Abstract: The rapid development of emerging industries such as artificial intelligence, big data, and cloud computing has led to the widespread application of large-scale cloud computing centers, which continuously generate massive amounts of operational data during operation. Efficient and accurate anomaly detection is a key part in ensuring intelligent operation and maintenance as well as the system reliability. Due to factors such as equipment aging, sudden environmental changes, and dynamic loads, operational data often contains problems such as data noise pollution, abnormal signal coupling, and dynamic drift in equipment status. Current mainstream anomaly detection methods typically rely on assumptions of data purity, identifying anomalies by learning normal data features and evaluating reconstruction errors. However, industrial-grade intelligent systems face inherent challenges such as sensor noise contamination, dynamic state drift, and coupled anomaly signals due to their complex topological structures and heterogeneous operating environments, all of which degrade data quality and compromise detection efficacy. To address these issues, this paper proposes a time-frequency enhanced multi-view contrastive learning (TF-ECL) framework. It introduces a dual-view enhancement mechanism to implement controllable augmentations in temporal and frequency domains, thereby improving model noise immunity based on cross-view contrastive learning. Meanwhile, based on the differential representations of abnormal signals in time-frequency space, a dual-domain collaborative detection mechanism is developed to achieve abnormal localization using time-frequency contrastive errors. Experimental results from multiple real-time data sets demonstrate that TF-ECL achieves stable and excellent performance in terms of the anomaly detection capability compared to state-of-the-art (SOTA) methods and obtains higher scores of F1,VUS-PR and VUS-ROC on four benchmark datasets.