CN114722276A - Data management and analysis method for smart city service - Google Patents
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Abstract
本发明涉及一种用于智慧城市服务的数据管理及分析方法，属于智慧城市领域，为了解决智慧城市中的数据定性分析难、更新慢等问题，本发明首先利用六角格对智慧城市的数据进行组织和管理形成六角格数据地图，其次利用POI数据更新快的特点，基于POI数据实现对六角格地图数据的更新，最后基于六角格数据地图实现智慧城市中的多种数据的挖掘与分析，有效提升城市的治理能力和管理水平。
The present invention relates to a data management and analysis method for smart city service, belonging to the field of smart city. Organize and manage to form a hexagonal data map, secondly use the characteristics of POI data to update quickly, realize the update of the hexagonal map data based on POI data, and finally realize the mining and analysis of various data in the smart city based on the hexagonal data map, effectively Improve the city's governance capacity and management level.
Description
技术领域technical field
本发明属于智慧城市领域，涉及一种用于智慧城市服务的数据管理及分析方法。The invention belongs to the field of smart cities, and relates to a data management and analysis method for smart city services.
背景技术Background technique
智慧城市是基于地理信息系统、数字城市、物联网、云计算、大数据等技术建立的现实与数字世界的融合，以实现人和物的感知、控制和智能服务。实现智慧城市建设目标的过程中数据管理和分析发挥着至关重要的作用，如何利用好智慧城市中的数据，实现数据的采集、整合、深度分析成为业界关注的热点和难题，传统智慧城市的解决方案已涌现越来越多的问题，例如定量分析城市功能区困难、数据更新慢等问题。现有发明多数基于四角网格进行分析，尚未见到有基于六角格网进行数据管理和分析的，六角格网相比于四角格具有较高的角度分辨率，有助于提升道路提取、影像匹配的精度等优点。POI（Point ofInterest）数据，被称作兴趣点，泛指互联网电子地图中的点类数据，基本包含名称、地址、坐标、类别四个属性，POI 数据容易获取、成本低、且动态更新。因此，本发明基于六角网格针对智慧城市数据进行管理，结合POI数据和地理信息数据实现六角网格网的配准与更新，并基于六角格网数据地图实现对进智慧城市数据的进行数据挖掘与分析。Smart city is the integration of reality and digital world based on technologies such as geographic information system, digital city, Internet of Things, cloud computing, big data, etc., to realize the perception, control and intelligent services of people and things. Data management and analysis play a vital role in the process of realizing the goal of smart city construction. How to make good use of data in smart cities to achieve data collection, integration, and in-depth analysis has become a hot spot and a problem for the industry. Solutions have emerged for more and more problems, such as difficulties in quantitative analysis of urban functional areas and slow data updates. Most of the existing inventions are based on quadrangular grids for analysis, and no data management and analysis based on hexagonal grids have been seen. Matching accuracy and so on. POI (Point of Interest) data, known as points of interest, generally refers to point data in Internet electronic maps, basically including four attributes: name, address, coordinates, and category. POI data is easy to obtain, low cost, and dynamically updated. Therefore, the present invention manages smart city data based on hexagonal grid, realizes registration and update of hexagonal grid network in combination with POI data and geographic information data, and realizes data mining of incoming smart city data based on hexagonal grid data map and analyse.
发明内容SUMMARY OF THE INVENTION
为解决智慧城市数据定性分析难、更新慢等问题，本发明提出一种用于智慧城市服务的数据管理及分析方法，首先基于六角格对智慧城市数据进行管理，由于POI数据的具有动态更新的特性，再结合POI数据和地理信息数据对六角网格数据进行更新，最后基于六角网格网对智慧城市数据进行分析与应用。In order to solve the problems of difficult qualitative analysis and slow update of smart city data, the present invention proposes a data management and analysis method for smart city services. First, the smart city data is managed based on the hexagonal grid. Based on the characteristics of POI data and geographic information data, the hexagonal grid data is updated, and finally the smart city data is analyzed and applied based on the hexagonal grid network.
