Cloud-Integrated KNN Algorithm for Efficient Risk Evaluation in Industrial Oxygen Safety System

The inherent risks involved with oxygen storage and distribution make ensuring safety in industrial oxygen systems essential. This research presents a Cloud-Integrated KNearest Neighbours (KNN) algorithm to evaluate risks in industrial oxygen safety systems efficiently. Enhancing realtime risk assessment, the proposed method uses cloud computing's scalability and processing capabilities in conjunction with KNN's accuracy. Analysing data collected from pressure, temperature, and flow sensors throughout oxygen distribution and storage units. The data from these key locations must first be collected and processed. Based on trends in previous data and present sensor readings, the KNN algorithm is used to classify and evaluate any safety protection. The system can spot out-of-the-ordinary occurrences or possible dangers in industrial oxygen operations and intervene proactively to maintain running smoothly. Improved risk prediction accuracy, scalability to manage massive amounts of sensor data, and easy integration with current cloud infrastructure are key benefits of the proposed technique. Industrial facilities may achieve greater operational safety and dependability levels by enhancing the speed and reliability of risk appraisal procedures.