CN112598800A - Air quality mode forecast data acquisition method and device based on terrain - Google Patents
Air quality mode forecast data acquisition method and device based on terrain Download PDFInfo
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Abstract
The invention discloses a method and a device for acquiring air quality mode forecast data based on terrain, which are used for constructing a three-dimensional space grid corresponding to a specified air quality mode forecast area; acquiring a designated grid in a three-dimensional space grid; acquiring a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified grid is between the layer heights of the first pattern prediction grid and the second pattern prediction grid, and the layer height of the first pattern prediction grid is higher than the layer height of the second pattern prediction grid; acquiring first pollutant concentration data of a first mode prediction grid and second pollutant concentration data of a second mode prediction grid; and acquiring specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data. The method and the device solve the problem that the pollutant concentration can not be accurately expressed in the height direction based on the terrain because the air quality forecast data is lacked to a certain extent in the height direction in the prior art.
Description
Technical Field
The invention relates to the technical field of environmental protection information management, in particular to a method and a device for acquiring air quality mode forecast data based on terrain.
Background
In recent years, with the attention of people to the environmental protection problem, more and more people begin to concern the surrounding environmental problem, and the environmental air quality forecast becomes the information concerned by people like the weather forecast.
Terrain visualization is an intuitive graphical representation of terrain and is a basic tool for people to understand and appreciate terrain. The current air quality forecast data is usually displayed on a two-dimensional map, that is, conventional terrain visualization technologies (such as contour topographic maps, section maps, scenic drawings and the like) are based on two dimensions, and the pollutant concentration forecast data is displayed in a point and surface form. The two-dimensional display mode has the following problems:
1. the three-dimensional distribution characteristics of pollutant concentration forecast cannot be embodied:
in reality, a geographic space is a three-dimensional space, and when pollutant concentration change is expressed in a two-dimensional map, information of one dimension is lost (generally in the height direction), so that pollutant concentration distribution cannot be comprehensively embodied. However, the atmospheric pollution process occurs in a three-dimensional space, so that the understanding of the three-dimensional overall characteristics of the pollutant concentration distribution is also greatly helpful for improving the quality of the ambient air.
2. Lack of topographic aspect:
in reality, the ground is rugged, different regions have different terrains, correspondingly, different climatic environments exist, and the influence on the air quality is different. However, the pollutant concentration change in the two-dimensional map ignores the factors of the terrain, and the change between the terrain and the pollutant concentration cannot be reflected.
Aiming at the problem that the pollutant concentration cannot be accurately expressed in the height direction based on the terrain due to the fact that the existing air quality prediction data is lacked to a certain extent in the height direction in the prior art, an effective solution is not provided.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for acquiring air quality model prediction data based on a terrain, so as to solve a problem in the prior art that the pollutant concentration cannot be accurately expressed in a height direction based on the terrain due to a certain degree of missing of the air quality prediction data in the height direction.
In a first aspect of the present invention, a method for acquiring air quality model forecast data based on terrain is provided, including:
constructing a three-dimensional space grid corresponding to a specified air quality mode forecasting region;
acquiring a designated grid in the three-dimensional space grid;
acquiring a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, the layer height of the first pattern prediction mesh being higher than the layer height of the second pattern prediction mesh;
obtaining first pollutant concentration data of the first pattern prediction grid and second pollutant concentration data of the second pattern prediction grid;
and acquiring specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
Optionally, obtaining specified pollutant concentration data for the specified grid from the first pollutant concentration data and the second pollutant concentration data comprises:
obtaining specified pollutant concentration data for the specified grid by the following formula:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
wherein Mvalue represents specified pollutant concentration data of the specified grid, Tvalue represents the first pollutant concentration data, QT represents a concentration contribution factor of the first pattern prediction grid, Bvalue represents the second pollutant concentration data, QB represents a concentration contribution factor of the second pattern prediction grid, dataHgt represents a height of the specified grid, TopHgt represents a height of the first pattern prediction grid, and BottomHgt represents a height of the second pattern prediction grid.
