CN114722276A - Data management and analysis method for smart city service - Google Patents
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Abstract
The invention relates to a data management and analysis method for smart city service, belongs to the field of smart cities, and aims to solve the problems of difficult qualitative analysis and slow updating of data in smart cities.
Description
Technical Field
The invention belongs to the field of smart cities, and relates to a data management and analysis method for smart city services.
Background
The smart city is a fusion of reality and a digital world established based on technologies such as a geographic information system, a digital city, an internet of things, cloud computing and big data, so as to realize perception, control and intelligent service of people and objects. Data management and analysis play a crucial role in the process of achieving the goal of smart city construction, how to utilize data in smart cities, and achieving data acquisition, integration and deep analysis become hot spots and problems concerned by the industry, and more problems, such as difficulty in quantitatively analyzing urban functional areas, slow data updating and the like, have emerged in the traditional smart cities. Most of the existing methods are analyzed based on the four-corner grids, data management and analysis based on the hexagonal grids are not seen, and the hexagonal grids have higher angular resolution compared with the four-corner grids, and are beneficial to improving the accuracy of road extraction and image matching. POI (Point of interest) data is called a point of interest, generally refers to point data in an Internet electronic map, basically comprises four attributes of name, address, coordinate and category, and is easy to obtain, low in cost and dynamically updated. Therefore, the smart city data are managed based on the hexagonal grids, the registration and the updating of the hexagonal grids are realized by combining POI data and geographic information data, and data mining and analysis of smart city data are realized based on the hexagonal grid data map.
Disclosure of Invention
In order to solve the problems of difficulty in qualitative analysis, slow updating and the like of smart city data, the invention provides a data management and analysis method for smart city service.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a data management and analysis method for smart city services includes the following steps:
the method comprises the following steps: and selecting the area to be managed and analyzed by selecting or inputting province, city, county, district and the like on the smart city map.
Step two: set up the graticule mesh size, set up different graticule mesh sizes according to the analysis demand of difference, the unit of graticule mesh size is m.
Step three: and generating a hexagonal grid, and generating the hexagonal grid capable of covering the selected area according to the selected area and the size of the grid.
Step four: determining grid coding and a continuous three-layer hexagonal grid coding method, in order to embody the layer characteristics, adopting numbers which are sequentially arranged to represent units of different layers, and forming a code element sequence which is called as an address code. The cells of the grid resulting from the initial subdivision are denoted as a1, the cells on the second level may be denoted as a1a2, the cells on the third level are denoted as a1a2a3, and the cells on the nth level are denoted as a1a2a3 … an.
Step five: and registering and updating the hexagonal grid, and registering the hexagonal grid according to the original geographic information data and the POI data. First, city element information contained in each hexagonal grid, such as business super, company enterprise, infrastructure and the like, is determined based on geographic information data of smart cities, and element information contained in each grid is determined. And performing secondary registration and updating on element information in the grid based on the POI data, wherein the POI data is classified according to types as shown in FIG. 4, the POI data has the characteristic of dynamic updating, the geographic information data is generally huge, the updating is generally slow, the POI data is updated quickly, and the updating and the registration of the element information in the hexagonal grid can be effectively realized by combining the POI data. For example, if a milky tea shop is known on a certain street based on geographic information data, but actually a shop of another type is changed, and the geographic information data is delayed, we can inquire whether the street has a milky tea shop according to the latest POI data left by the user at the street, and update the shop on the street.
Step six: and (4) data preprocessing, namely acquiring a smart city data map based on the hexagonal grid according to the first step to the fifth step, and analyzing and mining various data according to the hexagonal grid data map. The data preprocessing step comprises the sensing of data, the collection of data and the conversion of data. The purpose of data preprocessing is to enable data to be subjected to a variety of data mining analysis and processing.
Step seven: and (4) data mining and analyzing, namely performing data mining and analyzing on the data from the step six. The data mining analysis comprises association analysis, cluster analysis, heat analysis and the like of the data. The association analysis can be used for analyzing the association between various business entities or the association between vehicle information, and the like, for example, the association analysis between the bank position and the school, house and the like can be analyzed through the hexagonal data map quantification analysis, and the address selection of the bank can be optimized. The clustering analysis can be used for obtaining spatial distribution characteristics of different types of data, for example, the taxi data is clustered, the taxi-out frequency, density and other information of taxis in different areas can be obtained, and the dispatching of the taxis can be optimized. The heat degree analysis can be used for analyzing the heat degree of various data types in the space, for example, the heat degree analysis can be used for analyzing the heat degree of people flow, vehicle flow and the like in different areas and different time periods, and further optimizing city management, trip decision and the like.
