基于DEM的可视性分析综合模型及其并行算法研究
发布时间：2019-02-23 14:40
【摘要】：可视性分析是空间分析不可或缺的内容,在诸多的地学分析与生产建设领域中发挥重要的作用。然而,现有的基于GIS的可视性分析往往注重单点的可视而忽略多点的复合可视,注重静点的可视而忽视动点的可视,注重直线的可视而忽略曲线的可视,因此,构建一个在可视性分析中能综合考虑多种要素综合作用与影响的可视性分析综合模型,不但可望取得GIS空间分析方法的理论创新,也对提升GIS空间分析能力与水平具有重要的意义。此外,目前基于单核串行的GIS软件,面对大数据量的可视性计算存在技术瓶颈。如何适应计算机多核集群计算能力的提升,构建面向分布式并行计算的可视性分析并行算法,也已经成为数字地形分析亟待突破的关键技术。本文在对可视性分析的主要影响因素的归纳与整合的基础上,构建可视性分析综合模型,并对并行可视性分析算法进行研究。主要内容和研究成果如下： (1)系统地总结了可视性分析的计算原理、计算方法与分析模型,对可视性分析的基本概念进行了扩展,从分析对象(可视性分析的各种实体及属性)、视线属性(分析对象间的视线轨迹)、约束条件(分析对象与视线之间的分析规则与控制参数)三大基本要素入手,系统综合地剖析了可视性问题。在此基础上,针对可视性分析的具体应用,提出了可视性分析综合模型的理论框架。 (2)基于可视性分析综合模型,以庐山地区的格网DEM作为实验数据,对可视性分析的应用模式进行了研究,设计了动点可视、特定视角可视、曲线可视等算法。基于这些算法与已有的相关研究基础,本文对可视问题,如景观评估、最佳选址、索道设计等问题进行了实验验证。 (3)提出面向并行可视性计算的粒度模型,该模型系统地将并行问题划分为数据粒度、任务粒度、结构粒度与容错粒度四大基本要素。本文重点对数据粒度进行了详细的分析与探讨。通过对并行数据粒度的量化与动态调度机制,数据粒度模型能够提升地学分析的计算效率。 (4)本文对分布式并行计算环境下的可视性分析算法进行了并行化设计。对不同性质的并行可视性分析算法进行通用的DEM数据部署与算法调度设计。提出了一套通用的并行可视性分析数据划分方案。该方法适用于分布式并行计算环境,不要求各计算节点分配所有的分析数据,能够实现低冗余、高效率的通用数据分配机制。实验结果表明,并行算法不仅取得了较高的加速比,而且具有较好的可扩展性。 本研究主要研究栅格DEM条件下的可视性分析,但在研究领域上,还适当进行了概念的拓展与应用领域的延伸。这些研究丰富与拓展了GIS空间分析的理论与方法体系,也是并行数字地形分析一次有益的创新实践。
[Abstract]:Visibility analysis is an indispensable part of spatial analysis and plays an important role in many fields of geoscience analysis and production construction. However, the existing visibility analysis based on GIS often pays attention to the visualization of single point but not the compound visual of multiple points, the visualization of static point but the visualization of moving point, the visualization of straight line but the visualization of curve, so, To construct a comprehensive model of visibility analysis which can comprehensively consider the comprehensive action and influence of various elements in visibility analysis is not only expected to achieve the theoretical innovation of GIS spatial analysis method. It is also of great significance to improve the spatial analysis ability and level of GIS. In addition, at present, the GIS software based on single core serial has a technical bottleneck in the visibility calculation of large amount of data. How to adapt to the improvement of computing capability of multi-core cluster and construct a parallel algorithm of visibility analysis for distributed parallel computing has become the key technology of digital terrain analysis. Based on the induction and integration of the main influencing factors of visibility analysis, this paper constructs a comprehensive model of visibility analysis, and studies the parallel visibility analysis algorithm. The main contents and research results are as follows: (1) the calculation principle, calculation method and analysis model of visibility analysis are summarized systematically, and the basic concept of visibility analysis is extended. This paper starts with three basic elements: analysis object (various entities and attributes of visibility analysis), line of sight attribute (analysis of line of sight between objects), constraint condition (analysis rules and control parameters between object and line of sight). The visibility problem is analyzed synthetically. On this basis, a theoretical framework of visibility analysis synthesis model is proposed for the specific application of visibility analysis. (2) based on the comprehensive model of visibility analysis, using grid DEM in Lushan area as experimental data, the application mode of visibility analysis is studied, and the algorithms of dynamic point visualization, specific visual angle visualization and curve visualization are designed. Based on these algorithms and the existing research basis, the visual problems, such as landscape evaluation, optimal location, ropeway design and so on, are experimentally verified in this paper. (3) A granularity model for parallel visibility computing is proposed, in which parallel problems are systematically divided into four basic elements: data granularity, task granularity, structural granularity and fault-tolerant granularity. In this paper, the data granularity is analyzed and discussed in detail. Through the quantization and dynamic scheduling mechanism of parallel data granularity, the data granularity model can improve the computing efficiency of geoscience analysis. (4) the visibility analysis algorithm in distributed parallel computing environment is designed in parallel. The parallel visibility analysis algorithms with different properties are designed for DEM data deployment and algorithm scheduling. A general parallel visibility analysis data partition scheme is proposed. This method is suitable for distributed parallel computing environment. It does not require all the analysis data to be allocated by each computing node. It can realize a universal data allocation mechanism with low redundancy and high efficiency. Experimental results show that the parallel algorithm not only achieves high speedup, but also has good scalability. In this study, the visibility analysis under raster DEM condition is mainly studied, but in the research field, the concept and the application field are extended appropriately. These studies enrich and extend the theory and method system of GIS spatial analysis, and are also a beneficial and innovative practice for parallel digital terrain analysis.
