Design And Implementation Of Monitoring Platform Of Vehicle Information Based On Massive Data

In recent years,with the rapid development and extensive application of information technology,the information of all walks of life has shown an exponential growth trend,and traditional industries have gradually approached the trend of informatization.With the improvement of people’s living standards,cars have become the main means of transportation,and the amount of information generated by vehicles has reached the scale of massive data.The current vehicle monitoring system is based on relational databases and traditional development techniques.When the number of connected vehicle-mounted terminals reaches a certain scale,the collection,processing,and querying efficiency of the system will be greatly weakened.In particular,the processing performance of the real-time requirements such as overspeed detection will be greatly affected.Based on this situation,this paper deeply analyzes the causes of the performance bottleneck of the original system,combines the characteristics of the vehicle data to propose a real-time optimization program for massive data,and uses the combination of Mina framework and Kafka message queues to replace memcached to collect data,while using Storm computing framework to complete the calculation of real-time data.Based on this optimization program,this paper designs and builds a vehicle monitoring platform for massive data based on the ”796” protocol standard issued by the Ministry of Communications,using the related technologies of big data processing.The platform adopts a distributed architecture.The vehicle terminal service uploads vehicle information to the Kafka message queue through the Mina framework.The Storm framework receives buffered data for real-time calculation.The analysis results are stored in mysql for persistent storage.The data stored in mysql will be regularly migrated to Hbase in a timely manner.Finally,the basic function of each module of the system is tested in this paper.The result shows that each module of the system can run normally.Then the paper carries out tests on the performance of the system according to different levels of data.The results show that the performance of the system is significantly higher than the original system,and it can provide users with a stable and efficient real-time monitoring platform for massive vehicles.