Design And Implementation Of Intelligent Agriculture Monitoring Platform Based On MQTT

Agriculture is the basic industry of our country,but compared with western developed countries,the modernization level and scale of agriculture in our country still need to be improved.The exchange of science and technology and agricultural technology of the Internet of things promotes the development of intelligent agriculture,facilitates the accurate grasp of crop growth status data,and can promote the scientific cultivation of crops and the increase of crop yield.In view of this,this paper proposes an intelligent agricultural monitoring platform based on MQTT message security transmission and ILZ4 data compression,which realizes the intelligence and precision of agricultural planting,and the platform can adapt to the intelligent agricultural scene with large data scale and fast response.The work done in this paper includes four points.First,starting from the reality,the architecture of the monitoring platform and the architecture design of the message engine are given,and the intelligent agricultural monitoring platform is built based on the MQTT protocol.Secondly,the ILZ4 compression algorithm is introduced,and the ILZ4 compression algorithm is integrated into the information storage and message transmission tasks,which can realize the real-time compression and transmission of large-scale monitoring information flow.In order to ensure the security of data transmission and the stability of the monitoring platform,TLS encryption protocol and double proxy server design are introduced into MQTT transmission.Thirdly,based on the capabilities provided by the platform,an intelligent agricultural monitoring system is developed to realize the functions of remote realtime monitoring of the production environment,visual viewing of environmental parameters,environmental anomaly alarm and platform data management,so that administrators can timely understand the agricultural production environment,remote control,and achieve precision agricultural planting.Fourth,simulation experiments are carried out to test and compare the performance of the proposed method by using the randomly generated data streams of 3 million virtual Internet of things monitoring data points.The experimental results show that this method has better performance in average throughput,compression ratio and execution time,and can reduce the data storage cost and transmission overhead in Internet of things-cloud applications.The work of this paper has good theoretical and application reference value for the application of intelligent agriculture,and provides a good,universal and scalable solution for the early warning and management of agricultural production environment.