Research on Intelligent Algorithms of IoT in Performance Monitoring of Key Ship Materials

Through the Internet of Things technology, the system can collect material performance data in various environments where the ship is operating. These data are detected for anomalies through the isolation forest algorithm, which can quickly identify and process abnormal data to ensure that the monitoring system has high accuracy and efficiency. The system design includes algorithm optimization, model simulation, and training of a large amount of historical data to ensure the adaptability and robustness of the algorithm. The experimental results show that the isolation forest algorithm has a high accuracy rate in anomaly detection in ship material performance monitoring and significantly reduces the false alarm rate. By comparing with other algorithms, the simulation results show that this system has advantages in data processing speed and accuracy, and can provide reliable support for ship maintenance decisions. Especially in complex operating environments, the system shows strong monitoring capabilities, which not only improves the intelligent level of ship material management, but also provides a strong technical reference for the future development of intelligent ship material monitoring technology.