CN107679229A - The synthetical collection and analysis method of city three-dimensional building high-precision spatial big data - Google Patents
The synthetical collection and analysis method of city three-dimensional building high-precision spatial big data Download PDFInfo
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Abstract
本发明公开了城市三维建筑高精度空间大数据的综合采集及分析方法,所述的方法包括以下步骤:(1)建立基础地形地貌数据库;(2)建立城市交通数据库;(3)建立城市建筑‑用地数据库;(4)构建高程‑等高线数据库;(5)建立城市三维模型,形成城市三维建筑高精度空间大数据;(6)对三维建模数据进行数据挖掘分析及可视化展示。本发明能够应对海量空间形态数据的处理,进行快速高效的城市三维建筑高精度空间大数据的获取及空间结构要素的测度,实现基于人工智能系统的城市空间分析基础数据的综合采集及信息合成,有助于城市规划与设计的全面、规范和高效的运作。
The invention discloses a method for comprehensive collection and analysis of high-precision spatial big data of urban three-dimensional buildings. The method includes the following steps: (1) establishing a basic topography database; (2) establishing an urban traffic database; (3) establishing an urban building ‑Land use database; (4) Construct elevation‑contour database; (5) Establish a 3D model of the city to form high-precision spatial big data of 3D buildings in the city; (6) Conduct data mining analysis and visual display of 3D modeling data. The present invention can cope with the processing of massive spatial form data, perform fast and efficient acquisition of high-precision spatial big data of urban three-dimensional buildings and measurement of spatial structural elements, and realize comprehensive collection and information synthesis of urban spatial analysis basic data based on artificial intelligence system, Contribute to the comprehensive, standardized and efficient operation of urban planning and design.
Description
技术领域technical field
本发明涉及一种城市空间大数据挖掘、采集及分析,具体涉及城市三维建筑高精度空间大数据的综合采集及分析方法。The invention relates to a mining, collection and analysis of urban spatial big data, in particular to a comprehensive collection and analysis method of urban three-dimensional building high-precision spatial big data.
背景技术Background technique
以三维建筑为最小单元的城市高精度空间大数据一直是空间规划乃至城市规划的核心基础,但是这一数据库具有难获取、周期长、小样本等特点。针对这一问题,运用高新信息化智能化的多源途径获取到城市空间大数据库对于研究城市空间形态演变、城市内在发展机制等有着十分重要的意义。相对于传统遥感卫星地图通过红外波段区分城市各类要素的作用而言,基于影像技术和数据挖掘的城市三维建筑高精度空间大数据库具有全样本、可定位、可视化、实时监测特点。更为重要的是,这一大数据库将从表层到深层、从实到虚对城市各空间系统、各空间单元进行全面、综合的信息表达与分析。Urban high-precision spatial big data with three-dimensional buildings as the smallest unit has always been the core foundation of spatial planning and even urban planning, but this database has the characteristics of difficulty in obtaining, long cycle, and small samples. In response to this problem, it is of great significance to obtain a large database of urban space by using a multi-source approach of high-tech informatization and intelligentization for the study of the evolution of urban spatial form and the internal development mechanism of the city. Compared with the role of traditional remote sensing satellite maps in distinguishing various elements of the city through infrared bands, the high-precision spatial large database of urban 3D buildings based on imaging technology and data mining has the characteristics of full samples, positioning, visualization, and real-time monitoring. More importantly, this large database will carry out comprehensive and comprehensive information expression and analysis of various spatial systems and spatial units of the city from the surface to the depth, from the real to the virtual.
在影像技术和数据挖掘的研究中,通过不同类型的数据源判别各种数据要素是其中的重要一环,城市三维建筑高精度空间大数据库的建立关乎专业信息识别、数据提取、动态变化预测和综合地图制作等重要方面。这一数据库将满足日益庞大的城市空间大数据对于城市规划与设计及辅助决策需求和城市管理需要,是城市规划面临的难题之一。In the research of image technology and data mining, it is an important part to distinguish various data elements through different types of data sources. The establishment of a high-precision spatial large database of urban 3D buildings is related to professional information identification, data extraction, dynamic change prediction and Comprehensive map making and other important aspects. This database will meet the needs of urban planning and design, auxiliary decision-making and urban management of the increasingly large urban space big data, which is one of the problems faced by urban planning.
