Fire Monitoring Method of Ancient Building Repair Stage Based on Machine Learning Algorithm

Fires in old structures have increased due to over-exploitation of tourists and the use of electrical equipment, inflicting substantial social and economic losses. With computers, statistical learning and optimization theory preceded machine learning. Numerous algorithms for various disciplines and issues have been proposed. Tibet has a plateau climate. A building's fire risk includes both property damage and personnel and property loss. The building's vulnerability depends on their joint deterioration. When the global positioning system falls short, machine learning-based outside mobile terminal placement compensates. No hardware is needed for machine learning-based outside mobile terminal location. This document evaluates fire risk based on building status, fire source control, fire control facilities, personnel evacuation facilities, and fire control safety management. This study summarizes and analyzes ancient building rehabilitation technology after fire and explores viability schemes under diverse circumstances to help develop it. In this work, the needle-robot learning algorithm can be adapted to fire during the repair stage of most historic buildings, but there are various types of ancient buildings, thus the evaluation index of different types of ancient buildings must be refined in the future.