Research And Application Of Sudden Massive Data Technology In Monitoring System Of The Internet Of Things

With booming of the Internet of Things technology in recent years, more and more Internet of Things technology has been put into practical use. Use of the Internet of Things to monitor is a new and effective regulatory approach. For large-scale monitoring objects, thousands of monitoring sensors are usually required to irregularly acquire data. However, different ways of monitoring data acquision, data content as well as large amount of data on whole line always make backend data processing be the entire system bottleneck, causing the whole system to slow down and even paralysis. Meanwhile, database as the data container that cannot effectively reports real-time information to frontend, resulting in that the information can not be presented normally. Faced with such a huge amount of data, how to use technical means to strengthen and improve the data reception and processing capabilities as well as the overall speed of response of the system to avoid system run rate reduced or even system collapse is this paper’s main goal.This paper proposes a backend model based on Java and Oracle technology on sudden massive data processing. This model uses design patterns thought to manage the overall code structure. Web Service technology provides a cross-platform communication way to frontend and memcache thought provides data cache to solve the problems caused due to sudden data high speed. At the same time, the relational database Oracle divides data into different tables and partitions, and adopts real-time tables and historical tables to separate collection data, in order to improve query efficiency of current or historical state. This model will build different indexs for further improvement of the ability to read the massive data in accordance with monitoring system features. This paper is based on the needs of the general large-scale monitoring system, by deep-seated database design, data division processing and quick data query, can effectively solve the server blocking problem caused by burst data. Meanwhile, to any requests come from frontend, the database can be able to secearch and respond quickly. This method can be effectively applied to other real-time data monitoring system, as well as online analysis software such as OLAP mode database. Paper also tackles with server task scheduling and vast amount of data processing effectively; wich can be a reference in other related fields and system development in the Internet of Things.