CN111126710A - Atmospheric pollutant prediction method - Google Patents
Atmospheric pollutant prediction method Download PDFInfo
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Abstract
The embodiment of the invention provides an atmospheric pollutant prediction method. The method comprises the steps of obtaining atmospheric capacity and wind direction data of pollutants; performing weighted interpolation on the acquired data by adopting a double-cubic convolution method; setting a region to be predicted, and extracting data corresponding to the region to be predicted from the data after weighted interpolation; and performing fusion calculation on data corresponding to the target grid of the area to be forecasted to obtain a pollutant diffusion predicted value. In this way, the distribution condition of pollutants in the area to be forecasted can be monitored, wind power and wind direction factors are introduced, the physical displacement process of pollutant diffusion is simulated, and the movement trend of the pollutants is predicted so as to predict the future atmospheric environment condition of the area to be forecasted, so that a data basis is provided for relevant departments, intervention measures are taken in advance, and atmospheric pollution is effectively controlled and prevented.
Description
Technical Field
Embodiments of the present invention relate generally to the fields of electronics and information technology and aerospace, and more particularly, to an atmospheric pollutant prediction method.
Background
In recent years, with the progress of industrialization and urbanization, energy consumption of coal, petroleum fuel and the like is continuously increased, and the atmospheric environment is rapidly deteriorated, so that pollution events are frequent.
Among the atmospheric environmental pollutions, the regional problem is the most prominent, and the atmospheric environmental pollution conditions are different for each region because the degree of industrialization and urbanization is not the same for each region. Among the typical pollution particles causing the atmospheric environmental pollution are sulfur dioxide (SO2) and nitrogen dioxide (NO 2).
Pollutants in the atmospheric environment of one area can seriously affect the health and trip plans of people in the area, and if the pollution degree in the atmospheric environment can be known in advance for a period of time in the future, the pollution degree in the atmospheric environment can be greatly convenient for the life of local people. Therefore, in order to monitor the atmospheric environmental pollution condition of each area, monitoring equipment is set in many areas in China, and the monitoring equipment can measure the concentration of polluted particles in the atmospheric environment through different means so as to obtain the local atmospheric environmental pollution condition; the existing monitoring equipment can only monitor the concentration of pollutant particles in the current atmospheric environment in real time, and cannot predict the pollution degree in the environment for a period of time in the future so as not to effectively assist relevant departments in effectively controlling and preventing atmospheric pollution.
Disclosure of Invention
According to an embodiment of the present invention, an atmospheric pollutant prediction scheme is provided. According to the scheme, the distribution condition of pollutants in the area to be forecasted is monitored, wind power and wind direction factors are introduced, the physical displacement process of pollutant diffusion is simulated, and the movement trend of the pollutants is predicted so as to predict the future atmospheric environment condition of the area to be forecasted, so that a data basis is provided for relevant departments, intervention measures are taken in advance, and atmospheric pollution is effectively controlled and prevented.
The invention provides an atmospheric pollutant prediction method. The method comprises the following steps: acquiring atmospheric capacity and wind power and wind direction data of pollutants in a global range;
respectively performing weighted interpolation on the obtained atmospheric capacity and wind direction data of the pollutants by adopting a cubic convolution method to obtain the atmospheric capacity of the pollutants after weighted interpolation and the wind direction data after weighted interpolation;
and performing fusion calculation on the atmospheric capacity of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation to obtain a pollutant diffusion prediction value.
Further, the atmospheric capacity of the pollutants is grid data used for representing the total content of the pollutants from the ground to the top of the atmosphere in each grid area;
the wind speed data in each grid area comprises wind speed data of the grid in the longitude direction and/or wind speed data of the grid in the latitude direction.
Further, the grid region is a rectangular region formed by two adjacent longitude lines and two adjacent latitude lines, and the rectangular region contains the acquired minimum data unit.
