CN113837496A - Model prediction method for influence of straw burning on air quality - Google Patents
Model prediction method for influence of straw burning on air quality Download PDFInfo
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- 239000010902 straw Substances 0.000 title claims abstract description 44
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- 238000009792 diffusion process Methods 0.000 claims abstract description 37
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 25
- 231100000719 pollutant Toxicity 0.000 claims abstract description 25
- 238000002485 combustion reaction Methods 0.000 claims abstract description 17
- 238000012544 monitoring process Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000009467 reduction Effects 0.000 claims description 6
- 239000000446 fuel Substances 0.000 claims description 5
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 claims description 4
- 239000003086 colorant Substances 0.000 claims description 4
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000003912 environmental pollution Methods 0.000 abstract description 5
- 238000003908 quality control method Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 230000002427 irreversible effect Effects 0.000 description 2
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- 238000007405 data analysis Methods 0.000 description 1
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
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Abstract
The invention discloses a model prediction method for influence of straw burning on air quality, which comprises the following steps: acquiring weather forecast data; acquiring air quality forecast data; acquiring a predicted village and town position; calculating a prediction range; calculating a pollutant diffusion index; calculating the diffusion direction of the pollutants; calculating a superimposed pollutant diffusion index; calculating a combustion index value; and obtaining a prediction result and judging whether the burning-out work can be carried out. The invention can solve the problem of pollution of straw burning to the atmospheric environment, reasonably arranges a straw burning plan through model prediction, reduces the pollution of the burnt straw to the atmospheric environment to the minimum, realizes the balance between the straw burning and the environmental pollution, not only ensures the quality control of the atmospheric environment within a reasonable range, but also ensures that farmers can burn redundant straw.
Description
Technical Field
The invention relates to the field of environmental monitoring, in particular to a model prediction method for influence of straw burning on air quality.
Background
As a big agricultural country, China can generate a large amount of straws every year, and with the continuous development of economy and agricultural science and technology, the demand of the straws as living fuel is greatly reduced, so that most farmers burn the straws at will, and the problem of environmental pollution caused by straw burning is inevitable. How to solve the balance problem between straw burning and environmental pollution is urgent, the prior art mainly monitors the air quality change in real time through an air quality monitoring device, and when the air quality in a certain area is found to be poor, the straw burning work is stopped artificially, so that the pollution to the atmospheric environment caused by straw burning is required to be passively received.
The real-time monitoring of the air quality change by the air quality monitoring equipment belongs to post-monitoring, causes pollution problems to be reproduced, prevents the pollution environment from being improved by adopting a plurality of means, increases the labor cost and the economic cost, and can cause irreversible pollution. The technology for monitoring the straw burning after the straw burning process cannot effectively control the pollution of the straw burning to air, so that once the straw burning process enters a straw burning period, the problem of how to effectively reduce the quality pollution of the atmospheric environment is solved.
In order to solve the current situation that the atmospheric quality is continuously poor due to straw burning, the pollution of the straw burning to the atmosphere can be fundamentally and effectively reduced by reasonably planning how the straw burning is carried out by combining a prior prediction technology.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a model prediction method for the influence of straw burning on air quality, which is based on weather data combined with a big data analysis model to burn straws in a planned way, and the model is used for predicting the burning result and selecting the mode of burning to cause the least pollution to the atmospheric environment to burn in the planned way.
In order to solve the technical problems, the invention adopts the technical scheme that: a model prediction method for influence of straw burning on air quality comprises the following steps:
step a, acquiring weather forecast data;
step b, acquiring air quality forecast data;
step c, obtaining the position of the predicted village and town;
step d, calculating a prediction range;
step e, calculating a pollutant diffusion index;
step f, calculating the diffusion direction of the pollutants;
step g, calculating a superimposed pollutant diffusion index;
step h, calculating a combustion index value;
and step l, obtaining a prediction result and judging whether the burning-out work can be carried out.
Further, step a, acquiring weather forecast data of 72 hours in the future by calling a data interface of the environment-friendly weather monitoring station, acquiring weather information including wind power, wind level, wind direction, temperature, rain, snow and clear, and grouping according to the date of province, city and county as key.
Further, step b acquires air quality data of 72 hours in the future by calling a data interface of the environmental-friendly weather monitoring station, and the data are grouped according to the weather monitoring station areas.
Further, the name of the village to be predicted is dynamically obtained according to a preset value of the system, a Goodpasture map API is called to obtain longitude and latitude coordinates (x, y) of the predicted village, the predicted position is determined, and the point position of the predicted position is marked into the gis map.
Further, in the step d, a java language built-in class library Path2D is used to combine with a system preset prediction radius to calculate boundary coordinate values of the area to be predicted, and the boundary coordinate values are marked as a predicted area; the formula of any point on the circle is:
x1＝x0+r*cos(a0*π/180)；
y1＝y0+r*sin(a0*π/180)；
wherein x0 and y0 are coordinates of the center of a circle, r is a radius, a0 is an angle, and a0 x pi/180 converts the angle a0 into radian.
Further, step e calculates a pollutant diffusion index of the marked area according to the weather information obtained in step a, and the formula is as follows:
wherein K is a diffusion index; r1 is a wind level index, R1 is a decimal number which is larger than 0 and smaller than 1, and the decimal number is determined according to the average value of historical data; fx is the current wind level and is obtained through the step a; fn is the maximum value of the wind power level, and an expected value is set through a system; r2 is the air quality index, R2 is a decimal number which is more than 0 and less than 1, and is determined according to the average value of historical data; ax is the current air quality api value and is determined according to the step a; an is the AQI historical maximum.
