## 3 Experimental Verification
### Monitoring Performance Analysis
#### 3.2.2 Monitoring Performance During the Climbing Scaffold Ascending Phase  
For scaffolding structures, the majority of the time is spent in a stationary state. However, as the building progresses, monitoring the climbing status of the scaffold becomes crucial. To comprehensively evaluate the monitoring system's dynamic detection capability under different displacement magnitudes, this study analyzes two typical climbing events: a large displacement scenario on 30 July, characterized by significant variations in both the horizontal and vertical directions, and a small displacement scenario on 30 June, where vertical displacement dominates with minor horizontal perturbations. By comparing the data processing results from these two scenarios, the system's robustness and sensitivity are separately validated. Figure 8 presents the monitoring data from 30 July before and after filtering. The raw data exhibited spike noise at approximately 1.5 h and 5.5 h, but this noise was effectively suppressed through filtering, demonstrating the system's strong data smoothing capability. Around 10 h, the climbing scaffold began its ascent (Figure 8c), resulting in noticeable changes in the horizontal directions. In the E direction (Figure 8a), displacement gradually increased after the climbing process began, reaching a maximum of approximately 0.04 m. In the N direction (Figure 8b), displacement reached approximately 0.02 m. Despite minor horizontal disturbances, the overall trend remained stable. The monitoring results for the U direction (Figure 8c) indicate that, after climbing initiation, the vertical displacement increased rapidly, ultimately reaching approximately 4.4 m, which is consistent with the expected climbing height. Overall, the results from 30 July confirm that the system can accurately capture both horizontal and vertical scaffold movements while effectively suppressing noise interference, thereby improving data reliability. Additionally, all measured parameters meet design specifications, and the BeiDou/GNSS monitoring results align closely with the actual climbing progress. This further validates the system's reliability and anti-interference capability in super-high-rise construction monitoring. Figure 9 illustrates the climbing scaffold monitoring process on 30 June. Compared to the results from 30 July, this dataset exhibits more subtle horizontal displacements. Throughout the climbing phase, the system successfully captured uniform short pauses, presenting an overall linear displacement pattern, which aligns well with the actual climbing scaffold movement. Additionally, after the climbing phase ended, the system stabilized in a shorter period, indicating that the improved algorithm demonstrates better adaptability in both dynamic and static phases. During the pre-climbing static phase, noticeable noise fluctuations were observed around 1 h, 2 h, and 5 h (Figure 9a,b). After applying filtering techniques, these fluctuations were effectively suppressed. Directly observing the E and N direction data in Figure 9a,b shows minimal displacement deviations, making it difficult to obtain accurate analytical results. Therefore, a more detailed analysis was conducted using the dataset presented in Table 2, which provides hourly mean displacement values for the E and N directions. From the processed data, it is evident that the E-direction displacement exhibits a more significant variation after preprocessing, increasing from \(-\)0.0041 m at 1 h to 0.0127 m at 10 h, with an overall increase of 0.0168 m. This trend demonstrates a gradual upward displacement with a decreasing fluctuation magnitude. In contrast, the N-direction displacement remains relatively stable, changing only slightly from 0.0044 m at 1 h to 0.0030 m at 10 h, with a total variation of just 0.0014 m. These results indicate that the applied preprocessing method is not only effective for detecting large-scale displacement