基于SaaS的矿震远程预警系统关键技术初探
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摘要
现有的矿震预警系统都是以独立形式运行于各矿区,相互之间没有关联,重复投资严重,且矿震数据海量繁杂,仅依靠现场分析无法满足实时预警需求。文章基于感知矿山物联网示范工程(夹河矿),对采用云计算SaaS模式构建的矿震远程预警系统关键技术进行了相关研究:首先给出了系统整体框架,然后重点探讨了该系统所涉及的三个关键技术,包括基于SaaS的矿震预警专家分析软件架构、多源SaaS数据库构建技术以及基于MapReduce的并发数据过滤接收技术。现场应用表明,所构建的系统已基本具备全国统一远程矿震预警系统的架构,能满足多矿区远程矿震预警的基本要求。
Existing early-warning systems of coal mine earthquake run independently in different mines.There is serious repeated investment and no correlation between these similar systems,and on-site analysis ability on massive coal mine earthquake data is clearly insufficient.Key technologies of a SaaS based remote early warning system of coal mine earthquake was researched based on Sensor Mine Internet of Things Demonstration Project(Jiahe Coal Mine).Overall architecture of the system was given and three key technologies of the system were discussed including structure of SaaS-based expert analysis software,construction of multi-source SaaS database and MapReduce-based parallel data filtering and receiving.The application showed that the system basically has structure of remote early-warning system of coal mine earthquake in the whole country and can meet basic requirements of remote early-warning of multi-mine earthquake.
Existing early-warning systems of coal mine earthquake run independently in different mines.There is serious repeated investment and no correlation between these similar systems,and on-site analysis ability on massive coal mine earthquake data is clearly insufficient.Key technologies of a SaaS based remote early warning system of coal mine earthquake was researched based on Sensor Mine Internet of Things Demonstration Project(Jiahe Coal Mine).Overall architecture of the system was given and three key technologies of the system were discussed including structure of SaaS-based expert analysis software,construction of multi-source SaaS database and MapReduce-based parallel data filtering and receiving.The application showed that the system basically has structure of remote early-warning system of coal mine earthquake in the whole country and can meet basic requirements of remote early-warning of multi-mine earthquake.
引文
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[2]窦林名,何学秋.冲击矿压防治理论与技术[M].徐州:中国矿业大学出版社,2001.
[3]姜福兴,杨淑华,成云海,等.煤矿冲击地压的微地震监测研究[J].地球物理学报,2006,49(5):1511-1516.
[4]窦林名,何学秋.煤矿冲击矿压的分级预测研究[J].中国矿业大学学报,2007,36(6):717-722.
[5]刘鹏.云计算[M].2版.北京:电子工业出版社,2011:1-9.
[6]张申,丁恩杰.物联网与感知矿山专题讲座之二——感知矿山与数字矿山、矿山综合自动化[J].工矿自动化,2010(11):29-32.
[7]CHONG F,CARRARO G.Building DistributedApplications Architecture Strategies for Catching the Long Tail[EB/OL].[2012-01-16].http://msdn2.microsoft.com/en-us/library/aa479069.aspx,2006.
[8]AULLACH S,GRUST T,JACOBS D,et al.Multi-tenant Databases for Software as a Service:Schema-Mapping Techniques[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data,2008,New York:1195-1206.
[9]MIETZNER R,METZGER A,LEYMANN F,et al.Variability Modeling to Support Customization and Deployment of Multi-Tenant-Aware Software as a Service Applications[C]//Proceedings of ICSE Workshop on Principles of Engineering Service Oriented Systems,2009,Washington:18-25.
[10]ZAHOOR E,PERRIN O,GODART C.An Integrated Declarative Approach to Web Services Composition and Monitoring[C]//Proceedings of Web Information Systems Engineering,2009,Poznan:247-260.
[11]DEAN J,GHEMAWAT S.MapReduce:Simplifid Data Processing on Large Clusters[C]//Proceedings of the6th Symposium on Operating Systems Design and Implementation,2004,San Francisco:137-150.
[12]BAHU S.Towards Automatic Optimization of Mapreduce Programs[C]//Proceedings of the1st ACM Symposium on Cloud Computing,2010,Indianapolis:137-142.
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