Landslide Monitoring and Early Warning System based on Edge Computing

Abstract

With the advancement of modern sensing technology and network communication, the geological disaster monitoring and early warning technology is changing toward intelligence, continuity, and real-time monitoring. The most effective way to improve real-time monitoring and early warning is to conduct front-end analysis in the field of geological disasters. With the application of the Internet of Things technology in geological disasters, field data collection and field-level data strategies belong to the edge computing gateway for execution. According to the latest research trend of landslide on-site monitoring and early warning, edge computing gateways usually use thresholds for monitoring and early warning. On the basis of the empirical model of simple statistics or the theoretical model of landslide deformation early warning, key early warning indicators and weights are proposed to judge the stage of landslide evolution or disaster level. This study starts with a comprehensive analysis of the temporal and spatial evolution of the surface deformation area in the field of geological disaster monitoring. Then, an edge computing smart gateway is designed on the basis of the Linux operating system. The Long Range Radio field wireless sensor network is used to develop an automated monitoring system for slopes of displacement monitoring points, rainfall, and soil moisture content-inducing factors. Subsequently, the effective methods of obtaining multilevel topographic data of slopes are investigated using field information, such as the velocity and acceleration of surface displacement data. The slope multielement trigger monitoring model with multiple induced element coupling modes is explored to realize the front-end edge computing strategy method of slope monitoring and early warning. The analysis results can be sent to high-level data analysis servers, such as the software deployed on the Local Area Network and cloud servers of the relevant department for secondary use. This research overcomes the problems of regular collection of existing on-site monitoring instruments, loss of catastrophic information, and low effective data rate. Compared with traditional working modes, such as uploading the original monitoring data to a server for data extraction and analysis and then feedback to control the field equipment, using front-end edge computing is more efficient in realizing the first data processing utilization and adaptive monitoring processing. It can also provide more effective and reliable data for disaster monitoring and early warning. In conclusion, this research is of great significance in effectively capturing the laws of early evolution, improving real-time early warning capabilities, and promoting the industrialization of national geological disaster monitoring and early warning technology and equipment.