空间信息网络任务智能识别方法

Task Intelligent Identification Method for Spatial Information Network

随着星间链路技术的不断发展和星上处理技术的日益成熟,空间信息网络传输的任务类型不断增加并呈现多样化的趋势,这对空间信息网络的多业务协同传输、网络资源全局调度等能力提出了新的挑战。然而,传统的空间信息网络资源调度大多以单一业务为驱动,忽略了任务与业务之间一对多的关系,导致某些低优先级任务抢占高优先级任务的网络资源,致使由多业务构成的空间信息网络任务的服务质量较低。此外,空间信息网络传输环境具有拓扑高动态变化、节点资源有限等特点,将传统的业务识别方法直接迁移到任务识别中存在任务识别效率低和开销大的缺陷。鉴于此,文中设计了部署于空间信息网络边缘的任务服务支持站,该支持站由识别标记模块、路由控制模块和数据通信模块组成,用以完成对任务类型的识别,以及基于任务服务质量需求的路由选择和数据传输。文中通过对流自身特征项的降维处理和基于高斯核函数的特征空间映射,设计了基于支持向量机的业务识别算法,进一步地,在引入网络传输环境相关特征项的基础上,将业务类型、业务数量和环境特征项相结合,设计了基于梯度下降法的任务识别算法。仿真结果表明,设计的任务识别算法具有很好的识别精准率和召回率,且消耗较短的识别时间和较少的识别开销,对空间信息网络任务流量识别的平均准确率达到95%以上,提高了1%以上,相比未经过特征降维的平均训练时间缩短了15%以上。

With the continuous development of inter-satellite link technology and the maturity of on-board processing technology,the types of tasks transmitted by spatial information networks are growing and diversifying.It presents a new challenge to the multi-service coordinated transmission of spatial information networks and the global scheduling of network resources.However,traditional spatial information network resource scheduling is mostly driven by a single service,ignoring the one-to-many relationship between tasks and services.This causes some low-priority tasks to preempt network resources of high-priority tasks,resulting in lower quality of service for spatial information network tasks composed of multiple services.In addition,the spatial information network transmission environment has the characteristics of high dynamic topology change and limited node resources.The traditional task identification method cannot identify the spatial information network task with high efficiency and low cost.Therefore,this paper designed a task service support station and deployed it at the edge of the spatial information network.The support station is composed of an recognition tag module,a route control module and a data communication module,which completes the task type identification and routing and data transmission based on the task quality of service requirements.On this basis,the existing identification methods only identify the single sub-service of the task,ignoring the one-to-many relationship between the task and the service,and can not meet the service quality requirement of the task.By introducing the feature space mapping based on Gaussian kernel function,the service recognition algorithm based on support vector machine was designed.Further,based on the introduction of environmental feature items,the business type,quantity and environmental feature items were combined to design a task identification algorithm based on gradient descent method.The simulation results show that the proposed task identification algorithm has good recognition accuracy and recall rate,and has less recognition time and recognition overhead.The average accuracy of task information recognition for spatial information network reaches 95%,which is increased by 1%.The average training time for feature dimension reduction is reduced by 15%.