This research aims to elevate the operational efficiency of electromechanical systems within national nature reserves, focusing on enhancing visitor safety and ecological preservation through cutting-edge computer technology. Central to our systematic methodology—spanning design, implementation, and evaluation—is the optimization of monitoring mechanisms and sensor technology, integrated with advanced data processing capabilities. We introduced an innovative deployment of high-resolution imaging and infrared sensors, orchestrated through an automated system empowered by the Internet of Things (IoT). This setup ensures seamless data transmission and sophisticated processing, with a significant emphasis on computer science principles. Additionally, we leveraged deep learning algorithms to refine image analysis and manage data flows more effectively, which are crucial for the real-time processing needs of the system. These computational enhancements have dramatically improved the system’s responsiveness, reducing event processing time from 15 to 5 min and achieving a 95% problem resolution rate. The frequency of system failures has also notably decreased, leading to a marked increase in tourist satisfaction. This study demonstrates that leveraging computer technology, particularly through the application of big data analytics and automated data processing systems, can significantly improve the efficiency and stability of safety systems in national parks, providing a stronger technical foundation for both environmental conservation and the safety of park visitors.