條帶狀公路運(yùn)營管理空間大數(shù)據(jù)降維組織及混合存儲關(guān)鍵技術(shù)研究
發(fā)布時間：2018-08-09 09:58
【摘要】：公路以其靈活多變、運(yùn)量大和速度快的特點(diǎn)在現(xiàn)代交通中占據(jù)重要地位。運(yùn)營管理作為公路生命周期的重要一環(huán),包括養(yǎng)護(hù)、交通、安全、服務(wù)等方面�？臻g信息技術(shù)是交通信息化建設(shè)的重要組成,貫穿在公路運(yùn)營管理過程中。隨著近年來,我國高分辨率對地觀測系統(tǒng)和北斗衛(wèi)星導(dǎo)航系統(tǒng)建設(shè)穩(wěn)步推進(jìn),物聯(lián)網(wǎng)、車聯(lián)網(wǎng)技術(shù)不斷推廣,交通運(yùn)營管理空間數(shù)據(jù)逐步呈現(xiàn)出體量大、種類多、速度快、價值高的大數(shù)據(jù)特征。傳統(tǒng)存儲管理方式不僅難以滿足大數(shù)據(jù)的需求,更缺乏對條帶狀空間數(shù)據(jù)的針對性。因此,如何高效地存儲管理公路運(yùn)營管理空間大數(shù)據(jù),是當(dāng)下亟待解決的問題。本文基于NoSQL數(shù)據(jù)庫和分布式云存儲,圍繞公路運(yùn)營管理空間數(shù)據(jù)的特征和管理需求,提出公路空間大數(shù)據(jù)條帶降維組織模型與公路空間大數(shù)據(jù)多態(tài)混合存儲架構(gòu),解決海量多源異構(gòu)公路運(yùn)營管理空間大數(shù)據(jù)的存儲檢索效率問題。本文主要研究內(nèi)容如下：(1)公路運(yùn)營管理空間大數(shù)據(jù)特征分析：通過對不同數(shù)據(jù)來源進(jìn)行分析,總結(jié)出其大數(shù)據(jù)特征和條帶狀空間分布特性。進(jìn)而根據(jù)其特點(diǎn)對數(shù)據(jù)進(jìn)行分類,并明確存儲管理需求,使得數(shù)據(jù)組織與存儲的設(shè)計更具針對性。(2)公路空間大數(shù)據(jù)條帶降維組織模型：通過分析空間數(shù)據(jù)降維的理論和方法,指出劃分空間格網(wǎng)是空間數(shù)據(jù)降維的有效方式,也是空間編碼的基礎(chǔ)。通過對Geohash格網(wǎng)與公路空間的尺度對比,提出公路空間格網(wǎng)劃分方法。將基于Geohash格網(wǎng)的空間數(shù)據(jù)降維方法,與公路本身一維的線性參照系統(tǒng)相結(jié)合,提出公路空間大數(shù)據(jù)條帶降維組織模型,并設(shè)計了點(diǎn)、線、面三類公路空間大數(shù)據(jù)的存儲檢索方式。(3)公路空間大數(shù)據(jù)多態(tài)混合存儲架構(gòu)：在深入分析不同空間大數(shù)據(jù)存儲技術(shù)的基礎(chǔ)上,將NoSQL數(shù)據(jù)庫、分布式云存儲及空間數(shù)據(jù)庫引擎三者無縫結(jié)合,對動態(tài)與靜態(tài)、結(jié)構(gòu)化與非結(jié)構(gòu)化、空間與非空間等多態(tài)的公路空間大數(shù)據(jù)進(jìn)行混合存儲。提出索引關(guān)聯(lián)的混合存儲協(xié)調(diào)管理引擎,通過空間信息降維進(jìn)行索引,建立公路空間大數(shù)據(jù)之間的關(guān)聯(lián),實(shí)現(xiàn)了公路空間大數(shù)據(jù)的無縫集成和一體化存儲。針對公路空間帶狀特點(diǎn),采用影像預(yù)分塊策略提高混合存儲架構(gòu)中影像數(shù)據(jù)按需獲取的效率。(4)公路運(yùn)營管理空間大數(shù)據(jù)管理原型系統(tǒng)：設(shè)計了原型系統(tǒng)的框架,實(shí)現(xiàn)了數(shù)據(jù)入庫、數(shù)據(jù)檢索、系統(tǒng)運(yùn)行監(jiān)控、數(shù)據(jù)可視化等核心功能,并介紹了原型系統(tǒng)在公路地質(zhì)災(zāi)害應(yīng)急處理中的應(yīng)用。通過與傳統(tǒng)的空間數(shù)據(jù)庫引擎進(jìn)行檢索性能對比實(shí)驗(yàn),論證了本文提出的公路運(yùn)營管理空間大數(shù)據(jù)存儲管理技術(shù)是可行的,并具有很好的性能。
[Abstract]:Highway plays an important role in modern traffic because of its flexibility, large volume and high speed. As an important part of road life cycle, operation management includes maintenance, transportation, safety, service and so on. Spatial information technology is an important component of traffic information construction, which runs through the course of highway operation and management. In recent years, the construction of high resolution Earth observation system and Beidou satellite navigation system has been advancing steadily, the technology of Internet of things and vehicle network has been popularized continuously, and the spatial data of traffic operation management have gradually presented large volume, many kinds and fast speed. Big data features of high value. The traditional storage management method is not only difficult to meet the needs of big data, but also lack the pertinence of strip spatial data. Therefore, how to store and manage highway management space big data efficiently is an urgent problem. Based on NoSQL database and distributed cloud storage, this paper proposes a hybrid storage architecture of highway spatial big data band reduction organization model and highway spatial big data polymorphic storage, focusing on the characteristics and management requirements of highway operation management spatial data. To solve the problem of storage and retrieval efficiency of massive multi-source heterogeneous highway operation management space big data. The main contents of this paper are as follows: (1) the big data characteristics of highway management space: through the analysis of different data sources, the characteristics of big data and strip spatial distribution are summarized. Then the data is classified according to its characteristics, and the requirement of storage management is clearly defined, which makes the design of data organization and storage more pertinence. (2) Highway spatial big data strip dimensionality reduction organization model: through analyzing the theory and method of spatial data dimensionality reduction, It is pointed out that dividing spatial grid is an effective way to reduce the dimension of spatial data, and it is also the basis of spatial coding. Based on the scale comparison between Geohash grid and highway space, a method of dividing highway space grid is proposed. The dimensionality reduction method of spatial data based on Geohash grid is combined with the one dimensional linear reference system of highway, and the dimensionality reduction organization model of big data strip in highway space is proposed, and the points and lines are designed. (3) Highway spatial big data polymorphic hybrid storage architecture: on the basis of in-depth analysis of different spatial big data storage technology, NoSQL database, The distributed cloud storage and spatial database engine are combined seamlessly to store the highway spatial big data which is dynamic and static, structured and unstructured, spatial and non-spatial. A hybrid storage coordination management engine for index association is proposed. By reducing the dimension of spatial information to index, the association between highway spatial big data is established, and the