CN112730743A - Interaction method and device for air quality mode forecast data - Google Patents
Interaction method and device for air quality mode forecast data Download PDFInfo
- Publication number
- CN112730743A CN112730743A CN202011513389.1A CN202011513389A CN112730743A CN 112730743 A CN112730743 A CN 112730743A CN 202011513389 A CN202011513389 A CN 202011513389A CN 112730743 A CN112730743 A CN 112730743A
- Authority
- CN
- China
- Prior art keywords
- air quality
- data
- cube
- color attribute
- forecast data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
- G01N33/0063—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a threshold to release an alarm or displaying means
Abstract
The invention discloses an interaction method and device for air quality mode forecast data. Generating three-dimensional texture data by using air quality mode forecast data, wherein the three-dimensional texture data comprises a geographical range and air quality data corresponding to each block in the geographical range; establishing a cube of a geographic range; establishing rays from the observation point and each point on the surface of the cube visible by the observation point, and generating a plurality of sampling points on the part of the rays in the cube; determining a block to which each sampling point belongs and corresponding air quality data, and converting the air quality data of each sampling point into a color attribute value according to the correspondence between the color attribute value and the air quality data; respectively accumulating the color attribute values of all sampling points on the same ray aiming at all rays to obtain the color attribute value at the intersection point of the visible cube face and the ray; and (5) after the cube is subdivided, displaying air quality mode forecast data at the section. And the three-dimensional display and interaction of the air quality mode forecast data can be realized.
Description
Technical Field
The invention relates to the field of air quality mode prediction, in particular to an interaction method and device of air quality mode prediction data.
Background
In the prior art, in the prediction of the air quality numerical value mode, a geographic space is divided into regular grids in the horizontal direction, each grid is fixed in size (for example, 5km × 5km), and mode prediction data, such as PM (particulate matter), is output at the vertex of each grid2.5Concentration value, PM10Concentration value and/or SO2Concentration values, etc.; in the vertical direction, the forecast space is divided into multiple layers, and the number of grids in each layer is the same. On the interaction of data, the air quality condition in the area is often represented by the air quality forecast result data of the ground layer, and the method has the following defects: 1) the air pollution process is three-dimensional in the geographic space, transmission and sedimentation exist between different layers from high altitude to the ground, the single-layer pollutants cannot completely represent the space pollution state, and partial data are selected for the air quality mode prediction result; 2) compared with single-layer plane display, the three-dimensional display is more vivid and accords with the cognition of people on the actual air quality pollution condition.
It can be seen that the air quality model forecast data in the prior art is usually shown in the form of a two-dimensional distribution graph, and a single-layer pollutant rendering is selected on a data source.
Disclosure of Invention
The invention innovatively provides an interaction method of air quality mode forecast data, and three-dimensional display and interaction of the air quality mode forecast data at a browser end are realized.
In order to achieve the technical purpose, the invention discloses an interaction method of air quality mode forecast data. The interaction method of the air quality mode forecast data comprises the following steps: generating three-dimensional texture data by the air quality mode forecast data, wherein the three-dimensional texture data comprises a geographical range and air quality data corresponding to each geographical block included in the geographical range; establishing a cube of the geographic region on a map; establishing rays with each point on each face of the cube visible from the observation point, and generating a plurality of sampling points on the part of the rays in the cube; determining the geographic block to which each sampling point belongs and corresponding air quality data according to the three-dimensional texture data, and converting the air quality data of each sampling point into corresponding color attribute values according to the corresponding relation between the color attribute values and the air quality data; for each ray, accumulating the color attribute values of each sampling point on the same ray to obtain the color attribute values of the intersection points of each face of the cube visible to the observation point and the ray, so as to generate three-dimensional display of air quality mode forecast data; and after the cube is subdivided, displaying air quality mode forecast data at the section.
Further, for the interaction method of the air quality mode forecast data, the method for subdividing the cube comprises the following steps: and displaying the air quality mode forecast data on the section of the coordinate axis direction by controlling the threshold range of the point in the cube in the coordinate axis direction.
Further, for the interaction method of the air quality model forecast data, generating three-dimensional texture data from the air quality model forecast data includes: and respectively processing the original air quality data corresponding to each geographic area into corresponding integers between 0 and 255 through a down-rounding function to serve as the air quality data stored in the texture.
Further, for the interaction method of the air quality mode forecast data, determining the geographic area to which each sampling point belongs and the corresponding air quality data according to the three-dimensional texture data, including: and converting the air quality data stored by the texture corresponding to the geographic area to which each sampling point belongs into the original air quality data.
