Operation Status Monitoring and Analysis Based on the Architecture of Stream Computing and Memory Computing

Abstract

The user's real-time electricity consumption data has become an important data source to measure the operation of power grid enterprises. Based on the user's real-time electricity consumption data, combined with discrete Fourier transform, we can monitor the abnormal behavior of the user. Based on the constructed user behavior characteristics, the K-Means clustering algorithm is used to classify the users' behavior categories. Considering the data size and real-time requirements of practical application, stream computing and memory computing technology together constitute the system framework, which is used in real-time monitoring and analysis system. Finally, through the analysis of the results of electricity behavior, the architecture of stream computing and memory computing is compared with the traditional data analysis platform.