Application of Big Data Analysis based on Random Forest in the Intelligent Maintenance System

In the era of rapid technological advancement, the integration of big data analytics into property management, particularly in the maintenance of greening facilities, is revolutionizing the approach to urban landscaping. This study investigates the application of big data analytics, utilizing a random forest algorithm for the intelligent maintenance of property greening facilities. By analyzing a broad spectrum of indicators such as plant species, growth status, and conservation needs, the random forest model leverages extensive historical data to forecast maintenance requirements and potential issues accurately. Furthermore, the integration of the minimum resistance model and online detection techniques enhances the system's capability for real-time monitoring and early warning. These technological advancements allow for proactive maintenance actions based on predictive assessments of operational status and potential malfunctions. The implementation of this intelligent maintenance system demonstrates a significant enhancement in maintenance efficiency, with a noted increase of 9.36%. The system not only ensures efficient operational management and timely problem resolution but also substantially improves the overall effectiveness of maintenance strategies for greening facilities. Through precise demand prediction and continuous monitoring, this approach empowers facility management teams to optimize maintenance protocols and uphold the aesthetic integrity of the environments they manage.