【学位授予单位】：南京师范大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：P208
[Abstract]:Visibility analysis is an indispensable part of spatial analysis and plays an important role in many fields of geoscience analysis and production construction. However, the existing visibility analysis based on GIS often pays attention to the visualization of single point but not the compound visual of multiple points, the visualization of static point but the visualization of moving point, the visualization of straight line but the visualization of curve, so, To construct a comprehensive model of visibility analysis which can comprehensively consider the comprehensive action and influence of various elements in visibility analysis is not only expected to achieve the theoretical innovation of GIS spatial analysis method. It is also of great significance to improve the spatial analysis ability and level of GIS. In addition, at present, the GIS software based on single core serial has a technical bottleneck in the visibility calculation of large amount of data. How to adapt to the improvement of computing capability of multi-core cluster and construct a parallel algorithm of visibility analysis for distributed parallel computing has become the key technology of digital terrain analysis. Based on the induction and integration of the main influencing factors of visibility analysis, this paper constructs a comprehensive model of visibility analysis, and studies the parallel visibility analysis algorithm. The main contents and research results are as follows: (1) the calculation principle, calculation method and analysis model of visibility analysis are summarized systematically, and the basic concept of visibility analysis is extended. This paper starts with three basic elements: analysis object (various entities and attributes of visibility analysis), line of sight attribute (analysis of line of sight between objects), constraint condition (analysis rules and control parameters between object and line of sight). The visibility problem is analyzed synthetically. On this basis, a theoretical framework of visibility analysis synthesis model is proposed for the specific application of visibility analysis. (2) based on the comprehensive model of visibility analysis, using grid DEM in Lushan area as experimental data, the application mode of visibility analysis is studied, and the algorithms of dynamic point visualization, specific visual angle visualization and curve visualization are designed. Based on these algorithms and the existing research basis, the visual problems, such as landscape evaluation, optimal location, ropeway design and so on, are experimentally verified in this paper. (3) A granularity model for parallel visibility computing is proposed, in which parallel problems are systematically divided into four basic elements: data granularity, task granularity, structural granularity and fault-tolerant granularity. In this paper, the data granularity is analyzed and discussed in detail. Through the quantization and dynamic scheduling mechanism of parallel data granularity, the data granularity model can improve the computing efficiency of geoscience analysis. (4) the visibility analysis algorithm in distributed parallel computing environment is designed in parallel. The parallel visibility analysis algorithms with different properties are designed for DEM data deployment and algorithm scheduling. A general parallel visibility analysis data partition scheme is proposed. This method is suitable for distributed parallel computing environment. It does not require all the analysis data to be allocated by each computing node. It can realize a universal data allocation mechanism with low redundancy and high efficiency. Experimental results show that the parallel algorithm not only achieves high speedup, but also has good scalability. In this study, the visibility analysis under raster DEM condition is mainly studied, but in the research field, the concept and the application field are extended appropriately. These studies enrich and extend the theory and method system of GIS spatial analysis, and are also a beneficial and innovative practice for parallel digital terrain analysis.
【学位授予单位】：南京师范大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：P208
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