# GNSS-Based Monitoring Methods for Mining Headframes
[PERSON] and colleagues utilized the spatiotemporal Kalman filter (STKF) to analyze dam deformation monitoring data, demonstrating the STKF's efficacy in noise filtering, data prediction, and interpolation of unmonitored data [21]. [PERSON] and [PERSON] conducted an analysis of deformation monitoring technology at the Diponegoro University dam in Semarang, Indonesia, utilizing Global Navigation Satellite System (GNSS) and ground measurement techniques. They evaluated the positioning accuracy of these technologies and highlighted the significance of GNSS in dam monitoring [22]. In the context of safety monitoring and assessment of large structures, such as headframes, it is imperative to study deformation risk assessment and develop early warning methods. [PERSON] et al. introduced a statistical approach for evaluating the risk associated with deformation monitoring systems, with a particular emphasis on the integrity risk related to the system's failure to provide timely alerts [23]. [PERSON] et al. developed a comprehensive risk assessment and early warning system for the construction and operation of high-speed railway underground bridges. Their approach involved both quantitative and qualitative analyses, integrating the \"as low as reasonably possible\" criterion for risk assessment and employing the autoregressive integrated moving average (ARIMA) method for deformation prediction [24]. [PERSON] and colleagues conducted a comprehensive review of technologies for landslide deformation monitoring and early warning, highlighting the critical role of deep deformation monitoring in enhancing early warning systems and suggesting future directions for the development of intelligent early warning models [25]. Meanwhile, [PERSON] and co-authors introduced an advanced framework for dam deformation monitoring that integrates deep learning with spatiotemporal characteristic analysis. This framework significantly improves prediction accuracy and facilitates real-time updates of warning thresholds by employing a synergistic approach that combines proper orthogonal decomposition, a deep kernel extreme learning machine algorithm, and cloud modeling techniques [26]. This approach exhibits substantial enhancements in predictive performance and the adaptive adjustment of safety indicators. The findings of this research not only highlight innovation and advancement in the domain of deformation monitoring, particularly concerning data processing and predictive methodologies, but also serve as a valuable reference for augmenting monitoring accuracy and ensuring engineering safety. Research on deformation monitoring predominantly focuses on data processing and analysis, along with deformation prediction and risk assessment. The development of models and methodologies is primarily applied to scenarios such as mining surfaces, landslides, dams, roads, and tunnels. However, there is a relative paucity of research concerning the deformation monitoring of mining shaft frameworks. In the realm of current research on GNSS technology, the majority of studies concentrate on discrete aspects, such as GNSS data preprocessing, accuracy enhancement, or algorithm modeling, as well as isolated risk assessment and early warning systems. However, this study successfully established a full-chain technical system for \"data preprocessing-risk assessment and early warning\", creating a closed-loop workflow from raw observation data to engineering decision support. This approach addresses the issue of information loss caused by the fragmentation of traditional research processes, significantly improving the timeliness and accuracy of structural safety monitoring in mining areas. Furthermore, for critical structures like mine headframes, most existing studies treat them as rigid bodies and monitor them using only one to four stations. In contrast, this study innovatively adopts an eight-point distributed monitoring scheme, decoupling the steel structure and sheave system into independent monitoring units for the first time.