A data visualization method based on data matching and interpolation algorithm

Abstract

The application provides a data visualization method based on data matching and interpolation algorithm, and the specific steps are as follows: a map hierarchical display module is formed by built-in basic map data shp, and the import of map data is completed; a data file is read, the required drawing data is screened according to the data file, and the initial data missing content is matched and perfected to meet the drawing requirements; an interpolation algorithm required to be used is selected, interpolation is performed on the station data to obtain global data; the required visualization type is selected, the visualization attribute display effect is set, and the visualization is displayed through adaptive chromatography; the picture of the visualization display is output, the time sequence animation is made and output, and the picture saving is completed. Through the import of the basic map data, the data reading, screening and matching, the interpolation calculation and the self-defined chromatography for the data visualization display, the data visualization is more standardized and intuitive, and the display effect is good.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data visualization, in particular to a data visualization method based on data matching and interpolation algorithm. BACKGROUND

[0002] Data visualization refers to the way of passing data through charts, so that users can quickly and accurately understand the information to be expressed, thereby improving communication efficiency. At present, there are many data visualization methods, and there are also data visualization methods specially for environmental and meteorological fields. However, at present, most of them are mainly for data management and data visualization display, and lack of specific data visualization methods available for professional technicians, which can adjust the display effect and unify the display standard according to specific needs. SUMMARY

[0003] The purpose of the present application is to provide a data visualization method based on data matching and interpolation algorithm, which can be used for data visualization display.

[0004] The purpose of the present application is achieved by the technical scheme, and the specific steps are as follows:

[0005] 1) Importing map data: importing map data by constructing a map hierarchical display module through built-in basic map data shp;

[0006] 2) Data reading and screening matching: reading data files, screening the required drawing data according to the data files, and matching and perfecting the missing contents of the initial data to meet the drawing requirements;

[0007] 3) Interpolation calculation: selecting the interpolation algorithm to be used to interpolate the station data to obtain global data;

[0008] 4) Data visualization: selecting the required visualization type, setting the visualization attribute display effect, and visualizing the display through adaptive chromatography;

[0009] 5) File saving: outputting the picture of visualization display, making and outputting time sequence animation, and completing picture saving.

[0010] Further, the basic map data in step 1) includes coastline, world map national boundary and China map provincial boundary, China map includes prefectural city boundary, river and lake, highway and railway, and other user shp data and geographic spatial coordinate data can be read automatically;

[0011] The basic map data can be displayed and hidden according to the display requirements of the basic map type selected for display, and meanwhile, various properties of the map display are defined to adjust the display effect, including the conventional properties of point line face type, color, size, thickness, and the selection of the map projection type, so that various map projection effects can be realized.

[0012] Further, the specific steps of data reading and matching in step 2) are as follows:

[0013] 2-1) Data reading and screening: reading the data file, and screening the required drawing data according to the data file, corresponding to the input year, month, day and drawing value, if the original data does not contain the screening item, then the corresponding screening input window is emptied, and the initial data is screened by the keyword;

[0014] The readable data types include station site scatter point data or grid data, the scatter point data can be superimposed with other visualization styles or drawn alone on the map generated in step 1), and the current scatter point longitude and latitude and data value information can be displayed by means of point selection; the meteorological station scatter point data is divided into three levels, which can be displayed alone or together according to the level, and the grid data can be directly drawn into a cloud chart or an isopleth chart;

[0015] 2-2) Data matching: matching the data according to the meteorological station table, mainly involving station name matching and station number matching; used for matching and perfecting the missing contents of the initial data; the matching character is the common item of the matching table and the initial data, and the matching function mainly realizes that when the initial data lacks longitude and latitude coordinate data, the drawing of the map data point is realized by matching to meet the drawing requirements;

[0016] The matching mode is divided into fuzzy matching and accurate matching according to the difference of the matching character, when the matching character is the city name, the fuzzy matching is adopted, and the station number and the like are accurately matched;

[0017] The initial data and the matched data can be displayed and saved as a file, the matching table can be opened and modified and added through the interface, and the new matching table can also be viewed, modified and saved and the like; the data point information after matching can be directly displayed on the drawing interface by means of point selection to display the geographical and data value information of the data point.

[0018] Further, the interpolation algorithm in step 3) includes but is not limited to Kriging interpolation, IDW interpolation, spline interpolation, triangular subdivision linear interpolation, cressman interpolation and trend surface interpolation.