目前国内外研究城市三维建筑高精度空间大数据库的主要关注单一数据的采集、分析及可视化,较少关注城市空间各数据系统之间的相互影响关系,更缺乏精细到三维建筑层面的城市空间全覆盖的三维建筑高精度空间大数据库的制作和动态显示技术。在城市三维建筑高精度空间大数据库的采集上,缺乏城市地理空间坐标,停留在单一维度的数据处理,在大尺度复合数据的城市空间大数据采集、集成和显示领域落后。At present, domestic and foreign studies on urban 3D building high-precision spatial large databases mainly focus on the collection, analysis, and visualization of single data, less attention is paid to the mutual influence relationship between various data systems in urban space, and there is even a lack of comprehensive urban space at the level of 3D buildings. Production and dynamic display technology of overlaid three-dimensional building high-precision spatial large database. In the collection of high-precision spatial large databases of urban 3D buildings, there is a lack of urban geographic spatial coordinates, data processing in a single dimension, and lagging behind in the field of urban spatial big data collection, integration and display of large-scale composite data.
发明内容Contents of the invention
发明目的:针对上述现有技术的不足,本发明提供城市三维建筑高精度空间大数据的综合采集及分析方法,所述的方法基于影像分析和大数据挖掘构建城市三维建筑模型,实现城市三维建筑高精度空间大数据的无缝衔接采集与空间特征的模拟动态显示。Purpose of the invention: Aiming at the deficiencies of the above-mentioned prior art, the present invention provides a comprehensive acquisition and analysis method for high-precision spatial big data of urban three-dimensional buildings. Seamless collection of high-precision spatial big data and simulated dynamic display of spatial features.
技术方案:城市三维建筑高精度空间大数据的综合采集及分析方法,包括以下步骤:Technical solution: comprehensive acquisition and analysis method of urban three-dimensional building high-precision spatial big data, including the following steps:
(1)对待测区域内的高清遥感影像图数据进行采集,建立城市地形地貌数据库;(1) Collect high-definition remote sensing image data in the area to be measured, and establish a database of urban topography and geomorphology;
(2)通过互联网开源城市大数据提取该区域内的道路及各类交通站点信息,建立城市交通数据库;(2) Extract the information of roads and various traffic stations in the area through open source urban big data on the Internet, and establish an urban traffic database;
(3)从互联网城市地图代码大数据识别该区域范围内的建筑及用地单元外轮廓点,建立建筑-用地数据库;(3) Identify the outer contour points of buildings and land units within the area from the Internet city map code big data, and establish a building-land database;
(4)运用SRTM DEM进行数据获取该区域范围内的高程点及等高线数据,建立高程-等高线数据库;(4) Use SRTM DEM for data acquisition of elevation points and contour line data within the area, and establish an elevation-contour line database;
(5)将上述数据库进过平移、缩放、旋转空间处理后转换到统一的wgs84坐标系中,以统一的shp数据格式进行数据组织管理、合成,建立城市三维建筑高精度空间大数据;(5) Transform the above database into a unified wgs84 coordinate system after translation, scaling, and rotation space processing, and organize, manage and synthesize data in a unified shp data format, and establish high-precision spatial big data of urban 3D buildings;
(6)对三维建模数据进行数据挖掘分析,通过地理信息系统进行分布及可视化展示。(6) Carry out data mining analysis on the 3D modeling data, and distribute and visualize it through the geographic information system.
其中,步骤(1)所述的建立地形地貌数据库模块包括采用红外遥感分波段技术,区分出内部的山体、水体及绿化植被基本要素,同时将其提取出进行矢量化处理。Wherein, the module of establishing topography database described in step (1) includes adopting infrared remote sensing sub-band technology to distinguish the basic elements of internal mountains, water bodies and green vegetation, and simultaneously extracting them for vectorization processing.
