基于多层网络的空间信息网络拓扑结构建模方法

Topology modeling method for space information network based on multi-layer network

针对空间信息网络实体和链接的异质性问题,提出一种基于多层网络的空间信息网络拓扑结构建模方法。在分析空间信息网络多层特征的基础上,首先,对空间信息网络节点建模,重点描述实体的空间位置和负载功能属性;然后,考虑空间信息网络实体间交互信息类型和交互节点的空间位置将链接分为两类,构建空间信息网络连边模型;最后,从空间分布、功能组网、任务组网和时变拓扑4个维度对空间信息网络多层模型的网络层进行描述,构建空间信息网络多层拓扑结构模型。在案例仿真与分析部分以通信网络、导航网络、传感网络和用户网为主要功能子网构建案例网络,采用所提建模方法对案例网络构建空间信息多层网络模型,并从空间分布和功能协同维度分析了案例网络在体系结构设计上的优缺点,验证了所提建模方法的有效性。相对于现有的空间信息网络建模方法,所提建模方法从多个维度比较完整、全面地描述了空间信息网络中多类型的实体和链接关系,能够从多角度、多尺度获得更全面的拓扑结构信息,具有更灵活的适应性。

Aiming at the heterogeneity of entities and links in Space Information Network (SIN), a topology modeling method for space information network based on multi-layer network was proposed. Based on the analysis of the multi-layer characteristics of SIN, firstly, the mode in SIN was modeled, in which the space location and load function attributes of the entities were mainly described. Then, the space information network edge model was constructed by dividing the links into two types according to the interaction information types between the space information network entities and the space locations of the interaction nodes. Finally, the network layer of the SIN multi-layer model was described from four dimensions: space distribution, functional networking, task networking and time-varying topology, carrying out a multi-layer topology model of sSIN. In the session of simulation and analysis, the communication network, navigation network, sensor network and user network were used as the main function subnets to construct a case network. With the proposed method, the SIN multi-layer network model was constructed for the case network. The advantages and disadvantages of the case network in architecture design were analyzed respectively in space distribution and function coordination, which verified the effectiveness of the proposed modeling method. Compared with the existing SIN modeling methods, the multi-type entity and link relationships in SIN can be described from multiple dimensions comprehensively by the proposed method, and more topological information can be obtained from multiple aspects and multiple scales, thus, more flexible adaptability is achived.