A Design And Implementation Of Monitoring And Early Warging System Based On BP Neural Network

With the development of technology, people have higher requirement to the living environment, environment of good quality is not only effects people’s daily life, but also effects the efficiency and quality of electronic equipment, briefly the prediction of environment statement is extremely important.To build the monitoring and early warning system, firstly using wireless sensor network of the internet of things technology group to build informational and intelli ge nt remote control networks to accurate the real-time environmental parameters. To accomplish effective monitoring of the environmental information such as smoke, water, temperature and humidity, users of the system can clearly understand the status of the current environment. Secondly, the forecasting model of high efficiency and accuracy is needed to forecast the specific environment information, such as temperature and humidity.Compared to smoke detection and water detection, the temperature growing trends shows characteristics of nonlinear, smooth and stable. Though three layers of back propagation neural network people can approach all nonlinear curves infinitely, using this model we can predict the growing trend of temperature precisely. When determining an appropriate structure of the back propagation neural network, and training it with previous temperature data, we can predict the temperature following in a short time to some extent. The experiments show that the model has better performance in the prediction of temperature growing trend, thus leading to early warning before accident happened.Through the application of the monitoring and early warning system, the user can be timely and accurate informed of the current environment of the specific situatio n. On the one hand, it can reduce the huge workload of manual observation and record during monitoring environment parameters, and avoid the error in manual observation and record. On the other hand, the trend of the environmental parameters is predicted accurately, so the potential safety hazard can be avoided, and the disaster can be prevented effectively.