Optionally, after obtaining the specified pollutant concentration data for the specified grid from the first pollutant concentration data and the second pollutant concentration data, the method further comprises:
acquiring a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid;
acquiring colors corresponding to the pollutant concentration data of each grid according to the color indexes;
displaying pollutant forecast data through the three-dimensional space grids and the colors corresponding to the grids;
wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend.
Optionally, obtaining first pollutant concentration data of the first modal prediction grid and second pollutant concentration data of the second modal prediction grid comprises:
acquiring the first pollutant concentration data and the second pollutant concentration data through a nested grid air quality prediction mode system.
In a second aspect of the present invention, there is provided a terrain-based air quality model forecast data acquisition apparatus, comprising:
the construction module is used for constructing a three-dimensional space grid corresponding to the specified air quality mode prediction area;
the first acquisition module is used for acquiring a specified grid in the three-dimensional space grid;
a second obtaining module, configured to obtain a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, the layer height of the first pattern prediction mesh being higher than the layer height of the second pattern prediction mesh;
a third obtaining module, configured to obtain first pollutant concentration data of the first pattern prediction grid and second pollutant concentration data of the second pattern prediction grid;
and the fourth acquisition module is used for acquiring the specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
Optionally, the fourth obtaining module is further configured to obtain the specified pollutant concentration data of the specified grid by the following formula:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
wherein Mvalue represents specified pollutant concentration data of the specified grid, Tvalue represents the first pollutant concentration data, QT represents a concentration contribution factor of the first pattern prediction grid, Bvalue represents the second pollutant concentration data, QB represents a concentration contribution factor of the second pattern prediction grid, dataHgt represents a height of the specified grid, TopHgt represents a height of the first pattern prediction grid, and BottomHgt represents a height of the second pattern prediction grid.
Optionally, the apparatus further comprises:
a fifth obtaining module, configured to obtain a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid;
a sixth obtaining module, configured to obtain, according to the color index, a color corresponding to the pollutant concentration data of each grid;
the display module is used for displaying pollutant forecast data through the three-dimensional space grids and the colors corresponding to the grids;
wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend.
Optionally, the third obtaining module is further configured to obtain the first pollutant concentration data and the second pollutant concentration data through a nested grid air quality prediction mode system.
In a third aspect of the present invention, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of obtaining terrain-based air quality model forecast data according to any of the first aspects above.
In a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method for acquiring terrain-based air quality model forecast data according to any of the first aspects above.
The technical scheme of the embodiment of the invention has the following advantages:
the embodiment of the invention provides a method and a device for acquiring air quality mode forecast data based on terrain, wherein the method comprises the following steps: constructing a three-dimensional space grid corresponding to a specified air quality mode forecasting region; acquiring a designated grid in a three-dimensional space grid; acquiring a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified grid is between the layer heights of the first pattern prediction grid and the second pattern prediction grid, and the layer height of the first pattern prediction grid is higher than the layer height of the second pattern prediction grid; acquiring first pollutant concentration data of a first mode prediction grid and second pollutant concentration data of a second mode prediction grid; and acquiring specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data. The problem of among the prior art air quality forecast data lack to a certain extent in the direction of height, lead to can not accurately express pollutant concentration in the direction of height based on topography is solved to the factor that has realized combining the topography embodies pollutant concentration distribution comprehensively from the three-dimensional angle, and then can be more accurate, the distribution in the geographic space of audio-visual show pollutant data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a method of terrain-based air quality model forecast data acquisition in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a three-dimensional mesh model;
FIG. 3 is a schematic diagram of mapping remote sensing image data to a three-dimensional mesh model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a grid of a digital elevation model for a pattern prediction area to be visualized in accordance with an embodiment of the present invention;
FIG. 5 is another schematic diagram of a digital elevation model grid for a pattern prediction area to be visualized in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a three-dimensional structure of pattern prediction concentration data according to an embodiment of the present invention;