Further, for the interaction method of the air quality mode forecast data, in the process of accumulating the color attribute values, the method further comprises the following steps: and screening the air quality data corresponding to the sampling points and stored in the texture, and filtering the sampling points which are larger than the air quality upper threshold and/or smaller than the air quality lower threshold in the air quality data stored in the texture.
Further, for the interactive method of air quality pattern forecast data, the air quality data comprises a pollutant concentration value.
Further, for the method of interacting air quality mode forecast data, the color attribute values comprise color transparency values.
In order to achieve the technical purpose, the invention discloses an interactive device for air quality mode forecast data. The interaction device for the air quality mode forecast data comprises: the three-dimensional texture data generating unit is used for generating three-dimensional texture data from the air quality mode forecast data, and the three-dimensional texture data comprises a geographical range and air quality data corresponding to each geographical block included in the geographical range; the cube establishing unit is used for establishing a cube of the geographic range on a map; the ray sampling unit is used for establishing rays with each point on each face of the cube visible from the observation point and generating a plurality of sampling points on the part of the rays in the cube; the color attribute value conversion unit is used for determining the geographic block to which each sampling point belongs and corresponding air quality data according to the three-dimensional texture data, and converting the air quality data of each sampling point into corresponding color attribute values according to the corresponding relation between the color attribute values and the air quality data; the color attribute value accumulation unit is used for accumulating the color attribute values of all sampling points on the same ray respectively aiming at all rays to obtain the color attribute values of the intersection points of all surfaces of the cube visible by the observation point and the rays, so that the three-dimensional display of the air quality mode forecast data is generated; and the subdivision unit is used for displaying the air quality mode forecast data at the section after subdividing the cube.
To achieve the above technical object, in yet another aspect, the present invention discloses a computing device. The computing device includes: one or more processors, and a memory coupled with the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the above-described method.
To achieve the above technical objects, in yet another aspect, the present invention discloses a machine-readable storage medium. The machine-readable storage medium stores executable instructions that, when executed, cause the machine to perform the above-described method.
The invention has the beneficial effects that:
the method and the device for interacting the air quality mode forecast data provided by the embodiment of the invention generate three-dimensional texture data from the air quality mode data in advance, establish a cube model on a map according to three-dimensional texture data coordinates, generate sampling points in a cube at intervals by adopting a ray method, extract the air quality data of the sampling points from the three-dimensional texture data, convert air quality concentration values into corresponding color values according to a color legend, and finally accumulate the colors and transparencies of all the color values to generate a final effect. Meanwhile, the transformation of the air quality pollution process in different dimensions is realized by subdividing the cubic model. The method can realize three-dimensional display and interaction of air quality mode forecast data at a browser end, can realize subdivision in the horizontal direction and the vertical direction of a geographic space, has a simple calculation method, and can be quickly realized in a WebGL program.
Drawings
In the figure, the position of the upper end of the main shaft,
fig. 1 is a flowchart of an interactive method of air quality model forecast data according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of air quality mode grid data provided by one example of the present invention;
FIG. 3 is a schematic diagram of a ray intersecting a cube according to one example of the invention;
FIG. 4 is a graphical illustration of color RGB values and contaminant concentration value correspondence provided by an example of the present invention;
fig. 5 is a three-dimensional display effect diagram of air quality model forecast data according to an example of the present invention;
fig. 6 is a three-dimensional display effect diagram of the air quality model forecast data shown in fig. 5, which is provided by an example of the present invention, after being split in the coordinate axis direction;
fig. 7 is a three-dimensional display effect graph of the air quality prediction data shown in fig. 5, which is provided according to an example of the present invention, after threshold filtering;
fig. 8 is a schematic structural diagram of an interactive device for air quality pattern forecast data according to another embodiment of the present invention;
fig. 9 is a block diagram of a computing device for interactive processing of air quality pattern forecast data, according to an embodiment of the present invention.
Detailed Description
The following explains and explains the interaction method and device for air quality model forecast data in detail in conjunction with the drawings of the specification.
Fig. 1 is a flowchart of an interaction method of air quality model forecast data according to an embodiment of the present invention.
As shown in fig. 1, in step S110, three-dimensional texture data is generated from the air quality model prediction data, where the three-dimensional texture data includes a geographical range and air quality data corresponding to each geographical area included in the geographical range. Wherein the geographical area may be a grid.
As an alternative embodiment, the air quality pattern prediction data is a plurality of mesh data, and the size of each mesh is the same and the number of meshes per layer is the same. And calculating the vertex coordinates of the geographic range according to the total row number, the total column number and the total layer number of the grids, the size and the layer height of the grids, and the row number and the column number corresponding to the coordinates of the central point and the coordinates of the central point in the air quality mode forecast data. Specifically, the calculation mode geographical range may be calculated according to the total row number and the total column number (m × n) of the air quality mode forecast data grid, the grid size (dx × dy), the center point coordinates (x, y), and the row column number (i, j) of the grid point corresponding to the center point coordinates, the total number of layers (l), and the layer height (dz), and may be calculated by the following formula:
xmin＝x-dx×(j-1)
ymin＝y-dy×(i-1)
xmax＝x+dx×(n-j)
ymax＝y+dy×(m-i)
zmin＝0
zmax＝dz×l
wherein x isminForecasting minimum value, y, of data grid geographical range x-direction for air quality modelminForecasting the minimum value, z, of the data grid geographical range y-direction for an air quality modelminThe minimum value of the air quality mode forecast data grid geographic range in the z direction is set to be 0, xmaxForecasting maximum value, y, of data grid geographical range x direction for air quality modelmaxForecasting the maximum value of the data grid geographical range y-direction, z, for the air quality modelmaxAnd forecasting the maximum value of the data grid geographical range in the z direction for the air quality mode.
In the example shown in fig. 2, in the air quality pattern prediction data, the total number of rows m in the grid is 5, the total number of columns n in the grid is 5, the total number of layers l is 3, and the row number and the column number corresponding to the center point coordinate (x, y) are (i is 3, and j is 3).
And respectively processing the original air quality data corresponding to each geographic area into corresponding integers between 0 and 255 through a down-rounding function to serve as the air quality data stored in the texture. As a more specific example, since the three-dimensional texture stores a binary number of 8 bits (bit), in order to generate the three-dimensional texture, the air quality pattern prediction data is processed, and the prediction results of all grid points are processed into integers between 0 and 255. Then, generating WebGL three-dimensional texture data with the size of m multiplied by n multiplied by l according to the total line number m, the total column number n and the total layer number l of the air quality mode grid, wherein the geographic range of the three-dimensional texture is consistent with the mode geographic range, and the grid point prediction result is converted into a texture storage value by adopting the following formula:
wherein V is the converted stored value, V is the forecast result of the air quality mode at the grid point, V is the forecast of the air quality mode at the grid pointminAnd vmaxForecasting maximum and minimum values of results for pre-specified air quality modes (toPM2.5 for example, v may be specifiedmin＝0，vmax500), Floor is a Floor rounding function.
In step S120, a geographical range cube is established on the map.
As a specific example, a cube vertex and drawing style are constructed, because the WebGL program draws faces through triangles, the 6 faces of the cube to be drawn are divided into 12 triangles through diagonals, and then pass through 8 vertices P1 (x) of the cubemin、ymin、zmin)、P2(xmin、ymax、zmin)、P3(xmax、ymax、zmin)、P4(xmax、ymin、zmin)、P5(xmin、ymin、zmax)、P6(xmin、ymax、zmax)、P7(xmax、ymax、zmax)、P8(xmax、ymin、zmax) The drawing order of each triangle on the map is determined, wherein the 4 vertexes of the bottom surface of the cube are P1, P2, P3 and P4 in sequence, and the 4 vertexes of the top surface of the cube are P5, P6, P7 and P8 in sequence. The first triangle is composed of P1, P2 and P3, the second triangle is composed of P1, P3 and P4, the third triangle is composed of P5, P6 and P7, the fourth triangle is composed of P5, P7 and P8, the fifth triangle is composed of P2, P6 and P7, the sixth triangle is composed of P2, P7 and P3, the seventh triangle is composed of P3, P7 and P8, the eighth triangle is composed of P3, P8 and P4, the ninth triangle is composed of P4, P8 and P5, the thirteenth angle is composed of P4, P5 and P1, the eleventh triangle is composed of P1, P5 and P6, and the twelfth triangle is composed of P1, P6 and P2.
The geographic position of the three-dimensional texture is the same as that of the cube, and the mapping relation between the three-dimensional texture and each vertex of the cube is established.
In step S130, a ray is created from the viewpoint to each point on each face of the cube visible from the viewpoint, and a plurality of sample points are generated on a portion of the ray within the cube.
As an alternative embodiment, a plurality of sampling points are generated at preset intervals on the portion of the ray within the cube.
As a more specific example, the intersection of a ray with a cube is determined, and the ray is created in three-dimensional space with each point on the face of the cube by looking at the point, where the ray equation is as follows:
f(t)＝P0+t×d
wherein, P0Is the starting point of the ray, i.e. the observation point, d is the direction vector of the ray, and t is a rational number.
The plane equation for the 6 faces of the cube is as follows:
wherein D is the distance from the origin to the surface of the cube, n is the normal vector of the plane of the cube, and p is the point on the surface of the cube.
When the ray intersects with the plane of the cube, the point f (t) is equal to the point f (p), the normal vector of the 6 faces of the cube is a unit vector, and the intersection equation of the ray and the face of the cube is obtained after the formula simplification as follows:
as shown in FIG. 3, each face of the cube visible from the viewpoint is 3 faces of the cube, there are two intersections when the ray intersects the cube face, and two different values t are solved1And tkIntroducing a ray equation, and calculating to obtain two intersection points PT1And PTK。
On line segment PT1(x1，y1，z1) And PTk(xk，yk，zk) Sampling, setting fixed sampling times k, and calculating a sampling interval delta t, wherein the formula is as follows:
according to sampling intervalΔ t and PT1、PTkAnd all the sampling point sets PT are obtained by calculation,
PT＝{PT1,PT2,…,PTk}
wherein k is the sampling times and is a positive integer. k may be an integer, for example, between 50-100.
In step S140, the geographical block to which each sampling point belongs and the corresponding air quality data are determined according to the three-dimensional texture data, and the air quality data of each sampling point is converted into the corresponding color attribute value according to the corresponding relationship between the color attribute value and the air quality data. Wherein the color attribute value may comprise a color transparency value.
As an optional implementation manner, the air quality data stored in the texture corresponding to the geographic area to which each sampling point position belongs is converted into the original air quality data. The air quality data may include, among other things, a pollutant concentration value, such as PM2.5The concentration of (2) can be in units of μ g/m3. As a more specific example, the pollutant concentration values of the sampling points are calculated, and the texture storage values v of the corresponding points are extracted from the three-dimensional texture by using three-dimensional linear interpolation according to the coordinates of the sampling points in the set PTkTexture storage value vkIs an integer between 0-255, and converting the stored value into a contaminant concentration value VkThe following formula may be adopted:
wherein, VkTo predict contaminant concentration values, vminAnd vmaxForecasting the maximum value and the minimum value of the result for the pre-specified air quality mode.
Specifically, as shown in fig. 4, rendering color RGB values corresponding to all sampling points may be extracted according to a corresponding relationship between a pollutant concentration value and the color RGB values, and texture storage values v corresponding to the sampling points may also be storedkAs a transparency value a of the rendering color, thereby converting the air quality data of each sampling point position into a corresponding colorAn attribute value. In this example, the color attribute values include a color transparency value and a color RGB value.
In step S150, for each ray, the color attribute values of each sampling point on the same ray are accumulated to obtain the color attribute value at the intersection of each face of the cube visible to the observation point and the ray, thereby generating a three-dimensional display of the air quality pattern forecast data.
As a specific example, before adding up the color attribute values of all the sampling points on the same ray, the WebGL program may convert the color transparency value and the color RGB value into a decimal number between 0 and 1, such as 0 into 0 and 255 into 1, respectively, so as to achieve the normalization of the color transparency value and the color RGB value of all the sampling points on the same ray. The color RGB values and the color transparency values of all sampling points on the same ray can be accumulated according to the following formula in sequence to obtain the final rendering color.
color.rbgi＝(1-color.ai-1)×ai×rgbi+color.rbgi-1
color.ai＝(1-color.ai-1)×ai+color.ai-1
Wherein, rgbiRepresenting the color RGB value of the ith sample point on the ray, aiColor rgb, representing the color transparency value of the ith sample point on the rayiRGB accumulated value of color representing the first i sample points on the ray, colori-1Color RGB accumulated value, color.a, representing the first i-1 sample points on the rayiColor. a representing the color transparency accumulation of the first i points on the rayi-1Representing the accumulated value of the color transparency of the first i-1 points on the ray; wherein i is a positive integer from 1 to the number of samples k, color0And color0Are all zero.
Fig. 5 is a three-dimensional display effect diagram of air quality model forecast data according to an example of the present invention.