本发明解决技术问题所采取的技术方案为：The technical scheme adopted by the present invention to solve the technical problem is:
一种用于智慧城市服务的数据管理及分析方法包括以下步骤：A data management and analysis method for smart city services includes the following steps:
步骤一：选定区域，可通过在智慧城市地图上框选或者输入省、市、县、区等方法选择所要管理分析的区域。Step 1: Select an area, you can select the area to be managed and analyzed by making a box on the smart city map or entering a province, city, county, district, etc.
步骤二：设置格网尺寸，根据不同的分析需求设置不同的格网大小，格网大小的单位是m。Step 2: Set the grid size, and set different grid sizes according to different analysis requirements. The unit of grid size is m.
步骤三：生成六角格网，根据选定的区域和格网尺寸，生成能够覆盖所选择区域的六角格网格。Step 3: Generate a hexagonal grid, according to the selected area and grid size, generate a hexagonal grid that can cover the selected area.
步骤四：确定格网编码，连续三层六角格网编码方法，为了体现层次特性，采用依次排列的数字表示不同层次的单元，所形成的码元序列称为“地址码”。初始剖分产生的网格单元表示为a1，第二层上的单元可以表示成a1a2，第三层上的单元表示成a1a2a3，第n层上的单元表示成a1a2 a3…an。Step 4: Determine the grid code, the continuous three-layer hexagonal grid coding method, in order to reflect the hierarchical characteristics, the numbers arranged in sequence are used to represent the units of different levels, and the formed code element sequence is called "address code". The mesh elements generated by the initial division are denoted as a1, the elements on the second layer can be denoted as a1a2, the elements in the third layer are denoted as a1a2a3, and the elements in the nth layer are denoted as a1a2 a3...an.
步骤五：六角格网配准与更新，根据原始地理信息数据和POI数据对六角格网进行配准。首先基于智慧城市的地理信息数据确定每个六角格网里所包含的城市要素信息，例如商超、公司企业、基础设施等，确定每个网格所包含的要素信息。再基于POI数据对格网内的要素信息进行二次配准和更新，如图4所示为POI数据类型分类，POI数据具有动态更新的特征，地理信息数据一般比较庞大其更新通常比较慢，而POI数据则更新比较快，通过结合POI数据可以有效的实现对六角格网内要素信息的更新与配准。例如，在某一街道上基于地理信息数据可以知道此处有一家奶茶店，但是实际上已经换成其他类型的店铺，此时的地理信息数据已经出现滞后的现象，我们则可以根据用户在此处留下的最新POI数据查询是否该街道还有奶茶店，实现对该街道的店铺进行更新。Step 5: Hexagonal grid registration and update, the hexagonal grid is registered according to the original geographic information data and POI data. First, based on the geographic information data of the smart city, determine the urban element information contained in each hexagonal grid, such as supermarkets, companies, infrastructure, etc., and determine the element information contained in each grid. Then, based on POI data, the element information in the grid is re-registered and updated. Figure 4 shows the classification of POI data types. POI data has the characteristics of dynamic update. Geographic information data is generally large and its update is usually slow. The POI data is updated faster, and the update and registration of the element information in the hexagonal grid can be effectively achieved by combining the POI data. For example, based on geographic information data on a certain street, it can be known that there is a milk tea shop here, but it has actually been replaced with other types of shops. At this time, the geographic information data has been lagging behind. The latest POI data left at the store is used to query whether there is a milk tea shop in the street, and the stores in the street can be updated.
