【摘要】：隨著物聯(lián)網(wǎng)的發(fā)展,接入的感知設(shè)備無論在種類還是數(shù)量上,都在日益增長,導(dǎo)致物聯(lián)網(wǎng)環(huán)境中的數(shù)據(jù)量日益激增。如何從海量的數(shù)據(jù)中高效、智能地發(fā)現(xiàn)我們感興趣的數(shù)據(jù)成為了我們的關(guān)注點(diǎn)。規(guī)則引擎是由基于規(guī)則的專家系統(tǒng)逐步演化而來的,可以通過源源不斷的接收數(shù)據(jù),將其作為事實(shí)與事先設(shè)定好的規(guī)則做匹配,從而可以在簡單的數(shù)據(jù)中發(fā)現(xiàn)復(fù)雜事件。然而目前的主流規(guī)則引擎都只能在單機(jī)的環(huán)境下運(yùn)行,缺少能夠分布、并行的實(shí)現(xiàn)機(jī)制,這樣使得當(dāng)系統(tǒng)的數(shù)據(jù)量較大時(shí),單個(gè)計(jì)算機(jī)的處理能力會(huì)成為系統(tǒng)的瓶頸。本文通過對(duì)國內(nèi)外工業(yè)界和學(xué)術(shù)界的優(yōu)秀規(guī)則引擎的研究,提出了一種適用于物聯(lián)網(wǎng)EDSOA架構(gòu)環(huán)境的分布式規(guī)則引擎的實(shí)現(xiàn)方法。本文首先從系統(tǒng)的總體需求開始分析,對(duì)傳統(tǒng)的分布式框架做出改進(jìn),并在其基礎(chǔ)上提出了分布式規(guī)則引擎的總體架構(gòu),將通過分解規(guī)則的方式,拆分規(guī)則集合,并將子規(guī)則集部署于不同的工作節(jié)點(diǎn)上,每個(gè)工作節(jié)點(diǎn)都將作為獨(dú)立的規(guī)則引擎,進(jìn)行規(guī)則匹配,再由主節(jié)點(diǎn)歸并各個(gè)工作節(jié)點(diǎn)產(chǎn)生的中間結(jié)果,以達(dá)到并行的匹配規(guī)則的目的。由于需要對(duì)單個(gè)規(guī)則進(jìn)行分解操作,本文對(duì)規(guī)引擎中的規(guī)則進(jìn)行了研究。通過對(duì)知識(shí)的研究,對(duì)規(guī)則做出了定義并根據(jù)特性對(duì)規(guī)則進(jìn)行了分類,不同的規(guī)則將會(huì)使用不同的方法進(jìn)行分解。同時(shí)發(fā)現(xiàn)不同的規(guī)則集合分解策略會(huì)影響到系統(tǒng)的匹配效率,本文又對(duì)如何“合理”地分解規(guī)則來提高系統(tǒng)的效率做出了研究。本文使用了 Apriori算法對(duì)規(guī)則之間的關(guān)聯(lián)做了分析。最后本文給出了原型系統(tǒng)的實(shí)現(xiàn)類圖,以及對(duì)系統(tǒng)進(jìn)行了性能測(cè)試來驗(yàn)證系統(tǒng),測(cè)試結(jié)果基本達(dá)到了預(yù)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.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.44;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 孫其博;劉杰;黎，

本文編號(hào)：2396252