Electric power data parallelization anomaly detection method based on MapReduce

Abstract

The invention relates to an electric power data parallelization anomaly detection method based on a MapReduce. The method comprises the following steps that first, electric power data anomaly characteristics are defined by a grid company according to the operation feature of an acquisition system and expert historical experience before, and accordingly an anomaly detection algorithm is established; second, a cluster calculation model is established; third, when processing data arrive, a Master node of the cluster calculation model starts a data anomaly detection task and distributes the calculation task to all Slave nodes in a relatively even mode; fourth, all the Slave nodes conduct anomaly detection calculation according to the task distributed by the Master node and the anomaly detection algorithm established in the step S01; fifth, the Master node outputs abnormal data according to the calculation of all the Slave nodes. By means of the electric power data parallelization anomaly detection method based on the MapReduce, the problem that the calculating and analyzing efficiency is low under the background of a huge quantity of electric power data in the prior art can be solved, and a forceful guarantee is provided for improving power grid efficiency and reducing loss.

Description

technical field [0001] The invention relates to a mass data processing technology, in particular to an abnormality detection method based on the parallelization of electric power data of a MapReduce calculation model. Background technique [0002] The power grid company needs to collect information such as the operating status of the metering device and the user's power load, electricity, and voltage in real time. Due to the influence of various factors during the operation of the power metering device, there will be electric energy in the power grid system and its collection system. Problems such as metering device failure, distribution network anomalies, metering device wiring errors, and collection system file data errors will affect the safe and stable operation of the power grid and the accurate collection of power consumption data. For the measurement anomalies in the power grid system, if the power data center can analyze the massive collection of data in a timely man...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for abnormal detection of power data parallelization based on the MapReduce calculation model, which can solve the problem of low efficiency of calculation and analysis in the prior art under the background of massive power data, and provide a powerful solution for improving the efficiency of the power grid and reducing losses. Guarantee

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