seamless integration and integrated storage of highway spatial big data are realized. According to the characteristics of highway spatial banding, image prepartitioning strategy is adopted to improve the efficiency of image data acquisition on demand in the hybrid storage architecture. (4) the big data management prototype system of highway operation management space: the framework of the prototype system is designed. The core functions of data storage, data retrieval, system operation monitoring, data visualization and so on are realized. The application of prototype system in highway geological disaster emergency treatment is introduced. By comparing the retrieval performance with the traditional spatial database engine, it is proved that the proposed big data storage and management technology of highway operation management space is feasible and has good performance.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：U495
本文編號：2173720
[Abstract]:Highway plays an important role in modern traffic because of its flexibility, large volume and high speed. As an important part of road life cycle, operation management includes maintenance, transportation, safety, service and so on. Spatial information technology is an important component of traffic information construction, which runs through the course of highway operation and management. In recent years, the construction of high resolution Earth observation system and Beidou satellite navigation system has been advancing steadily, the technology of Internet of things and vehicle network has been popularized continuously, and the spatial data of traffic operation management have gradually presented large volume, many kinds and fast speed. Big data features of high value. The traditional storage management method is not only difficult to meet the needs of big data, but also lack the pertinence of strip spatial data. Therefore, how to store and manage highway management space big data efficiently is an urgent problem. Based on NoSQL database and distributed cloud storage, this paper proposes a hybrid storage architecture of highway spatial big data band reduction organization model and highway spatial big data polymorphic storage, focusing on the characteristics and management requirements of highway operation management spatial data. To solve the problem of storage and retrieval efficiency of massive multi-source heterogeneous highway operation management space big data. The main contents of this paper are as follows: (1) the big data characteristics of highway management space: through the analysis of different data sources, the characteristics of big data and strip spatial distribution are summarized. Then the data is classified according to its characteristics, and the requirement of storage management is clearly defined, which makes the design of data organization and storage more pertinence. (2) Highway spatial big data strip dimensionality reduction organization model: through analyzing the theory and method of spatial data dimensionality reduction, It is pointed out that dividing spatial grid is an effective way to reduce the dimension of spatial data, and it is also the basis of spatial coding. Based on the scale comparison between Geohash grid and highway space, a method of dividing highway space grid is proposed. The dimensionality reduction method of spatial data based on Geohash grid is combined with the one dimensional linear reference system of highway, and the dimensionality reduction organization model of big data strip in highway space is proposed, and the points and lines are designed. (3) Highway spatial big data polymorphic hybrid storage architecture: on the basis of in-depth analysis of different spatial big data storage technology, NoSQL database, The distributed cloud storage and spatial database engine are combined seamlessly to store the highway spatial big data which is dynamic and static, structured and unstructured, spatial and non-spatial. A hybrid storage coordination management engine for index association is proposed. By reducing the dimension of spatial information to index, the association between highway spatial big data is established, and the seamless integration and integrated storage of highway spatial big data are realized. According to the characteristics of highway spatial banding, image prepartitioning strategy is adopted to improve the efficiency of image data acquisition on demand in the hybrid storage architecture. (4) the big data management prototype system of highway operation management space: the framework of the prototype system is designed. The core functions of data storage, data retrieval, system operation monitoring, data visualization and so on are realized. The application of prototype system in highway geological disaster emergency treatment is introduced. By comparing the retrieval performance with the traditional spatial database engine, it is proved that the proposed big data storage and management technology of highway operation management space is feasible and has good performance.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：U495
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