Further, performing fusion calculation on the atmospheric capacity of the pollutant after the weighted interpolation and the wind direction data after the weighted interpolation to obtain a pollutant diffusion prediction value, and the method comprises the following steps:
setting a region to be forecasted, and extracting corresponding data in the region to be forecasted from the atmospheric capacity of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation according to longitude and latitude information included in the region to be forecasted;
and performing fusion calculation on the atmospheric capacity of the pollutants in the area to be forecasted and the wind direction data after the weighted interpolation to obtain a pollutant diffusion prediction value in the area to be forecasted.
Further, the fusion calculation of the atmospheric capacity of the pollutants in the area to be forecasted and the wind direction data after the weighted interpolation includes:
if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitude direction or wind speed data of the grid in the latitude direction, calculating a predicted value of the pollutant of the target grid in the corresponding direction of the wind speed after the preset time is diffused, and taking the predicted value as the predicted value of the pollutant of the target grid after the preset time is diffused.
Further, the fusion calculation of the atmospheric capacity of the pollutants in the area to be forecasted and the wind direction data after the weighted interpolation includes:
and if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitudinal direction and wind speed data of the grid in the latitudinal direction, calculating the predicted value of the pollutants of the target grid after the preset time passes and the pollutants of the target grid diffuse in the longitudinal direction and the latitudinal direction.
Further, if the wind speed data in the wind direction data after the weighted interpolation of the target grid includes wind speed data of the grid in the latitudinal direction, calculating a predicted value of the pollutant of the target grid in the latitudinal direction after diffusion of the pollutant for a preset time, including:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
after diffusion for a preset time, calculating a predicted value of the pollutants of the target grid along the latitude direction:
p(x,y)(e)＝p(x,y)(z)-w(z)+w(g)
wherein p (x, y) (e) is a predicted value of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time of diffusion; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and w (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; w (g) is the diffusion increment of the contaminant in the latitudinal direction at the point p (x, y) of the target grid after the time t has elapsed;
if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitudinal direction, calculating an atmospheric pollutant diffusion prediction value of the pollutant of the target grid diffusing along the longitudinal direction after the pollutant of the target grid diffuses in a preset time, and the method comprises the following steps:
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion in a preset time, calculating a predicted value of the pollutants of the target grid along the longitude direction:
p(x,y)(r)＝p(x,y)(z)-q(z)+h(g)
wherein p (x, y) (r) is a predicted value of the pollutants at the p (x, y) point of the target grid along the longitude direction after the diffusion of the t time; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and q (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; h (g) is the diffusion increment of the contaminant in the longitudinal direction at the p (x, y) point of the target grid after the time t has elapsed.
Further, if the wind speed data in the wind direction data after the weighted interpolation of the target grid includes wind speed data of the grid in the longitudinal direction and wind speed data of the grid in the latitudinal direction, calculating predicted values of pollutants of the target grid after a preset time elapses and the pollutants diffuse in the longitudinal direction and the latitudinal direction, including:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion is carried out for a preset time, calculating the predicted values of pollutants diffused along the longitude direction and the latitude direction of the pollutants of the target grid:
p(x,y)(s)＝p(x,y)(z)-q(z)+h(g)-w(z)+w(g)
and p (x, y)(s) is a predicted value of the pollutant after the pollutant at the p (x, y) point of the target grid diffuses along the longitude direction and the latitude direction after the diffusion of the time t.
Further, still include:
dividing the pollutant diffusion predicted value into a plurality of intervals, performing color assignment on each interval, and performing color filling on each grid in the area to be predicted through the interval where the pollutant diffusion predicted value in each grid in the area to be predicted is located and the color assigned by the interval.
Further, still include:
and correspondingly marking the pollutant diffusion predicted value corresponding to each grid into each grid, and displaying the pollutant diffusion predicted value corresponding to each grid in the area to be forecasted through numbers.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of any embodiment of the invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present invention will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 shows a flow diagram of an atmospheric pollutants prediction method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a mesh for weighted interpolation of an original atmospheric volume mesh of contaminants and its surroundings, according to an embodiment of the invention;
FIG. 3 shows a schematic view of a center grid obtained after atmospheric volume weighted interpolation of contaminants according to an embodiment of the invention;
fig. 4 is a diagram illustrating the correspondence between the original grid and the grid position after weighted interpolation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
According to the method, the distribution condition of the pollutants in the area to be forecasted is monitored, wind power and wind direction factors are introduced, the physical displacement process of pollutant diffusion is simulated, and the movement trend of the pollutants is predicted so as to predict the future atmospheric environment condition of the area to be forecasted, so that a data basis is provided for relevant departments, intervention measures are taken in advance, and atmospheric pollution is effectively controlled and prevented.
Fig. 1 shows a flow chart of an atmospheric pollutant prediction method according to an embodiment of the invention.
And S101, acquiring atmospheric capacity and wind direction data of pollutants in the global range.
The atmospheric capacity of the pollutant is used for representing the total content of the pollutant from the ground to the top of the atmosphere, and the data is grid data.
As an embodiment of the present invention, the atmospheric volume of the contaminants is obtained from a publicly released auri satellite omi (ozone Monitoring instrument). OMI is an ozone layer observer, one of the 4 satellite-borne sensors of the AURA satellite, provided by the netherlands airline and finland weather, manufactured by two netherlands and three finlands together. In this embodiment, the spatial resolution of the SO2 and NO2 data and the SO2 and NO2 data of the openly published Aura satellite omi (ozone Monitoring instrument) is 13km × 24 km. The data values for SO2 and NO2 represent the total content of the total column of atmosphere within the entire 13km by 24km grid. The atmospheric main column is the area from the ground to the top of the atmosphere.
The wind direction data is grid data, and comprises wind speed data and wind direction data in each grid area, and the wind speed data in each grid area comprises wind speed data of the grid in the longitude direction and/or wind speed data of the grid in the latitude direction. And the future generally refers to hours in the future, with specific times consistent with the predicted times of the data provided by the data source.
As an embodiment of the invention, the acquisition source of the wind direction data is a publicly released NOAA satellite GFS. GFS is a global forecasting system published by the national environmental forecasting center of america, which publishes weather data in the global range 4 times per day with a resolution up to 0.25 ° x 0.25 °. The data published each time is saved in a folder named gfs. GFS data are binned globally (longitude 0 ° -360 °, latitude-90 ° -90 °) using a standard 0.25 ° × 0.25 ° (about 28km × 28 km). The data precision required this time is 0.25 ° (0p25), so the data format is: gfs.t { HH } z.pgrb2.0p25.f { XXX }, where HH represents the time of release and XXX represents the weather forecast data for hours in the future. For example, gfs.t00z.pgrb2.0p25.f001 represents the future 1-hour meteorological prediction data issued at 0. The NOAA weather satellite used by the global weather forecasting system (GFS) has two satellites running in the mean time, the orbits of the two satellites are close to the sun synchronous orbit of a perfect circle, the orbits of the two satellites are 870 kilometers and 833 kilometers respectively, and the inclination angles of the orbits are 98.9 degrees and 98.7 degrees. Therefore, the meteorological parameters of all the places on the earth can be acquired through the two satellites.
The method comprises the steps of firstly acquiring one or more weather parameter data required by people from data issued by a Global Forecasting System (GFS), and then generating a weather data statistical table according to the acquired one or more weather parameter data so as to acquire data of parameters such as temperature, wind direction, wind speed, ground pressure, humidity and the like of different regions of the world. Specifically, when acquiring data, only the desired data can be downloaded from the data released by the Global Forecasting System (GFS), and unnecessary data is not downloaded, so that data screening is not needed during post-processing. Of course, the data published by the Global Forecasting System (GFS) can be downloaded first, and before the weather data statistical table is generated, the wanted data is sorted out, and the unwanted data is removed, so as to reduce the workload of the post-processing. For example, the meteorological prediction data includes data such as temperature, precipitation, wind direction, wind speed, and the like, and wind direction and wind speed data are extracted from the meteorological prediction data to serve as wind direction and wind speed prediction data.