Step eight: and data visualization, wherein the data visualization comprises big data information visualization, multi-scale display and the like. The big data information visualization means that data are visually displayed through data mining and analysis, and the information visualization means that data to be analyzed or smart city data are displayed in a multi-azimuth mode, including the display of various statistical charts and indexes. The multi-scale visualization refers to multi-scale display of the hexagonal data map, the size of the grid can be freely changed, and data information under different granularities is analyzed.
Step nine: by applying scene analysis, the purpose of the smart city is to better manage the city and score data so as to improve the living standard of people. The intelligent city data map based on hexagonal grid management can be applied to various scenes, including traffic planning, city management, urban and rural planning and the like.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a data management and analysis method for smart city service aiming at the problems of difficult qualitative analysis, slow update and the like of smart city data, firstly, managing the data by utilizing a hexagonal grid, secondly, updating the hexagonal grid data based on POI data, and finally, realizing the analysis of various data based on a hexagonal grid data map so as to optimize the management of a city;
the method provides a reliable solution for smart city data analysis, and provides a reliable basis for smart city data analysis based on the hexagonal grid.
Drawings
FIG. 1 illustrates a process for managing smart city data based on hexagonal grids;
FIG. 2. hexagonal mesh produced;
FIG. 3 is a hexagonal grid encoding of three successive layers;
FIG. 4 POI data classification;
FIG. 5 is a flow of smart city data analysis based on hexagonal lattices.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The method comprises the following steps: and selecting the area to be managed and analyzed by selecting or inputting province, city, county, district and the like on the smart city map.
Step two: set up the graticule mesh size, set up different graticule mesh sizes according to the analysis demand of difference, the unit of graticule mesh size is m. For example, analysis of bank addressing and decision making requires smaller size hexagragons in order for the bank to cover the needs of most people nearby.
Step three: and generating a hexagonal grid, and generating the hexagonal grid capable of covering the selected area according to the selected area and the size of the grid. As shown in fig. 2.
Step four: determining grid coding, as shown in fig. 3, a continuous three-layer hexagonal grid coding method, in order to embody the layer characteristics, numbers (symbols) arranged in sequence are used to represent units of different layers, and the formed symbol sequence is called "address code". The grid cell resulting from the initial split (n = 1) is denoted a1, the cell on the second level may be denoted a1a2, the cell on the third level is denoted a1a2a3, and the cell on the nth level is denoted a1a2a3 … an. The mesh cells of the first layer are central parent cells, which are hierarchically split, and 1 central child cell and 6 vertex child cells can be formed in the second layer. And adding 0 after encoding for the central subunit of the second layer, namely 00, adding one number in 1, 2, …, 6 to the last bit encoding of the unit address code in a counterclockwise order for the four vertex subunits of 6 vertices, adding 1 to the vertex subunit on the right side of the central subunit, and then sequentially adding 2, 3, …, 6 to the vertex subunits around the central subunit in a counterclockwise order, so that the grid unit encoding generated in the second layer is 00, 01, …, 06.
Step five: and registering and updating the hexagonal grid, and registering the hexagonal grid according to the original geographic information data and the POI data. First, city element information contained in each hexagonal grid, such as business super, company enterprise, infrastructure and the like, is determined based on geographic information data of smart cities, and element information contained in each grid is determined. And performing secondary registration and updating on element information in the grid based on the POI data, wherein the POI data is classified according to types as shown in FIG. 4, the POI data has the characteristic of dynamic updating, the geographic information data is generally huge, the updating is generally slow, the POI data is updated quickly, and the updating and the registration of the element information in the hexagonal grid can be effectively realized by combining the POI data. For example, if a milky tea shop is known on a certain street based on geographic information data, but actually a shop of another type is changed, and the geographic information data is delayed, we can inquire whether the street has a milky tea shop according to the latest POI data left by the user at the street, and update the shop on the street.