In step S160, after the cube is subdivided, the air quality model prediction data at the section is displayed. As an alternative embodiment, the presentation of the air quality pattern forecast data at the section in the coordinate axis direction may be realized by controlling the threshold range of the point in the cube in one coordinate axis direction. Specifically, the threshold range of the point in the cube in the x-axis direction may be controlled during rendering to realize the profile of the air quality data in the x-axis direction, for example, x in the coordinates of the point is set to be not less than the x-axis lower threshold and/or not more than the x-axis upper threshold, and when the x coordinate of the point is less than the x-axis lower threshold and/or more than the x-axis upper threshold, the point is eliminated in rendering. The same can be achieved in the y-axis direction and the z-axis direction. Fig. 6 is a three-dimensional display effect diagram of the air quality model forecast data shown in fig. 5, which is provided by an example of the present invention, and is split in the coordinate axis direction.
As an optional implementation manner, in the color attribute value accumulation process in step S150, the method for interacting air quality mode forecast data of this embodiment further includes the following steps: and screening the air quality data corresponding to the texture storage corresponding to the sampling points, and filtering out the sampling points which are larger than the upper threshold value and/or smaller than the lower threshold value in the air quality data stored in the texture storage. As a more specific example, in the color attribute value accumulation process in step S150, the texture storage value v corresponding to the sampling point is screenedkSet v tokThe value is not less than the lower air quality threshold and/or not more than the upper air quality threshold when vkLess than lower air mass threshold or vkAnd when the air quality is larger than the upper limit threshold of the air quality, the point is eliminated in the rendering process without color accumulation, and the filtering of the pollutant concentration threshold is realized. Fig. 7 is a three-dimensional display effect diagram of the air quality prediction data shown in fig. 5, which is provided by an example of the present invention and is filtered by a threshold.
Fig. 8 is a schematic structural diagram of an interactive device for air quality model forecast data according to another embodiment of the present invention. As shown in fig. 8, the interaction apparatus 800 for air quality model forecast data according to this embodiment includes a three-dimensional texture data generation unit 810, a cube creation unit 820, a ray sampling unit 830, a color attribute value conversion unit 840, a color attribute value accumulation unit 850, and a subdivision unit 860.
The three-dimensional texture data generating unit 810 is configured to generate three-dimensional texture data from the air quality model prediction data, where the three-dimensional texture data includes a geographic range and air quality data corresponding to each geographic region included in the geographic range. The operation of the three-dimensional texture data generating unit 810 may refer to the operation of step S110 described above with reference to fig. 1.
The cube building unit 820 is used for building a geographic range cube on a map. The operation of the cube creation unit 820 may refer to the operation of step S120 described above with reference to fig. 1.
The ray sampling unit 830 is configured to create rays from the viewpoint and from points on each face of the cube visible to the viewpoint, and generate a plurality of sample points on a portion of the rays within the cube. The operation of the ray sampling unit 830 may refer to the operation of step S130 described above with reference to fig. 1.
The color attribute value conversion unit 840 is configured to determine a geographic area to which each sampling point belongs and corresponding air quality data according to the three-dimensional texture data, and convert the air quality data of each sampling point into a corresponding color attribute value according to a correspondence between the color attribute value and the air quality data. The operation of the color attribute value conversion unit 840 may refer to the operation of step S140 described above with reference to fig. 1.
The color attribute value accumulation unit 850 is configured to accumulate the color attribute values of the sampling points on the same ray respectively for each ray to obtain the color attribute values at the intersection points of the rays and each face of the cube visible to the observation point, so as to generate a three-dimensional display of the air quality mode forecast data. The operation of the color attribute value accumulation unit 850 may refer to the operation of step S150 described above with reference to fig. 1.
The subdivision unit 860 is configured to display air quality model prediction data at a section after subdividing the cube. The operation of the dividing unit 860 may refer to the operation of step S160 described above with reference to fig. 1.
According to the interaction method and device for the air quality mode forecast data, the air quality mode grid data are generated into three-dimensional texture data in advance, a cube model is built on a map according to three-dimensional texture data coordinates, sampling points in a cube are generated at fixed intervals by adopting a ray method, sampling point air quality data are extracted from the three-dimensional texture data, an air quality concentration value is converted into corresponding color values according to a color legend, and finally color and transparency accumulation is carried out on all the color values to generate a final effect. Meanwhile, through subdivision on the cubic model and pollutant threshold filtering, the transformation of the air quality pollution process in different dimensions is realized. The method has the advantages that three-dimensional display and interaction of air quality mode forecast data can be achieved at a browser end, subdivision in the horizontal direction and the vertical direction of a geographic space can be achieved, filtering on pollutant threshold values can be achieved, the calculation method is simple, and the method can be quickly achieved in a WebGL program.
Fig. 9 is a block diagram of a computing device for interactive processing of air quality pattern forecast data, according to an embodiment of the present invention.