FIG. 7 is a schematic three-dimensional representation of terrain elevation data according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a spatial interpolation structure for elevation data according to an embodiment of the present invention;
FIG. 9 is a PM2.5 rendering illustration according to an embodiment of the invention;
fig. 10 is a block diagram of a configuration of a terrain-based air quality pattern prediction data acquisition apparatus according to an embodiment of the present invention;
fig. 11 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be considered as limiting the present application. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not set forth in detail in order to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
According to an embodiment of the present invention, there is provided an embodiment of a terrain-based air quality model forecast data acquisition method, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the present embodiment, a method for acquiring air quality model forecast data based on terrain is provided, which can be used in an environmental information processing system, and fig. 1 is a flowchart of the method for acquiring air quality model forecast data based on terrain according to the embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
step S101, a three-dimensional space grid corresponding to the specified air quality mode forecasting area is constructed. Specifically, a three-dimensional space coordinate system corresponding to a specified air quality mode forecasting region is constructed; the three-dimensional space coordinate system takes an axis parallel to the longitude line direction as an X axis, takes an axis parallel to the latitude line direction as a Y axis, and takes an elevation axis as a Z axis. Constructing a three-dimensional space grid according to a specified air quality prediction mode system based on a three-dimensional space coordinate system; and the range of coordinate axes of the three-dimensional space grid and the size of the grid correspond to the specified air quality prediction mode system.
Step S102, obtaining a designated grid in the three-dimensional space grid. Where the forecasted geographic range is progressively reduced (e.g., D1> D2> D3), the spatial resolution of the forecast data is progressively increased. The prediction area of the mode to be visualized in the embodiment of the present invention is a prediction area D3, for example, the geographical range is: the longitude is between 109.0 degrees and 123.9808 degrees, the latitude is between 34.0 degrees and 42.964 degrees, the layer height is 1 to 12 layers, the longitude direction has 240 grids, each data grid is 0.0624 degrees, the latitude direction has 180 grids, and each data grid is 0.0498 degrees. It should be understood by those skilled in the art that the geographic range and the grid division are not limited to the embodiment, and the adjustment of the geographic range and the grid division of different areas according to actual needs is also within the protection scope of the embodiment. A schematic diagram of the constructed three-dimensional mesh model is shown in fig. 2.
Step S103, acquiring a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, and the layer height of the first pattern prediction mesh is higher than the layer height of the second pattern prediction mesh.
Step S104, acquiring first pollutant concentration data of the first mode prediction grid and second pollutant concentration data of the second mode prediction grid. In an alternative embodiment, the first pollutant concentration data and the second pollutant concentration data may be acquired by a nested grid air quality prediction mode system. Specifically, the specified Air Quality Prediction mode System may be a Nested grid Air Quality Prediction mode System (referred to as NAQPMS), the Air Quality Prediction data output by the NAQPMS mode is a Nested grid structure, and the NAQPMS is autonomously developed by the atmospheric physics research institute of the chinese academy of sciences. The model system has gone through the development of recent 20 years and is developed by integrating a series of city and region scale air quality models which are independently developed. The mode can be used for researching the air pollution problem of regional scales, researching the occurrence mechanism and the change rule of the problems of air quality and the like of urban scales, and researching the mutual influence process among different scales. The mode is an important tool for researching the interaction among pollutant discharge amount, meteorological conditions, chemical conversion and dry-wet removal, and can provide scientific pollution discharge control strategies for an environmental decision part. The air quality prediction subsystem (NAQPM) is the core of the whole model system and mainly treats the physical and chemical processes of pollutant emission, advection transportation, diffusion, dry and wet sedimentation, gas phase, liquid phase and heterogeneous reaction and the like. The spatial structure of the device is a three-dimensional Euler conveying mode, and the vertical coordinate adopts a terrain following coordinate. The horizontal structure is a multi-nested grid, a unidirectional and bidirectional nesting technology is adopted, the resolution is 3-81 km, and the vertical unequally-spaced structure is divided into 20 layers. The air quality forecast data generated by the NAQPMS is divided into 20 layers in the vertical direction along with terrain unequally, multiple nested grids are arranged in the horizontal direction, and the resolution of the grids can be correspondingly changed and adjusted according to different forecast areas. Those skilled in the art should understand that the obtaining manner is only used for illustrating the embodiment and does not constitute a limitation on the embodiment.