Step six: and (4) data preprocessing, namely acquiring a smart city data map based on the hexagonal grid according to the first step to the fifth step, and analyzing and mining various data according to the hexagonal grid data map. The data preprocessing step comprises the sensing of data, the collection of data and the conversion of data. The purpose of data preprocessing is to enable data to be subjected to a variety of data mining analysis and processing. The perception of the data refers to acquiring the data through a terminal, for example, acquiring required data information through means of inputting, clicking and the like on a smart city data map based on a hexagonal grid. The data collection refers to inquiring, displaying, collecting and the like of data information, and the data conversion refers to converting the acquired data information into data types capable of machine learning and deep learning, so as to provide data support for subsequent data mining and analysis.
Step seven: and (4) data mining and analyzing, namely performing data mining and analyzing on the data from the step six. The data mining analysis comprises association analysis, cluster analysis, heat analysis and the like of the data. The association analysis can be used for analyzing the association between various business entities or the association between vehicle information, and the like, for example, the association analysis between the bank position and the school, house and the like can be analyzed through the hexagonal data map quantification analysis, and the address selection of the bank can be optimized. The clustering analysis can be used for obtaining spatial distribution characteristics of different types of data, for example, the taxi data is clustered, the taxi-out frequency, density and other information of taxis in different areas can be obtained, and the dispatching of the taxis can be optimized. The heat analysis can be used for analyzing the heat of various data types in the space, for example, the heat analysis can be used for analyzing the heat of people flow, vehicle flow and the like in different regions and different time periods, so as to optimize city management, trip decision and the like.
Step eight: and data visualization, wherein the data visualization comprises big data information visualization, multi-scale display and the like. The big data information visualization means visualization display of data through data mining and analysis, and the information visualization means multidirectional display of data to be analyzed or smart city data, including display of various statistical charts and indexes. The multi-scale visualization refers to multi-scale display of the hexagonal data map, the size of the grid can be freely changed, and data information under different granularities is analyzed.
By applying scene analysis, the purpose of the smart city is to better manage the city and score data so as to improve the living standard of people. The intelligent city data map based on hexagonal grid management can be applied to various scenes, including traffic planning, city management, urban and rural planning and the like.
The technical solutions described above only represent the preferred technical solutions of the present invention, and some possible modifications to some parts of the technical solutions by those skilled in the art all represent the principles of the present invention, and fall within the protection scope of the present invention.
Claims (1)
1. A data management and analysis method for smart city service is characterized in that: the method comprises the following steps: the method comprises the following steps: selecting an area, namely selecting the area to be managed and analyzed by methods of selecting frames on the smart city map or inputting provinces, cities, counties, districts and the like;
step two: setting the size of a grid, and setting different sizes of the grid according to different analysis requirements, wherein the unit of the size of the grid is m;
step three: generating a hexagonal grid, and generating the hexagonal grid capable of covering the selected area according to the selected area and the size of the grid;
step four: determining grid coding, namely a continuous three-layer hexagonal grid coding method, in order to embody the layer characteristics, sequentially arranging numbers to represent units of different layers, forming a symbol sequence called as "address code", wherein grid units generated by initial subdivision are denoted by a1, units on a second layer can be denoted by a1a2, units on a third layer are denoted by a1a2a3, and units on an nth layer are denoted by a1a2a3 … an;
step five: registering and updating the hexagonal grids, registering the hexagonal grids by combining original geographic information data and POI data, determining city element information contained in each hexagonal grid based on the geographic information data of the smart city, and performing secondary registration and updating on the element information in the hexagonal grids based on the POI data;
step six: the method comprises the following steps of data preprocessing, namely, a smart city data map based on a hexagonal grid and a hexagonal grid data map are combined to analyze and mine various data, wherein the data preprocessing link comprises data sensing, data collection and data conversion; the purpose of data preprocessing is to enable data to be subjected to various data mining analysis and processing;
step seven: data mining analysis, namely performing data mining and analysis on the preprocessed data, wherein the data mining analysis comprises association analysis, cluster analysis and heat degree analysis of the data;
step eight: and data visualization, wherein the data visualization comprises big data information visualization, information visualization and multi-scale display.
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