步骤六：数据预处理，根据步骤一到步骤五，可以获取到基于六角网格的智慧城市数据地图，根据六角网格数据地图可以进行多种数据的分析和挖掘。数据预处理环节包括对数据的感知、数据的搜集以及数据的转换。数据预处理的目的是为了使数据能够进行多种数据挖掘分析与处理。Step 6: Data preprocessing. According to
步骤七：数据挖掘分析，对来着步骤六的数据进行数据挖掘与分析。数据挖掘分析包括对数据的关联分析、聚类分析以及热度分析等。其中关联分析可以用于分析多种商业实体之间的关联亦或者是车辆信息之间的关联等，例如通过六角格数据地图量化分析银行位置与学校、住宅等位置之间关联分析，可以优化银行的选址。聚类分析可以用于得到不同类型数据的空间分布特征，例如对于出租车数据进行聚类分析，可以得到不同区域的出租车的出车频率和密度等信息，可以优化出租车的调度。热度分析可以用于多种数据类型的在空间的热度进行分析，例如可以用于分析不同区域不同时间段的人流量、车流量等热度，进而优化城市管理、出行决策等。Step 7: Data mining and analysis, to perform data mining and analysis on the data from Step 6. Data mining analysis includes association analysis, cluster analysis, and heat analysis of data. The association analysis can be used to analyze the association between various commercial entities or the association between vehicle information, etc. address selection. Cluster analysis can be used to obtain the spatial distribution characteristics of different types of data. For example, by cluster analysis of taxi data, information such as the frequency and density of taxis in different regions can be obtained, and the scheduling of taxis can be optimized. Heat analysis can be used to analyze the heat of various data types in space. For example, it can be used to analyze the heat of people and vehicles in different regions and different time periods, so as to optimize urban management and travel decision-making.
步骤八：数据可视化，数据可视化包括对大数据信息可视化、信息可视化和多尺度展示等。其中大数据信息可视化指通过数据挖掘与分析对数据进行可视化展现，信息可视化指的对所要分析的数据或者智慧城市数据进行多方位展示，包括多种统计图表和指标的展示。多尺度可视化指的是对六角格数据地图进行多尺度的展示，可以自由变换网格的尺寸大小，进而分析不同粒度下的数据信息。Step 8: Data visualization, data visualization includes visualization of big data information, information visualization and multi-scale display. Among them, big data information visualization refers to the visualization of data through data mining and analysis, and information visualization refers to the multi-directional display of the data to be analyzed or smart city data, including the display of various statistical charts and indicators. Multi-scale visualization refers to the multi-scale display of the hexagonal grid data map, which can freely change the size of the grid, and then analyze the data information at different granularities.
步骤九：应用场景分析，智慧城市的目的是为了更好的管理城市、分数据以便于提升人民的生活水平。本发明所提的基于六角格管理的智慧城市数据地图可以进行多种场景的应用，包括交通规划、城市治理、城乡规划等。Step 9: Analysis of application scenarios, the purpose of smart city is to better manage the city and divide data in order to improve people's living standards. The smart city data map based on hexagonal grid management proposed by the present invention can be applied in various scenarios, including traffic planning, urban governance, urban and rural planning, and the like.
与现有技术相比，本发明的有益效果在于：Compared with the prior art, the beneficial effects of the present invention are:
本发明针对智慧城市数据定性分析难、更新慢等问题，提供了一种用于智慧城市服务的数据管理及分析方法，首先利用六角格对数据进行管理，其次基于POI数据对六角格数据进行更新，最后基于六角格数据地图实现多种数据的分析，进而优化城市的管理；Aiming at the problems of difficult qualitative analysis and slow updating of smart city data, the present invention provides a data management and analysis method for smart city services. First, the hexagonal grid is used to manage the data, and then the hexagonal grid data is updated based on POI data. , and finally realize the analysis of various data based on the hexagonal data map, and then optimize the management of the city;
本发明为智慧城市数据分析提供了可靠的解决方案，为基于六角格进行智慧城市数据分析提供了可靠的基础。The invention provides a reliable solution for smart city data analysis, and provides a reliable basis for smart city data analysis based on the hexagonal grid.
