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Research Of Natural Disaster Emergency Warning System Based On Embedded

Posted on:2018-03-13Degree:MasterType:Thesis
Country:ChinaCandidate:J X ZhaoFull Text:PDF
GTID:2371330542472552Subject:Control engineering
Abstract/Summary:PDF Full Text Request
China is a vast country,with complex geological formations,and frequent geological disasters such as flash floods and mudslides,they has caused great loss of life and property to the people and the country.Usually large natural disasters cause a lot of secondary disasters,it is a huge potential danger for the relief workers and the people.Therefore,the occurrence of mudslides in the aftermath of major natural disasters may occur,The development of emergency warning monitoring systems is extremely important.This paper mainly designs the wireless collection transmission module based on MSP430 and the emergency warning terminal with STM32 as the core.The prediction model and the landslide prediction model based on RBF neural network are also established.Wireless acquisition and transmission module design of the analog,digital and digital sensor interface.The program is programmed to implement the data collection of multiple sensors in the field,and transmit the data to the emergency warning terminal using the 433 MHz module.The emergency warning terminal is based on STM32,which extends the SD card storage module,TFT-LCD touch screen module,433 MHz wireless communication module,Through the transplant operating system and stemwin-based human-to-computer interaction system,and the preparation of applications,can easily access the wireless acquisition and transmission module reported data,and can store and display relevant information.Emergency warning terminal can be in the emergency site according to the set threshold for early warning and forecasting,but also the collection data upload center station according to the forecast model for forecasting early warning.Experiments and simulation results verify the rationality of the system design and the accuracy of the forecast model.
Keywords/Search Tags:disaster monitoring, STM32, RBF neural network
PDF Full Text Request
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