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Monitoring the stability and safety of climbing scaffolds in super-high-rise construction is critical to ensuring construction quality and worker safety. This study proposes a Global Navigation Satellite System (GNSS)-based real-time monitoring method to track scaffold displacement and assess structural performance. A multi-level data optimization framework integrating gross error elimination, data interpolation, robust Kalman filtering, and a Cumulative Sum Control Chart (CUSUM)-based early warning system is developed to enhance monitoring accuracy. The key objectives of this research are to improve real-time displacement tracking, suppress measurement noise, and establish an automated anomaly detection mechanism for climbing scaffolds under complex construction conditions. The proposed method was validated in a super-high-rise construction project in Tianjin, China. Experimental results demonstrated that the system effectively reduced high-frequency noise and gross errors, achieving root mean square error (RMSE) reductions of 51.4% in the E direction, 45.5% in the N direction, and 49.6% in the U direction. The system successfully tracked vertical climbing displacements of 4.4 m per ascent and horizontal deviations of 4 cm (E direction) and 2 cm (N direction). Additionally, the multi-level warning mechanism identified displacement anomalies based on predefined thresholds, providing an early warning function to enhance scaffold safety management. Compared to conventional monitoring methods, the proposed BeiDou/GNSS-based system provides higher precision, real-time adaptability, and enhanced automation, offering a scalable solution for intelligent construction safety management. The findings contribute to structural health monitoring (SHM) applications and can serve as a reference for future high-rise construction safety assessments. Full article