Open AccessMonitoring and Analysis of Distributed New Energy Resources Based on the Internet of Things
Author(s) -
Hengzhi Cui,
Chong Wang,
Haixuan Liu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/714/4/042025
Subject(s) - computer science , cloud computing , big data , distributed computing , database , distributed data store , data collection , distributed database , data warehouse , the internet , efficient energy use , energy consumption , resource (disambiguation) , architecture , data intensive computing , distributed generation , modular design , operating system , renewable energy , grid computing , computer network , engineering , art , statistics , geometry , mathematics , grid , electrical engineering , visual arts
In order to accurately monitor and analyze new energy resources in real time, the analysis and design of a distributed new energy resource monitoring device based on the Internet of Things cloud platform is carried out, and a Hadoop distributed new energy system based on the Internet of Things cloud computing is designed. It analyzes the computing architecture of parallel collection and distributed storage of new energy resource monitoring data, analyzes the infrastructure system of the distributed system, and builds the big data processing architecture and computing service architecture for the electricity information collection system based on the Hadoop cluster. Tasks are processed in parallel to improve the computing efficiency of electricity consumption big data; at the same time, in order to solve the problem of insufficient storage space caused by the sharp increase in the amount of data in the electricity consumption information collection system and the difficulty of data interaction at all levels, a distributed database is developed. The modular design of the parallel data collection system of HBase and the data warehouse tool Hive, and the distributed data calculation design based on the MapReduce method have improved the efficiency of data collection and the unified storage of heterogeneous data. The research provides reference for realizing an efficient and safe new energy resource monitoring data processing system.