[0019] The interpolation algorithm is selected and the precision of the interpolation grid is defined, and the scatter point data is subjected to interpolation operation to generate grid data.

[0020] Further, the specific steps of data visualization in step 4) are as follows:

[0021] 4-1) The types of visualization required include scatter plot, heat map, block diagram, grid diagram, contour diagram, surface diagram, slice diagram, and path diagram;

[0022] The scatter plot is drawn based on the matched data, the path diagram is drawn based on the generated scatter plot, the block diagram is drawn by first selecting the block data, then selecting the corresponding block diagram range according to the content of the block data, and the heat map, grid diagram, contour diagram, surface diagram, and slice diagram are all drawn by first gridding the data through interpolation, wherein the heat map is drawn using statistical method, the contour is drawn using self-defined hierarchical display or default equal division, and the surface diagram and slice diagram are drawn based on three-dimensional data containing altitude;

[0023] 4-2) Select the style, size, and cloud map attributes of the points, and draw the scatter plot by checking the required levels according to the meteorological station level, and then close the window after drawing is completed;

[0024] 4-3) Visualization is performed by adaptive color spectrum: different color spectrums are used for different data types, different orders of full spectrum are used for different regional levels to display the quantitative differences between the mapping elements, and different ranges of color spectrum are used for different time dimensions to represent the range, and the adaptive selection of color spectrum for visualization is completed.

[0025] Further, the specific steps of visualization by adaptive color spectrum in step 4-3) are as follows:

[0026] 4-3-1) Visualization is performed by using different color spectrums for different data types:

[0027] The average and cumulative values of temperature, humidity, precipitation, and radiation are represented by jet color spectrum to represent data distribution, with blue representing small values and red representing large values; wherein: the minimum value distribution uses cool color spectrum, with light blue representing large values and purple representing small values; the maximum value distribution uses parula color spectrum, with blue representing small values and light yellow representing large values;

[0028] Single-color spectrum is used to represent salt fog concentration, chloride ion concentration, sulfur dioxide, and other atmospheric media, with values gradually expressed from dark to light; wherein: green single-color spectrum is used for salt fog concentration and chloride ion concentration, yellow single-color spectrum is used for sulfur dioxide concentration, and red single-color spectrum is used for other atmospheric medium concentration;

[0029] 4-3-2) Visualization is performed by using different orders of full spectrum for different regional levels:

[0030] Different color order numbers are used to represent digital map regions, and the color order numbers are divided into low, medium, and high three levels of recognition;

[0031] Using Jet chromatography Low-resolution chromatograms are used to display provincial digital maps. The medium-resolution color spectrum displays the national-level digital map, using A highly recognizable color spectrum display of a digital world map;

[0032] 4-3-3) Visualize different time dimensions using chromatograms of varying ranges:

[0033] Different color gradations are used to represent the length of time, including monthly, quarterly, and yearly data ranges.

[0034] In JET chromatography, the monthly data range is displayed using the first half of the chromatographic range, with the maximum value being... The values ​​are [0.5, 1, 0.5]. The quarterly data range uses the first three-quarters of the chromaticity values ​​within the chromaticity range for display, with the maximum value being [missing value]. The value is [1, 0.5, 0]. The annual value data range is displayed using colorimetric values ​​across the entire chromaticity range, with the maximum value being [missing value]. The value is [0.5, 0, 0];

[0035] 4-3-4) During the visualization process, adaptive matching of data values ​​and color values ​​is performed:

[0036] Calculate the color level interval between data values. :

[0037]

[0038] In the formula, and These are the maximum and minimum values ​​of the data, respectively. For color class number;

[0039] Based on color level interval values Calculate the chromaticity index :

[0040]

[0041] In the formula, This is the floor function;

[0042] Based on the color index and reference chromatographic allocation Chromaticity values, and leave whitespace for areas without data, assigning... The value is [1,1,1].

[0043] Because of the adoption of the above technical solution, the present invention has the following advantages:

[0044] 1、The present application can expand the reading of new map data through the built-in various types of maps, and can display the maps separately or superimposed in layers, so that the attributes such as color, thickness, style, projection type, etc. can realize various display effects according to the user's needs.

[0045] 2、The present application can automatically complete the missing coordinate data through data matching, and can find and improve the missing geographic coordinate data in the data table through accurate matching or fuzzy matching of different character types, so as to complete the drawing.

[0046] 3、The present application integrates various interpolation algorithms and provides an interface for users to extend interpolation algorithms, helping users to quickly select the required interpolation algorithm for calculation, improving the efficiency and accuracy of data interpolation processing.

