Research demonstrates improved automation and resource scheduling in multi-tasking systems, indicating enhanced system resilience.
The integrated modular avionics system is widely used in the aviation field due to its flexibility, ease of modification, and high fault tolerance. However, the system faces changing environments and evolving multi-tasking requirements. Existing manual configuration methods and traditional algorithms for generating reconstruction blueprints have limitations in terms of automation and quality assurance. They struggle to meet the increasing complexity and difficulty of resource scheduling during task switching in the comprehensive electronic system. In this paper, the DDQN-MS-NN reconstruction algorithm is proposed to address these challenges. The algorithm focuses on generating multi-tasking reconstruction blueprints for the comprehensive electronic system, improving the automation level and quality of resource scheduling, by introducing multi-step learning and noise network mechanism. Experimental results show that, the DDQN-MS-NN reconstruction algorithm can enhance system stability and resilience compared with traditional algorithms.
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