Visual Group Intelligent Decision-making Real-time Monitoring And Early Warning System For Landslides

China is a country with frequent geological disasters.Landslides pose a huge threat to the safety of people's lives and property.However,the existing technical solutions that can realize landslide monitoring have high installation and deployment costs and unstable performance.It is difficult to monitor the landslide area in real time,and it is also difficult to realize the large-scale layout of monitoring equipment.Aiming at the shortcomings of existing landslide early warning and monitoring methods and the urgent needs of the people,this thesis designs and develops a visual group intelligent decision-making landslide monitoring and early warning system that combines particle swarm algorithm and grayscale prediction algorithm.It mainly implements the following functional modules: realtime collection of displacement data and groundwater content data of monitoring nodes,real-time acquisition of distance data between monitoring nodes,and realization of selforganizing network functions between monitoring nodes.The data can be reliably transmitted through the self-organizing network,the data can be displayed through the web visual interface,the historical data can be analyzed and predicted,and the FLAC3 D software can be modeled to simulate the real mountain sliding.At the same time,the current status of the early warning monitoring node can be dynamically observed on the web side in real time.Since the monitoring nodes of landslides are basically buried on the hills with more dangerous terrain,it is difficult to regularly maintain the monitoring nodes manually.Therefore,it is necessary to design a plan that allows the monitoring nodes to work in the field for a long time.In this thesis,low-power LoRa(Long Range)chip equipment is selected as a data communication transmission tool between terminal nodes,and low-power acceleration sensors and groundwater content sensors are selected to collect and measure environmental information of landslides.The data collected by the monitoring nodes is converged and transmitted to the LoRa gateway node,and then it is remotely transmitted by using NBIoT(Narrow Band Internet of Things).The data is saved in the central database of the cloud server.The Vue Architecture Web interface is used to display,query,update and save the data in real time.The high-reliability real-time data obtained through node monitoring can be used to understand the current movement status of the landslide body.The law of the displacement of the landslide body is calculated through modeling,and then the movement trend of the landslide is predicted,which provides a scientific basis for landslide prevention.