Data Collection and Analysis of Visible Light Micro-Ecological Remote Sensing Based on Task Debugging and Optimization
Two popular strategies for scheduling optimization are greedy scheduling and genetic algorithm. In this paper, a
compound scheduler that can search for the request subsets not competing with other requests is proposed based
on task scheduling optimization, and different scheduling optimization algorithms are applied to the subsets.
The feasibility of this method depends mostly on the visible light eco-satellite model and the time when the
detection request subsets become truly independent. The results suggest that understanding the competition
among requests can provide opportunities for applying different scheduling strategies to the request subsets. It
can not only save the overall computation time but also allow the application of strategies such as exhaustive
planning, which is not feasible for the while request set. In conclusion, this paper indicated that the task
scheduling optimization can save the overall computation time for the acquisition and analysis of visible light
micro-ecological remote sensing satellites.