[0047] 4、The present application uses adaptive chromatography for visualization, providing an adaptive method for visualization, so that data visualization can be standardized according to the needs, and the display process is more intelligent.

[0048] 5、The adaptive method of the present application helps users to unify the data visualization management from the data type and time, and quickly and effectively identifies various data types, so that the user can intuitively understand the type of environmental data through the chromatography color and display range, and can more conveniently compare and analyze different data of the same environmental type.

[0049] 6、The present application can output pictures and time sequence animations, providing more efficient and refined means to display the research results of professional technicians and improve the effect and efficiency of information transmission.

[0050] Other advantages, objects, and features of the present application will be explained in the following description, and to some extent will be obvious to those skilled in the art based on the following description, or can be learned from the practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the following description. BRIEF DESCRIPTION OF DRAWINGS

[0051] The drawings of the present application are as follows.

[0052] Figure 1 The flowchart of the method of the present application.

[0053] Figure 2 The data reading and display processing interface diagram of the present application. DETAILED DESCRIPTION

[0054] The present application will be further described below in conjunction with the drawings and examples.

[0055] As Figures 1-2The data visualization method based on data matching and interpolation algorithm is used for realizing visualization display of temperature data of Chinese map meteorological stations on January 1, 2010, and the specific steps are as follows:

[0056] 1) importing map data: the map data is imported by means of a built-in basic map data shp constituting a map hierarchical display module;

[0057] The basic map data includes a coastline, world map national boundaries and Chinese map provincial boundaries, the Chinese map includes city boundaries, rivers and lakes, highways and railways, and other user shp data and geographic spatial coordinate data can be read automatically;

[0058] The basic map data can be selected according to display requirements to display and hide the basic map type required to be displayed, meanwhile, various properties of the map display are defined to adjust the display effect, the various properties include conventional properties such as point line surface type, color, size and thickness, and the selection of map projection type can realize various map projection effects.

[0059] In the example of the application, the display style and projection type of the default coastline and Chinese provincial boundary map are modified through the base map attribute, the coastline and the Chinese provincial boundary base map are checked on the main interface, and the map display is performed, and the meridian and parallel line display is opened.

[0060] 2) data reading and screening matching: the data file is read, the required drawing data is screened according to the data file, and the missing content of the initial data is matched and perfected to meet the drawing requirements; the specific steps are as follows:

[0061] 2-1) data reading and screening: the data file is read, the required drawing data is screened according to the data file, the input year, month, day and drawing value are matched, if the original data does not contain the screening item, the corresponding screening input window is emptied, and the initial data is screened through a keyword;

[0062] The readable data types include station site scatter point data or grid data, the scatter point data can be superimposed with other visualization styles or independently drawn on the map generated in step 1), and the current scatter point longitude and latitude and data value information can be displayed through the point selection mode; the meteorological station scatter point data is divided into three levels, and can be displayed individually or collectively according to the level, and the grid data can be directly drawn into a cloud chart or an isopleth chart;

[0063] 2-2) data matching: the data is matched according to the meteorological station table, mainly involving station name matching and station number matching; the missing content of the initial data is matched and perfected; the matching character is a common item of the matching table and the initial data, and the matching function mainly realizes that when the initial data lacks longitude and latitude coordinate data and cannot be drawn into a map data point, the matching is filled to meet the drawing requirements;

[0064] The matching mode is divided into fuzzy matching and accurate matching according to different matching characters, the fuzzy matching is adopted when the matching character is a city name, and the accurate matching is adopted for station number, etc.

[0065] The initial data and the matched data can be displayed and saved as a file, the matching table can be opened and modified through the interface, and the new matching table can also be viewed, modified, saved and the like; the data point information after matching can be directly displayed on the drawing interface through the point selection mode to display the geographic and data value information of the data point.

[0066] In the embodiment of the application, the contour map is selected in the drop-down list, the contour map drawing interface is opened, the excel data file is selected in the interface data reading window to read the scatter point data, the scatter point data includes station site number, longitude and latitude, elevation, year, month, day, data value including RHU, SSD, T, PRE and HG five kinds of label data, the year, month, day and the data value to be visualized are input in the interface, the data value T is input on January 1, 2010, the initial button is clicked in the data management interface, and the data read by the current screening is displayed;

[0067] The data is matched again, the matching value is the station number, the matching button is clicked in the data management interface, the background meteorological matching table file is used as the matching standard table, the perfect data after matching is displayed in the data management interface, and the data can be saved according to the needs (not saved here). Since the matching item is the station number, the accurate matching is performed, and the data that fails to match is displayed on the interface, the data will not be drawn, and the corresponding modification and addition can be performed in the meteorological matching table if drawing is needed.

[0068] The data point after matching can be directly drawn into a scatter plot, a single color can be selected to identify the geographic position only during drawing, or a data value cloud chart color can be displayed simultaneously, the data prompt is opened, and the scatter point drawn in the interface can be displayed and viewed through the mouse point selection mode to display the specific longitude and latitude and data value.

[0069] 3) Interpolation calculation: selecting an interpolation algorithm to be used, interpolating the station data to obtain global data; the interpolation algorithm includes but is not limited to Kriging interpolation, IDW interpolation, spline interpolation, triangular partition linear interpolation, Cressman interpolation and trend surface interpolation;

[0070] The interpolation algorithm is selected and the precision of the interpolation grid is defined, and the scatter point data is subjected to interpolation operation to generate grid data.

[0071] In the examples of the present application, for the scatter data, grid data needs to be generated by interpolation, so by using the interpolation algorithms available in the Matlab and python metpy software, and at the same time, the commonly used interpolation algorithms of other six kinds of environmental meteorological data are implemented by using the Matlab language, so that the final purpose of selecting different interpolation algorithms to obtain data for drawing according to specific needs can be achieved. Click the interpolation algorithm button to enter the interpolation calculation interface, select Kriging interpolation for calculation here, and after the calculation is completed, return to the data management interface and click the interpolation button, then the interface displays the data values after interpolation. Then calculate the China border area, select the data within the China border in the grid data, and the data outside the border is empty. Thus, the data processing is completed.

[0072] 4) Data visualization: select the required visualization type, set the visualization attribute display effect, and visualize the display through adaptive chromatography; the specific steps are:

[0073] 4-1) The types of visualization required include scatter plot, heat map, block diagram, grid diagram, contour diagram, surface diagram, slice diagram, and path diagram;

[0074] The scatter plot is based on the matched data for drawing, the path diagram is based on the generated scatter plot for drawing, the block diagram is drawn by first selecting the block data, then selecting the corresponding block diagram range according to the content of the block data, the heat map, grid diagram, contour diagram, surface diagram, and slice diagram are all gridized by interpolation when drawing, among which the heat map is drawn by statistical method, the contour is drawn by self-defined hierarchical display or default equal division, and the surface diagram and slice diagram are drawn based on three-dimensional data containing altitude;

[0075] 4-2) Select the style, size, and cloud chart attributes of the point, and draw the scatter plot according to the selected levels of the meteorological station, then close the window after drawing is completed;

[0076] 4-3) Visualize the display through adaptive chromatography: use different chromatography for different data types, use different order full chromatography for different regional levels to display the quantitative differences between the mapping elements, and use different range of chromatography for different time dimensions to represent the range, complete the adaptive selection of chromatography for visualization display, and the specific steps are:

[0077] 4-3-1) Visualize the display by using different chromatography for different data types:

[0078] The average value and cumulative value of temperature, humidity, precipitation, and radiation are represented by jet chromatography to represent data distribution, with blue representing small value and red representing large value; among them: the minimum value distribution uses cool chromatography, with light blue representing large value and purple representing small value; the maximum value distribution uses parula chromatography, with blue representing small value and light yellow representing large value;

[0079] Salt spray concentration, chloride ion concentration, sulfur dioxide and other atmospheric media concentrations are represented by monochromatic chromatograms, with values ​​gradually expressed from dark to bright; among them: salt spray concentration and chloride ion concentration are represented by green monochromatic chromatograms, sulfur dioxide concentration is represented by yellow monochromatic chromatograms, and concentrations of other atmospheric media are represented by red monochromatic chromatograms.

[0080] 4-3-2) Visualize different orders of full chromatograms for different regional levels:

[0081] Different color gradations are used to represent regions on digital maps, and the color gradations are divided into three levels of recognition: low, medium, and high.

[0082] Using Jet chromatography Low-resolution chromatograms are used to display provincial digital maps. The medium-resolution color spectrum displays the national-level digital map, using A highly recognizable color spectrum display of a digital world map;

[0083] 4-3-3) Visualize different time dimensions using chromatograms of varying ranges:

[0084] Different color gradations are used to represent the length of time, including monthly, quarterly, and yearly data ranges.

[0085] In JET chromatography, the monthly data range is displayed using the first half of the chromatographic range, with the maximum value being... The values ​​are [0.5, 1, 0.5]. The quarterly data range uses the first three-quarters of the chromaticity values ​​within the chromaticity range for display, with the maximum value being [missing value]. The value is [1, 0.5, 0]. The annual value data range is displayed using colorimetric values ​​across the entire chromaticity range, with the maximum value being [missing value]. The value is [0.5, 0, 0];

[0086] 4-3-4) During the visualization process, adaptive matching of data values ​​and color values ​​is performed:

[0087] Calculate the color level interval between data values. :

[0088]

[0089] In the formula, and These are the maximum and minimum values ​​of the data, respectively. For color class number;

[0090] Based on color level interval values Calculate the chromaticity index :

[0091]

[0092] In the formula, is a floor function;

[0093] Assigning a color spectrum according to the color scale index The chroma value, and the blank processing of the data-free part, is given The value is [1, 1, 1].

[0094] In the embodiment of the present application, in the visualization display process, return to the contour drawing interface, select the adaptive color spectrum, select the average value as the numerical type, click OK to draw the contour map; after drawing, close the contour window, here the scatter points need to be superimposed on the contour map, therefore, no data drawing clearing is needed here, directly select the drawing style scatter plot again, select the scatter plot style as 'o', and set the scatter point size as 20, here the scatter points are drawn according to the data value, therefore, no scatter point color needs to be selected, the color spectrum is selected as adaptive, here check the first, second and third stations, confirm the drawing, then the scatter points are superimposed on the contour; open the data cursor switch, click the scatter point with the left mouse button, then the latitude and longitude value and the data value of the scatter point position are displayed.

[0095] 5) File saving: output the picture of the visualization display, make and output the time sequence animation, and complete the picture saving.

[0096] In the embodiment of the present application, click the file picture saving, save the picture in the folder, here the picture is named as 01.jpg. According to the need, draw other time and date data maps, save the pictures in sequence according to the naming, and put them in the same folder. Open the animation making interface, select the folder to generate the animation, then automatically generate the time sequence animation according to the naming order of all the picture files in the folder.

[0097] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0098] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0099] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0100] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0101] Finally, it should be noted that the above-mentioned embodiments are merely intended to illustrate the technical solutions of the present application, but not to limit the same. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered within the scope of protection of the claims of the present application.

Claims

1. A data visualization method based on data matching and interpolation algorithms, characterized in that, The specific steps are as follows: 1) Import map data: The map data is imported by constructing a layered map display module using built-in basic map data shapefiles. 2) Data reading and filtering: Read the data file, filter the required plotting data based on the data file, and match and improve the missing content of the initial data to meet the plotting requirements; 3) Interpolation calculation: Select the required interpolation algorithm to interpolate the station data to obtain global data; 4) Data Visualization: Select the desired visualization type, set the visualization attributes and display effects, and use adaptive color charts for visualization; 5) File saving: Output visually displayed images, create and output time-series animations, and save the images; The specific steps for data reading and filtering matching in step 2) are as follows: 2-1) Data reading and filtering: Read the data file and filter the required plotting data according to the data file. Input the year, month, day and plotting value accordingly. If the original data does not contain the filter items, clear the corresponding filter input window. Filter the initial data by keywords. The readable data types include scattered data or grid data of meteorological stations. Scattered data can be overlaid with other visualization styles or drawn separately on the map generated in step 1). The latitude and longitude and data value information of the current scatter point can be displayed by selecting points. Scattered data of meteorological stations are divided into three levels, which can be displayed separately or together according to the level. Grid data can be directly used to draw cloud maps or contour maps. 2-2) Data Matching: Matching data based on the meteorological station table, mainly involving station name matching and station number matching; used to match and improve missing content in the initial data; the matching characters are common items between the matching table and the initial data. The matching function is mainly to fill in the missing latitude and longitude coordinate data in the initial data when map data points cannot be drawn, in order to meet the drawing requirements. The matching method is divided into two types based on the different matching characters: fuzzy matching and exact matching. Fuzzy matching is used when the matching character is the city name, and exact matching is used when the station number is the station number. Both the initial data and the matched data can be viewed or saved as files. The matching table can be opened, modified, and added through the interface. New matching tables can also be viewed, modified, and saved. After the matching is completed, the geographic and data value information of the data point can be displayed directly on the drawing interface by selecting the data point. The specific steps for data visualization in step 4) are as follows: 4-1) The types of visualizations required include scatter plots, heatmaps, block maps, grid maps, contour maps, surface maps, slice maps, and path maps; Scatter plots are drawn based on matched data, path plots are drawn based on generated scatter plots, and block plots are drawn by first selecting block data and then selecting the corresponding block plot range based on the content of the block data. Heat maps, grid maps, contour maps, surface maps, and slice maps are all drawn by first interpolating the data into a grid. Heat maps are drawn using statistical methods, contour lines are drawn using custom hierarchical display or default equal division, and surface maps and slice maps are drawn based on three-dimensional data including altitude. 4-2) Select the style, size, and cloud map attributes of the points. For scatter plots, check the boxes to display the required levels according to the meteorological station level and start drawing. Close the window when drawing is complete. 4-3) Visualization through adaptive chromatography: Different chromatograms are used for different data types, and different orders of full chromatograms are used for different regional levels to display quantitative differences between mapping elements. Different ranges of chromatograms are used for different time dimensions to represent the range, thus completing the adaptive selection of chromatograms for visualization.

