Task allocation method for industrial distributed data collection system

Abstract

The invention provides a task allocation method for an industrial distributed data collection system. According to the method, multiple factors such as resource utilization rate of collection nodes, collection efficiency, load balance, collection reliability, heterogeneous collection nodes and the like are comprehensively considered; simulation-based initial collection node number optimization model and initial task allocation optimization model are built; and according to actual configuration and usage conditions of the collection nodes, the number of the collection nodes in operation is optimized, so that the collected resource utilization rate is increased, initial allocation of tasks including 1:1 redundant tasks and non-redundant tasks under different resource configuration and resource usage conditions of the collection nodes in the industrial distributed data collection system is realized, and the demands of industrial distributed data collection on collection timeliness, reliability, resource effective utilization and the like in an industrial big data environment are met.

Description

technical field [0001] The invention belongs to the technical field of data acquisition, and in particular relates to a task allocation method in an industrial distributed data acquisition system. Background technique [0002] With the advent of the industrial big data environment, the data sources in the industrial process are increasingly diversified and the data scale is increasing. Facing the collection of large-scale high-frequency data in the industry and some new application requirements, in order to ensure the sequential and real-time data collection In terms of reliability and reliability, more and more enterprises are beginning to consider adopting distributed systems for industrial data collection. In the design process of the distributed data acquisition system, the task allocation strategy is a very critical link, which will directly affect the resource utilization efficiency and data acquisition efficiency of the acquisition system. [0003] In a distributed e...

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Problems solved by technology

Although the above-mentioned patent can realize the effective allocation of tasks, it is only aimed at the situation where the resource usage of each collection node is the same at the initial allocation time. For heterogeneous collection nodes with different resource configuration or usage, the above allocation scheme is not applicable. Moreover, the allocation method described in the existing patent is only for the occasions where all tasks are important tasks and need to be backed up, or all tasks are not backed up. The acquisition reliability of the acquisition task is often only backed up for some important data in the acquisition task, and other tasks are not backed up. The task allocation method in the existing patent cannot achieve load balancing of each acquisition node in this case. Moreover, the existing patents do not provide a specific optimization method for the number of initial work collection nodes, which is likely to cause waste of collection resources

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