Xiaoxue Zhang

Today’s electromagnetic environment is still not optimistic enough, and spectrum resources are relatively few. In the actual allocation, unevenness will inevitably occur. In addition, the current level of intelligent monitoring is limited, and it is impossible to fully grasp the dynamics of frequency use. Spectrum monitoring emergency maneuverability still needs to be further improved, and there is a lack of refined spectrum resource management measures. In order to solve this problem as soon as possible, the article proposed a solution to the electromagnetic spectrum monitoring problem based on Cloud Computing, Big Data and Artificial Intelligence technology, proposed construction plans for various main applications and corresponding monitoring services, mainly strengthening the construction of handheld monitoring systems, electromagnetic spectrum monitoring and control systems, cloud monitoring systems, Big Data analysis systems, and intelligent monitoring and dispatching systems.

As one of the products in the US Department of Defense Architecture Framework version 2.0 (DoDAF 2.0), the Operational Activity to Services Traceability Matrix product (SvcV-5) links the operational viewpoint (OV) and service viewpoint (SvcV). SvcV-5 is an essential product for optimizing the design of other service view products; thus, it is necessary to study how to optimize the design of SvcV-5. This paper studies the semi-automatic design method of SvcV-5 based on the service identification method. The main idea of this paper is to extract relevant information from previous architecture design products, such as OV-6b, CV-4 and CV-6, and then to study the method of taking service identification and designing the SvcV-5 product. First, a message-based service initial identification method is used to generate the initial service set. Second, considering the measure of the cohesion and the coupling degree of the service, a service improving algorithm is used to update the initial service set. In this process, a dependency matrix between operational activities is established; this matrix includes data dependencies and capability requirement dependencies. Then, according to the mapping result of the service identification method, service-related information can be determined manually by designers. Finally, SvcV-5 can be generated through an automatic algorithm based on the service identification result and the OV-6b product. This article verifies the proposed method using the motivation example of the earthquake rescue information system (ERIS) architecture product design. Through conducting experimental analysis and comparing our method with other service identification methods, we illustrate the effectiveness of the proposed SvcV-5 optimization design method.