Design of a Business Process Automation Monitoring Platform for Power Grid Enterprises for Digital Operations

This study aims to design an automated monitoring platform for business processes in power grid enterprises aimed at digital operations. Through four experiments, we evaluated the performance of the platform in fault detection, predictive maintenance, load management, and optimization, and conducted a user satisfaction survey. This article evaluates the effectiveness of predictive maintenance algorithms for power grid business process automation monitoring platforms. The research results show that predictive maintenance algorithms exhibit high accuracy in historical data and successfully predict the maintenance needs of most equipment. The performance data of device 002 is more prominent compared to other devices, but the accuracy of the predicted maintenance algorithm for its maintenance time is 100%. Device 003 shows an increasing trend in current and voltage, with a prediction accuracy of 92%, demonstrating the algorithm's good predictive ability for device degradation trends and possible faults. Overall, the power grid business process automation monitoring platform designed in this study has achieved significant results in digital operations, providing feasible solutions for power grid enterprises to achieve digital transformation.