物联网（Internet of Things，IoT）技术正在通过快速集成和部署智能电表重塑全球能源格局，这些电表支持高分辨率的能耗监测、双向通信以及先进的计量基础设施服务。然而，这一数字化转型也使电力系统面临不断演变的威胁，涵盖网络入侵、电力盗窃以及设备故障等。这些异常行为的不可预测性，加之标注异常数据的稀缺，使实时检测异常变得尤为困难。为应对这些挑战，提出了一种用于智能电表异常检测的实时决策支持框架，该框架基于滑动时间窗口以及两个自监督对比学习模块。第一个模块通过合成多样化的异常样本来弥补标注的异常数据不足，第二个模块则捕捉内在的时间模式以增强上下文区分能力。提出的端到端框架可持续地利用滚动更新的电表数据实时更新模型，及时识别电网中不断演化出现的新兴异常行为。在8个公开可用的智能电表数据集上针对7种不同的异常模式进行了广泛评估，结果表明所提的完整框架表现优秀，平均召回率和F1分数均超过0.85。

The rapid integration of Internet of Things （IoT） technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring, two-way communication, and advanced metering infrastructure services. However, this digital transformation also exposes power system to evolving threats, ranging from cyber intrusions and electricity theft to device malfunctions, and the unpredictable nature of these anomalies, coupled with the scarcity of labeled fault data, makes real-time detection exceptionally challenging. To address these difficulties, a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules. The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies, while the second captures intrinsic temporal patterns for enhanced contextual discrimination. The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids. Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework, achieving average recall and F1 score of more than 0.85.