Multi-human-machine Collaborative Virtual-Real Fusion Carrier-based Aircraft Aviation Support Operations Electronic Sand Table Decision System

The aviation support operational environment of aircraft carriers is characterized by high dynamism, with diverse and complex sources of operational situation information. Existing sand table systems have signif-icant limitations in terms of the comprehensiveness of situational information display and the efficiency of operational plan simulations. This constrains commanders’ ability to intuitively understand and make re-al-time adjustments to aviation support operational plans. In response to these issues, this study designs and implements a multi-human and machine collaborative virtual-real fusion carrier-based aircraft aviation support operations electronic sand table decision system. First, we establish a multi-source situational in-formation fusion environment oriented toward cross-device collaborative interaction analysis. Through fusing situation awareness data obtained from multiple channels to achieve dynamic and complete situa-tional presentation. Then, we propose a mixed reality based multi-human-machine collaborative interactive visualization method for operational plan simulation and correction. This method optimizes the ma-chine-recommended carrier-based aircraft operational plan through multi-human-machine collaboration while utilizing virtual-real mapping technology to provide global visualized situational evolution process of the deck to assist in deduction and adjustments. Finally, by leveraging commanders’ intuitive reasoning capabilities, it relies on group collaboration mechanisms to integrate the experiences of multiple com-manders, mitigates information asymmetry, and ultimately computes more optimized simulation plans. The experiment is carried out by multi-human-machine collaborative simulation of aviation support operations on the deck of a large naval vessel as an application scenario, which demonstrate the system’s validity and advantages. Compared with the current mainstream machine intelligent algorithms, the planned operational paths achieved an average 2.77% reduction in length, a 76.25% decrease in total station traversals, and a 58.18% improvement in path smoothness. This provides scientific and reliable reference support for on-site decision-making in aviation support tasks.