过载情形下群依赖作战网络恢复方法

作战网络能够加速作战体系中杀伤链的闭合从而倍增作战效果，但是其也面临着被重点毁伤的威胁，为快速有效恢复甚至提升作战网络的鲁棒性，对作战网络级联失效建模和鲁棒性恢复方法展开了研究。首先，针对作战网络建模存在偏差的问题，从实际出发构建了双层异质群依赖作战网络模型，然后分析并设计了条件性群依赖失效、非连通失效和临界过载失效等级联失效过程，并提出具有作战意义的网络鲁棒性指标。考虑到时效性和恢复资源的限制，利用作战网络节点的属性特征，提出一种基于容量和重要性的边界节点优先恢复(prior recovery based on capacity and importance, PRCI)方法。最后，通过不同方法对比、调整模型参数等仿真实验检验所提方法的有效性和可行性。仿真结果表明，PRCI方法的恢复效果明显优于其他基准方法，具有起效快，迭代少的特点，在相同资源条件下可快速有效恢复作战网络的能力；同时还发现该方法的恢复效果与容忍度、容量参数、过载承受系数及恢复比例成正比，与负载参数成反比，进一步为作战网络的结构优化设计提供了参考。

Recovery method for group-dependent combat network in overload situation

Combat networks can accelerate the closure of the kill chain in a combat system-of-system and thus multiply combat effectiveness, but they also face the threat of focused destruction. In order to effectively and quickly recover and even improve the robustness of combat networks, the cascade failure modeling and robustness recovery methods of combat networks are studied. Firstly, to address the bias in combat network modeling, a two-layer heterogeneous group-dependent combat network model is constructed from real world. Then the conditional group-dependent failure, non-connected failure and critical overload cascading failure processes are analyzed and designed. Further, a network robustness index with operational significance is proposed. Considering the limitation of timelines and recovery resources, a prior recovery based on capacity and importance (PRCI) of boundary nodes is proposed, taking into account the attribute features of the combat network. Finally, the effectiveness and feasibility of the proposed method are validated through simulation experiments, which involve comparing and adjusting model parameters using different approaches. The results show that the recovery performance of the PRCI method is significantly better than other benchmark methods with fast onset and fewer iterations, enabling rapid and effective restoration of combat network under same conditions. Additionally, it is also found that the recovery performance of the method is proportional to the tolerance, capacity parameters, overload bearing coefficients and recovery ratio, while inversely proportional to the load parameters. These findings further provide insights for the structural optimization of combat networks.