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They generate a large number of high\u2010dimensional time series monitoring data in the process of operation, which often hide various potential abnormal conditions, which bring hidden dangers to the stable operation of the system. Existing anomaly detection methods mainly focus on the sequence characteristics of time series data, but often ignore the correlation between different variables of multivariate data, and the detection efficiency is low when facing high\u2010dimensional time series data. To solve the above problems, we propose a deep anomaly detection method based on graph neural network, and combined with the big data computing framework Apache Flink, we construct a real\u2010time anomaly detection system for large\u2010scale high\u2010dimensional time series data. 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