Further, the grid region is a rectangular region formed by two adjacent longitude lines and two adjacent latitude lines, and the rectangular region contains the acquired minimum data unit.
As an embodiment of the present invention, it is preferable that the grid area coincides with the acquired atmospheric capacity of the pollutant of the Aura satellite OMI and the acquired grid area of the weather data of the global prediction system (GFS).
S102, performing weighted interpolation on the obtained atmospheric capacity of the pollutants and the wind direction data by adopting a cubic convolution method to obtain the atmospheric capacity of the pollutants after weighted interpolation and the wind direction data after weighted interpolation.
Since the data acquired in S101 includes at least two types, such as air volume of pollutants and wind direction data, the grid size of each type of data is different from each other, for example, the grid size of the air volume of pollutants SO2 and NO2 is 13km × 24km, and the grid size of the wind direction data is 28km × 28 km. Therefore, it is necessary to scale the grid data of different specifications to unify the grid data of the same specification. The image data needs to be subjected to weighted interpolation so that the image data can be scaled.
As an embodiment of the invention, the obtained atmospheric capacity of the pollutant is respectively subjected to weighted interpolation by adopting a cubic convolution method, so that the grid data of the atmospheric capacity of the pollutant and the grid data of the wind direction data of the wind power have the same specification again. For example, all recreate 13km x 13km of mesh data. Since the equidistant sampling is adopted, the value of any one of the meshes B (X, Y) on the generated mesh is the result of calculating a weighted average corresponding to the 16 mesh values (4 × 4) of the original image, as shown in fig. 2. Assuming that the original graph grid value is W (z), and the interpolated grid value is W (z1), the following results are obtained:
wherein h is the sampling interval, c is the parameter of the sampling point, and n is the number of samples.
In fig. 2, the point P (x, y) is the position corresponding to the original image in the target image B (x, y) shown in fig. 3, and since the original mesh size does not match the interpolated mesh size, the coordinate position of P will have a fractional part, as shown in fig. 4. Therefore, let P be coordinates P (x + u, y + v), where x, y represent the integer part, u, v represent the fractional part, u represents the deviation of the row number, and v represents the deviation of the column number. We can then get the position of the nearest 16 grids, here denoted by p (i, j), (i, j ═ 1,0,1, 2). The 16 pixels take on the values of (i-1, j-1), (i-1, j +1), (i-1, j +2), (i, j-1), (i, j +1), (i, j +2), (i +1, j-1), (i +1, j +1), (i +1, j +2), (i +2, j-1), (i +2, j +1) and (i +2, j + 2).
The purpose of weighted interpolation is to find out the influence parameters of the 16 grids to the interpolated grid values by finding out a weight parameter, so as to obtain the pixel values of the corresponding points of the target image according to the influence parameters.
Taking P (0,0) as an example, P (0,0) is (1+ u,1+ v) away from the original grid P (x + u, y + v). First, a core calculation function u(s) in the convolution interpolation is constructed:
wherein, a is-0.5; this yields the row coefficients: and c _ i _0 ═ w (1+ u), the column coefficient c _ j _0 ═ w (1+ v), coefficients corresponding to rows and columns are obtained through calculation, weight parameters of each connected grid p (i, j) can be obtained through c ═ cj, and finally, the pixel value corresponding to the target grid B (X, Y) can be obtained through the summation formula.
As an embodiment of the invention, a double cubic convolution method is adopted to carry out weighted interpolation on the acquired wind direction data, and grid data of 13km multiplied by 13km is regenerated. Since the equidistant sampling is adopted, the value of any grid G (X, Y) on the generated grid is the result of calculating a weighted average corresponding to the 16 grid values (4 × 4) of the original image. Assuming that the original grid value of the graph is W (Z), and the interpolated grid value is W (Z1), the method comprises the following steps:
wherein h is the sampling interval, c is the parameter of the sampling point, and n is the number of samples.
