Cross-platform three-dimensional map thermodynamic diagram generation method

Abstract

The invention discloses a cross-platform three-dimensional map thermodynamic diagram generation method, and belongs to the technical field of cross-platform three-dimensional map data visualization. In order to solve the problems that data statistics and GIS map display are difficult to combine and the display effect is poor, the method comprises the steps of preparing a geographic information file of a target region, and constructing a three-dimensional map; configuring three-dimensional map loading parameters for the constructed three-dimensional map; original data are collected and stored in a data server to serve as initial data; performing standard processing on the obtained initial data according to the type and form of the statistical graph; performing statistical chart parameter configuration; based on the obtained data subjected to standard processing, carrying out data loading of a statistical graph; and confirming a display platform for the statistical graph of the loaded data for displaying. According to the method, geographic coordinate information and color styles can be visually displayed while the data statistical graph is displayed, data configuration is flexible, adjustment is convenient, the speed is high, and portability is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of cross-platform three-dimensional map data visualization, and in particular relates to a cross-platform three-dimensional map heat map generation method. Background Art

[0002] At present, users generally display various data in two-dimensional flat statistical charts. For large and complex data, especially data involving geographic information, there is a lack of spatial and more intuitive display and analysis on maps; and for the display effect of data, dynamic display is more preferred. Facing different users, they hope to have personalized processing, stronger portability and reusability. Summary of the invention

[0003] The problem to be solved by the present invention is the difficulty in combining data statistics with GIS map display and the poor display effect. A cross-platform three-dimensional map heat map generation method is proposed.

[0004] To achieve the above object, the present invention is implemented through the following technical solutions:

[0005] A cross-platform three-dimensional map heat map generation method includes the following steps:

[0006] S1. Prepare geographic information files of the target area and construct a three-dimensional map;

[0007] S2. For the three-dimensional map constructed in step S1, configure the three-dimensional map loading parameters;

[0008] S3. Collect the original data and save it to the data server as initial data;

[0009] S4. Standardize the initial data obtained in step S3 according to the type and form of the statistical chart;

[0010] S5. Configure statistical chart parameters;

[0011] S6. Loading data for statistical chart based on the standardized data obtained in step S4;

[0012] S7. Confirm that the statistical chart of the loaded data is displayed on the display platform.

[0013] Furthermore, the geographic information data in the geographic information file of the target area in step S1 is in GeoJson format. The map data is registered by using the echarts.registerMap method. The echarts.registerMap method accepts two types of parameters: the first type of parameter is the name of the map, which is a string type; the second type of parameter is the data object of the map.

[0014] Furthermore, setting the three-dimensional map loading parameters in step S2 includes defining the map name, setting the area fill color, area border color, area border width, transparency, viewing angle rotation angle, animation transition duration, and animation transition effect.

[0015] Furthermore, in step S3, the original data is collected from statistical agencies, official data platforms or custom data collection.

[0016] Furthermore, in step S4, the initial data obtained in step S3 is set to n pieces, and the geographic information data of the i-th piece of initial data is a i , the associated information of the i-th initial data is name i , the geographic coordinate information of the i-th initial data is (X i ,Y i );

[0017] The statistics to be counted for the i-th initial data is b i , the attribute information value of the i-th initial data is value i , b i The associated information with the i-th initial data is name i , for a i With b i association;

[0018] According to the association information between geographic information data and the statistical data to be counted, the integrated data c of the i-th initial data is obtained. i for:

[0019] c i ={name i ,value:[X i ,Y i ,value i ]};

[0020] The data set C after standard processing is:

[0021] Furthermore, in step S5, echarts is used to configure the style of the statistical chart, including the color gradient range, statistical range, and statistical field of the heat map.

[0022] Furthermore, step S6 generates a data statistical model by assigning the processed data to the data field of the echarts 3D bar chart, completes the data configuration part of the statistical chart configuration item, combines it with the previous map configuration part, and finally completes the statistical chart through setOption in echarts.

[0023] Furthermore, in step S7, the display platform includes one of a browser, a window system, and a mac system.

