Research on forest ecological protection strategy optimization and decision support system based on IoT and deep reinforcement learning

Forest ecological protection faces unprecedented challenges with global climate change and the intensification of human activities. Traditional protection methods are difficult to adapt to the complex and changeable ecological environment, and innovative technical means are urgently needed. This study aims to integrate the Internet of Things and deep reinforcement learning technology to build a forest ecological protection strategy optimization and decision support system to improve protection efficiency and scientificity. A comprehensive perception of forest ecology is achieved by deploying sensoreal times to monitor forest environmental parameters in real life, such as temperature, humidity, soil nutrients, etc. Based on the deep reinforcement learning algorithm, a protection strategy optimization model is established, and the optimal strategy is learned by simulating the protection measures in different situations. Experimental results show that compared with traditional methods, this system can improve the effectiveness of the protection strategy by 30% and significantly reduce the waste of resources. In the simulated 1000-hectare forest area, after adopting this system, the vegetation coverage rate increased by 20%, and the species diversity index increased by 15%. In addition, the system also has a real-time decision support function, which can dynamically adjust protection strategies according to real-time data to respond to sudden environmental changes. This study provides a new technical path for forest ecological protection and a useful reference for research in related fields.