As shown in fig. 9, the computing device 900 may include at least one processor 910, a memory 920, a memory 930, a communication interface 940, and an internal bus 950, and the at least one processor 910, the memory 920, the memory 930, and the communication interface 940 are connected together via the bus 950. The at least one processor 910 executes at least one computer-readable instruction (i.e., an element described above as being implemented in software) stored or encoded in a computer-readable storage medium (i.e., memory 920).
In one embodiment, stored in the memory 920 are computer-executable instructions that, when executed, cause the at least one processor 910 to perform: generating three-dimensional texture data by the air quality mode forecast data, wherein the three-dimensional texture data comprises a geographical range and air quality data corresponding to each geographical block included in the geographical range; establishing a cube of the geographic region on a map; establishing rays with the observation point as a starting point and each point on each face of the cube visible by the observation point, and generating a plurality of sampling points on the part of the rays in the cube; determining a geographical block to which each sampling point position belongs and corresponding air quality data according to the three-dimensional texture data, and converting the air quality data of each sampling point position into corresponding color attribute values according to the corresponding relation between the color attribute values and the air quality data; for each ray, accumulating the color attribute values of each sampling point on the same ray to obtain the color attribute values of the intersection points of each face of the cube visible to the observation point and the ray, so as to generate three-dimensional display of air quality mode forecast data; and after the cube is subdivided, displaying air quality mode forecast data at the section.
It should be appreciated that the computer-executable instructions stored in the memory 920, when executed, cause the at least one processor 910 to perform the various operations and functions described above in connection with fig. 1-8 in the various embodiments of the present invention.
In the present disclosure, computing device 900 may include, but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, Personal Digital Assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronics, and so forth.
According to one embodiment, a program product, such as a non-transitory machine-readable medium, is provided. A non-transitory machine-readable medium may have instructions (i.e., elements described above as being implemented in software) that, when executed by a machine, cause the machine to perform various operations and functions described above in connection with fig. 1-8 in various embodiments of the disclosure.
Specifically, a system or apparatus may be provided which is provided with a readable storage medium on which software program code implementing the functions of any of the above embodiments is stored, and causes a computer or processor of the system or apparatus to read out and execute instructions stored in the readable storage medium.
In this case, the program code itself read from the readable medium can realize the functions of any of the above-described embodiments, and thus the machine-readable code and the readable storage medium storing the machine-readable code form part of the present invention.
Examples of the readable storage medium include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or from the cloud via a communications network.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the claims, and all equivalent structures or equivalent processes that are transformed by the content of the specification and the drawings, or directly or indirectly applied to other related technical fields are included in the scope of the claims.
Claims (10)
1. An interactive method for air quality model forecast data is characterized by comprising the following steps:
generating three-dimensional texture data by the air quality mode forecast data, wherein the three-dimensional texture data comprises a geographical range and air quality data corresponding to each geographical block included in the geographical range;
establishing a cube of the geographic region on a map;
establishing rays with each point on each face of the cube visible from the observation point, and generating a plurality of sampling points on the part of the rays in the cube;
determining the geographic block to which each sampling point belongs and corresponding air quality data according to the three-dimensional texture data, and converting the air quality data of each sampling point into corresponding color attribute values according to the corresponding relation between the color attribute values and the air quality data;
for each ray, accumulating the color attribute values of each sampling point on the same ray to obtain the color attribute values of the intersection points of each face of the cube visible to the observation point and the ray, so as to generate three-dimensional display of air quality mode forecast data;
and after the cube is subdivided, displaying air quality mode forecast data at the section.
2. The method for interacting air quality model forecast data of claim 1, wherein the subdivision of the cube comprises:
and displaying the air quality mode forecast data on the section of the coordinate axis direction by controlling the threshold range of the point in the cube in the coordinate axis direction.
3. The method of claim 1, wherein generating three-dimensional texture data from the air quality model forecast data comprises:
and respectively processing the original air quality data corresponding to each geographic area into corresponding integers between 0 and 255 through a down-rounding function to serve as the air quality data stored in the texture.
4. The method for interacting the air quality model forecast data according to claim 3, wherein the step of determining the geographic area to which each sampling point belongs and the corresponding air quality data according to the three-dimensional texture data comprises:
and converting the air quality data stored by the texture corresponding to the geographic area to which each sampling point belongs into the original air quality data.
5. The method of claim 3 or 4, wherein in the process of accumulating the color attribute values, the method further comprises:
and screening the air quality data corresponding to the sampling points and stored in the texture, and filtering the sampling points which are larger than the air quality upper threshold and/or smaller than the air quality lower threshold in the air quality data stored in the texture.
