust operation and strong environmental adaptability. This work provides a resilient, intelligent, and fully integrated solution for autonomous wind field monitoring in data‐scarce, infrastructure‐limited, and extreme outdoor scenarios.