基于可靠性分析的立方星网络维护架构优化

立方星编队或星座构成分布式空间传感器网络，可提高立方星执行复杂空间任务的能力。然而立方星易发生故障，其故障时间不确定性也导致了传感器网络性能的不稳定，这凸显了对立方星网络进行在轨维护的重要性。考虑立方星传感器网络的功能维持问题，描述了一种网络维护架构，通过定期发射、在轨备份立方星以及时更换故障立方星，从而提高网络对单星随机失效事件的快速响应与恢复能力。建立了该架构的运行成本模型，包括固定成本、储存成本和短缺成本。收集整理了真实的立方星寿命数据，并使用最大化拟合优度参数估计方法得到最优立方星寿命的随机模型。采用基于蒙特卡罗仿真的遗传算法优化备用立方星的补给时刻和补给数量，在备份成本与系统性能下降所带来的损失之间进行权衡，使得系统的综合收益最优。

Maintenance architecture optimization of CubeSat networks based on reliability analysis

CubeSat formations or constellations can form distributed space sensor networks, enhancing the ability of the CubeSat to execute sophisticated space missions. However, CubeSats are prone to malfunction, and the uncertainty of their failure time causes the performance instability of the sensor network, highlighting the importance of on-orbit maintenance of CubeSat networks. In view of the function maintenance of space CubeSat sensor networks, a maintenance architecture is described. It can improve the rapid responsiveness and recovery capability to a single CubeSat fault event of the network by launching CubeSats regularly, making backup CubeSats on the orbit and replacing damaged CubeSats in time. The operation cost model of this architecture is built, involving fixed costs, storage costs and shortage costs. The practical CubeSat lifetime data is collected to acquire the optimal CubeSat lifetime stochastic model with the parameter estimation optimization method of maximizing the coefficient of determination. The supply time and quantity of spare CubeSats are optimized by a Monte-Carlo-simulation-based genetic algorithm. This solution is a trade-off between the costs of backups and the losses caused by the system performance decline, finally achieving an optimal comprehensive income.