Computer and Modernization ›› 2023, Vol. 0 ›› Issue (10): 84-91.doi: 10.3969/j.issn.1006-2475.2023.10.013

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Real-time Behavioral Safety Warning for Power Operators Based on Multi-source Data

  

  1. (1. State Grid Information and Communication Industry Group Co., Ltd., Beijing 102211, China;
    2. State Grid Tianjin Electric Power Company, Tianjin 300010, China)
  • Online:2023-10-26 Published:2023-10-27

Abstract:
 In order to reduce the occurrence of safety accidents and ensure the safety of power operators in the process of power grid construction, a behavior recognition model based on decision fusion of three dimensional residual convolutional neural network (R3D) models is proposed. First, the captured video dataset is subjected to data cleaning and enhancement; then, the corresponding R3D models are trained with the datasets collected from multiple angles; further, the multiple R3D models are fused at the decision level; finally, the possible violations or dangerous behaviors of power operators are warned in real-time by building a cloud platform. The experimental results show that the model has the advantages of high recognition accuracy and a low number of parameters, which proves that the behavior safety early warning method proposed in this paper can make early warning quickly and accurately and provide a safety guarantee for power grid construction.

Key words: Key words: power construction, unsafe behavior, R3D model, cloud platform, early warning system, decision fusion, multi-source data

CLC Number: