Abstract
Abstract To realize the monitoring and diagnosis of multi-source data in the power industry and meet the real-time processing requirements of the power system, the storm-distributed real-time computing platform is introduced to process the data. On this platform, the streaming data processing model based on a sliding window is deployed to realize the anomaly diagnosis of data flow. By calculating the predicted value of a point and the confidence interval of the predicted value of the point, we can judge whether the actual point is an abnormal value. By testing the data processing capacity of a cluster and a single machine, the experiment shows that in the cluster environment, reasonably setting the component parallelism can improve the throughput of flow computing.
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