## 1 Introduction
Although emerging technologies have been leveraged to enhance scaffold safety monitoring ([PERSON] et al., 2024 [17]; [PERSON] et al., 2025 [18]; [PERSON] et al., 2024 [19]), fully intelligent monitoring has yet to be realized. Recently, artificial intelligence (AI) and multi-source data fusion have provided new perspectives on scaffold monitoring. [PERSON] et al. [20] and [PERSON] et al. [21] integrated BIM models with multi-sensor data to achieve the real-time visualization of scaffold planning. [PERSON] et al. [22] introduced a ConvLSTM-based multi-source information fusion model, which enables the dynamic analysis of bolt failure and steel pipe deformation in scaffolding. In addition, deep-learning-based image recognition methods have been developed to detect scaffold safety hazards through video surveillance ([PERSON] et al., 2020 [23]). However, existing approaches still present notable limitations: (1) a heavy reliance on dense sensor networks, leading to high costs and complex deployment requirements; (2) inadequate real-time tracking capabilities for scaffold ascent and short-term dynamic displacements; and (3) a lack of an effective early warning mechanism, making it difficult to respond to sudden structural anomalies. Additionally, wind-induced vibrations and environmental disturbances significantly affect monitoring accuracy, but existing methods have not fully accounted for these error sources. In recent years, Global Navigation Satellite Systems (GNSS) have emerged as a powerful tool for structural health monitoring due to their high-precision positioning and real-time tracking capabilities ([PERSON] et al., 2022 [24]; [PERSON] et al., 2024 [25]). However, GNSS applications in dynamic climbing scaffold monitoring remain largely unexplored. The existing research has primarily focused on the long-term deformation monitoring of entire structures, with little emphasis on the high-precision tracking of short-period dynamic movements during scaffold ascent ([PERSON] et al., 2024 [26]; [PERSON] et al., 2025 [27]). To address the aforementioned challenges, this study proposes a BeiDou/GNSS-based monitoring framework for climbing scaffolds in super-high-rise buildings and conducts field validation in an actual construction project. The main contributions of this study include the following: (1) High-Precision Real-Time Monitoring Method: A BeiDou/GNSS-based 3D dynamic monitoring system is developed to achieve the real-time tracking of scaffold movements, enhancing measurement accuracy and system stability. (2) Multi-Level Data Optimization Strategy: A combination of gross error elimination, adaptive filtering, and Kalman filtering is applied to improve GNSS data reliability, mitigating environmental noise effects. (3) CUSUM-Based Anomaly Detection Mechanism: A Cumulative Sum (CUSUM) control chart model is adopted to accurately identify scaffold operation anomalies, improving the sensitivity and reliability of the early warning system. (4) Field Validation and Performance Assessment: The monitoring system is tested in a real-world super-high-rise construction project to evaluate its applicability and stability in complex construction environments, verifying its effectiveness in climbing scaffold safety monitoring. The remainder of this paper is structured as follows: Section 2 introduces the proposed BeiDou/GNSS monitoring framework and multi-level warning mechanism. Section 3 details the experimental setup, data processing methods, and system performance validation results. Section 4 discusses the research findings, system limitations, and future optimization directions. Finally, Section 5 summarizes the study and provides an outlook for future research.