进一步的,步骤(1)所述的建立的城市地形地貌数据库模块包括以下具体步骤:Further, the urban topography database module of setting up described in step (1) includes the following specific steps:
(1.1)对一定区域内的高清遥感影像技术数据进行采集,并基于红外线波段区分平台中的吸管工具对区域内的高清遥感影像图像中的山体、水体及绿化植被要素颜色进行吸取,进而设定出山体、水体及绿化植被要素的色彩标准阈值,并采集区域范围内的符合山体、水体及绿化植被要素阈值内的图像部分,设定为单独的文件格式;(1.1) Collect high-definition remote sensing image technology data in a certain area, and absorb the colors of mountains, water bodies and green vegetation elements in the high-definition remote sensing image images in the area based on the straw tool in the infrared band distinction platform, and then set Get the color standard threshold value of mountains, water bodies and green vegetation elements, and collect the image parts within the threshold of mountains, water bodies and green vegetation elements within the scope of the area, and set it as a separate file format;
(1.2)将山体、水体及绿化植被要素数据分别导入VPstudio矢量化软件,进行A轮廓线矢量化命令操作,同时将通用的拉直命令强度均调整为“强”等级,并对“层”这一阈值等级调整为首尾最强等级,最后采用V矢量化命令,分别对四类要素进行矢量化处理,形成作为矢量化的基础底图;(1.2) Import the element data of mountains, water bodies and green vegetation into VPstudio vectorization software respectively, and carry out the A contour line vectorization command operation, and at the same time adjust the general straightening command intensity to "strong" level, and "layer" this Adjust the first threshold level to the strongest level at the beginning and end, and finally use the V vectorization command to perform vectorization processing on the four types of elements respectively to form a basic base map for vectorization;
(1.3)将山体、水体及绿化植被要素矢量化数据分别导入到地理信息系统,并将其分别存储为shp格式文件,其中山体、水体要素采用转多段面处理,将三类数据转换成为地形地貌数据库模块三维建模基础数据。(1.3) Import the vectorized data of mountains, water bodies and green vegetation elements into the geographic information system, and store them as shp format files, in which the elements of mountains and water bodies are converted into multi-section faces, and the three types of data are converted into topography and landforms Database module 3D modeling basic data.
进一步的,步骤(2)所述的建立城市交通数据库模块具体包括以下步骤:Further, the urban traffic database module described in step (2) specifically includes the following steps:
(2.1)运用URL编码方法接入openstreet map后台API端口,并框选出与步骤A相同范围的区域,应用“Overpass API”命令进行数据下载;(2.1) Use the URL encoding method to access the backstage API port of openstreet map, and select the area in the same range as step A, and use the "Overpass API" command to download the data;
(2.2)将步骤(2.1)得到的数据导入JSOM软件,并采用关键词搜索方法对道路及交通站点空间要素进行搜索查找,并将其分别单独导出成为独立的OSM格式数据进行空间要素数据的提取及筛选;(2.2) Import the data obtained in step (2.1) into JSOM software, and use the keyword search method to search and find the spatial elements of roads and traffic stations, and export them separately into independent OSM format data for the extraction of spatial element data and screening;
(2.3)将OSM格式数据导入地理信息系统,并将其转化为独立SHP格式文件。(2.3) Import the OSM format data into the geographic information system and convert it into an independent SHP format file.
进一步的,步骤(3)所述的建立城市建筑-用地数据库模块具体包括以下步骤:Further, the establishment of urban building-land database module described in step (3) specifically includes the following steps:
(3.1)运用高德地图坐标拾取工具对相同区域范围内的边界节点的经纬度坐标进行拾取,并进行核对及记录;(3.1) Use the Gaode map coordinate picking tool to pick up the latitude and longitude coordinates of the boundary nodes within the same area, and check and record them;
(3.2)运用Python软件中的IDLE(Python GUI)模块中的building编码程序对精准经纬度坐标下的边界节点范围内的建筑物外轮廓点进行抓取,同时运用txtToPolygon编码程序将建筑物外轮廓点转化为建筑物外轮廓线,并赋予其建筑高度属性信息;(3.2) Use the building coding program in the IDLE (Python GUI) module in the Python software to capture the building outline points within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to capture the building outline points Convert it into the outline of the building and give it building height attribute information;
(3.3)运用Python软件中的IDLE(Python GUI)模块中的land use编码程序对精准经纬度坐标下的边界节点范围内的用地地块外轮廓点进行抓取,同时运用txtToPolygon编码程序将用地地块外轮廓点转化为用地地块外轮廓线,并赋予其用地性质属性信息;(3.3) Use the land use coding program in the IDLE (Python GUI) module of the Python software to capture the outer contour points of the land plot within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to convert the land use plot The outer contour points are converted into the outer contour line of the land plot, and endowed with land use property attribute information;
进一步的,所述的步骤(4)建立高程-等高线数据库包括通过API代码开发方法和矢量化,运用谷地地理信息系统平台对区域范围内的高程点及等高线数据进行抓取,并选取所需要的精度及坐标系,导出成csv格式文件;然后将csv格式文件导入Arcgis平台,转化为SHP格式文件。Further, the described step (4) establishes the elevation-contour database including developing methods and vectorization through API codes, using the valley geographic information system platform to grab elevation points and contour data in the region, and Select the required accuracy and coordinate system, and export it into a csv format file; then import the csv format file into the Arcgis platform, and convert it into a SHP format file.
