A framework involving MEC: imaging satellites mission planning

Abstract

Satellite will play an important role in many important industries and exist as a carrier of information transmission in the era of Internet of Things. Massive data can be used in planning and scheduling processes A general data-driven framework-imaging satellite mission planning framework (ISMPF) for solving imaging mission planning problems is proposed. ISMPF mainly includes three parts: task assignment, planning and scheduling and task execution. The framework gives a general solution to the problem of satellite mission planning. The two core parts of the planning and scheduling module are machine learning algorithms and planning and scheduling algorithms, which greatly affect the quality of the results. Machine learning algorithm is mainly used to quickly obtain feasible initial solution. This idea can be used to quickly analyze and model the imaging satellite observation mission planning, imaging satellite measurement and control, data downlink mission planning problems. It has a strong generality and is suitable for most situations of imaging satellites. In order to verify the validity of ISMPF, we designed test examples for measurement and control, data downlink missions. Experimental verification demonstrates the effectiveness of our proposed framework.