Multi-satellite multi-station integrated scheduling method and system

Abstract

The invention provides a multi-satellite multi-station integrated scheduling method and system, and relates to the technical field of satellite scheduling. According to the embodiment of the invention, a diversified neighborhood structure is introduced according to an initial multi-satellite multi-station integrated scheduling scheme to obtain an optimal scheduling scheme, a local optimal solution is obtained quickly, and the solving quality is improved; whether the optimal scheduling scheme is caught in local optimum or not is judged, if yes, a disturbance mechanism is started to remove a part of observation tasks from the optimal scheduling scheme, the removed tasks are kept in an inactive table, and when one of the tasks is triggered, proposed three removal operators are called so as to improve the solving quality; the optimal scheduling scheme for removing part of observation tasks is traversed, the data transmission process is optimized, observation data downloading is arranged, unnecessary data downloading activities are canceled, unnecessary energy consumption is reduced, the satellite resource utilization rate is increased, meanwhile, time is saved, and more opportunities are provided for observation activities.

Description

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AI Technical Summary

Benefits of technology

The invention introduces a method called MultiStar Multiprotocol (MSM) that optimizes resource allocation by creating a more efficient way of collecting observed samples from different locations at once without overloading any resources or slowdowns associated with traditional methods like sequential sampling techniques. This allows researchers to gather important insights about environmental impact caused due to pollution sources while also improving their efficiency.

Problems solved by technology

This patents discuss various technical issues associated with integrative scheduling systems used by different observing devices (such as cameras) or ground platforms: (1) There exist many challengings involved in optimizing resource allocation among diverse observational requirements due to factors like limited communication capacity and unpredictable environmental conditions such as wind speed; (2) Current methods involve assuming fixed periods during which observations take place without considering any changes caused by external influences, making it hard to identify specific areas where an observer's actions should occur beforehand. Additionally, existing solutions require either preprocessing all observed images prior to analysis or relying solely upon imagery alone, leading to incomplete understanding of the whole system.

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