附图说明Description of drawings
图1. 基于六角格的智慧城市数据管理流程；Figure 1. Smart city data management process based on hexagonal grid;
图2. 生成的六角格网；Figure 2. The generated hexagonal grid;
图3. 连续三层的六角格网编码；Figure 3. Hexagonal grid coding of three consecutive layers;
图4. POI数据分类；Figure 4. POI data classification;
图5. 基于六角格的智慧城市数据分析流程。Figure 5. Hexagon-based smart city data analysis process.
具体实施方式Detailed ways
以下结合附图对本发明作进一步说明。The present invention will be further described below with reference to the accompanying drawings.
步骤一：选定区域，可通过在智慧城市地图上框选或者输入省、市、县、区等方法选择所要管理分析的区域。Step 1: Select an area, you can select the area to be managed and analyzed by making a box on the smart city map or entering a province, city, county, district, etc.
步骤二：设置格网尺寸，根据不同的分析需求设置不同的格网大小，格网大小的单位是m。例如分析银行选址与决策需要设置六角格网尺寸就较小，其目的是为了使银行能够覆盖附近多数人的需求。Step 2: Set the grid size, and set different grid sizes according to different analysis requirements. The unit of grid size is m. For example, the size of the hexagonal grid needs to be set smaller for the analysis of bank location and decision-making, the purpose is to enable the bank to cover the needs of the majority of people nearby.
步骤三：生成六角格网，根据选定的区域和格网尺寸，生成能够覆盖所选择区域的六角格网格。如图2所示。Step 3: Generate a hexagonal grid, according to the selected area and grid size, generate a hexagonal grid that can cover the selected area. as shown in picture 2.
步骤四：确定格网编码，如图3所示为连续三层六角格网编码方法，为了体现层次特性，采用依次排列的数字（码元）表示不同层次的单元，所形成的码元序列称为“地址码”。初始剖分（n=1）产生的网格单元表示为a1，第二层上的单元可以表示成a1a2，第三层上的单元表示成a1a2a3，第n层上的单元表示成a1a2 a3…an。第一层的网格单元是中心父单元，对其进行层次剖分，在第二层中可以形成1个中心子单元和6个顶点子单元。对于第二层的中心子单元编码后加0，即00，对于四周的6个顶点子单元按逆时针顺序将单元地址码的最后一位编码添加上1，2，…，6中的一个数字，中心子单元右边的顶点子单元添加1，然后逆时针绕着中心子单元，对顶点子单元依次添加2，3，…，6，则第二层次中生成的网格单元编码为00，01，…，06。Step 4: Determine the grid coding. As shown in Figure 3, the continuous three-layer hexagonal grid coding method is used. In order to reflect the hierarchical characteristics, the numbers (symbols) arranged in sequence are used to represent the units of different levels. is "address code". The grid cells generated by the initial division (n=1) are represented as a1, the cells on the second layer can be represented as a1a2, the cells on the third layer are represented as a1a2a3, and the cells on the nth layer are represented as a1a2 a3…an . The mesh unit of the first layer is the central parent unit, which is hierarchically divided, and 1 central subunit and 6 vertex subunits can be formed in the second layer. For the central subunit of the second layer, add 0, that is, 00, and add a number of 1, 2, ..., 6 to the last digit of the unit address code in the counterclockwise order for the 6 vertex subunits around , add 1 to the vertex subunit to the right of the center subunit, and then go around the center subunit counterclockwise, add 2, 3, ..., 6 to the vertex subunit in turn, then the grid unit code generated in the second level is 00, 01 , …, 06.