Let H be the position in the target image G (X, Y) corresponding to the original image, and since the original mesh size does not match the interpolated mesh size, the coordinate position of H will have a fractional part. Therefore, let H be coordinates H (x + u, y + v), where x, y respectively represent integer parts, u, v respectively represent fractional parts, u represents deviation of row number, and v represents deviation of column number. We can then get the position of the nearest 16 grids, here denoted by h (i, j), (i, j ═ 1,0,1, 2). The 16 pixels take on the values of (i-1, j-1), (i-1, j +1), (i-1, j +2), (i, j-1), (i, j +1), (i, j +2), (i +1, j-1), (i +1, j +1), (i +1, j +2), (i +2, j-1), (i +2, j +1) and (i +2, j + 2).
The purpose of weighted interpolation is to find out the influence parameters of the 16 grids to the interpolated grid values by finding out a weight parameter, so as to obtain the pixel values of the corresponding points of the target image according to the influence parameters.
Taking H (0,0) as an example, H (0,0) is (1+ u,1+ v) away from the original grid H (x + u, y + v). First, a core calculation function u(s) in the convolution interpolation is constructed:
wherein, a is-0.5; this yields the row coefficients: and c _ i _0 ═ w (1+ u), the column coefficient c _ j _0 ═ w (1+ v), coefficients corresponding to rows and columns are obtained through calculation, a weight parameter of each connected grid h (i, j) can be obtained through c ═ cj, and finally, a pixel value corresponding to the target grid G (X, Y) can be obtained through the summation formula.
Further, an area to be forecasted is set, and corresponding data in the area to be forecasted are extracted from the atmospheric capacity of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation respectively according to longitude and latitude information included in the area to be forecasted.
The acquired data is data of a global scope, so that an area to be forecasted needs to be set according to longitude and latitude information of an area scope to be forecasted, or a defined target area is used as the area to be forecasted, and longitude and latitude information of an area boundary is acquired. And extracting corresponding data in the region boundary from the acquired atmospheric capacity and wind direction data of the pollutants after the weighted interpolation respectively.
As an embodiment of setting the area to be forecasted, the range of the area to be forecasted needs to be set according to the latitude and longitude information of the boundary of the area to be forecasted, in order to predict the atmospheric pollutant diffusion condition in the area range with specific latitude and longitude.
As another embodiment of the present invention for setting the area to be forecasted, if a target area, for example, beijing city, has been selected, the range of the area to be forecasted is set with the longitude and latitude of the area boundary of beijing city as the boundary.
Further, the atmospheric capacity of the pollutants in the area to be forecasted after weighted interpolation and the wind direction data after weighted interpolation are used as the data base of S104. Therefore, the area boundary predicted by the atmospheric pollutants can be defined, and the practicability and operability of the method are further improved.
S103, carrying out fusion calculation on the atmospheric volume of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation to obtain a pollutant diffusion predicted value.
Further, if the wind speed data in the wind direction data after the weighted interpolation of the target grid includes wind speed data of the grid in the longitude direction or wind speed data of the grid in the latitude direction, calculating a predicted value of the pollutant of the target grid in the direction corresponding to the wind speed after the preset time is diffused, as the predicted value of the pollutant of the target grid after the preset time is diffused.
Before calculating the predicted value of the pollutants of the target grid, firstly judging the wind direction condition of the target grid, extracting wind direction data from the obtained wind direction data, and if the wind direction data is single wind direction data, namely the wind direction data only has a longitudinal direction and no latitude direction component, or only has a latitudinal direction and no longitude direction component; only the influence of the unidirectional wind direction on the pollutants of the grid needs to be considered when the pollutant predicted value of the target grid is calculated. For example, if the acquired wind direction data of the current grid is in the longitudinal direction, the influence of the latitudinal direction is not considered when the pollutant predicted value of the grid is calculated, and the calculated pollutant predicted value in the longitudinal direction is the final pollutant predicted value.
