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This invention discloses a data-augmented anomaly localization method for IoT-sensing cloud data centers, relating to the field of cloud data. The method includes: S1 acquiring a training dataset; S2 constructing an anomaly detection model; S3 importing the training dataset into the anomaly detection model for optimization training; S4 acquiring the raw data from the cloud data center in real time and importing it into the optimized anomaly detection model to obtain predicted values ​​for the raw data; and S5 determining anomalies in the raw data of the cloud data center based on the predicted values ​​and the true values ​​of the raw data. The method utilizes data augmentation-neural transformation and convolutional long short-term memory networks to mine different aspects of the temporal and spatial characteristics of multivariate time series, increasing the amount of training data and reducing false positives caused by non-stationary and nonlinear real-time IoT data. Furthermore, it employs an attention-based autoregressive LSTM network to reduce the dimensionality of the data and extract features again, fully acquiring information beneficial to prediction from the data and improving the robustness of the model's anomaly detection.