步骤五：六角格网配准与更新，根据原始地理信息数据和POI数据对六角格网进行配准。首先基于智慧城市的地理信息数据确定每个六角格网里所包含的城市要素信息，例如商超、公司企业、基础设施等，确定每个网格所包含的要素信息。再基于POI数据对格网内的要素信息进行二次配准和更新，如图4所示为POI数据类型分类，POI数据具有动态更新的特征，地理信息数据一般比较庞大其更新通常比较慢，而POI数据则更新比较快，通过结合POI数据可以有效的实现对六角格网内要素信息的更新与配准。例如，在某一街道上基于地理信息数据可以知道此处有一家奶茶店，但是实际上已经换成其他类型的店铺，此时的地理信息数据已经出现滞后的现象，我们则可以根据用户在此处留下的最新POI数据查询是否该街道还有奶茶店，实现对该街道的店铺进行更新。Step 5: Hexagonal grid registration and update, the hexagonal grid is registered according to the original geographic information data and POI data. First, based on the geographic information data of the smart city, determine the urban element information contained in each hexagonal grid, such as supermarkets, companies, infrastructure, etc., and determine the element information contained in each grid. Then, based on POI data, the element information in the grid is re-registered and updated. Figure 4 shows the classification of POI data types. POI data has the characteristics of dynamic update. Geographic information data is generally large and its update is usually slow. The POI data is updated faster, and the update and registration of the element information in the hexagonal grid can be effectively achieved by combining the POI data. For example, based on geographic information data on a certain street, it can be known that there is a milk tea shop here, but it has actually been replaced with other types of shops. At this time, the geographic information data has been lagging behind. The latest POI data left at the store is used to query whether there is a milk tea shop in the street, and the stores in the street can be updated.
步骤六：数据预处理，根据步骤一到步骤五，可以获取到基于六角网格的智慧城市数据地图，根据六角网格数据地图可以进行多种数据的分析和挖掘。数据预处理环节包括对数据的感知、数据的搜集以及数据的转换。数据预处理的目的是为了使数据能够进行多种数据挖掘分析与处理。其中数据的感知指的是通过终端获取数据，例如通过在基于六角格的智慧城市数据地图上输入、点击等手段，获取所需求的数据信息。数据的搜集指的是对于数据信息进行查询、显示、搜集等，数据的转换指的是将所获取到的数据信息转换为能够进行机器学习、深度学习的数据类型，进而为后续数据挖掘与分析提供数据支撑。Step 6: Data preprocessing. According to
步骤七：数据挖掘分析，对来着步骤六的数据进行数据挖掘与分析。数据挖掘分析包括对数据的关联分析、聚类分析以及热度分析等。其中关联分析可以用于分析多种商业实体之间的关联亦或者是车辆信息之间的关联等，例如通过六角格数据地图量化分析银行位置与学校、住宅等位置之间关联分析，可以优化银行的选址。聚类分析可以用于得到不同类型数据的空间分布特征，例如对于出租车数据进行聚类分析，可以得到不同区域的出租车的出车频率和密度等信息，可以优化出租车的调度。热度分析可以用于多种数据类型的在空间的热度进行分析，例如可以用于分析不同区域不同时间段的人流量、车流量等热度，进而优化城市管理、出行决策等。Step 7: Data mining and analysis, to perform data mining and analysis on the data from Step 6. Data mining analysis includes association analysis, cluster analysis, and heat analysis of data. The association analysis can be used to analyze the association between various commercial entities or the association between vehicle information, etc. address selection. Cluster analysis can be used to obtain the spatial distribution characteristics of different types of data. For example, by cluster analysis of taxi data, information such as the frequency and density of taxis in different regions can be obtained, and the scheduling of taxis can be optimized. Heat analysis can be used to analyze the heat of various data types in space. For example, it can be used to analyze the heat of people and vehicles in different regions and different time periods, so as to optimize urban management and travel decision-making.
步骤八：数据可视化，数据可视化包括对大数据信息可视化、信息可视化和多尺度展示等。其中大数据信息可视化指通过数据挖掘与分析对数据进行可视化展现，信息可视化指的对所要分析的数据或者智慧城市数据进行多方位展示，包括多种统计图表和指标的展示。多尺度可视化指的是对六角格数据地图进行多尺度的展示，可以自由变换网格的尺寸大小，进而分析不同粒度下的数据信息。Step 8: Data visualization, data visualization includes visualization of big data information, information visualization and multi-scale display. Among them, big data information visualization refers to the visual display of data through data mining and analysis, and information visualization refers to the multi-directional display of the data to be analyzed or smart city data, including the display of various statistical charts and indicators. Multi-scale visualization refers to the multi-scale display of the hexagonal grid data map, which can freely change the size of the grid, and then analyze the data information at different granularities.