2. The data visualization method based on data matching and interpolation algorithms as described in claim 1, characterized in that, The basic map data mentioned in step 1) includes coastlines, world map borders and Chinese map provincial borders. The Chinese map includes prefecture-level city boundaries, rivers and lakes, highways and railways, as well as other users' shapefile data and geospatial coordinate data. The basic map data can be displayed and hidden by selecting the type of basic map to be displayed according to display needs. At the same time, various map display attributes are defined to adjust the display effect. These attributes include common attributes such as point, line, and polygon type, color, size, and thickness. The map projection type can be selected to achieve various map projection effects.

3. The data visualization method based on data matching and interpolation algorithms as described in claim 1, characterized in that, The interpolation algorithms in step 3) include, but are not limited to, Kriging interpolation, IDW interpolation, spline interpolation, triangulation linear interpolation, Cressman interpolation, and trend surface interpolation. By selecting an interpolation algorithm and customizing the precision of the interpolation grid, interpolation operations are performed on scattered data to generate grid data.

4. The data visualization method based on data matching and interpolation algorithms as described in claim 1, characterized in that, The specific steps for visualization using adaptive chromatography in step 4-3) are as follows: 4-3-1) Different chromatograms are used to visualize different data types: The average and cumulative values ​​of temperature, humidity, precipitation, and radiation were represented by JET chromatograms, with blue indicating smaller values ​​and red indicating larger values. Specifically, the distribution of minimum values ​​was represented by Cool chromatograms, with bright cyan indicating large values ​​and purple indicating small values. The distribution of maximum values ​​was represented by Parula chromatograms, with blue indicating small values ​​and bright yellow indicating large values. Salt spray concentration, chloride ion concentration, sulfur dioxide and other atmospheric media concentrations are represented by monochromatic chromatograms, with values ​​gradually expressed from dark to bright; among them: salt spray concentration and chloride ion concentration are represented by green monochromatic chromatograms, sulfur dioxide concentration is represented by yellow monochromatic chromatograms, and concentrations of other atmospheric media are represented by red monochromatic chromatograms. 4-3-2) Visualize different orders of full chromatograms for different regional levels: Different color gradations are used to represent regions on digital maps, and the color gradations are divided into three levels of recognition: low, medium, and high. Using Jet chromatography Low-resolution chromatograms are used to display provincial digital maps. The medium-resolution color spectrum displays the national-level digital map, using A highly recognizable color spectrum display of a digital world map; 4-3-3) Visualize different time dimensions using chromatograms of varying ranges: Different color gradations are used to represent the length of time, including monthly, quarterly, and yearly data ranges. In JET chromatography, the monthly data range is displayed using the first half of the chromatographic range, with the maximum value being... The values ​​are [0.5, 1, 0.5]. The quarterly data range uses the first three-quarters of the chromaticity values ​​within the chromaticity range for display, with the maximum value being [missing value]. The value is [1, 0.5, 0]. The annual value data range is displayed using colorimetric values ​​across the entire chromaticity range, with the maximum value being [missing value]. The value is [0.5, 0, 0]; 4-3-4) During the visualization process, adaptive matching of data values ​​and color values ​​is performed: Calculate the color level interval between data values. : In the formula, and These are the maximum and minimum values ​​of the data, respectively. For color class number; Based on color level interval values Calculate the chromaticity index : In the formula, This is the floor function; Based on the color scale index and reference chromatographic allocation Chromaticity values, and leave blank areas with no data, assigning... The value is [1,1,1].

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