Research on Emergency Response Strategy for Large-Scale Urban Power Outage Based on Machine Learning

Cities are the centers of economic, cultural and social life. Cities are not only densely populated areas, but also places where a large amount of infrastructure, businesses and services are concentrated. However, cities also face various challenges, one of which is power outages. A large-scale urban power outage is an unexpected event that can be caused by a variety of reasons, including extreme weather, equipment failure, and vandalism. Such incidents cause many serious harms to cities and their residents, not only threatening lives, but also undermining social order, economic stability, infrastructure sustainability, environmental quality and mental health. It is of great significance to implement the emergency response strategy of large-scale urban power outage. These strategies can not only ensure the safety of residents, but also maintain the sustainability of urban infrastructure, reduce social and economic losses, improve the resilience of cities to disasters, protect the environment and resources, and increase public awareness and preparedness [1]. Therefore, city governments and relevant agencies must carefully develop and implement emergency response strategies to mitigate these hazards. Only through effective preventive and emergency measures can cities better protect residents and property and ensure social stability and sustainable development. This paper will discuss the importance of large-scale urban power outage emergency response strategies and how to use machine learning to build large-scale urban power outage emergency response strategies. Finally, the effectiveness of the proposed model is verified by experiments.