Mission planning method for mega-remote sensing constellation

By optimizing the planning of giant remote sensing constellations through a parallel volunteer mechanism and simulated annealing algorithm, the problems of slow planning speed and personalized user needs in existing technologies are solved, and fast and flexible mission planning and optimization are achieved.

CN117217483BActive Publication Date: 2026-06-09HARBIN INST OF TECH
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HARBIN INST OF TECH
Filing Date
2023-09-22
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing mission planning methods for giant remote sensing constellations suffer from problems such as slow planning speed, static and rigid planning modes, inability to handle sudden and urgent missions, and inability to reflect the importance of missions and personalized user needs.

Method used

A parallel volunteer mechanism and a simulated annealing algorithm are adopted. The importance score of the task is evaluated through an examination mechanism. The task planning scheme is optimized by combining the selection intention score and the social intention coefficient. The parallel volunteer mechanism is used to sort by importance score and match volunteer forms with satellite time windows. The simulated annealing algorithm is used to optimize the social intention coefficient to achieve the optimal scheme.

Benefits of technology

It accelerates task planning, can handle more random concurrent tasks, reflects task priorities, enables personalized planning for users, and improves the flexibility and efficiency of planning.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN117217483B_ABST
    Figure CN117217483B_ABST
Patent Text Reader

Abstract

The task planning method of the giant remote sensing constellation solves the problem of slow task planning speed of the existing remote sensing constellation, and the method comprises the following steps: the to-be-imaged tasks obtain respective importance scores through an examination mechanism, and are sorted from large to small; each to-be-imaged task determines a volunteer book according to a selection willingness score of an imaging time window of different satellites, and the volunteer book comprises imaging time windows of different satellites sorted according to the selection willingness score; according to the sorted to-be-imaged tasks and the volunteer books thereof, the imaging time windows of the satellites are matched with the to-be-imaged tasks according to the rules of a parallel volunteer mechanism, to generate a general task planning scheme; in the parallel volunteer mechanism, the to-be-imaged tasks are regarded as students, and the imaging time windows of the satellites are regarded as majors of colleges and universities. The method can also arrange the planning of special tasks and special imaging loads by using an early batch mechanism under the premise of accelerating the planning speed.
Need to check novelty before this filing date? Find Prior Art