应用场景分析，智慧城市的目的是为了更好的管理城市、分数据以便于提升人民的生活水平。本发明所提的基于六角格管理的智慧城市数据地图可以进行多种场景的应用，包括交通规划、城市治理、城乡规划等。Application scenario analysis, the purpose of smart city is to better manage the city and divide data in order to improve people's living standards. The smart city data map based on hexagonal grid management proposed by the present invention can be applied in various scenarios, including traffic planning, urban governance, urban and rural planning, and the like.
上述技术方案仅体现了本发明技术方案的优选技术方案，本技术领域的技术人员对其中某些部分所可能做出的一些变动均体现了本发明的原理，属于本发明的保护范围之内。The above technical solutions only represent the preferred technical solutions of the technical solutions of the present invention, and some changes that those skilled in the art may make to some parts of them all reflect the principles of the present invention and fall within the protection scope of the present invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115374316A (en) * | 2022-10-24 | 2022-11-22 | 北京科技大学 | Intelligent variable-scale data analysis method and system supported by real-time decision |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013257655A (en) * | 2012-06-11 | 2013-12-26 | Aisin Aw Co Ltd | Operation input system |
| CN105022790A (en) * | 2015-06-19 | 2015-11-04 | 中国科学院遥感与数字地球研究所 | Object-orientated city entity geocoding integration method |
| CN105976110A (en) * | 2016-05-05 | 2016-09-28 | 云神科技投资股份有限公司 | Method and system for managing smart city |
| CN110335187A (en) * | 2019-06-18 | 2019-10-15 | 国家信息中心 | A kind of smart city operating system |
| CN112288048A (en) * | 2020-12-28 | 2021-01-29 | 湖南师范大学 | A multi-source data-driven urban crowd travel identification method |
| US11163921B1 (en) * | 2020-09-01 | 2021-11-02 | TeleqoTech | Managing a smart city |
-
2022
- 2022-03-29 CN CN202210320032.4A patent/CN114722276A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013257655A (en) * | 2012-06-11 | 2013-12-26 | Aisin Aw Co Ltd | Operation input system |
| CN105022790A (en) * | 2015-06-19 | 2015-11-04 | 中国科学院遥感与数字地球研究所 | Object-orientated city entity geocoding integration method |
| CN105976110A (en) * | 2016-05-05 | 2016-09-28 | 云神科技投资股份有限公司 | Method and system for managing smart city |
| CN110335187A (en) * | 2019-06-18 | 2019-10-15 | 国家信息中心 | A kind of smart city operating system |
| US11163921B1 (en) * | 2020-09-01 | 2021-11-02 | TeleqoTech | Managing a smart city |
| CN112288048A (en) * | 2020-12-28 | 2021-01-29 | 湖南师范大学 | A multi-source data-driven urban crowd travel identification method |
Non-Patent Citations (2)
| Title |
|---|
| 刘卓;陈艳艳;路尧;孙浩冬;何佳;: "时空大数据驱动的新型智慧城市交通规划决策支持框架", 地理信息世界, no. 03, 25 June 2020 (2020-06-25) * |
| 张弛;武善梅;: "服务智慧城市的云计算技术――以居民出行数据分析服务为例", 现代城市, no. 01, 28 March 2019 (2019-03-28) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115374316A (en) * | 2022-10-24 | 2022-11-22 | 北京科技大学 | Intelligent variable-scale data analysis method and system supported by real-time decision |
| CN115374316B (en) * | 2022-10-24 | 2023-01-03 | 北京科技大学 | Intelligent variable-scale data analysis method and system supported by real-time decision |
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