And step S105, acquiring specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
Through the steps, the pollutant concentration data of any height based on the terrain can be acquired, and the problem that the pollutant concentration can not be accurately expressed in the height direction based on the terrain due to the fact that air quality forecast data are lost in the height direction to a certain degree in the prior art is solved, so that the pollutant concentration distribution can be comprehensively embodied from a three-dimensional angle by combining with the factors of the terrain, and the distribution of the pollutant data in the geographic space can be displayed more accurately and visually.
In an optional embodiment, remote sensing image data of a specified air quality mode prediction area is downloaded, and the remote sensing image data is correspondingly drawn to the three-dimensional space grid. In order to achieve the effect of simulating reality, the current mode prediction region, namely the remote sensing image data of the mode prediction region to be visualized is downloaded based on the constructed three-dimensional space coordinate system and is drawn into the three-dimensional grid, so that the basic model of the regional three-dimensional grid is realized. Wherein the range and the grid size of all coordinate axes are consistent with the information of the NAQPMS mode data. The three-dimensional space coordinate system is used for simulating a real three-dimensional space, and the finally realized space grid coordinate system effect is shown in figure 3. Specifically, the remote sensing data is used as texture data and drawn to an XY plane of a three-dimensional space coordinate system to obtain a three-dimensional grid basic model, wherein the XY axis range and the XY axis grid size of the three-dimensional grid basic model are consistent with those of an XY axis range and an XY axis grid size of a specified air quality prediction mode system, terrain elevation data of the specified air quality prediction mode system are obtained, and the three-dimensional grid model is constructed according to the three-dimensional grid basic model and the terrain elevation data. The visualization of the terrain data is also called a digital elevation model, specifically, the terrain elevation data and the model data of the pattern prediction area are corresponding to each other, for example, there are 12 layers, and the attributes such as the starting point, the size, the interval and the like of the grid are consistent. One of the terrain elevation data represents an elevation of its corresponding pattern data, with a value at each grid point representing the elevation at that point. Based on the three-dimensional space grid coordinate system constructed in the above embodiment, by setting elevation information for each grid point of the XY plane grid, a digital elevation model that can reflect the actual topographic relief change can be obtained, as shown in fig. 4 and 5, the topography of the geographic area is represented by topographic elevation data of the first layer, and the topographic pollutant concentration based drawing is realized on this basis.
In order to make the relief of the terrain visually noticeable, in an alternative embodiment, after the terrain elevation data for a given air quality prediction mode system is acquired, each terrain elevation data is multiplied by the same stretch factor. The maximum change of the terrain is 9259.86 m, which is about 9.3 km (the place with the highest earth and land surface altitude, namely the mumoummar peak altitude 8848.86 m, and the place with the lowest earth and land surface altitude, namely the dead sea altitude of 411 m), however, the mode prediction area with the minimum range, such as D3, is 1381 km from west to east. Therefore, in order to visually and clearly detect the relief of the terrain, the terrain elevation data needs to be subjected to 'stretching' processing, and each elevation value is uniformly multiplied by the same stretching factor.
In the process of acquiring pollutant forecast data of each grid of the three-dimensional space grid through the designated air quality forecast mode system, in an optional embodiment, pollutant concentration data is acquired through the designated air quality forecast mode system, and spatial interpolation is performed according to the terrain elevation data by using the pollutant concentration data to acquire the pollutant forecast data of each grid. Specifically, the pollutant forecast data generated by the NAQPMS mode is concentration data representing pollutants at different heights in a layer of grid structure, as shown in fig. 6, the terrain elevation data corresponds to the grid of the mode forecast data, that is, each grid point in the mode forecast data can find corresponding elevation data in the terrain elevation data. The terrain elevation data structure of the pattern prediction area, as shown in fig. 7, is that the air quality prediction mode system has a designated pattern prediction area, and the air quality prediction mode system has terrain data, so that the pattern prediction areas also have a relationship that they are parents. And each modal forecast data can correspondingly acquire the elevation value of the point in the terrain elevation data grid.
