Virtual Risk Trajectory and Super-Conflict Gray Target Negotiation-Driven Intelligent Risk Management and Control for Complex Equipment Development

The development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception, and the absence of collaborative negotiation. Traditional risk management and control methods, based on the ideal assumptions of equal subjects and independent indicators, struggle to characterize and resolve super-conflict games dominated by super decision-makers. Furthermore, they lack dynamic early warning and closed-loop execution mechanisms linked to real-time perception, commonly suffering from drawbacks such as low early warning accuracy, high decision-making conflict, delayed response, and inefficient collaboration. To address these issues, this paper integrates multi-agent conflict negotiation with intelligent perception and learning technologies to propose an intelligent risk early warning and closed-loop control method for complex equipment development. The three-dimensional risk evolution dynamics model and Virtual Risk Center (VRC) are employed to decouple super-conflict indicators, while the industrial inspection unmanned aerial vehicle (UAV) perception relative motion model enables the unified mapping of physical risks and decision-making games. A super-conflict gray target negotiation (SCGTN) model is constructed to achieve stable consensus decisions among multiple parties under conflicting indicators. Based on Markov Decision Processes and the PPO algorithm, the optimal virtual risk trajectory is generated, which is then combined with the Archimedean spiral convergence trajectory to synthesize executable control trajectories. This forms an integrated system of UAV real-time perception → super-conflict resolution → intelligent decision-making → closed-loop regulation. Validated through a case study of large-scale complex aviation equipment development, the proposed method achieves field-validated risk early warning accuracy of 94.7% evaluated against real-world on-site ground truth labels. The numerical simulation results, whose parameters are fully calibrated against real-world engineering datasets, indicate that under simulated test conditions, our method yields simulation-predicted performance: it reduces decision-making conflict intensity by 49.3%, controls risk deviation error within 1.38%, and shortens closed-loop response time to 158 ms. Note that conflict reduction level, risk deviation error, and closed-loop response time are pure simulation outputs and have not been directly measured from physical on-site closed-loop experiments. Under the same simulation setup, the end-to-end response speed is 7.6 times faster than the peer dynamic closed-loop Digital Twin-Proximal Policy Optimization (DT-PPO) benchmark algorithm with identical online sensing and reinforcement learning architecture and roughly 4700 times faster than simulated counterparts of traditional static offline evaluation modes that rely on periodic manual statistics and offline meetings. It is adaptable to complex equipment development scenarios characterized by strong super-conflict, high dynamics, and unequal subjects, providing a theoretical framework and technical support for intelligent risk prevention and control throughout the full lifecycle of complex equipment.