There are multiple safety risk factors in corporate oil and gas loading and unloading operations, including vessel, environment, operational safety, equipment, people and management. These factors are intersecting and interrelated, forming a complex system. In this study, the explanatory structural model and fuzzy PID control algorithm based on particle swarm optimization are used to improve the safety of enterprise oil and gas loading and unloading operations. Firstly, 25 main risk factors are screened out through questionnaire survey and expert interviews, and ISM is established to analyze the risk hierarchy structure and reveal its intrinsic relationship; then, a fuzzy PID control system based on PSO optimization is designed to realize the intelligent control of oil and gas loading and unloading robotic arm. The results show that: the ISM model divides the risk factors into five layers, in which the safety of oil transfer arm, pipeline safety and reliability of anti-static device are the key risk factors in layer 2; compared with the traditional PID controller, the fuzzy PID controller based on PSO optimization shortens the rise and convergence time during startup from 2.11s to 0.54s, which is a reduction of 74.4%; and the convergence time during stopping process is reduced from 3.95s to 1.01s, a reduction of 74.4%, eliminating the system oscillation and overshooting problems. In addition, through the fuzzy rule design and parameter optimization, the nonlinear and time-varying characteristics of the system are effectively solved, and the control accuracy and system response speed are improved. Enterprises should pay attention to the regular inspection and maintenance of loading and unloading equipments, establish a perfect emergency management system, and improve the professional quality of operators, so as to comprehensively improve the safety of oil and gas loading and unloading operations.
