Electric power Internet-of-things equipment anomaly detection method based on graph neural network

Abstract

The invention relates to an electric power Internet-of-things equipment anomaly detection method based on a graph neural network. The method comprises the following steps of: S1, collecting the flow data and business data of different electric power Internet-of-things equipment through a data collection tool; S2, carrying out Koopman analysis on the collected data; S3, constructing a graph structure of an electric power Internet of things; S4, constructing a graph neural network model by taking a graph model as input, and updating the feature state of a node by utilizing graph convolution and a graph attention network; and S5, performing anomaly detection on the node at a certain moment by using K-Means clustering. According to the method, through introducing Koopman analysis, nonlinear dynamic characteristics of data of the electric power Internet of things are captured; and a graph convolutional neural network is introduced to extract the spatial features of the electric power Internet of things, the attributes of the device nodes and the information of the neighborhood device nodes in the topological structure of the electric power Internet of things are fused to achieve anomaly detection of the electric power Internet of things, and the accuracy and stability of detection are effectively improved.

Description

technical field [0001] The present application relates to the technical field of power equipment detection, and in particular to a graph neural network-based abnormality detection method for power Internet of Things equipment. Background technique [0002] With the rapid development of communication technology and the complexity of the application environment, more and more smart devices are integrated into the power Internet of Things to sense the status of the power grid and transmit information, which also brings more risks and risks. challenge. The external data collected by the terminal devices in the electric power Internet of Things, namely smart meters, smart temperature sensors, smart monitoring, and some smart terminal products that provide data collection and communication services, are relatively random. When there is a large difference in the network, it is difficult to achieve consistency between the data of each node, and the scattered distribution of power I...

AI Technical Summary

Problems solved by technology

However, this scheme has the following defects: ① During the process of constructing the feature matrix, only the statistical features based on time series are extracted, and the spatial features of the power Internet of Things equipment cannot be extracted, resulting in low accuracy of subsequent anomaly detection

However, this solution only processes the historical power consumption data of the same device, and does not take advantage of the spatial characteristics between devices

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