对有人/无人机协同作战指挥控制(Command and Control,C2)系统技术问题开展研究.总结了有人/无人机协同作战验证项目试验情况并分析了协同作战体系组成、C2结构和复杂性.结合作战特点和任务需求,提出了包括计算通信层、协同控制层和规...对有人/无人机协同作战指挥控制(Command and Control,C2)系统技术问题开展研究.总结了有人/无人机协同作战验证项目试验情况并分析了协同作战体系组成、C2结构和复杂性.结合作战特点和任务需求,提出了包括计算通信层、协同控制层和规划决策层的递阶式C2系统结构.根据决策粒度差异,划分了编组、编队和单机三层决策层次,分析了决策模块中的决策模式优选与跃迁、辅助决策和人机协同等关键技术,并对未来研究方向进行了展望.展开更多
In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction...In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.展开更多
文摘对有人/无人机协同作战指挥控制(Command and Control,C2)系统技术问题开展研究.总结了有人/无人机协同作战验证项目试验情况并分析了协同作战体系组成、C2结构和复杂性.结合作战特点和任务需求,提出了包括计算通信层、协同控制层和规划决策层的递阶式C2系统结构.根据决策粒度差异,划分了编组、编队和单机三层决策层次,分析了决策模块中的决策模式优选与跃迁、辅助决策和人机协同等关键技术,并对未来研究方向进行了展望.
基金The Planning Program of Science and Technology of Ministry of Housing and Urban-Rural Development of China (No. 2010-K5-16)
文摘In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.