Further, after diffusion for a preset time, calculating a predicted value of the pollutant of the target grid along the latitude direction, including:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
after diffusion for a preset time, calculating a predicted value of the pollutants of the target grid along the latitude direction:
p(x,y)(e)＝p(x,y)(z)-w(z)+w(g)
wherein p (x, y) (e) is a predicted value of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time of diffusion; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and w (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; w (g) is the diffusion increment of the contaminant in the latitudinal direction at the point p (x, y) of the target grid after the time t has elapsed.
Further, after diffusion for a preset time, calculating an atmospheric pollutant diffusion prediction value of the pollutant of the target grid diffusing along the longitude direction, including:
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion in a preset time, calculating a predicted value of the pollutants of the target grid along the longitude direction:
p(x,y)(r)＝p(x,y)(z)-q(z)+h(g)
wherein p (x, y) (r) is a predicted value of the pollutants at the p (x, y) point of the target grid along the longitude direction after the diffusion of the t time; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and q (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; h (g) is the diffusion increment of the contaminant in the longitudinal direction at the p (x, y) point of the target grid after the time t has elapsed.
Further, if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitude direction and wind speed data of the grid in the latitude direction, respectively calculating predicted values of pollutants of the target grid in the longitude direction and the latitude direction after diffusion of preset time, and after vector superposition, obtaining the predicted value of the target grid after diffusion of the preset time.
Before calculating the predicted value of the pollutants of the target grid, the wind direction condition of the target grid needs to be judged, wind direction data is extracted from the obtained wind direction data, and if the wind direction data is not single wind direction data, namely the current wind direction is not only along the longitude direction or the latitude direction, but also the wind direction with an angle. That is, the current wind direction may be further divided into a component in the longitudinal direction and a component in the latitudinal direction. At this time, when the predicted value of the pollutant of the target grid is calculated, only the influence of the pollutant on the grid in a single wind direction cannot be considered, but the diffusion amount and the supplement amount of the pollutant of the grid in the longitude direction and the latitude direction need to be calculated respectively, so that the predicted value of the pollutant of the target grid after the preset time is obtained finally.
And the target grid is a certain grid in the area to be forecasted, and after the pollutant forecast value of the grid target after the preset time is obtained through calculation, all the target grids in the area to be forecasted are traversed to obtain the forecast data of the whole area to be forecasted. And (3) the pollutant diffusion predicted value obtained through the fusion calculation process considers factors of wind speed and wind direction, the atmospheric pollutant diffusion predicted value in the wind direction is accurately calculated by supplementing the diffusion increment in the wind direction, and the values in different wind directions are subjected to vector superposition to obtain the atmospheric pollutant diffusion predicted value. By simulating the physical displacement of the pollutants in the atmosphere, the moving trend of the pollutants is predicted so as to predict the pollution condition after each area.
If the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitudinal direction and wind speed data of the grid in the latitudinal direction, calculating the predicted value of the pollutant after the pollutant of the target grid diffuses in the longitudinal direction and the latitudinal direction after the preset time, wherein the method comprises the following steps:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion is carried out for a preset time, calculating the predicted values of pollutants diffused along the longitude direction and the latitude direction of the pollutants of the target grid:
p(x,y)(s)＝p(x,y)(z)-q(z)+h(g)-w(z)+w(g)
and p (x, y)(s) is a predicted value of the pollutant after the pollutant at the p (x, y) point of the target grid diffuses along the longitude direction and the latitude direction after the diffusion of the time t.
And further, correspondingly marking the pollutant diffusion predicted value corresponding to each grid into each grid, and presenting the pollutant diffusion predicted value corresponding to each grid in the area to be forecasted through numbers.
After the pollutant diffusion predicted value corresponding to each grid is obtained, the pollutant diffusion predicted values corresponding to each grid can be distinguished through colors according to the size of the pollutant diffusion predicted value corresponding to each grid, the pollutant diffusion predicted value corresponding to each grid is correspondingly marked into each cell, and therefore the pollutant diffusion predicted value corresponding to each grid in the area to be forecasted can be presented in a digital mode.