进一步的,步骤(5)建立城市三维建筑高精度空间大数据具体包括以下步骤:Further, the step (5) of establishing high-precision spatial big data of urban three-dimensional buildings specifically includes the following steps:
(5.1)对单种类型的大数据进行综合建模,分别导入地理信息系统数据库,将四类数据模块按照统一的数据格式进行空间位置的对位,各类数据库模块转换成统一的wgs84坐标系,使得每一类数据的数值能与其它类型数据数值实现空间耦合;(5.1) Carry out comprehensive modeling of a single type of big data, import them into the geographic information system database respectively, and align the spatial positions of the four types of data modules in accordance with a unified data format, and convert various database modules into a unified wgs84 coordinate system , so that the value of each type of data can be spatially coupled with the value of other types of data;
(5.2)进行多层次的数据格式转换,将不同格式的数据类型转化为统一或者可相互转换的数据格式。(5.2) Perform multi-level data format conversion, and convert data types in different formats into unified or mutually convertible data formats.
(5.3)将对位后的六类数据统一到以城市道路围合而成的街区为基本统计单元,每一街区基本单元包含形地貌数据库模块、交通数据库模块、建筑-用地数据库模块以及高程-等高线数据库模块四大模块的数据信息。(5.3) Unify the six types of data after alignment into the block surrounded by urban roads as the basic statistical unit. Each block basic unit includes topography database module, traffic database module, building-land database module and elevation- The data information of the four modules of the contour database module.
进一步的,步骤(6)包括统计各用地地块单元内部的建筑数量数据、建筑基底面积数据、建筑总面积数据,并计算出各街区内的建筑密度、容积率等空间指标。Further, step (6) includes counting the building quantity data, building base area data, and building total area data within each land use unit, and calculating spatial indicators such as building density and floor area ratio in each block.
有益效果:本发明与现有技术相比其显著的效果在于:1、本发明基于影像分析和大数据挖掘,能够应对海量数据的处理,进行实时的快速进行城市三维建筑高精度空间大数据的综合采集;2、通过将多种类型、多系统的城市空间数据叠合在同一个数字地图系统下,实现基于城市坐标体系的城市三维建筑高精度空间大数据的无缝衔接采集与空间特征的模拟动态显示;3、通过将地形地貌数据库模块、交通数据库模块、建筑-用地数据库模块以及高程-等高线数据库模块叠合在同一个数字地图系统下,实现基于城市坐标体系的城市空间大数据的采集及综合分析显示;4、通过不同途径类型划分多图层对应相应的城市空间大数据要素,有利于分类管理和选择操作,设定选区功能可以快速选择显示范围,从而减少人工重复劳动,便于数据输入输出,快速分析及导出图像;5、本发明将城市空间形态的各要素数据进行多接口无缝结合,实现海量数据的快速获取和直观查询显示,为政府职能部门和建筑设计、城市规划领域提供数据访问;6、城市三维建筑高精度空间大数据要素可以通过选择查询显示,将所需数据和图像在计算机上动态地展示,进一步地为城市各系统机制改善和空间形态优化提供决策方案。Beneficial effects: Compared with the prior art, the present invention has significant effects as follows: 1. Based on image analysis and big data mining, the present invention can deal with the processing of massive data, and carry out real-time and rapid processing of urban three-dimensional building high-precision spatial big data. Comprehensive acquisition; 2. By superimposing various types and multi-system urban spatial data under the same digital map system, the seamless collection of urban three-dimensional building high-precision spatial big data based on the urban coordinate system and the identification of spatial characteristics are realized. Simulate dynamic display; 3. Realize urban spatial big data based on the urban coordinate system by superimposing the terrain database module, traffic database module, building-land database module and elevation-contour database module under the same digital map system The collection and comprehensive analysis display; 4. Dividing multiple layers through different ways and types corresponds to the corresponding urban space big data elements, which is conducive to classification management and selection operations. Setting the selection function can quickly select the display range, thereby reducing manual duplication of labor. It is convenient for data input and output, rapid analysis and image export; 5. The present invention seamlessly integrates the data of various elements of urban spatial form with multiple interfaces, realizes rapid acquisition of massive data and intuitive query display, and provides services for government functional departments and architectural design, urban Provide data access in the field of planning; 6. Urban three-dimensional building high-precision spatial big data elements can be displayed through selection query, and the required data and images can be dynamically displayed on the computer to further provide decision-making for the improvement of urban system mechanisms and spatial form optimization plan.
