Design of Management Platform Architecture and Key Algorithm for Massive Monitoring Big Data

Abstract

With the construction and development of industrial informatization, industrial big data has become a trend within the smart industry. To obtain valuable information on massive data, achieving the acquisition, storage, analysis, and mining is becoming an important area of research. Focusing on the application requirements for industrial fields, we propose a data acquisition and analysis system based on the NB-IoT for industrial applications. The system is an integrated system that includes sensor data acquisition, data transmission, data storage, and analysis mining. In this study, we mainly focused on the use of the NB-IoT network to collect and transmit real-time data for sensors. First, for the long time series (e.g., if we collect the data streams for one year for the sensor with a frequency of 1 Hz, the length of the series will reach 107). Then, we propose DSCS-LTS, a distributed storage and calculation model, and CCCA-LTS, an algorithm for the correlation coefficient of long time series in a distributed environment. Third, we propose a granularity selection algorithm and query process logic for visualization. We tested the platform in our laboratory and an automated production line for one year, and the experimental results using real data sets show that our approach is effective and scalable, can achieve efficient data management, and provide the basis for intelligent enterprise decision-making.