[0024] Beneficial effects of the present invention:

[0025] The cross-platform three-dimensional map heat map generation method described in the present invention uses data compatible with geographic information, performs modeling processing, and then performs GIS map data visualization configuration and rendering based on the echarts framework. While displaying data statistical graphs, geographic coordinate information can also be intuitively displayed. The color style and data configuration are flexible, easy to adjust, fast, and highly portable. The heat map display can clearly show the demand distribution of resources, help enterprises or organizations to reasonably allocate resources, and improve efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a cross-platform three-dimensional map heat map generation method according to the present invention;

[0027] Figure 2 It is a schematic diagram of the vector data processing flow of the present invention;

[0028] Figure 3 This is a display effect diagram of a cross-platform three-dimensional map heat map described in the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0030] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0031] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 3 The detailed instructions are as follows:

[0032] Embodiment 1:

[0033] A cross-platform three-dimensional map heat map generation method includes the following steps:

[0034] S1. Prepare geographic information files of the target area and construct a three-dimensional map;

[0035] Furthermore, the geographic information data in the target area geographic information file in step S1 is GeoJson format data, and the map data is registered by using the echarts.registerMap method, which accepts two types of parameters: the first type of parameter is the name of the map, which is a string type; the second type of parameter is the data object of the map;

[0036] Furthermore, GeoJSON is a JSON-based geospatial information data exchange format specifically used to encode geospatial data. GeoJSON combines the simplicity of JavaScript Object Notation (JSON) and the complexity of geospatial data representation, and is suitable for encoding and exchanging various geographic data.

[0037] Furthermore, ECharts is an open source JavaScript charting library developed by Baidu;

[0038] S2. For the three-dimensional map constructed in step S1, configure the three-dimensional map loading parameters;

[0039] Furthermore, setting the three-dimensional map loading parameters in step S2 includes defining the map name, setting the area fill color, area border color, area border width, transparency, viewing angle, animation transition duration, and animation transition effect;

[0040] Furthermore, define the map name as the map name registered by S1, the area fill color, the color definition uses RGBA value or color string, and the transparency range is 0-1;

[0041] S3. Collect the original data and save it to the data server as initial data;

[0042] Furthermore, in step S3, the raw data is collected from statistical agencies, official data platforms, or custom data collection;

[0043] S4. Standardize the initial data obtained in step S3 according to the type and form of the statistical chart;

[0044] Furthermore, in step S4, the initial data obtained in step S3 is set to n pieces, and the geographic information data of the i-th piece of initial data is a i , the associated information of the i-th initial data is name i , the geographic coordinate information of the i-th initial data is (X i ,Y i );

[0045] The statistics to be counted for the i-th initial data is b i , the attribute information value of the i-th initial data is value i , b i The associated information with the i-th initial data is name i , for a i With b i association;

[0046] According to the association information between geographic information data and the statistical data to be counted, the integrated data c of the i-th initial data is obtained. i for:

[0047] c i ={name i ,value:[X i ,Y i ,value i ]};

[0048] The data set C after standard processing is:

[0049] Furthermore, the statistical chart type is defined as a 3D column chart, and the heat map column chart is a column Figure 1 kind;

[0050] Furthermore, the step of processing the data specification in S4 includes:

[0051] S4.1. Remove erroneous data, empty data, and redundant data from the initial data;

[0052] S4.2. Determine the latitude and longitude geographic coordinates for display of statistical data;

[0053] S4.3. Combine the statistical data with the latitude and longitude geographic coordinates to form statistical data;

[0054] S4.4. Finally, the data are packaged together to form a data array and a data statistical model;

[0055] S5. Configure statistical chart parameters;

[0056] Furthermore, in step S5, echarts is used to configure the style of the statistical chart, including the color gradient range, statistical range, and statistical field of the heat map;

[0057] Furthermore, the rendering styles include heat map color gradient range, statistical range, statistical field and other styles. Heat maps are divided into continuous and discrete types. When a continuous heat map is selected, the statistical range [min, max] must be set; when a discrete heat map is selected, the value and color of each statistical stage point must be set separately. Expose the data statistical model rendering style calling interface;

[0058] S6. Loading data for statistical chart based on the standardized data obtained in step S4;

[0059] Furthermore, step S6 generates a data statistical model by assigning the processed data to the data field of the echarts 3D bar chart, completes the data configuration part of the statistical chart configuration item, combines it with the previous map configuration part, and finally completes the statistical chart through setOption in echarts;

[0060] Furthermore, according to the overall effect, configure the map view rotation angle, animation transition duration, and animation transition effect;

[0061] S7. Confirm that the statistical chart of the loaded data is displayed on the display platform.

[0062] Furthermore, in step S7, the display platform includes one of a browser, a window system, and a mac system.

