Mission reliability assessment and maintenance scheduling optimization for unmanned system-of-systems considering spatiotemporal dynamics

Mission reliability is a critical determinant of operational effectiveness in unmanned system-of-systems (USoS). However, existing assessment methods are typically static, making it challenging to accurately assess and optimize the USoS mission reliability under dynamic spatiotemporal constraints. This study proposes a novel framework integrating operation chain, multi-source uncertain shocks, and spatiotemporal constraints. Firstly, a spatiotemporally constrained operation chain model is established by formalizing the USoS as a dynamic directed graph, integrating node mobility and time constraints. Secondly, a mission reliability assessment method centered on an operation chain meta-model is developed, quantifying the effective functional paths under spatiotemporal constraints, overcoming the limitations of traditional models to verify spatiotemporal interaction validity. Then, an importance-measure-based maintenance scheduling optimization method incorporating resource constraints is proposed to maximize reliability recovery. Finally, a regional air-defense USoS case study under multi-source uncertain shocks (e.g., firepower strikes and electromagnetic interference, modeled as random, deliberate, and area attacks) is given to validate the framework. Results show that the proposed methods enhance mission reliability, supporting quantification and optimization in dynamic spatiotemporal environments.