附图说明Description of drawings
图1是本发明基于影像分析和大数据挖掘的城市三维建筑高精度空间大数据的综合采集方法图;Fig. 1 is a diagram of the comprehensive collection method of urban three-dimensional building high-precision spatial big data based on image analysis and big data mining in the present invention;
图2是本发明杭州城市市域空间范围图;Fig. 2 is the spatial scope figure of Hangzhou City of the present invention;
图3是本发明杭州城市形地貌数据库模块图;Fig. 3 is a module diagram of Hangzhou urban topography database of the present invention;
图4是本发明杭州城市交通数据库模块图;Fig. 4 is a block diagram of the urban traffic database in Hangzhou of the present invention;
图5是本发明杭州城市建筑-用地数据库模块图;Fig. 5 is a module diagram of Hangzhou city building-land database of the present invention;
图6是本发明杭州城市高程-等高线数据库模块图。Fig. 6 is a block diagram of the Hangzhou city elevation-contour database of the present invention.
具体实施方式detailed description
为了详细的说明本发明公开的技术方案,下面结合说明书附图和具体实施例做进一步的阐述。In order to describe the technical solution disclosed in the present invention in detail, further elaboration will be made below in conjunction with the accompanying drawings and specific embodiments.
以下将结合中国杭州市域范围(总面积16596平方公里,常住人口918.8万人,城镇化率76.2%)的城市三维建筑高精度空间大数据的综合采集及分析方法,本发明数据采集流程如图1所示,具体操作步骤如下:The comprehensive collection and analysis method of the three-dimensional building high-precision space big data of the city in conjunction with Hangzhou, China (total area 16596 square kilometers, permanent population 9.188 million, urbanization rate 76.2%), the data collection process of the present invention is shown in Figure 1 As shown, the specific operation steps are as follows:
(1)对杭州市域范围内的高清遥感影像图数据进行采集,并采用红外遥感分波段技术,区分出内部的山体、水体及绿化植被基本要素,同时将其提取出后进行矢量化处理,形成地形地貌数据库模块,如图3所示;(1) Collect the high-definition remote sensing image data within the urban area of Hangzhou, and use infrared remote sensing sub-band technology to distinguish the basic elements of internal mountains, water bodies and green vegetation, and extract them and vectorize them to form The topography database module, as shown in Figure 3;
(1.1)对杭州市域范围内的高清遥感影像技术数据进行采集,并基于红外线波段区分平台中的吸管工具对杭州市域范围内的高清遥感影像图像中的山体、水体及绿化植被要素颜色进行吸取,进而设定出山体、水体及绿化植被要素的色彩标准阈值,并采集杭州市域范围内的符合山体、水体及绿化植被要素阈值内的图像部分,设定为单独的文件格式;(1.1) Collect the technical data of high-definition remote sensing images within the urban area of Hangzhou, and absorb the colors of mountains, water bodies and green vegetation elements in the high-definition remote sensing image images within the urban area of Hangzhou based on the straw tool in the infrared band discrimination platform, Then set the color standard threshold value of the mountain, water body and green vegetation elements, and collect the image parts within the threshold of the mountain body, water body and green vegetation elements within the scope of Hangzhou City, and set it as a separate file format;
(1.2)将山体、水体及绿化植被要素数据分别导入VPstudio矢量化软件,进行A轮廓线矢量化命令操作,同时将通用的拉直命令强度均调整为“强”等级,并对“层”这一阈值等级调整为首尾最强等级,最后采用V矢量化命令,分别对四类要素进行矢量化处理,形成作为矢量化的基础底图;(1.2) Import the element data of mountains, water bodies and green vegetation into VPstudio vectorization software respectively, and carry out the A contour line vectorization command operation, and at the same time adjust the general straightening command intensity to "strong" level, and "layer" this Adjust the first threshold level to the strongest level at the beginning and end, and finally use the V vectorization command to perform vectorization processing on the four types of elements respectively to form a basic base map for vectorization;
