Multi-objective optimized satellite joint task planning method

Abstract

The invention discloses a multi-target optimized satellite joint task planning method. Observation tasks of a target group are allocated to multiple satellites under the principles of giving consideration to important task priority, satellite resource load balancing, task success rate ensuring and user preference satellite priority; satellite resources are preferentially allocated to the targets with high priorities according to the importance degrees, urgency degrees and demand source attributes of the targets; in order to prevent a large number of satellite resources from concentrating on asmall number of key targets, the number of observations of each target is limited to be five at most. In order to improve the observation success rate of the targets, optical, radar and electronic load satellites are distributed to each target at most for observation; in addition, other satellites are used for supplementation on the basis of preferentially using the satellites preferred by the user. The method is suitable for guaranteeing the relative consistency of results before and after adjustment during emergency task adjustment, and can reduce the influence range of emergency tasks.

Description

technical field [0001] The invention relates to a multi-objective optimized satellite joint task planning method, which is suitable for quickly completing multi-satellite task allocation for the rapid monitoring of global hotspot targets, and the obtained planning scheme can give priority to important tasks, balance the load of satellite resources, and ensure the task success rate , User preference satellite priority and other aspects to seek a balance. Background technique [0002] The whole process of traditional satellite observation needs to go through the control process of the satellite-ground closed-loop link from putting forward the demand to obtaining information, which is difficult to meet the timeliness requirements. The self-operating Earth observation constellation can have the ability to complete the observation task by itself with no or little support from the ground system, and has the characteristics of fast and flexible. In order to improve the efficiency ...

AI Technical Summary

Problems solved by technology

[0004] However, most of the traditional multi-satellite task allocation methods are aimed at finding the optimal solution for a single optimization objective, and cannot balance the task success rate, resource utilization rate, user preference, etc.

At present, the existing multi-objective optimization method uses iterative operations such as genetic algorithm and the idea of ​​large-scale population sampling to find non-inferior solution sets when assigning multi-star tasks, which has high complexity, high requirements for hardware performance, and large solution space. It is not suitable for calculation and decision-making on the star, and it cannot ensure the stability and consistency of repeated calculation results, and it is not suitable for fine-tuning the plan during emergency adjustments.

Therefore, in the current practical application, there is no autonomous mission planning capability on the satellite. In order to obtain a high-quality satellite mission allocation plan, it is necessary to use a combination of man and machine to formulate the plan on the ground. star observation mission

Images