[1]刘 岩,柳乐怡,王 冬,等.基于深度学习算法的调度自动化云平台任务优化策略研究[J].机械与电子,2022,(10):32-35.
　LIU Yan,LIU Leyi,WANG Dong,et al.Research on Task Optimization Strategy of Scheduling Automation Cloud Platform Based on Deep Learning Algorithm[J].Machinery & Electronics,2022,(10):32-35.
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基于深度学习算法的调度自动化云平台任务优化策略研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年10期
页码:
32-35
栏目:
设计与研究
出版日期:
2022-10-31

文章信息/Info

Title:
Research on Task Optimization Strategy of Scheduling Automation Cloud Platform Based on Deep Learning Algorithm
文章编号:
1001-2257 ( 2022 ) 10-0032-05
作者:
刘 岩 1 ; 柳乐怡 1 ; 王 冬 1 ; 黄红伟 2 ; 雷彦辉 2
1. 深圳供电局有限公司,广东 深圳 518000 ; 2. 北京清大科越股份有限公司,北京 100102
Author(s):
LIU Yan1 ;  LIU Leyi1 ;  WANG Dong1 ;  HUANG Hongwei2 ;  LEI Yanhui2
( 1.Shenzhen Power Supply Company , Shenzhen 518000 , China ; 2.Beijing QU Creative Technology Co. , Ltd. , Beijing 100102 , China )
关键词:
云平台; 调度自动化; 改进粒子群算法; 调度优化
Keywords:
cloud platform ;  scheduling automation ;  optimized particle swarm optimization ;  scheduling optimization
分类号:
TM734
文献标志码:
A
摘要:
分析了云平台任务调度的特点和目标,从任务调度算法入手,提出了基于改进粒子群算法的电力调度自动化系统的人工智能方法,开发了云计算操作的模型。基于该算法和物理模型的运行控制考虑了 QoS 要求和平台云居民的环境负载平衡,可以有效提高所提电力调度自动化系统的云平台任务调度的效率。以电力自动化云平台为分析对象,研究其架构,将修正的 PSO 算法与云资源调度模型的结构拓扑相结合,建立三级数据节点,给出了基于改进 PSO 的云平台调度模型,旨在提高云计算资源配置效率,改善云服务质量,解决电力调度自动化系统的任务调度问题。
Abstract:
The characteristics and objectives of cloud platform task scheduling are analyzed.Starting with task scheduling algorithm , an artificial intelligence method of power dispatching automation system based on improved particle swarm optimization algorithm is proposed.A cloud computing operation model has been developed.The operation control based on the algorithm and physical model considers QoS requirements and environmental load balance of platform cloud residents , which can effectively improve the efficiency of cloud platform task scheduling of power dispatching automation system proposed in this paper.The architecture of power automation cloud platform is studied.Combining the modified PSO algorithm with the structural topology of the cloud resource scheduling model , a three-level data node is established , and a cloud platform scheduling model based on the improved PSO is presented , aiming to improve the efficiency of cloud computing resource allocation , improve cloud service quality , and solve the task scheduling problem of the power dispatching automation system.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2022-04-02
作者简介:刘 岩 ( 1988- ),男,广东深圳人,硕士,工程师,研究方向为电网调度自动化管理及自动发电控制系统管理;柳乐怡 ( 1998- ),女,广东深圳人,学士,工程师,研究方向为电网调度自动化系统运行管理。
更新日期/Last Update: 2022-11-07