The step S105 mentioned above involves obtaining the specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data, and in an alternative embodiment, the specified pollutant concentration data of the specified grid is obtained by the following formula:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
wherein Mvalue represents specified pollutant concentration data for the specified grid, Tvalue represents the first pollutant concentration data, and QT represents the first pollutant concentration dataA concentration contribution factor of the pattern prediction grid, Bvalue representing the second pollutant concentration data, QB representing the concentration contribution factor of the second pattern prediction grid, dahgt representing the height of the specified grid, TopHgt representing the height of the first pattern prediction grid, and BottomHgt representing the height of the second pattern prediction grid. In a specific alternative embodiment, the terrain elevation data grid of the forecast area D3 is copied as a data display layer (the data display layer can be drawn by setting density values and colors), and according to the conditions input by the user (for example, the minimum value is 1, the maximum value is 100, the number increases at intervals of 0.01, and represents a coefficient for increasing the height of the data display layer), the elevation values of all grid points are integrally increased by corresponding elevation values, so that the data display layer is raised. And (3) making a straight line parallel to the Z axis through the longitude and latitude coordinates of the data display grid points, and determining the elevation value of the intersection point of the line and each layer of terrain elevation data. And calculating a spatial linear interpolation factor according to the elevation value of the terrain, and calculating a pollutant forecast concentration value of the data display grid point. As shown in fig. 8, the M point is a grid point in the data presentation layer, the terrain elevation value of the point is dataHgt which is 1000M, the pattern prediction data T point which is higher than the M point is located at a terrain height TopHgt which is 1200M, the pattern prediction data B point which is lower than the M point is located at a terrain height BottomHgt which is 500M. It is assumed that the concentration Tvalue of PM2.5 at point T is 10. mu.g/m3The concentration Bvalue of PM2.5 at point B is 62. mu.g/m3. Firstly, calculating concentration contribution factors of the T point and the B point to M according to the height difference, wherein the farther the height distance is, the smaller the contribution is:
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
therefore, the pollutant concentration value Mvalue at point M can be calculated by the above-mentioned factors,
Mvalue＝Tvalue*QT+Bvalue*QB；
in this example, the calculation result is that Mvalue 10 × 0.714+62 × 0.286 × 24.9(μ g/m)3) (ii) a By the above method, the point can be set for all the grid points of the data display layerThe concentration value.
In an optional embodiment, after obtaining the specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data, obtaining a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid, obtaining a color corresponding to the pollutant concentration data of each grid according to the color index, and displaying pollutant forecast data through the three-dimensional space grid and the color corresponding to each grid; wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend. The color index and the color of the environment data of each grid are preset, for example, the number of 1-10 is set for 10 grids, 10 colors are put in turn, and the 6 th color is obtained each time the color is corresponding to the calculated number, for example, the number is 6. And according to the color information of each grid point, drawing the change of the whole pollutant concentration to obtain the three-dimensional pollutant concentration change display which finally follows the terrain. If the threshold value of pollutant concentration display is set, the grids of the data display layer within the threshold value range are drawn, and the grids of the data display layer not in the threshold value range are directly discarded. Specifically, for example, the concentration of the contaminant (PM2.5) in any one grid is N, and 0 in this alternative embodiment<＝N<＝350μg/m3By setting the rendering color of the concentration of the contaminant according to the present alternative embodiment, the concentration distribution of the rendered contaminant is as shown in fig. 9.
