Abstract: The HVAC industry relies heavily on a variety of important files, including design schematics, maintenance records, compliance documents, and sensor data. Managing this growing volume of information efficiently is critical for maintaining operations, ensuring regulatory compliance, and tracking equipment maintenance.
This paper presents an AI-powered system for managing metadata-rich files within HVAC environments. The system leverages machine learning, natural language processing (NLP), and cloud integration to automatically categorize files, enable intelligent search, detect duplicates, and streamline compliance monitoring. It extracts metadata from sources such as IoT sensor logs, CAD drawings, and service reports to improve document organization and retrieval.
To ensure security, the system includes role-based access control (RBAC), encryption, and anomaly detection to identify unauthorized access. By integrating with cloud storage and IoT networks, it enhances file accessibility, reduces data redundancy, and helps HVAC organizations meet industry security standards.
Overall, this AI-driven metadata management approach improves file retrieval speed, compliance oversight, storage efficiency, and data protection. It demonstrates how intelligent automation can optimize HVAC digital workflows while supporting real-time operations and regulatory adherence.
Keywords: HVAC, Metadata Management, AI-Based File Management, Machine Learning, Natural Language Processing (NLP), Cloud Integration, Document Security, Role-Based Access Control (RBAC), Anomaly Detection, Encryption, Duplicate Detection, Intelligent Search, Regulatory Compliance, IoT Integration, Predictive Analytics, Blockchain for Document Authentication, Automated Workflow, Secure Data Storage, Audit Logs, Smart File Categorization, DNS.
Title: Securing HVAC Systems with AI-Powered Metadata Intelligence
Author: Aakarsh Mavi
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 13, Issue 3, July 2025 - September 2025
Page No: 121-126
Research Publish Journals
Website: www.researchpublish.com
Published Date: 28-July-2025
