Research on Real-Time Monitoring and Early Warning System of Offshore Oil Exploitation Environment Based on Intelligent Algorithm of Internet of Things

This project intends to take offshore oil and gas resources as the research object. The Arduino open-source platform and XBeePro are used to extend the communication range to build a real-time monitoring system for offshore oil and gas resources. This project builds an overall architecture for real-time monitoring of the offshore environment. Aiming at the core problems of long-distance communication, multi-communication mechanism fusion and node power supply optimization, the related hardware implementation methods are proposed. A multi-object recognition method based on YOLOv5-Light is designed by combining lightweight model Ghost Net with ECA's attention mechanism. A hybrid algorithm based on multi-stage and multi-scale filters is proposed by combining data of several key characteristics. Kalman filter is used to process the ranging data and output the corresponding alarm information. Through the research of this project, comprehensive, efficient and low-cost environmental data acquisition of offshore oil exploitation can be achieved. This system has great practical value in both scientific research and practice.