Research on the Application and Implementation of Internet of Things Technology in the Perception and Early Warning System of Smart Park

This paper proposes an Internet of Things perception and early warning system based on the deep reinforcement learning (DRL) model. The system collects multidimensional data in the park in real time through Internet of Things devices, and optimizes the decision-making process by combining the DRL model to achieve accurate perception and efficient early warning of abnormal events. The sensor network and edge computing architecture are used to complete the rapid fusion of multi-source data, and the improved deep Q network (DQN) model is used to train the intelligent early warning strategy to improve the system's adaptive ability in complex environments. The simulation experiment is based on real park operation data to evaluate the system's performance in indicators such as event perception, early warning time and accuracy. The experimental results show that the accuracy of anomaly detection in complex scenarios of this system is 96.8%, and the early warning time is shortened by 35% compared with traditional methods, showing significant advantages in the flexibility and reliability of event response. This study provides an efficient technical solution for the intelligent management of smart parks. Through the combination of Internet of Things and deep reinforcement learning, the park early warning system is promoted to be more intelligent and efficient.