And further, dividing the pollutant diffusion predicted value into a plurality of intervals, performing color assignment on each interval, and performing color filling on each grid in the area to be forecasted through the interval where the pollutant diffusion predicted value in each grid in the area to be forecasted is located and the color assigned by the interval.
After the pollutant diffusion predicted value corresponding to each grid is obtained, the pollutant diffusion predicted values can be distinguished through colors according to the size of the pollutant diffusion predicted value corresponding to each grid, the pollutant diffusion predicted values are divided into different intervals according to the obtained pollutant diffusion predicted value corresponding to each grid, then different colors are used for representing different intervals, and for example, the intervals from low to high can be represented sequentially through blue, yellow, red and gradient colors of the blue, yellow and red. And then, the cells can be subjected to color filling according to the pollutant diffusion predicted value in each grid and the color represented by the interval where the pollutant diffusion predicted value is located, so that a color pollutant diffusion prediction graph which represents different pollutant diffusion predicted values by different colors can be generated, and the method can be more vivid and visual.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules illustrated are not necessarily required to practice the invention.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. An atmospheric pollutant prediction method, comprising:
acquiring atmospheric capacity and wind power and wind direction data of pollutants in a global range;
respectively performing weighted interpolation on the obtained atmospheric capacity and wind direction data of the pollutants by adopting a cubic convolution method to obtain the atmospheric capacity of the pollutants after weighted interpolation and the wind direction data after weighted interpolation;
and performing fusion calculation on the atmospheric capacity of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation to obtain a pollutant diffusion prediction value.
2. The method of claim 1, wherein the atmospheric capacity of the contaminants is grid data representing the total content of contaminants from the surface to the top of the atmosphere in each grid area;
the wind speed data in each grid area comprises wind speed data of the grid in the longitude direction and/or wind speed data of the grid in the latitude direction.
3. The method according to claim 2, wherein the grid region is a rectangular region formed by two adjacent longitude lines and two adjacent latitude lines, and the rectangular region contains the minimum acquired data unit.
4. The method of claim 1, wherein performing a fusion calculation on the atmospheric capacity of the weighted interpolated pollutants and the weighted interpolated wind direction data to obtain a pollutant diffusion prediction value comprises:
setting a region to be forecasted, and extracting corresponding data in the region to be forecasted from the atmospheric capacity of the pollutants subjected to the weighted interpolation and the wind direction data subjected to the weighted interpolation according to longitude and latitude information included in the region to be forecasted;
and performing fusion calculation on the atmospheric capacity of the pollutants in the area to be forecasted and the wind direction data after the weighted interpolation to obtain a pollutant diffusion prediction value in the area to be forecasted.
5. The method according to claim 4, wherein the fusion calculation of the atmospheric volume of pollutants in the area to be forecasted and the weighted interpolated wind direction data comprises:
if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitude direction or wind speed data of the grid in the latitude direction, calculating a predicted value of the pollutant of the target grid in the corresponding direction of the wind speed after the preset time is diffused, and taking the predicted value as the predicted value of the pollutant of the target grid after the preset time is diffused.
6. The method according to claim 4, wherein the fusion calculation of the atmospheric volume of pollutants in the area to be forecasted and the weighted interpolated wind direction data comprises:
and if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitudinal direction and wind speed data of the grid in the latitudinal direction, calculating the predicted value of the pollutants of the target grid after the preset time passes and the pollutants of the target grid diffuse in the longitudinal direction and the latitudinal direction.
