An abnormal detection method for power Internet of things equipment based on graph neural network

Abstract

This application relates to a graph neural network-based abnormality detection method for electric power Internet of Things equipment, which includes the following steps: S1: using data collection tools to collect traffic data and business data of different electric power Internet of Things devices; S2: analyzing the collected data Koopman analysis; S3: Construct the graph structure of the power Internet of Things; S4: Construct a graph neural network model using graph models as input, and use graph convolution and graph attention networks to update node feature states; S5: Use K-Means clustering Anomaly detection is performed on nodes at a certain moment. The present invention captures the nonlinear dynamic characteristics of the power Internet of Things data by introducing Koopman analysis; introduces the graph convolutional neural network, extracts the spatial characteristics of the power Internet of Things, and fuses the attributes of the device nodes themselves and the neighborhood device nodes in the topological structure of the Power Internet of Things The information realizes the anomaly detection of the power Internet of things, effectively improving the accuracy and stability of the detection.

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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