Sustainability is a key factor in petroleum industry development, determining whether an enterprise has the ability to maintain high-quality safety management in the long term. In order to reduce occupational injuries and accidents, and to improve safety in the petroleum industry, this study proposes a hybrid approach based on the fuzzy analytical hierarchy process (FAHP), human factors, and the fuzzy comprehensive evaluation (FCE) method in order to assess safety performance in a petroleum enterprise. This paper is comprised of four stages. In the first stage, a model is constructed for assessing the safety of the petroleum industry based on a literature review. In the second stage, we use the FAHP to determine the weights of five factors and 19 sub-factors. In the third stage, employees are the subjects of a questionnaire on the safety performance of the petroleum enterprise. According to the analysis of the assessment results, we focus on improving employees’ safety behaviors and mental health. A second round of questionnaires is distributed to the employees, and a second set of assessment results obtained. Finally, the results of the two evaluations are compared, and the effectiveness of the combination of FAHP, human factors, and FCE is verified.

To better guarantee the health and safety of employees and reduce the probability of occupational injuries and accidents, it is necessary to evaluate safety production management levels in enterprises. In this study, an evaluation index model was established for the safety production management of an oilfield enterprise. By utilizing an analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE), this research is based on the characteristics of the oilfield enterprise and its behavior‐based safety (BBS) management. A comparison of the results of two FCEs shows that the unsafe behaviors of employees were considerably reduced, and the safety production management level was significantly increased. The results of the case study also verify that combining the FCE model and the BBS approach is effective. The combination of the AHP and FCE method with BBS management support aims to improve safety behavior and increase safety training and the identification of critical unsafe behavior, thus reducing occupational injuries and accidents.

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

This paper proposes the complex dynamics of collective behavior through an interdisciplinary approach that integrates individual cognition with potential fields. Firstly, the interaction between individual cognition and external potential fields in complex social systems is explored, integrating perspectives from physics, cognitive psychology, and social science. Subsequently, a new modeling method for the multidimensional potential field mechanism is proposed, aiming to reduce individual behavioral errors and cognitive dissonance, thereby improving system efficiency and accuracy. The approach uses cooperative control and distributed learning algorithms to simulate collective behavior, allowing individuals to iteratively adapt based on local information and collective intelligence. Simulations highlight the impact of factors such as individual density, noise intensity, communication radius, and negative potential fields on collective dynamics. For instance, in a high-density environment with 180 individuals, increased social friction and competition for resources significantly decrease collective search efficiency. Validation is achieved by comparing simulation results with existing research, showing consistency and improvements over traditional models. In noisy environments, simulations maintain higher accuracy and group cohesion compared to standard methods. Additionally, without communication, the Mean Squared Error (MSE) initially drops rapidly as individuals adapt but stabilizes over time, emphasizing the importance of communication in maintaining collective efficiency. The study concludes that collective behavior emerges from complex nonlinear interactions between individual cognition and potential fields, rather than being merely the sum of individual actions. These insights enhance the understanding of complex system dynamics, providing a foundation for future applications in adaptive urban environments and the design of autonomous robots and AI systems.