基于物联网EDSOA架构的分布式规则引擎的研究与实现
发布时间：2018-12-30 22:26
【摘要】：随着物联网的发展,接入的感知设备无论在种类还是数量上,都在日益增长,导致物联网环境中的数据量日益激增。如何从海量的数据中高效、智能地发现我们感兴趣的数据成为了我们的关注点。规则引擎是由基于规则的专家系统逐步演化而来的,可以通过源源不断的接收数据,将其作为事实与事先设定好的规则做匹配,从而可以在简单的数据中发现复杂事件。然而目前的主流规则引擎都只能在单机的环境下运行,缺少能够分布、并行的实现机制,这样使得当系统的数据量较大时,单个计算机的处理能力会成为系统的瓶颈。本文通过对国内外工业界和学术界的优秀规则引擎的研究,提出了一种适用于物联网EDSOA架构环境的分布式规则引擎的实现方法。本文首先从系统的总体需求开始分析,对传统的分布式框架做出改进,并在其基础上提出了分布式规则引擎的总体架构,将通过分解规则的方式,拆分规则集合,并将子规则集部署于不同的工作节点上,每个工作节点都将作为独立的规则引擎,进行规则匹配,再由主节点归并各个工作节点产生的中间结果,以达到并行的匹配规则的目的。由于需要对单个规则进行分解操作,本文对规引擎中的规则进行了研究。通过对知识的研究,对规则做出了定义并根据特性对规则进行了分类,不同的规则将会使用不同的方法进行分解。同时发现不同的规则集合分解策略会影响到系统的匹配效率,本文又对如何“合理”地分解规则来提高系统的效率做出了研究。本文使用了 Apriori算法对规则之间的关联做了分析。最后本文给出了原型系统的实现类图,以及对系统进行了性能测试来验证系统,测试结果基本达到了预期效果
[Abstract]:With the development of the Internet of things (IoT), the number of sensor devices is increasing day by day, which leads to the increasing amount of data in the Internet of things (IoT) environment. How to find the data we are interested in efficiently and intelligently from the massive data has become our focus. The rule engine is evolved from the rule-based expert system. It can be used to match the facts with the rules set in advance by receiving the data continuously, so that complex events can be found in the simple data. However, the current mainstream rule engines can only run in a single machine environment, and lack of distributed and parallel implementation mechanism, which makes the processing ability of a single computer become the bottleneck of the system when the data volume of the system is large. Based on the research of the excellent rule engine in industry and academia at home and abroad, this paper presents a method of implementing the distributed rule engine suitable for the EDSOA architecture of the Internet of things. Based on the analysis of the general requirements of the system, this paper improves the traditional distributed framework, and proposes the general architecture of the distributed rule engine, which will split the rule set by decomposing the rules. The subrule set is deployed to different working nodes, each working node will act as an independent rule engine to match the rules, and then merge the intermediate results generated by each working node by the primary node. In order to achieve the purpose of parallel matching rules. Because of the need to decompose a single rule, the rules in the gage engine are studied in this paper. Through the study of knowledge, the rules are defined and classified according to their characteristics. Different rules will be decomposed in different ways. At the same time, it is found that different decomposition strategies of rule set will affect the matching efficiency of the system. This paper also studies how to decompose the rules reasonably to improve the efficiency of the system. In this paper, Apriori algorithm is used to analyze the association between rules. Finally, the implementation class diagram of the prototype system is given, and the system performance is tested to verify the system. The test results basically reach the expected results.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.44;TN929.5
[Abstract]:With the development of the Internet of things (IoT), the number of sensor devices is increasing day by day, which leads to the increasing amount of data in the Internet of things (IoT) environment. How to find the data we are interested in efficiently and intelligently from the massive data has become our focus. The rule engine is evolved from the rule-based expert system. It can be used to match the facts with the rules set in advance by receiving the data continuously, so that complex events can be found in the simple data. However, the current mainstream rule engines can only run in a single machine environment, and lack of distributed and parallel implementation mechanism, which makes the processing ability of a single computer become the bottleneck of the system when the data volume of the system is large. Based on the research of the excellent rule engine in industry and academia at home and abroad, this paper presents a method of implementing the distributed rule engine suitable for the EDSOA architecture of the Internet of things. Based on the analysis of the general requirements of the system, this paper improves the traditional distributed framework, and proposes the general architecture of the distributed rule engine, which will split the rule set by decomposing the rules. The subrule set is deployed to different working nodes, each working node will act as an independent rule engine to match the rules, and then merge the intermediate results generated by each working node by the primary node. In order to achieve the purpose of parallel matching rules. Because of the need to decompose a single rule, the rules in the gage engine are studied in this paper. Through the study of knowledge, the rules are defined and classified according to their characteristics. Different rules will be decomposed in different ways. At the same time, it is found that different decomposition strategies of rule set will affect the matching efficiency of the system. This paper also studies how to decompose the rules reasonably to improve the efficiency of the system. In this paper, Apriori algorithm is used to analyze the association between rules. Finally, the implementation class diagram of the prototype system is given, and the system performance is tested to verify the system. The test results basically reach the expected results.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.44;TN929.5
【参考文献】
相关期刊论文 前4条
1 孙其博;刘杰;黎，
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