Construction and realization of information platform for geological disaster monitoring and early warning in Qinghai Province

Under the influence of global changes, the frequency of geological disasters in Qinghai Province, which is located in the northeastern part of the Qinghai-Tibet Plateau, has increased significantly. It is important to build a geological disaster monitoring and early warning information platform in Qinghai Province to reduce the safety of life and property in the area. In this paper, the process of construction and realization of the information platform for geological disaster monitoring and early warning in Qinghai Province is described in terms of equipment deployment, system construction, platform function and operation status. At present, the information platform of geological disaster monitoring and early warning in Qinghai Province has been able to achieve the unified management and dynamic update of disaster information of different sources and batches of geological disasters, and achieve data integration, result visualization, information synthesis and system integration. The information platform of geological disaster monitoring and early warning in Qinghai Province includes 10 modules such as geological disaster investigation and evaluation system, geological disaster monitoring and early warning system, and geological disaster meteorological early warning system. At this stage, all universal monitoring data can be sent to the national geological disaster monitoring data platform synchronously, which can support the work of geological disaster early warning efficiently. The monitoring and early warning information platform can automatically analyze the monitoring data collected in real time and support a variety of early warning models to discriminate; when the monitoring data changes and touches the preset discriminating models, it can automatically send geohazard early warning information. Through the trial run of the system, there have been cases of results monitoring and early warning, and the data is reliable enough to meet the needs of monitoring and early warning.