Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.

中文翻译：

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基于边缘计算架构的电力领域不安全行为检测方法

由于电力行业中的高风险因素，可以通过使用监视系统实时预测和警告操作员的不规范和不安全行为来提高电力系统的安全性。针对视频智能监控的实时性和准确性要求，提出了一种基于边缘计算架构的及时判断电力运行中不安全行为的方法。该方法将不安全行为判断服务部署到边缘云中，提高了实时性。为了识别正在执行的动作，本文提出的端到端动作识别模型使用时间卷积神经网络（TCN）来提取局部时域特征，并使用门循环单元（GRU）层来提取全局时域特征。 ，这提高了动作片段识别的准确性。将动作识别的结果与基于yolov3模型的设备目标识别的结果相结合，并使用分类规则确定当前动作是否安全。实验表明，该方法具有较好的实时性，并且在MSRAction数据集上对所提出的动作识别进行了验证，从而提高了动作段的识别精度。同时，不安全行为的判断结果也证明了该方法的有效性。实验表明，该方法具有较好的实时性，并且在MSRAction数据集上对所提出的动作识别进行了验证，从而提高了动作段的识别精度。同时，不安全行为的判断结果也证明了该方法的有效性。实验表明，该方法具有较好的实时性，并且在MSRAction数据集上对所提出的动作识别进行了验证，从而提高了动作段的识别精度。同时，不安全行为的判断结果也证明了该方法的有效性。