In this embodiment, a device for acquiring air quality model forecast data based on terrain is also provided, and the device is used to implement the above embodiments and preferred embodiments, which have already been described and will not be described again. As used hereinafter, the term "module" is a combination of software and/or hardware that can implement a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The present embodiment provides a terrain-based air quality pattern forecast data acquisition apparatus, as shown in fig. 10, including:
the construction module 101 is used for constructing a three-dimensional space grid corresponding to a specified air quality mode prediction area;
a first obtaining module 102, configured to obtain a specified grid in the three-dimensional space grid;
a second obtaining module 103, configured to obtain a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, and the layer height of the first pattern prediction mesh is higher than the layer height of the second pattern prediction mesh;
a third obtaining module 104, configured to obtain first pollutant concentration data of the first pattern prediction grid and second pollutant concentration data of the second pattern prediction grid;
a fourth obtaining module 105, configured to obtain specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
Optionally, the fourth obtaining module is further configured to obtain the specified pollutant concentration data of the specified grid by the following formula:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
where Mvalue represents the specified pollutant concentration data of the specified grid, Tvalue represents the first pollutant concentration data, QT represents the concentration contribution factor of the first pattern prediction grid, Bvalue represents the second pollutant concentration data, QB represents the concentration contribution factor of the second pattern prediction grid, dataHgt represents the height of the specified grid, TopHgt represents the height of the first pattern prediction grid, and BottomHgt represents the height of the second pattern prediction grid.
Optionally, the apparatus further comprises:
a fifth obtaining module, configured to obtain a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid;
a sixth obtaining module, configured to obtain, according to the color index, a color corresponding to the pollutant concentration data of each grid;
the display module is used for displaying the pollutant forecast data through the three-dimensional space grids and the colors corresponding to the grids;
wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend.
Optionally, the third obtaining module is further configured to obtain the first pollutant concentration data and the second pollutant concentration data through a nested grid air quality prediction mode system.
The terrain-based air quality model forecast data acquisition device in this embodiment is in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory executing one or more software or fixed programs, and/or other devices that may provide the above-described functionality.
Further functional descriptions of the modules are the same as those of the corresponding embodiments, and are not repeated herein.
An embodiment of the present invention further provides an electronic device, which has the device for acquiring air quality model forecast data based on terrain shown in fig. 10.
Referring to fig. 11, fig. 11 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present invention, and as shown in fig. 11, the terminal may include: at least one processor 1101, such as a CPU (Central Processing Unit), at least one communication interface 1103, memory 1104, and at least one communication bus 1102. Wherein a communication bus 1102 is used to enable connective communication between these components. The communication interface 1103 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional communication interface 1103 may also include a standard wired interface and a standard wireless interface. The Memory 1104 may be a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 1104 may optionally be at least one memory device located remotely from the processor 1101. Wherein the processor 1101 may be combined with the apparatus described in fig. 10, an application program is stored in the memory 1104, and the processor 1101 calls the program code stored in the memory 1104 for performing any one of the above-mentioned terrain-based air quality model forecast data acquisition methods.
The communication bus 1102 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1102 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
The memory 1104 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 1104 may also comprise a combination of memories of the kind described above.
The processor 1101 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 1101 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, memory 1104 is also used to store program instructions. The processor 1101 may invoke program instructions to implement a method of terrain-based air quality model forecast data acquisition as illustrated in the embodiment of fig. 1 of the present application.
Embodiments of the present invention further provide a non-transitory computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions may execute the method for acquiring air quality model forecast data based on a terrain in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A terrain-based air quality model forecast data acquisition method is characterized by comprising the following steps:
constructing a three-dimensional space grid corresponding to a specified air quality mode forecasting region;
acquiring a designated grid in the three-dimensional space grid;
acquiring a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, the layer height of the first pattern prediction mesh being higher than the layer height of the second pattern prediction mesh;
obtaining first pollutant concentration data of the first pattern prediction grid and second pollutant concentration data of the second pattern prediction grid;
and acquiring specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
2. A terrain-based air quality model forecast data acquisition method according to claim 1, characterized in that acquiring specified pollutant concentration data for the specified grid from the first pollutant concentration data and the second pollutant concentration data comprises:
obtaining specified pollutant concentration data for the specified grid by the following formula:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
wherein Mvalue represents specified pollutant concentration data of the specified grid, Tvalue represents the first pollutant concentration data, QT represents a concentration contribution factor of the first pattern prediction grid, Bvalue represents the second pollutant concentration data, QB represents a concentration contribution factor of the second pattern prediction grid, dataHgt represents a height of the specified grid, TopHgt represents a height of the first pattern prediction grid, and BottomHgt represents a height of the second pattern prediction grid.
