Design And Implementation Of Cold Chain Transportation Monitoring System Based On NB-IoT

With the rapid growth of refrigerated transportation equipment in our country and the improvement of cargo transportation safety and freshness requirements,the cold chain transportation monitoring system has become a necessary system for refrigerated transportation equipment.The existing cold chain transportation monitoring system mainly monitors the location of transportation vehicles,with single information collection,short message method for information transmission,simple information processing,and it is difficult to meet the needs of modern cold chain transportation monitoring.Therefore,the use of modern information technology to research and develop a cold chain transportation monitoring system with comprehensive information collection,timely information transmission,and advanced information processing has an important role.Aiming at the problem that the cold chain transportation environment of urban fresh agricultural products is difficult to be effectively guaranteed,a cold chain transportation monitoring system is designed.The data acquisition,transmission and processing technology are researched,and the monitoring system is designed from the two levels of on-board monitoring terminal and monitoring center.The on-board monitoring terminal is connected to a variety of sensors and positioning modules through a low-power processor to collect environmental parameters and vehicle location information in the cold chain compartment,and data transmission is carried out through the NB-IoT module.The design of the monitoring center mainly uses Node.js,HTML,CSS,JavaScript and database technology,including three parts:back-end server,database and front-end management platform to realize the receiving,processing,storage and display of data.In addition,based on the realization of system functions,in order to realize the prediction of the environment data in the cabin,this paper uses two prediction algorithms:ARMA autoregressive average algorithm and BP neural network algorithm to construct a prediction model.The prediction performance of the two algorithms is compared through experimental simulation.The experimental results show that the prediction model of BP neural network is closer to the real value,and its mean absolute error,root mean square error,and correlation coefficient are 0.1353,0.1660,and 0.9667 respectively.It shows that BP neural network has better prediction effect.Finally,the cold chain transportation monitoring system is tested for function and power consumption.The test results show that the system can realize the collection and transmission of environmental data of cold chain carriages.The data transmission time is within 3s,and it has good real-time performance.The monitoring center monitors the temperature,humidity,carbon dioxide concentration,and air pressure data changes in the cabin.The expected functions such as data chart display,user registration and login,historical record query,and map positioning have been completed.