Remote sensing common product production scheduling method, equipment and medium

Abstract

The invention provides a remote sensing common product production scheduling method. The method comprises the following steps: constructing a product model set corresponding to a product; distinguishing the level of each product model in the product model set; and distributing the product models at the same level to production nodes level by level. Based on the technical scheme, before the tasks are distributed to the computer nodes, the product models corresponding to the product names can be divided and graded, then the product models at the same level are produced according to the levels, and compared with the prior art, the production process is analyzed and simplified, and the efficiency of remote sensing product production scheduling is greatly improved.

Description

technical field [0001] The present disclosure specifically discloses a remote sensing common product production scheduling method, equipment and medium. Background technique [0002] The existing remote sensing product production scheduling method supports the "first come, first served" scheduling strategy. When a new task arrives at the scheduling server, the scheduling server will dispatch the task to the first idle computing node according to the "first come, first served" processing strategy. , the specific implementation steps are as follows: [0003] The scheduling server receives the remote sensing product production order, checks whether the queue of idle computing nodes is empty, and if it is empty, the scheduling server defends the order into the order buffer. Otherwise, assign the task to the first node in the queue of idle compute nodes. [0004] The computing node executes the production task, and at the same time, the scheduling server removes this node from ...

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Problems solved by technology

[0006] Based on the above information, it can be seen that the existing production scheduling methods for remote sensing products have the following defects: On the one hand, it is easy to cause waste of resources: when there is a remote sensing product production task that has a high resource demand for computing nodes, the computing nodes cannot meet them. Assigning tasks with low resource requirements to nodes with high reproduction capabilities is a waste of computing node resources

On the other hand, it takes a long time: the amount of remote sensing data is large, and the production process of a single product is complicated and time-consuming

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