7. The method of claim 5, wherein if the wind speed data in the weighted interpolated wind direction data of the target grid includes wind speed data of the grid in a latitudinal direction, calculating a predicted value of the target grid for the contaminant in the latitudinal direction after a predetermined time has elapsed, comprises:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
after diffusion for a preset time, calculating a predicted value of the pollutants of the target grid along the latitude direction:
p(x,y)(e)＝p(x,y)(z)-w(z)+w(g)
wherein p (x, y) (e) is a predicted value of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time of diffusion; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and w (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; w (g) is the diffusion increment of the contaminant in the latitudinal direction at the point p (x, y) of the target grid after the time t has elapsed;
if the wind speed data in the wind direction data after the weighted interpolation of the target grid comprises wind speed data of the grid in the longitudinal direction, calculating an atmospheric pollutant diffusion prediction value of the pollutant of the target grid diffusing along the longitudinal direction after the pollutant of the target grid diffuses in a preset time, and the method comprises the following steps:
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion in a preset time, calculating a predicted value of the pollutants of the target grid along the longitude direction:
p(x,y)(r)＝p(x,y)(z)-q(z)+h(g)
wherein p (x, y) (r) is a predicted value of the pollutants at the p (x, y) point of the target grid along the longitude direction after the diffusion of the t time; p (x, y) (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid before diffusion, and q (z) is the atmospheric volume of the contaminant at the point p (x, y) of the target grid after time t has elapsed; h (g) is the diffusion increment of the contaminant in the longitudinal direction at the p (x, y) point of the target grid after the time t has elapsed.
8. The method of claim 6, wherein if the wind speed data in the weighted interpolated wind direction data of the target grid includes wind speed data in a longitudinal direction and wind speed data in a latitudinal direction of the grid, calculating a predicted value of the pollutant after a preset time has elapsed for the pollutant of the target grid to diffuse in the longitudinal direction and in the latitudinal direction, comprises:
calculating the atmospheric capacity of pollutants diffused in the latitude direction in a preset time by the target grid:
w(z)＝k×p(x,y)(z)×p(x,y)(g)×t
wherein w (z) is the atmospheric volume of the contaminant diffused in the latitude direction in the preset time by the target grid; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (g) is the wind speed data in the latitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the latitude direction after the preset time is passed:
w(g)＝k×p(x-1,y)(z)×p(x-1,y)(g)×t
wherein w (g) is the diffusion increment of the pollutant at the p (x, y) point of the target grid along the latitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (g) is the latitudinal wind velocity data of the contaminant at point p (x-1, y) prior to diffusion;
calculating the atmospheric capacity of pollutants diffused in the longitudinal direction in a preset time by the target grid:
q(z)＝k×p(x,y)(z)×p(x,y)(m)×t
wherein q (z) is the atmospheric volume of the pollutant diffused in the longitudinal direction in the target grid within a preset time; k is the diffusion coefficient; p (x, y) (z) is the atmospheric volume of the pollutant at the p (x, y) point of the target grid before diffusion, and p (x, y) (m) is the wind speed data in the longitudinal direction of the pollutant at the p (x, y) point of the target grid before diffusion; t is a preset time;
calculating the pollutant diffusion increment of the target grid along the longitude direction after the preset time is passed:
h(g)＝k×p(x,y-1)(z)×p(x,y-1)(m)×t
wherein h (g) is the diffusion increment of the pollutants of the p (x, y) point of the target grid along the longitude direction after t time diffusion; p (x-1, y) (z) is the atmospheric volume of the contaminant at point p (x-1, y) prior to diffusion; p (x-1, y) (m) is wind speed data in the longitudinal direction of the contaminant at point p (x-1, y) before diffusion;
after diffusion is carried out for a preset time, calculating the predicted values of pollutants diffused along the longitude direction and the latitude direction of the pollutants of the target grid:
p(x,y)(s)＝p(x,y)(z)-q(z)+h(g)-w(z)+w(g)
and p (x, y)(s) is a predicted value of the pollutant after the pollutant at the p (x, y) point of the target grid diffuses along the longitude direction and the latitude direction after the diffusion of the time t.
9. The method of claim 1, further comprising:
dividing the pollutant diffusion predicted value into a plurality of intervals, performing color assignment on each interval, and performing color filling on each grid in the area to be predicted through the interval where the pollutant diffusion predicted value in each grid in the area to be predicted is located and the color assigned by the interval.
10. The method of claim 1, further comprising:
and correspondingly marking the pollutant diffusion predicted value corresponding to each grid into each grid, and displaying the pollutant diffusion predicted value corresponding to each grid in the area to be forecasted through numbers.
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