(1.3)将山体、水体及绿化植被要素矢量化数据分别导入到地理信息系统,并将其分别存储为shp格式文件,其中山体、水体要素进行转多段面的技术处理方法,使得三类型数据能转换成为地形地貌数据库模块三维建模基础数据。(1.3) Import the vectorized data of mountain body, water body and green vegetation elements into the geographic information system respectively, and store them as shp format files respectively. It is converted into basic data for 3D modeling of the terrain and landform database module.
(2)运用URL编码方法,从互联网开源城市大数据抓取方法对杭州市域范围内的道路及各类交通站点进行提取,并将其存储为单独的文件,形成交通数据库模块,如图4所示;(2) Use the URL encoding method to extract the roads and various traffic stations within the Hangzhou city area from the Internet open source city big data capture method, and store them as separate files to form a traffic database module, as shown in Figure 4 Show;
(2.1)运用URL编码方法接入openstreet map后台API端口,并框选出与步骤A相同范围的区域,应用“Overpass API”命令进行数据下载;(2.1) Use the URL encoding method to access the backstage API port of openstreet map, and select the area in the same range as step A, and use the "Overpass API" command to download the data;
(2.2)根据步骤(2.1)下载得到的数据导入JSOM软件,并采用关键词搜索方法对道路及交通站点空间要素进行搜索查找,并将其分别单独导出成为独立的OSM格式数据进行空间要素数据的提取及筛选;(2.2) Import the data downloaded according to step (2.1) into JSOM software, and use the keyword search method to search and find the spatial elements of roads and traffic stations, and export them separately into independent OSM format data for spatial element data extraction and screening;
(2.3)将OSM格式数据导入地理信息系统,并将其转化为独立SHP格式文件。(2.3) Import the OSM format data into the geographic information system and convert it into an independent SHP format file.
(3)用Java语言代码方法,从互联网城市地图代码大数据方法识别出杭州市域范围内的建筑及用地单元外轮廓点并将其进行空间整合成建筑及用地多段面,形成建筑-用地数据库模块,如图5所示;(3) Use the Java language code method to identify the outer contour points of buildings and land use units within the urban area of Hangzhou from the Internet city map code big data method, and integrate them into multiple sections of buildings and land use to form a building-land use database module , as shown in Figure 5;
(3.1)运用高德地图坐标拾取工具对相同杭州市域范围内的边界节点的经纬度坐标进行拾取,并进行核对及记录;(3.1) Use the AutoNavi map coordinate picking tool to pick up the latitude and longitude coordinates of the boundary nodes within the same Hangzhou city area, and check and record them;
(3.2)运用Python软件中的IDLE(Python GUI)模块中的building编码程序对精准经纬度坐标下的边界节点范围内的建筑物外轮廓点进行抓取,同时运用txtToPolygon编码程序将建筑物外轮廓点转化为建筑物外轮廓线,并赋予其建筑高度属性信息;(3.2) Use the building coding program in the IDLE (Python GUI) module in the Python software to capture the building outline points within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to capture the building outline points Convert it into the outline of the building and give it building height attribute information;
#左上角经纬度坐标(火星坐标)117.250586,31.879621#The latitude and longitude coordinates of the upper left corner (Mars coordinates) 117.250586,31.879621
zs_lon_lat='117.250586,31.879621'zs_lon_lat='117.250586,31.879621'
zs_lon_deg=float(zs_lon_lat.split(',')[0])zs_lon_deg=float(zs_lon_lat.split(',')[0])
zs_lat_deg=float(zs_lon_lat.split(',')[1])zs_lat_deg=float(zs_lon_lat.split(',')[1])
