Design and Implementation of Petrochemical Port Safety Monitoring and Early Warning System Based on Deep Learning Algorithm

Purpose: To address the numerous risk issues in the petrochemical port area, a safety monitoring and early warning system is proposed to monitor potential risks in real time and provide timely warning of risks. Method: Based on the actual situation of the petrochemical port area, this article designed a safety monitoring and early warning system to address the risk issues in the petrochemical port area. By utilizing this system to enhance the security issues of petrochemical port areas, and combining deep learning algorithms, some performance testing experiments were conducted on the system. Result: Under the algorithm proposed in this paper, the average recognition accuracy of each dataset was 92.19%, and the average warning time was 0.54 seconds. Under the machine learning algorithm, the average recognition accuracy of each dataset was 88.45%, and the average warning time was 1.32 seconds. Conclusion: From the above data, it can be seen that the algorithm proposed in this paper can effectively improve the recognition accuracy of each dataset, shorten the warning time, and optimize the performance of the system.