Multi-satellite earth observation mission planning problem processing method and device

Abstract

The invention relates to a multi-satellite earth observation mission planning problem processing method, which comprises the following steps: when processing multi-satellite earth observation mission planning problems through an ant colony algorithm, in the initialization step, obtaining a default value [alpha]min of a pheromone influence factor; performing an iteration process of the ant colony algorithm to obtain a best solution St generated in each generation of iteration, and recording the best solution St generated in each generation of iteration into a solution set as a solution element; starting from the second generation of iteration process and after obtaining the best solution St generated in each generation of iteration, calculating the value [alpha]t+1 of the pheromone influence factor used in the follow-up (t+1)th generation of iteration process according to a first formula; and when the iteration process meets termination criteria, serving the solution element, having the maximum fitness function value, in the solution set as an optimal scheme for the multi-satellite earth observation mission planning problems. The processing method can ensure that the obtained solution has better quality and processing efficiency is high.

Description

technical field [0001] The invention relates to computer technology, in particular to a method and device for processing observation task planning problems. Background technique [0002] In order for Earth-imaging satellites to function better, mission planning techniques are particularly critical. The meaning of mission planning refers to the process of scheduling observation tasks to be executed, resource matching, determining the working time domain, airspace and mode of satellites and their loads, and formulating detailed work plans. The purpose is to drive satellite resources Perform tasks scientifically and efficiently. Multi-satellite Earth observation mission planning must be completed under complex constraints and multiple optimization objectives, so the problem has a wide dimension and a large optimization space. At present, intelligent algorithms are often used to obtain its approximate optimal solution. [0003] The ant colony algorithm in the prior art has cer...

AI Technical Summary

Problems solved by technology

Multi-satellite earth observation mission planning must be completed under complex constraints and multiple optimization objectives, so the problem has a wide dimension and a large optimization space. At present, intelligent algorithms are often used to obtain its approximate optimal solution

[0003] In the prior art, the ant colony algorithm has certain advantages in dealing with multi-satellite earth observation mission planning, but the determined optimal mission sequence is usually not the global optimal solution, and the convergence is slow and the processing time is long

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