3. A terrain-based air quality model forecast data acquisition method according to claim 1, characterized in that after acquiring specified pollutant concentration data for said specified grid from said first pollutant concentration data and said second pollutant concentration data, said method further comprises:
acquiring a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid;
acquiring colors corresponding to the pollutant concentration data of each grid according to the color indexes;
displaying pollutant forecast data through the three-dimensional space grids and the colors corresponding to the grids;
wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend.
4. A method of obtaining terrain-based air quality model prediction data according to any of claims 1-3, characterized in that obtaining first pollutant concentration data of the first model prediction grid and second pollutant concentration data of the second model prediction grid comprises:
acquiring the first pollutant concentration data and the second pollutant concentration data through a nested grid air quality prediction mode system.
5. A terrain-based air quality model forecast data acquisition device, comprising:
the construction module is used for constructing a three-dimensional space grid corresponding to the specified air quality mode prediction area;
the first acquisition module is used for acquiring a specified grid in the three-dimensional space grid;
a second obtaining module, configured to obtain a first pattern prediction grid and a second pattern prediction grid; wherein the layer height of the specified mesh is between the layer heights of the first and second pattern prediction meshes, the layer height of the first pattern prediction mesh being higher than the layer height of the second pattern prediction mesh;
a third obtaining module, configured to obtain first pollutant concentration data of the first pattern prediction grid and second pollutant concentration data of the second pattern prediction grid;
and the fourth acquisition module is used for acquiring the specified pollutant concentration data of the specified grid according to the first pollutant concentration data and the second pollutant concentration data.
6. A terrain-based air quality model forecast data acquisition apparatus according to claim 4, wherein said fourth acquisition module is further adapted to acquire specified pollutant concentration data for said specified grid by:
Mvalue＝Tvalue*QT+Bvalue*QB；
QT＝(dataHgt-BottomHgt)/(TopHgt–BottomHgt)；
QB＝(TopHgt-dataHgt)/(TopHgt–BottomHgt)；
wherein Mvalue represents specified pollutant concentration data of the specified grid, Tvalue represents the first pollutant concentration data, QT represents a concentration contribution factor of the first pattern prediction grid, Bvalue represents the second pollutant concentration data, QB represents a concentration contribution factor of the second pattern prediction grid, dataHgt represents a height of the specified grid, TopHgt represents a height of the first pattern prediction grid, and BottomHgt represents a height of the second pattern prediction grid.
7. A terrain-based air quality model forecast data pertaining to claim 6, characterized in that said device further comprises:
a fifth obtaining module, configured to obtain a color index corresponding to the pollutant concentration data of each grid in the three-dimensional space grid;
a sixth obtaining module, configured to obtain, according to the color index, a color corresponding to the pollutant concentration data of each grid;
the display module is used for displaying pollutant forecast data through the three-dimensional space grids and the colors corresponding to the grids;
wherein the color index corresponding to the contaminant concentration data for each grid is obtained by the following formula:
cIndex＝((P–min)/(max–min))*ColorNum；
where cndex represents a color index, P represents pollutant concentration data corresponding to each grid, min represents a minimum pollutant concentration, max represents a maximum pollutant concentration, and ColorNum represents the number of colors of a pollutant rendering legend.
8. A terrain-based air quality model forecast data acquisition apparatus according to any of claims 5-7, wherein said third acquisition module is further configured to acquire said first pollutant concentration data and said second pollutant concentration data via a nested grid air quality forecast model system.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of terrain-based air quality pattern forecast data according to any of claims 1-4.
10. A computer readable storage medium having stored thereon computer instructions, characterized in that the instructions, when executed by a processor, implement the method for terrain-based air quality pattern forecast data acquisition of any of claims 1-4 above.
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