Design And Realization Of RTU Software For Geological Disaster Monitoring

Once a mudslide disaster breaks out,it often brings huge casualties and economic losses,and poses a serious threat to the lives and property of the people in mountainous areas and road traffic safety.The geological conditions in my country's mountainous areas are complex,and debris flow disasters occur frequently.On some important traffic arteries,traditional methods of manual observation cannot be effectively monitored all the time.The existing debris flow early warning device has relatively poor adaptability to the environment,and its measurement parameters are relatively single.Compared with similar products in the world,the stability is lower,and it is difficult to work stably for a long time under severe working conditions in the field.Therefore,it is necessary to develop a monitoring device that can maintain long-term work in the field and can remotely and fully automate all-weather monitoring of debris flow information.The remote terminal unit RTU for geological disaster monitoring designed in this paper can collect meteorological,hydrological,sound and other data of debris flow-prone areas according to the evolution,development and formation of debris flow disasters,and perform intelligent analysis and decision-making.The main content of this paper is as follows:(1)Complete the software design of the RTU equipment side,mainly including low-power management design and sensor acquisition design.In order to make the RTU work in the field for a long time,this paper designs a low-power management mode for the RTU from a software perspective.The RTU system can be awakened through timed sleep to reduce the overall operating power consumption.Then design the collection function according to the sensor collection signal input type,and design the appropriate data collection method according to the different working characteristics of different sensors.(2)In order to enable RTU equipment to collect data and send it to the host computer,this paper designs a dual-channel network communication technology based on 4G communication and Beidou short message communication technology.Among them,Beidou communication is the auxiliary communication method,and 4G communication,as the main communication method of the RTU device,needs to be combined with the message queue telemetry protocol MQTT,and connected to the Alibaba Cloud platform to complete the publishing and receiving of messages.(3)The data collected by the Earth Disaster RTU will eventually be sent to the upper computer.When the upper computer collects abnormal data,the system needs to automatically send out early warning information.This article will introduce a single-class learning method based on the autoencoder to identify the abnormal data in the RTU collected data.When a debris flow disaster occurs,the characteristics of the collected data change significantly,and the system will automatically identify the abnormal data in the reported data and issue an early warning message.Finally,the algorithm is simulated and verified by the rainfall signal and mud level signal collected in Wenchuan area.The verification results show that the single classification method based on the autoencoder has a high recognition rate for debris flow abnormal data,and the RTU system can be used for debris flow disaster early warning.At the end of this paper,we use the built RTU platform prototype to test the RTU acquisition system as a whole.First,test the low power consumption mode of the RTU device side.Then collect the signal input type from the sensor,and test each acquisition sensor in the form of analog quantity,switch quantity and RS485 signal input.Then the dual-channel networking communication of the RTU system was tested,and the 4G module was tested to verify the design integrity of the entire acquisition system and the effectiveness of data transmission.Finally,the identification test of the abnormal data state was completed.The debris flow early warning system was tested by using the rainfall data collected by the monitoring equipment.The test results showed that the autoencoder algorithm can better identify the abnormal data in the collected data.