Optimizing HVAC Security : AI-Driven Metadata Management Framework

Abstract

The HVAC industry depends on detailed metadata files like design schematics, maintenance records, compliance documents, and data from sensors. Managing these files effectively is key to operational efficiency, regulatory compliance, and maintenance tracking. This study introduces an AI-powered Metadata File Management System designed for HVAC companies, using machine learning, natural language processing (NLP), and cloud integration to automate file categorization, retrieval, duplication detection, and compliance monitoring. The system pulls metadata from sources such as IoT sensor logs, CAD files, and service reports, providing smart search options and organized documents. It also includes robust security features like role-based access control (RBAC), document encryption, and anomaly detection to spot unauthorized access. By working with cloud storage and IoT networks, this solution improves accessibility, cuts down on data redundancy, and meets industry security standards. This framework will show marked improvements in file retrieval speed, compliance monitoring, storage efficiency, and data security. This research illustrates how AI-driven metadata management can enhance HVAC digital workflows, ensuring efficiency, compliance, and secure real-time data use.