[0063] Taking some simulated data as an example, in order to fully demonstrate the data visualization effect, Harbin area is selected for data display. According to S2 definition map, three-dimensional map construction and style definition are performed, and the color of each area is defined: '#007aff', area border: '#000', border width: '0.4', area transparency: '0.8', and the perspective is defined: up and down, left and right rotation perspective, animation time. The original geographic information data and statistical data are shown in Table 1:

[0064] Table 1

[0065]

[0066]

[0067] The place name in the table is the name of the geographical location where the statistical model is located. The longitude and latitude are the locations on the map where the statistical model is located. The value is the value of the statistical data to be counted. Here, a single value is taken as an example. The statistical data and the longitude and latitude coordinate information are associated through the geographical name.

[0068] The original vector data is uniformly normalized to form a statistical model data array. The data of the array is: new = [{name:'Nangang District',value:[126.6742,45.7641,640]},{name:'Daoli District',value:[126.59176,45.59608,650]},{name:'Daowai District',value:[126.59216,45.92398,940]},{name:'Xiangfang District',value:[126.98566,45.6373,840]},{name:'Songbei District',value:[126.50048,45.3301,740]},{name:'Pingfang District',value:[126.6219,45.6442,540]},{name:'Mulan County',value ue:[127.8489,46.0280,740]},{name:'Yanshou County',value:[128.4167,45.2833,840]},{name:'Tonghe County',value:[128.8125,46.0625,640]},{name:'Fangzheng County',value:[128.8333,45.6167,640]},{name:'Yilan County',value:[129.4500,46.4333,580]},{name:'Shangzhi City',value:[128.06667,45.18333,590]},{name:'Wuchang City',value:[127.53333,44.866667,740]}].

[0069] Define the statistical model style and set the gradient color of the three-dimensional heat map. The heat map statistics are divided into continuous and discrete types. Taking the continuous type as an example, the statistical range is 0-950, and a three-dimensional data statistical model is generated. Load the data statistical model onto the three-dimensional map, and the effect is as follows Figure 3 shown.

[0070] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0071] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A cross-platform three-dimensional map heat map generation method, characterized in that: The steps include: S1. Prepare geographic information files of the target area and construct a three-dimensional map; S2. For the three-dimensional map constructed in step S1, configure the three-dimensional map loading parameters; S3. Collect the original data and save it to the data server as initial data; S4. Standardize the initial data obtained in step S3 according to the type and form of the statistical chart; S5. Configure statistical chart parameters; S6. Loading data for statistical chart based on the standardized data obtained in step S4; S7. Confirm that the statistical chart of the loaded data is displayed on the display platform.

2. A cross-platform three-dimensional map heat map generation method according to claim 1, characterized in that: The geographic information data in the target area geographic information file in step S1 is GeoJson format data. The map data is registered by using the echarts.registerMap method. The echarts.registerMap method accepts two types of parameters: the first type of parameter is the name of the map, which is a string type; the second type of parameter is the data object of the map.

3. A cross-platform three-dimensional map heat map generation method according to claim 2, characterized in that: Setting the three-dimensional map loading parameters in step S2 includes defining the map name, setting the area fill color, area border color, area border width, transparency, viewing angle, animation transition duration, and animation transition effect.

4. A cross-platform three-dimensional map heat map generation method according to claim 3, characterized in that: In step S3, the original data is collected from statistical agencies, official data platforms or custom data collection.

5. A cross-platform three-dimensional map heat map generation method according to claim 4, characterized in that: In step S4, the initial data obtained in step S3 is set to n pieces, and the geographic information data of the i-th piece of initial data is a i , the associated information of the i-th initial data is name i , the geographic coordinate information of the i-th initial data is (X i ,Y i ); The statistics to be counted for the i-th initial data is b i , the attribute information value of the i-th initial data is value i , b i The associated information with the i-th initial data is name i , for a i With b i association; According to the association information between geographic information data and the statistical data to be counted, the integrated data c of the i-th initial data is obtained. i for: c i ={name i ,value:[X i ,Y i ,value i ]}; The data set C after standard processing is:

6. A cross-platform three-dimensional map heat map generation method according to claim 5, characterized in that: In step S5, echarts is used to configure the style of the statistical chart, including the color gradient range, statistical range, and statistical field of the heat map.

7. A cross-platform three-dimensional map heat map generation method according to claim 6, characterized in that: Step S6 generates a data statistical model by assigning the processed data to the data field of the echarts 3D bar chart, completes the data configuration part of the statistical chart configuration item, combines it with the previous map configuration part, and finally completes the statistical chart through setOption in echarts.

8. A cross-platform three-dimensional map heat map generation method according to claim 7, characterized in that: Step S7: the display platform includes one of a browser, a window system, and a mac system.

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