## 5 Conclusions  
This paper proposes a BeiDou/GNSS-based real-time monitoring system for climbing scaffolds in super-high-rise construction, integrating multi-level data optimization and an anomaly detection mechanism to enhance monitoring accuracy and safety. The system was experimentally validated on an actual super-high-rise construction project, and the findings confirm its effectiveness in tracking scaffold displacement and detecting potential safety risks. Compared to conventional monitoring approaches, the proposed method significantly improves the real-time accuracy, robustness, and early warning capabilities, providing a reliable solution for ensuring scaffold stability in complex construction environments.  
Experimental validation demonstrated a high static positioning accuracy and robust dynamic tracking performance. During static monitoring on 29 June, the RMSE values were significantly reduced through adaptive filtering and gross error correction, achieving reductions of 51.4% in the East (E) direction, 45.5% in the North (N) direction, and 49.6% in the Vertical (U) direction, ensuring sub-centimeter stability.  
For dynamic monitoring, two climbing events were analyzed. The first event on June 30 confirmed the system's ability to track the scaffold ascent with a measured displacement of 4.42 m, capturing millimeter-level horizontal perturbations (1.68 cm in E and 0.14 cm in N direction), demonstrating a high sensitivity to minor oscillations. The second event on 30 July recorded a vertical displacement of 4.38 m, with horizontal deviations of 4.0 cm (E) and 2.0 cm (N) due to wind loads and environmental factors. These findings validate the system's capability to detect both large-scale scaffold movements and fine horizontal oscillations with high precision.  
Beyond displacement tracking, the system integrated a CUSUM-based anomaly detection algorithm, which successfully identified deviations in scaffold movement and triggered early warnings for potential structural risks. Compared to conventional manual inspection and single-sensor monitoring approaches, the proposed BeiDou/GNSS-based system significantly enhances real-time adaptability, measurement accuracy, and automated risk assessment, making it a practical solution for scaffold monitoring in complex construction environments.  
Future research should focus on expanding validation across multiple construction projects with varying structural designs and environmental conditions to improve system generalizability. Additionally, integrating MEMS-based IMUs with GNSS data could enhance short-term displacement tracking. Machine-learning-enhanced anomaly detection could further refine the warning system, improving predictive capabilities for scaffold instability. Furthermore, coupling the BeiDou/GNSS monitoring system with structural health monitoring (SHM) models, such as finite element modeling (FEM), could provide deeper insights into scaffold stability and long-term performance.  
Conceptualization, G.L.; investigation, P.W. and P.Z.; methodology, P.W. and J.W.; validation, P.W., J.G., J.Z., H.Z. and Y.L.; writing--original draft, P.W.; writing--review and editing, P.W., G.L. and J.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (42274029; 42404035); and R&D Program of Beijing Municipal Education Commission (KM202410016007).  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.