【摘要】：隨著林業(yè)信息化建設(shè)的推進,越來越多的新技術(shù)、新方法在林業(yè)上得到了應(yīng)用。要對森林領(lǐng)域進行深入的研究,森林資源監(jiān)測數(shù)據(jù)的獲取是必不可少的�，F(xiàn)在,我國森林資源監(jiān)測對像多(如氣候、土壤、水文、水質(zhì)、視頻等),分布范圍廣,采集的數(shù)據(jù)量大,數(shù)據(jù)的形式多樣,結(jié)構(gòu)不統(tǒng)一。只有對辛苦得來的寶貴森林監(jiān)測數(shù)據(jù)進行有效的存儲、管理和利用,才能對林業(yè)問題的分析、決策提供客觀、全面的支持。本文針對森林資源監(jiān)測數(shù)據(jù)的大數(shù)據(jù)問題,不同類型數(shù)據(jù)的存儲問題以及數(shù)據(jù)的并行存儲和計算問題,進行了以下研究。 首先,針對森林資源監(jiān)測數(shù)據(jù)分布范圍廣,相互間相對孤立以及監(jiān)測數(shù)據(jù)量大等問題。本文提出了一種基于Glusterfs的森林資源監(jiān)測云平臺的構(gòu)建方法；通過云平臺的構(gòu)建,可以解決各地區(qū)監(jiān)測數(shù)據(jù)相對孤立的問題,把所有的監(jiān)測數(shù)據(jù)存儲在構(gòu)建的統(tǒng)一虛擬資源存儲池中,實現(xiàn)數(shù)據(jù)的邏輯統(tǒng)一,有利于數(shù)據(jù)的共享和存儲資源利用率的提高。 其次,針對森林資源監(jiān)測數(shù)據(jù)格式和類型的多樣性等問題。本文通過分析森林監(jiān)測數(shù)據(jù)的邏輯結(jié)構(gòu),大致將監(jiān)測數(shù)據(jù)分為結(jié)構(gòu)化數(shù)據(jù)和非結(jié)構(gòu)化數(shù)據(jù),并設(shè)計了一種混合式的存儲機制,即根據(jù)數(shù)據(jù)的不同類型將其分別存儲在關(guān)系型數(shù)據(jù)庫和NoSQL數(shù)據(jù)庫中；從而解決了不同數(shù)據(jù)類型的數(shù)據(jù)在同一系統(tǒng)中存儲的問題。 再次,針對系統(tǒng)數(shù)據(jù)存儲的均衡性問題,本文運用了一致性哈希算法,從而使得整個系統(tǒng)的數(shù)據(jù)存儲和計算更加均衡；針對數(shù)據(jù)處理過程中的并行化問題,本文設(shè)計的基于Glusterfs的森林監(jiān)測云平臺,采用MapReduce計算模型和并行數(shù)據(jù)庫技術(shù),實現(xiàn)了數(shù)據(jù)的并行化計算和存儲。 最后,本文對搭建的簡易Glusterfs云平臺的可靠性、擴展性、彈性及消除元數(shù)據(jù)性能等方面做了測試。實驗結(jié)果表明系統(tǒng)整體性能優(yōu)異。同時,在鎖問題和文件遍歷問題上還需要繼續(xù)改進。
[Abstract]:With the development of forestry information construction, more and more new technologies and methods have been applied in forestry. The acquisition of forest resources monitoring data is essential to the in-depth study of the forest field. At present, there are many monitoring objects (such as climate, soil, hydrology, water quality, video, etc.) in China, which have a wide range of distribution, large amount of data collected, various forms of data, and disunity of structure. Only through the effective storage, management and utilization of valuable forest monitoring data can we provide objective and comprehensive support for the analysis and decision making of forestry problems. In this paper, big data problem of forest resource monitoring data, storage problem of different types of data and parallel storage and computation of data are studied as follows. Firstly, aiming at the problems of wide distribution of forest resources monitoring data, relative isolation of each other and large amount of monitoring data, etc. In this paper, a method of constructing forest resource monitoring cloud platform based on Glusterfs is put forward, which can solve the problem of relative isolation of monitoring data in different regions. All the monitoring data are stored in the unified virtual resource storage pool to realize the logical unification of the data, which is conducive to the sharing of data and the improvement of the utilization rate of storage resources. Secondly, aiming at the diversity of forest resources monitoring data format and type, etc. By analyzing the logical structure of forest monitoring data, this paper roughly divides the monitoring data into structured data and unstructured data, and designs a hybrid storage mechanism. According to different types of data, they are stored in relational database and NoSQL database respectively, thus solving the problem of different data types stored in the same system. Thirdly, aiming at the equalization of system data storage, this paper uses the consistent hash algorithm to make the data storage and computation more balanced in the whole system, and aiming at the parallelization problem in the process of data processing, The forest monitoring cloud platform based on Glusterfs is designed in this paper. The parallel computing and storage of data is realized by using MapReduce computing model and parallel database technology. Finally, the reliability, extensibility, elasticity and performance of the simple Glusterfs cloud platform are tested. The experimental results show that the overall performance of the system is excellent. At the same time, the problems of lock and file traversal still need to be improved.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：S757.2

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