A DATA-DRIVEN FRAMEWORK FOR INTELLIGENT COLD STORAGE MONITORING AND TEMPERATURE REGULATION

Cold storage systems are essential for ensuring the quality and safety of temperature-sensitive goods across industries such as food, pharmaceuticals, and biotechnology. However, traditional temperature regulation approaches often struggle with delayed fault detection, lack of adaptive response mechanisms, and inefficient energy consumption. As modern supply chains grow increasingly complex, the demand for intelligent, automated cold storage solutions has become more urgent. This paper proposes a comprehensive data-driven framework for intelligent cold storage monitoring and temperature regulation. By integrating Internet of Things (IoT) sensors, real-time data acquisition, machine learning (ML) algorithms, and predictive control models, the system continuously tracks environmental and equipment metrics. Anomaly detection techniques are used to identify deviations from normal behavior, while reinforcement learning is applied to optimize response strategies in varying operational contexts. The framework includes a cloud-based data processing layer, an ML-based anomaly detection engine, and a closed-loop control module capable of adjusting temperature settings proactively. Through simulations and real-world deployment scenarios, the system demonstrated improved temperature stability, faster fault diagnosis, and reduced energy consumption compared to conventional control mechanisms. The findings suggest that combining ML and IoT technologies provides a scalable and adaptive solution for next-generation cold storage management.