6. The method of interacting air quality model forecast data of claim 1 or 4, characterized in that said air quality data comprises pollutant concentration values.
7. The method of interacting air quality pattern forecast data of claim 1, wherein said color attribute values comprise color transparency values.
8. An interactive device for air quality model forecast data, comprising:
the three-dimensional texture data generating unit is used for generating three-dimensional texture data from the air quality mode forecast data, and the three-dimensional texture data comprises a geographical range and air quality data corresponding to each geographical block included in the geographical range;
the cube establishing unit is used for establishing a cube of the geographic range on a map;
the ray sampling unit is used for establishing rays with each point on each face of the cube visible from the observation point and generating a plurality of sampling points on the part of the rays in the cube;
the color attribute value conversion unit is used for determining the geographic block to which each sampling point belongs and corresponding air quality data according to the three-dimensional texture data, and converting the air quality data of each sampling point into corresponding color attribute values according to the corresponding relation between the color attribute values and the air quality data;
the color attribute value accumulation unit is used for accumulating the color attribute values of all sampling points on the same ray respectively aiming at all rays to obtain the color attribute values of the intersection points of all surfaces of the cube visible by the observation point and the rays, so that the three-dimensional display of the air quality mode forecast data is generated;
and the subdivision unit is used for displaying the air quality mode forecast data at the section after subdividing the cube.
9. A computing device, comprising:
one or more processors, and
a memory coupled with the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
10. A machine-readable storage medium having stored thereon executable instructions that, when executed, cause the machine to perform the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011513389.1A CN112730743A (en) | 2020-12-17 | 2020-12-17 | Interaction method and device for air quality mode forecast data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011513389.1A CN112730743A (en) | 2020-12-17 | 2020-12-17 | Interaction method and device for air quality mode forecast data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112730743A true CN112730743A (en) | 2021-04-30 |
Family
ID=75603554
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011513389.1A Pending CN112730743A (en) | 2020-12-17 | 2020-12-17 | Interaction method and device for air quality mode forecast data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112730743A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114266862A (en) * | 2021-12-28 | 2022-04-01 | 中科三清科技有限公司 | Air quality three-dimensional distribution image generation method, terminal and data processing server |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008567A (en) * | 2014-03-10 | 2014-08-27 | 昆山数字城市信息技术有限公司 | City haze weather visualization drawing method based on GPU |
| US20140375678A1 (en) * | 2013-06-25 | 2014-12-25 | Iteris, Inc. | Data overlay for animated map weather display and method of rapidly loading animated raster data |
| KR101517827B1 (en) * | 2014-05-20 | 2015-05-18 | 주식회사 첨단공간정보 | Digital map drawing system for plotting space image of geographic feature using an reference point |
| US9311397B2 (en) * | 2004-08-31 | 2016-04-12 | Visual Real Estates, Inc. | Method and apparatus of providing street view data of a real estate property |
| CN106874349A (en) * | 2016-12-26 | 2017-06-20 | 深圳市位和科技有限责任公司 | Multidimensional data analysis method and system based on interactive visual |
| CN106898045A (en) * | 2017-02-24 | 2017-06-27 | 郑州大学 | A kind of very three-dimensional geography scene adaptive construction method in big region based on SGOG tile fragments |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
| CN207502498U (en) * | 2017-12-01 | 2018-06-15 | 浙江海洋大学 | A kind of air quality detecting device based on unmanned aerial vehicle platform |
| CN109410313A (en) * | 2018-02-28 | 2019-03-01 | 南京恩瑞特实业有限公司 | A kind of meteorology three-dimensional information 3D simulation inversion method |
| CN110120083A (en) * | 2019-05-17 | 2019-08-13 | 中科三清科技有限公司 | Water quality forecast is distributed drawing drawing method, device and electronic equipment |
| CN111683388A (en) * | 2020-08-13 | 2020-09-18 | 中国人民解放军国防科技大学 | Near-field radiation attenuation test method and three-dimensional display system |
| CN112000683A (en) * | 2020-08-25 | 2020-11-27 | 中科三清科技有限公司 | Data processing method, device and equipment |
-
2020