#右下角经纬度坐标(火星坐标)117.312298,31.851338#The latitude and longitude coordinates of the lower right corner (Mars coordinates) 117.312298,31.851338
yx_lon_lat='117.312298,31.851338'yx_lon_lat='117.312298,31.851338'
yx_lon_deg=float(yx_lon_lat.split(',')[0])yx_lon_deg=float(yx_lon_lat.split(',')[0])
yx_lat_deg=float(yx_lon_lat.split(',')[1])yx_lat_deg=float(yx_lon_lat.split(',')[1])
#地图缩放级别# map zoom level
zoom=int(17)zoom=int(17)
li1=deg2num(zs_lat_deg,zs_lon_deg,zoom)li1=deg2num(zs_lat_deg,zs_lon_deg,zoom)
m=li1[0]m=li1[0]
n=li1[1]n=li1[1]
level=li1[2]level=li1[2]
li2=deg2num(yx_lat_deg,yx_lon_deg,zoom)li2=deg2num(yx_lat_deg,yx_lon_deg,zoom)
m1=li2[0]m1=li2[0]
n1=li2[1]n1=li2[1]
X=m1-mX=m1-m
Y=n1–nY=n1–n
(3.3)运用Python软件中的IDLE(Python GUI)模块中的land use编码程序对精准经纬度坐标下的边界节点范围内的用地地块外轮廓点进行抓取,同时运用txtToPolygon编码程序将用地地块外轮廓点转化为用地地块外轮廓线,并赋予其用地性质属性信息;(3.3) Use the land use coding program in the IDLE (Python GUI) module of the Python software to capture the outer contour points of the land plot within the boundary node range under the precise latitude and longitude coordinates, and use the txtToPolygon coding program to convert the land use plot The outer contour points are converted into the outer contour line of the land plot, and endowed with land use property attribute information;
def deg2num(lat_deg,lon_deg,zoom):def deg2num(lat_deg, lon_deg, zoom):
lat_rad=math.radians(lat_deg)lat_rad = math.radians(lat_deg)
n=2.0**zoomn=2.0**zoom
tx=int((lon_deg+180.0)/360.0*n)tx=int((lon_deg+180.0)/360.0*n)
ty=int((1.0-math.log(math.tan(lat_rad)+(1/math.cos(lat_rad)))/math.pi)/2.0*n)ty=int((1.0-math.log(math.tan(lat_rad)+(1/math.cos(lat_rad)))/math.pi)/2.0*n)
li=[]li=[]
li.append(tx)li.append(tx)
li.append(ty)li.append(ty)
li.append(zoom)li.append(zoom)
return lireturn li
def transformCell(tx,ty,zoom):def transformCell(tx,ty,zoom):
if tx>2**zoom-1:if tx>2**zoom-1:
tx=2**zoom-1tx=2**zoom-1
if ty>2**zoom-1:if ty>2**zoom-1:
ty=2**zoom-1ty=2**zoom-1
d=int(math.pow(2,int((zoom+1)/2)))d=int(math.pow(2,int((zoom+1)/2)))
x=tx%dx=tx%d
y=ty%dy=ty%d
m=(tx-x)/dm=(tx-x)/d
n=(ty-y)/dn=(ty-y)/d
tile=[tx-m*d+n*d,ty-n*d+m*d]tile=[tx-m*d+n*d,ty-n*d+m*d]
return tilereturn tile
(4)通过API代码开发方法,运用SRTM DEM进行数据抓取获取杭州市域范围内的高程点及等高线数据,并导入CAD进行矢量化,形成高程-等高线数据库模块,如图6所示;(4) Through the API code development method, SRTM DEM is used for data capture to obtain the elevation point and contour line data within the scope of Hangzhou City, and imported into CAD for vectorization to form an elevation-contour line database module, as shown in Figure 6 Show;
(4.1)运用谷地地理信息系统平台对杭州市域范围内的高程点及等高线数据进行抓取,并选取所需要的精度及坐标系,导出成csv格式文件;(4.1) Use the valley geographic information system platform to capture the elevation points and contour line data within the Hangzhou city area, and select the required accuracy and coordinate system, and export it into a csv format file;
(4.2)将csv格式文件导入Arcgis平台,转化为SHP格式文件;(4.2) Import the csv format file into the Arcgis platform and convert it into a SHP format file;
