Health Status Analysis Method for Power Distribution Network Operators Based on Intelligent Wearable Device data and Logistic Regression Algorithm

Power industry has already put forward the requirement of minimal or even no power outages for the maintenance and repair of the power system. The improvement of relevant requirements puts forward new requirements for the personal care of on-site maintenance operators in the power industry. As power operations are usually carried out in complex and dangerous environments, such as high-altitude work, proximity to high-voltage equipment, etc., exploring more scientific and stable ways to ensure the safety of operators during maintenance operations has important practical significance and research value. Current reports on power system security issues, are mostly limited to the safety of the system itself or the operation safety of power system enterprises, and rarely involve the safety care of operators. With the emergence of intelligent wearable devices, real-time monitoring of operators’ mental, physical, and other states has become possible. In this paper, a health status fusion evaluation algorithm based on swarm intelligence optimization and logistic regression based on various physiological parameters extracted by intelligent wearable devices has been proposed, which effectively classifies and identifies the health status of workers reflected by multivariate nonlinear physiological data.