隧联网结构及智能监控数据分析
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摘要
社会信息化水平的发展和中国城市化进程的推进,将人们带入了多感官的、物物相连的数据海洋中。我们置身于网络化世界,其中包括虚拟化的互联网也包括物物相联的物联网。在复杂广阔的网络里,城市交通作为支持经济发展的重要基础,其建设与发展的速度也越来越快。城市隧道是“感知中国”的一个关键和特殊交通感知点,但我国城市隧道智能监控发展较晚,至今没有统一的城市隧道监控规范和标准,更没有形成统一的城市隧道智能监控平台,无法实现多隧道“感知”。虽然各大城市已经先后建立了多个城市隧道智能监控系统,但并没有实现城市多个隧道的联网监控。本文通过研究已开发多个城市隧道监控系统,总结和归纳了城市隧道的相关特性,本文提出“隧联网”TI(Tunnels Internet)的构想,它是“物联网’'IoT(Internet of Things)在城市交通智能监控领域的一个特殊应用。
针对多个城市隧道智能监控系统中存在的数据孤立化、非标准化、海量化、多源异构性、建设与管理分离等问题。本文希望通过对隧联网的体系结构、功能层次、数据特性、数据整合和数据挖掘等方面的研究与分析,解决这些已经存在的问题。隧联网将能够实现城市隧道监控系统进行深层次的数据共享与协同应用,为应急管理、城市规划建设和隧道长期监控提供服务,实现各个城市隧道监控系统间的协同调度与联动控制,丰富物联网技术的应用,提高隧道联网智能监控水平的目标。本文的主要研究内容及相关创新点归纳为以下三个方面：
(1)本文首先根据城市隧道监控系统的实际需求和相关技术发展,给出了隧联网TI的构想；然后以此为出发点提出了“隧联网体系结构TIA”,并详细分析了隧联网体系结构TIA五层模型；其后详细分析隧联网的五个功能层。针对隧联网的数据特性进行了分析,并通过对隧联网数据的分类研究和异构性分析,提出了对应的数据集成方法。
(2)本文以数据挖掘方法为主要研究手段,分别从状态规则和数据预测两个方面入手对隧联网进行智能数据分析。在隧联网状态规则挖掘方面,首先根据隧道的特性提出了基于隧联网的交通拥堵等级划分模型；并建立了基于隧联网的交通拥堵关联规则挖掘模型TI-CAR。以隧道交通拥堵时刻的相关数据为对象,挖掘隧联网中各个隧道间潜在的关联性,并进行实验分析比较为隧联网智能化管理提供了依据。
(3)研究了隧联网交通数据流预测。分析了隧联网数据流；并讨论了隧联网预测分析方法；然后针对隧联网的交通数据流分析问题提出了两个算法预测隧联网的车流量：TI-GMDH和TI-LS-SVM预测算法,并对这两个算法进行讨论分析；最后通过隧联网的实际监控数据进行了实验分析与比较,证实了本文提出的两个预测算法较SVM预测方法更加适合隧联网车流量的预测。
With the development of information technology in society and the promotion of urbanization in China, people are brought into the multi-sensory, objects-connected digital ocean. We are in the networked world, including the virtual internet and the Internet of Things (IoT). In the complex and wide network, urban transport is an important foundation to support the economic development of China, and its construction and development speed becomes faster and faster. City Tunnels are key and special traffic-aware points of the "Perception of China", however, because of the late development of urban tunnel monitoring of China, so far non-unified norms and standards of urban tunnel monitoring and non-unified decision-support platform of urban tunnel monitoring, the multiple tunnel "Perceptions" can not be achieved. Although the urban tunnel monitoring systems have been established in major cities, but there are no network urban tunnel monitoring. By studying on exploitation of multiple urban tunnel monitoring systems, the relevant characteristics of urban tunnel were summarized and generalized, the idea of Tunnels Internet (TI), as a special case of Internet of Things (IoT) applied in the field of urban transport and intelligent transportation, was proposed in this paper.
For the data isolation, non-standardization, data magnanimity, information heterogeneous complexity, tunnel construction and management separation etc. in multiple ubran tunnel monitoring systems. The research and analysis were carried on the architecture, functional hierarchy, data characteristics, data integration and data mining of TI to solve those existing problems. TI will achieve deep-level data sharing and collaborative applications of ubran tunnel monitoring systems, provide services for collaborative emergency management, urban planning and construction, and long-period monitoring, implement co-scheduling and control linkage of different departments, expand applications of IoT technology, and improve the ubran tunnel monitoring. The main contents and innovations of thesis are generalize into three parts:
(1) According to the actual needs and related technology development of urban tunnel monitoring system, the idea of Tunnels Internet (TT) was proposed at first, then Tunnels Internet Architecture (TIA) was put forward, and analysis of five-layer model of TIA were carried out; and then details of five functional layers of TIA were analyzed. According to the analysis of data characteristics of TI, classification and heterogeneity analysis of TI data, the corresponding data integration methods were put forward.
(2) Intelligent methods and theory of data mining as the main research means, intelligent data analysis of TI were carried out from the status rules and data prediction. On the status rules mining of TI, at first classification model of traffic congestion based on TI was proposed, and then exploitation model of Tunnels Internet congestion association rule (TI-CAR) was established. Related data at the tunnel traffic congestion time as main objects of study, the potential correlations of every tunnels in the TI were explored, and reliable theoretical models and experimental analysis and comparison were carried out to supply basis for the intelligent management of TI.