- 2020-12-17 CN CN202011513389.1A patent/CN112730743A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9311397B2 (en) * | 2004-08-31 | 2016-04-12 | Visual Real Estates, Inc. | Method and apparatus of providing street view data of a real estate property |
| US20140375678A1 (en) * | 2013-06-25 | 2014-12-25 | Iteris, Inc. | Data overlay for animated map weather display and method of rapidly loading animated raster data |
| CN104008567A (en) * | 2014-03-10 | 2014-08-27 | 昆山数字城市信息技术有限公司 | City haze weather visualization drawing method based on GPU |
| KR101517827B1 (en) * | 2014-05-20 | 2015-05-18 | 주식회사 첨단공간정보 | Digital map drawing system for plotting space image of geographic feature using an reference point |
| CN106874349B (en) * | 2016-12-26 | 2018-04-10 | 深圳市位和科技有限责任公司 | Multidimensional data analysis method and system based on interactive visual |
| CN106874349A (en) * | 2016-12-26 | 2017-06-20 | 深圳市位和科技有限责任公司 | Multidimensional data analysis method and system based on interactive visual |
| CN106898045A (en) * | 2017-02-24 | 2017-06-27 | 郑州大学 | A kind of very three-dimensional geography scene adaptive construction method in big region based on SGOG tile fragments |
| CN107340365A (en) * | 2017-06-19 | 2017-11-10 | 中国科学院南京地理与湖泊研究所 | A kind of three-dimensional monitoring and data digging system and method towards lake blue algae disaster |
| CN207502498U (en) * | 2017-12-01 | 2018-06-15 | 浙江海洋大学 | A kind of air quality detecting device based on unmanned aerial vehicle platform |
| CN109410313A (en) * | 2018-02-28 | 2019-03-01 | 南京恩瑞特实业有限公司 | A kind of meteorology three-dimensional information 3D simulation inversion method |
| CN110120083A (en) * | 2019-05-17 | 2019-08-13 | 中科三清科技有限公司 | Water quality forecast is distributed drawing drawing method, device and electronic equipment |
| CN111683388A (en) * | 2020-08-13 | 2020-09-18 | 中国人民解放军国防科技大学 | Near-field radiation attenuation test method and three-dimensional display system |
| CN112000683A (en) * | 2020-08-25 | 2020-11-27 | 中科三清科技有限公司 | Data processing method, device and equipment |
Non-Patent Citations (1)
| Title |
|---|
| 朱帅: "虚拟地球技术在三维空气质量数据可视化中的应用尝试", 《2017中国环境科学学会科学与技术年会》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114266862A (en) * | 2021-12-28 | 2022-04-01 | 中科三清科技有限公司 | Air quality three-dimensional distribution image generation method, terminal and data processing server |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101116111B (en) | 2d/3d line rendering using 3d rasterization algorithms | |
| CN114283231A (en) | LOD-based BIM model lightweight construction and display method | |
| CN108717729A (en) | A kind of online method for visualizing of landform multi-scale TIN of the Virtual earth | |
| KR100959349B1 (en) | A method for accelerating terrain rendering based on quadtree using graphics processing unit | |
| CN112597260A (en) | Visualization method and device for air quality mode forecast data | |
| KR101591427B1 (en) | Method for Adaptive LOD Rendering in 3-D Terrain Visualization System | |
| CN113724401A (en) | Three-dimensional model cutting method and device, computer equipment and storage medium | |
| KR101552827B1 (en) | Method Of Dividing Three-dimensional Object Model | |
| CN116402966A (en) | Three-dimensional terrain visual simulation modeling method | |
| CN112730743A (en) | Interaction method and device for air quality mode forecast data | |
| CN111583268B (en) | Point cloud virtual selection and cutting method, device and equipment | |
| US10347034B2 (en) | Out-of-core point rendering with dynamic shapes | |
| Masood et al. | High-performance GPU based planetary-scale terrain visualization | |
| CN102364522B (en) | Large-scale weather volume data drawing method based on global division | |
| CN114048279B (en) | Method and device for generating forecast information | |
| CN115953541A (en) | Quad-tree LOD terrain generation method, device, equipment and storage medium | |
| CN112843700B (en) | Terrain image generation method and device, computer equipment and storage medium | |
| CN115186347A (en) | Building CityGML modeling method combining house type plan and inclined model | |
| Scholz et al. | Level of Detail for Real-Time Volumetric Terrain Rendering. | |
| Lee et al. | Bimodal vertex splitting: Acceleration of quadtree triangulation for terrain rendering | |
| Masood et al. | A novel method for adaptive terrain rendering using memory-efficient tessellation codes for virtual globes | |
| CN112598791A (en) | Method and device for displaying air quality data in forecast consultation PPT | |
| shiyun | Level of detail optimization for real-time terrain | |
| CN115774896B (en) | Data simulation method, device, equipment and storage medium | |
| CN115828110B (en) | Water system space feature similarity detection method, device, storage medium and apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210430 |
|
| RJ01 | Rejection of invention patent application after publication |