Distributed-Swarm: A Real-Time Pattern Detection Model Based on Density Clustering

The advancement of power technology and the improvement of people’s living standards promote the expansion of the power grid scale and the sharp rise in electricity consumption. In the power system, due to the use of various sensors, we can collect a large number of power data (eg. the spatial-temporal information of electric vehicle charging). Usually, such spatial-temporal data is generated in the form of a data stream. The analysis and mining of such data can be widely applied in power equipment condition monitoring and maintenance, user equipment anomaly warning, urban power grid analysis and other scenarios. Among them, the pattern detection of power data plays a key role in power data analysis. Since the power data such as the spatial-temporal information of electric vehicle charging is time-sensitive, it is crucial to perform real-time pattern mining in real-time monitoring systems. However, state-of-the-art pattern detection methods are built on batch mode. Extending such works directly to an online environment tends to result in (1) expensive network cost, (2) high processing latency, and (3) low accuracy results. In this paper, we propose a framework for frequent motion pattern detection of power data in the real-time distributed environment. Through the softmax differentiation function, the power data is filtered to reduce the workload and improve the performance of the framework. At the same time, we propose the concept of historical state matrix to solve the problem that the nodes of each physical partition in a distributed environment can not perceive each other. Extensive experiments are conducted on real dataset and the experimental results show that our pattern detection is about 70% faster than baseline methods, which proves the huge advantage of our approach over available solutions in the literature.