(3) Traffic data stream predictions of TI were studied. Data streams of TI were analyzed, and prediction analysis methods of TI were disscussed. And then two prediction algorithms (TI-GMDH and TI-LS-SVM) on traffic data stream analysis were proposed and discussed. In the end, by experimental analysis and comparison on real data of TI, conclusion was achieved that, compared with SVM prediction, prediction algorithms of TI-GMDH and TI-LS-SVM proposed in the thesis were more suitable for the prediction of TI.
针对多个城市隧道智能监控系统中存在的数据孤立化、非标准化、海量化、多源异构性、建设与管理分离等问题。本文希望通过对隧联网的体系结构、功能层次、数据特性、数据整合和数据挖掘等方面的研究与分析,解决这些已经存在的问题。隧联网将能够实现城市隧道监控系统进行深层次的数据共享与协同应用,为应急管理、城市规划建设和隧道长期监控提供服务,实现各个城市隧道监控系统间的协同调度与联动控制,丰富物联网技术的应用,提高隧道联网智能监控水平的目标。本文的主要研究内容及相关创新点归纳为以下三个方面：
(1)本文首先根据城市隧道监控系统的实际需求和相关技术发展,给出了隧联网TI的构想；然后以此为出发点提出了“隧联网体系结构TIA”,并详细分析了隧联网体系结构TIA五层模型；其后详细分析隧联网的五个功能层。针对隧联网的数据特性进行了分析,并通过对隧联网数据的分类研究和异构性分析,提出了对应的数据集成方法。
(2)本文以数据挖掘方法为主要研究手段,分别从状态规则和数据预测两个方面入手对隧联网进行智能数据分析。在隧联网状态规则挖掘方面,首先根据隧道的特性提出了基于隧联网的交通拥堵等级划分模型；并建立了基于隧联网的交通拥堵关联规则挖掘模型TI-CAR。以隧道交通拥堵时刻的相关数据为对象,挖掘隧联网中各个隧道间潜在的关联性,并进行实验分析比较为隧联网智能化管理提供了依据。
(3)研究了隧联网交通数据流预测。分析了隧联网数据流；并讨论了隧联网预测分析方法；然后针对隧联网的交通数据流分析问题提出了两个算法预测隧联网的车流量：TI-GMDH和TI-LS-SVM预测算法,并对这两个算法进行讨论分析；最后通过隧联网的实际监控数据进行了实验分析与比较,证实了本文提出的两个预测算法较SVM预测方法更加适合隧联网车流量的预测。
With the development of information technology in society and the promotion of urbanization in China, people are brought into the multi-sensory, objects-connected digital ocean. We are in the networked world, including the virtual internet and the Internet of Things (IoT). In the complex and wide network, urban transport is an important foundation to support the economic development of China, and its construction and development speed becomes faster and faster. City Tunnels are key and special traffic-aware points of the "Perception of China", however, because of the late development of urban tunnel monitoring of China, so far non-unified norms and standards of urban tunnel monitoring and non-unified decision-support platform of urban tunnel monitoring, the multiple tunnel "Perceptions" can not be achieved. Although the urban tunnel monitoring systems have been established in major cities, but there are no network urban tunnel monitoring. By studying on exploitation of multiple urban tunnel monitoring systems, the relevant characteristics of urban tunnel were summarized and generalized, the idea of Tunnels Internet (TI), as a special case of Internet of Things (IoT) applied in the field of urban transport and intelligent transportation, was proposed in this paper.
For the data isolation, non-standardization, data magnanimity, information heterogeneous complexity, tunnel construction and management separation etc. in multiple ubran tunnel monitoring systems. The research and analysis were carried on the architecture, functional hierarchy, data characteristics, data integration and data mining of TI to solve those existing problems. TI will achieve deep-level data sharing and collaborative applications of ubran tunnel monitoring systems, provide services for collaborative emergency management, urban planning and construction, and long-period monitoring, implement co-scheduling and control linkage of different departments, expand applications of IoT technology, and improve the ubran tunnel monitoring. The main contents and innovations of thesis are generalize into three parts:
(1) According to the actual needs and related technology development of urban tunnel monitoring system, the idea of Tunnels Internet (TT) was proposed at first, then Tunnels Internet Architecture (TIA) was put forward, and analysis of five-layer model of TIA were carried out; and then details of five functional layers of TIA were analyzed. According to the analysis of data characteristics of TI, classification and heterogeneity analysis of TI data, the corresponding data integration methods were put forward.
(2) Intelligent methods and theory of data mining as the main research means, intelligent data analysis of TI were carried out from the status rules and data prediction. On the status rules mining of TI, at first classification model of traffic congestion based on TI was proposed, and then exploitation model of Tunnels Internet congestion association rule (TI-CAR) was established. Related data at the tunnel traffic congestion time as main objects of study, the potential correlations of every tunnels in the TI were explored, and reliable theoretical models and experimental analysis and comparison were carried out to supply basis for the intelligent management of TI.
(3) Traffic data stream predictions of TI were studied. Data streams of TI were analyzed, and prediction analysis methods of TI were disscussed. And then two prediction algorithms (TI-GMDH and TI-LS-SVM) on traffic data stream analysis were proposed and discussed. In the end, by experimental analysis and comparison on real data of TI, conclusion was achieved that, compared with SVM prediction, prediction algorithms of TI-GMDH and TI-LS-SVM proposed in the thesis were more suitable for the prediction of TI.
引文
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