基于智能算法的油田档案管理系统任务分配研究  

Research on Task Allocation of Oilfield Archives Management System Based on Intelligence Algorithms

作　　者：任伟建[1,2] 高铭泽 张永丰 汪世涛[4] 朱珊 REN Wei-jian;GAO Ming-ze;ZHANG Yong-feng;WANG Shi-tao;ZHU Shan(College of Electrical Engineering and Information, Northeast Petroleum University;Heilongjiang Provincial Key Laboratory of Networking ＆ Intelligent Control;Planning and Design Institute of NO. 2 Oil Production Plant, Daqing Oilfield Co. , Ltd.;PetroChina Jiangsu LNG Company Limited;Gansu Tobacco Industry Co. , Ltd.)

机构地区：[1]东北石油大学电气信息工程学院 [2]黑龙江省网络化与智能控制重点实验室 [3]大庆油田有限责任公司第二采油厂规划设计研究所 [4]中石油江苏液化天然气有限公司 [5]甘肃烟草工业有限责任公司

出　　处：《化工自动化及仪表》2018年第4期302-306,共5页Control and Instruments in Chemical Industry

基　　金：国家自然科学基金项目(61374127);黑龙江省博士后科研启动资金项目(LBH-Q12143)

摘　　要：为解决工作流管理系统中由任务堆积和超时导致的系统工作效率低的问题,将自适应人工萤火虫算法和蚁群算法结合,形成一种新型智能AGSO-ACO算法,并利用该算法进行油田档案管理系统任务分配。实验结果表明:AGSO-ACO算法可以保证用户总负载均衡增长、用户的任务与经验值基本保持一致、用户在局部时间段内达到负载均衡。In order to solve problems like the low efficiency of operations incurred by the task stacking and frequent overtime in the workflow management system, having adaptive artificial glowworm algorithm and ant colony algorithm combined to form a new intelligent algorithm（AGSO-ACO） was implemented for task alloca- tion of archives management system. Experimental results show that, this AGSO-ACO algorithm can ensure a balance growth of user＇ s total load and bring user＇ s task into correspondence with empirical value as well as make users come to load balancing in a local time period.

关 键 词：AGSO—ACO算法 任务分配 油田档案管理系统 负载均衡 

分 类 号：TH865[机械工程—仪器科学与技术]