The invention provides a multi-
satellite task
planning method based on K-means clustering, the method comprises the steps of: S1, collecting task requirements of users T= {t1, t2, t3... Tn}, and obtaining an orbital working time set O= {o1, o2, o3,... Om} corresponding to each circle of sun illumination region of all currently available satellites;S 2, calculating the distance Disij between the task ti and each element oj in the set O, forming a distance set D= {di1, di2, di3... Din} between the task ti and the
orbit set O, and clustering the task ti to the
orbit k, Disik=Min (D) which is theshortest distance from the
orbit set O; S3, judging whether the current clustering scheme sk belongs to the set S= {s1, s2, s3,... Sz}, outputting the clustering scheme sk if sk belongs to S, otherwise adding the scheme sk to the scheme set S, and returning to step S2. By analyzing the factors affecting the assignment of multi-
satellite tasks and quantifying these factors and by using the K-meansclustering
algorithm, a multi-
satellite cooperative task assignment scheme is designed, which has fewer iterations and faster computational speed, and can meet the constraints of
time complexity for large-scale optimization problems, and greatly improve the quality of imaging, and improve the
completion rate of the task.