(5)将上述数据库模块导入地理信息系统平台,并进行平移、缩放、旋转等空间处理,将各类数据库模块转换成统一的wgs84坐标系,并保证能进行完全空间对位;同时,以统一的数据格式进行各类数据组织管理,叠加后的数据进行合成处理,各数据之间具有统一街区数据处理单元,依据合成后的数据进行三维建模,形成统一的杭州城市三维建筑高精度空间大数据;(5) Import the above database modules into the geographic information system platform, and perform spatial processing such as translation, zooming, and rotation, and convert various database modules into a unified wgs84 coordinate system, and ensure complete spatial alignment; The data format is used to organize and manage all kinds of data, and the superimposed data is synthesized and processed. There is a unified block data processing unit between each data, and 3D modeling is performed based on the synthesized data to form a unified high-precision 3D architectural space in Hangzhou. data;
(5.1)对单种类型的大数据进行综合建模,分别导入地理信息系统数据库,将四类数据模块按照统一的数据格式进行空间位置的对位,各类数据库模块转换成统一的wgs84坐标系,使得每一类数据的数值能与其它类型数据数值实现空间耦合;(5.1) Carry out comprehensive modeling of a single type of big data, import them into the geographic information system database respectively, and align the spatial positions of the four types of data modules in accordance with a unified data format, and convert various database modules into a unified wgs84 coordinate system , so that the value of each type of data can be spatially coupled with the value of other types of data;
(5.2)进行多层次的数据格式转换,将不同格式的数据类型转化为统一或者可相互转换的数据格式。(5.2) Perform multi-level data format conversion, and convert data types in different formats into unified or mutually convertible data formats.
(5.3)将对位后的六类数据统一到以杭州城市道路围合而成的街区为基本统计单元,即每一街区基本单元包含形地貌数据库模块、交通数据库模块、建筑-用地数据库模块以及高程-等高线数据库模块四大模块的数据信息。(5.3) Unify the six types of data after alignment into the block surrounded by urban roads in Hangzhou as the basic statistical unit, that is, the basic unit of each block includes topography database module, traffic database module, building-land database module and The data information of the four modules of the elevation-contour database module.
(6)对三维建模数据进行数据挖掘分析,获得杭州城市空间大数据空间指标特征的综合信息,并实时分析杭州城市三维建设情况,进而通过地理信息系统进行分布及可视化展示。(6) Carry out data mining analysis on the 3D modeling data, obtain comprehensive information on the spatial index characteristics of Hangzhou urban space big data, and analyze the 3D construction of Hangzhou city in real time, and then distribute and visualize it through the geographic information system.
(6.1)统计各用地地块单元内部的建筑数量数据、建筑基底面积数据、建筑总面积数据,并计算出各街区内的建筑密度、容积率等空间指标。(6.1) Statistics of building quantity data, building base area data, and total building area data within each land plot unit are calculated, and spatial indicators such as building density and volume ratio in each block are calculated.
(6.2)通过地理信息系统对数据库进行可视化展示,支持导出二维显示图像格式有DWG、JPEG、PDF、EPS、PNG、GIF、TIFF;支持导出三维显示图像格式有DWG、3ds、skp、CityGML。(6.2) Visually display the database through the geographic information system, support the export of two-dimensional display image formats in DWG, JPEG, PDF, EPS, PNG, GIF, TIFF; support the export of three-dimensional display image formats in DWG, 3ds, skp, CityGML.
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Application publication date: 20180209 Assignee: SHENZHEN GENERAL INSTITUTE OF ARCHITECTURAL DESIGN AND RESEARCH Co.,Ltd. Assignor: SOUTHEAST University Contract record no.: X2022320000042 Denomination of invention: Comprehensive collection and analysis method of high-precision spatial big data of urban three-dimensional buildings Granted publication date: 20210601 License type: Common License Record date: 20220414 |