Research On Grassland Fire Monitoring System Based On Internet Of Things

The rise of the Internet industry for the implementation of grassland fire monitoring and early warning system provides more meticulous precision of the method.In the monitoring of grassland fire,the application of various sensors to effectively monitor the grassland environment,and the construction of the optimal environment for the grassland in each season by means of big data analysis and human control are important measures to further restrain grassland fire.We are aiming at the problems of the existing grassland fire monitoring system data collection with high power consumption,data cannot be cut synchronously and not timeliness,etc.,this paper chooses a low-cost,moderate power consumption,and organized distributed wireless sensor network(WSN)based on ZigBee technology.A set of grassland fire monitoring system with synchronization mechanism based on CC2530 chip is designed.The entire grassland fire monitoring system is divided into three parts: sensor terminal,network terminal and application terminal.The sensing end is the ZigBee network,which mainly senses the physical quantities on the grassland(such as air temperature and humidity,smoke particles,soil humidity,etc.)through various sensors,and then uploads it to the gateway through the topology Because the tree cluster structure has good scalability and can build a huge network,the tree cluster structure is adopted.In addition,in order to achieve the effect of reducing the energy consumption of the entire sensor network,we have selected an improved RBS algorithm based on Bayesian estimation and an improved TPSN algorithm based on Bayesian estimation for the upper network and the lower network,respectively.Through experiments,it is found that the overall accuracy of the two improved algorithms is not as good as the classic algorithm,but the accuracy is not much different.The result of the classic algorithm is only reliable when the sample size is large enough(after 50 sets of sample points),while the improved algorithm is reliable when the sample size is small.In this way,only a small number of sample points need to be collected,and the entire network can be synchronized,which greatly reduces the wireless data exchange and further reduces the energy consumption of the entire network.After the gateway and network side receive the data from the perception,they first perform data preprocessing,correct and sort the defects and garbled data in the data,complete the data display,and finally upload the data to the private cloud.The follow-up visual effect shows that the back-end data synchronization effect is good,and the synchronization error of the entire network is between 1-3ms.The private cloud is the application side.After receiving the data from the gateway,it saves the data to the database for subsequent data analysis on the one hand,and presents the data on the other hand.