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.
Highlights
With the rapid development of “interconnected” and “intelligent” industries, industrial big data has become the focus of current research
We propose a distributed storage, calculation model DSCS-LTS, correlation coefficient estimation method CCCA-LTS for massive, long time series data, and propose a data
We propose a method to estimate the correlation coefficients of two sequences on HBase and design a fast estimation method, CCCA-LTS, for the upper and lower bounds of the correlation coefficients
Summary
With the rapid development of “interconnected” and “intelligent” industries, industrial big data has become the focus of current research. The construction and development of the “interconnected” industry and “intelligent” industry require a large amount of basic data These data are to stay in the collection stage and to carry out deep storage, analysis, and mining, for example, real-time analysis of industrial field data, alarm of abnormal data, supervision of every process in the production process, and intelligent decision-making. The scheme of “centralized collection and centralized management” provides an effective solution for massive data management in the industrial field In this paper, this specific project is an example: a large tomato sauce factory production line, collecting all kinds of monitoring data (such as sterilization temperature, tank pressure, pipeline flow rate, and motor power), real-time data through the network transmission to the data management platform, for massive data design distributed storage and analysis methods, realize data depth mining and visualization, and provide the basis for decision-making. Wireless Communications and Mobile Computing visualization method that addresses the key problems related to the management of massive monitoring data
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