Digital Construction Method for Emergency Scenarios in Power Industry Based on Machine Learning

With the increasing complexity and scale of the power grid system, the emergency management of emergencies is particularly important. Traditional emergency decision-making methods are facing the challenges of dealing with high-dimensional data and uncertainty. Therefore, emergency decision-making methods based on big data and data-driven have gradually become the focus of research. The purpose of this paper is to discuss the data-driven emergency decision-making method system of power grid system, analyze the characteristics of big data of power grid, and propose the emergency decision-making process based on data flow. This paper first gives an overview of emergency scenarios, and analyzes the actual scenarios of machine learning in emergency management. Secondly, according to the special requirements of power grid system, a data-driven emergency response decision-making architecture is constructed, and the key technologies such as data fusion and emergency evolution modeling are discussed. Practice has proven that the machine learning based digital construction method for emergency scenarios in the power industry proposed in this article is effective and worthy of promotion.