<p>The monitoring system plays a crucial role in the stability analysis of mine slopes and the early warning of engineering geological disasters. Monitoring equipment that integrates GNSS systems and various sensors is widely employed in the monitoring of landslide disasters. External factors such as high temperatures can compromise the reliability of GNSS devices and sensors, resulting in frequent abnormal fluctuations and loss of raw data. Unstable data sequences pose significant challenges to the accurate characterization of landslide trends and the early warning of such disasters. An optimized method based on the time window method, Kalman filtering technique (KFT), Pauta criterion, and Weibull distribution has been proposed to eliminate abnormal fluctuations and data loss. A preliminary-accurate two-step abnormal data identification method has been developed, leveraging the Pauta criterion and Weibull distribution. The dynamic process noise KFT was introduced to realize the accurate fitting of the denoising sequences. The effectiveness of the optimization method was validated through the processing of monitoring data from the Hainan-Shilu Iron Mine in China. The optimized curves effectively characterize the impact of rainfall and blasting on the development of landslides. This study contributes to a more accurate interpretation of landslide parameter sequences, revealing patterns of variation in landslide angles and displacements, thereby ensuring the safety and sustainability of mining resource extraction.</p>