Further, step f calculates the diffusion direction of the pollutant according to the wind direction information obtained in step a, the diffusion direction is divided into eight direction indexes of east, west, south, north, south-east, north-east, south-west and north-west, and the eight direction indexes are respectively marked in the predicted area of the gis map.
Step g, combining the temperature obtained in the step a, continuously superposing and calculating a diffusion index, judging whether the temperature reduction amplitude is more than 8 ℃, and subtracting 0.3 from the diffusion index if the temperature reduction amplitude is more than 8 ℃;
wherein, T0 is the index of the cold air flow temperature, T1 is the temperature of the previous day, obtained according to the step a, T2 is the temperature of the current day, obtained according to the step a, Tx is the parameter of the cold air flow, and determined according to the historical data average value.
Further, the index K obtained by calculation in the step e and the calculation result of the administrative district adjacent to the administrative district are subjected to cross calculation to obtain a final combustion index value S, the distance between the village and the town is calculated, a gis map distance measuring tool is used for calculating the distance between longitude and latitude coordinates of the central points of the two villages and towns to obtain a distance index value L, when the distance exceeds 50KM, the distance index value is 0, when the distance is between 30-50KM, the distance index value is 1, and when the distance is within 30KM, the distance index value is 2;
wherein S is a combustion index value, N is a natural number greater than 0 and is determined by the number of adjacent villages, K0 is a current village pollutant diffusion index, L is a distance parameter, Kn is a pollutant diffusion index value of an adjacent village, and N is determined by the number of adjacent villages.
Further, step l, judging whether the burning operation can be carried out or not according to the burning index value of step h, wherein the burning index value is more than 0.6, and judging that the burning operation cannot be carried out; the burning index value is more than or equal to 0.4 and less than or equal to 0.6, and the burning can be carried out as appropriate; if the combustion index value is less than 0.4, judging that the fuel can be burnt out; and marking colors of the target prediction area on an gis map according to the prediction result value, and visually observing the target prediction area on a gis map to predict the situation.
The invention discloses a model prediction method for influence of straw burning on air quality, which can solve the problem of pollution of the straw burning on the atmospheric environment, reasonably arranges a straw burning plan through model prediction, reduces the pollution of the burnt straw on the atmospheric environment to the minimum, realizes balance between the straw burning and the environmental pollution, ensures that the quality of the atmospheric environment is controlled in a reasonable range, and ensures that farmers can burn redundant straw.
Detailed Description
The invention effectively solves the problem of irreversible environmental pollution caused by finding and solving problems afterwards and the problems of manpower, economic cost and the like caused by solving the problems through a pre-prediction mechanism. Through the prediction model, the planned burning-out work can be reasonably arranged, and the arbitrary burning behaviors of farmers are reduced. Burning out is carried out according to the burning index value, so that the pollution of straw burning to the atmospheric environment can be effectively reduced.
The present invention will be described in further detail with reference to specific embodiments.
Acquiring weather forecast data: and calling a data interface of the environment-friendly weather monitoring station to acquire a weather forecast for 72 hours in the future and acquire weather information including wind power, wind level, wind direction, temperature, rain, snow and sunny weather. And grouping according to the date of province, city and county as key.
Acquiring air quality forecast data: and calling a data interface of the environmental-friendly weather monitoring station to acquire air quality data of 72 hours in the future, and grouping according to weather monitoring station areas.
Obtaining a predicted village and town position: dynamically acquiring the name of a village to be predicted according to a preset value of a system, calling a Goodpasture map API (application program interface) to acquire longitude and latitude coordinates x and y of the predicted village, determining the predicted position, and marking the point position into an gis map.
Calculating a prediction range: and calculating the boundary coordinate value of the area to be predicted by using a java language built-in class library Path2D and combining with a preset prediction radius of the system. Marked as a predicted area. Knowing the coordinates of the center of the circle (x0, y0), the radius r, and the angle a0, any point on the circle is calculated as:
x1＝x0+r*cos(a0*π/180)；
y1＝y0+r*sin(a0*π/180)；
wherein a0 pi/180 is to convert the angle a0 into radian, divide the circle on the plane into 100 parts, and calculate the plane coordinate corresponding to the intersection point of the arc radius and the circle by substituting the center coordinate, the radian and the radius according to the formula. Thus, 100 coordinate points on the plane circle are obtained, (x1, y1), (x2, y2) … (x100, y100), the obtained 100 coordinate points are converted into longitude and latitude coordinates, the longitude and latitude coordinates are sequentially marked in a gis map, a circular gis map area is formed, and the area is filled with red and marked as a predicted area.
And (3) calculating a pollutant diffusion index: and c, calculating a pollutant diffusion index of the marked area according to the wind power and weather information obtained in the step a.
And b, determining according to the historical data average value, wherein K is a diffusion index, R1 is a wind level index which is a decimal number larger than 0 and smaller than 1, Fx is the current wind level and is obtained through the step a, Fn is a wind level maximum value, An expected value is set through a system, R2 is An air quality index which is a decimal number larger than 0 and smaller than 1, Ax is a current air quality api value and is determined according to the historical data average value, and An is An AQI historical maximum value.
Calculating the diffusion direction of the pollutants: and c, calculating the diffusion direction of the pollutants according to the wind direction information obtained in the step a. The diffusion direction is divided into eight direction indexes of east, west, south, north, southeast, northeast, southwest and northwest, and the eight direction indexes are respectively marked in the predicted area of the gis map.
Calculating the diffusion index of the superposed pollutants: and c, continuously superposing and calculating the diffusion index by combining the temperature obtained in the step a, judging whether the temperature reduction amplitude is more than 8 ℃ (cold air flow), and subtracting 0.3 from the diffusion index if the temperature reduction amplitude is more than 8 ℃.
Wherein, T0 is the index of the cold air flow temperature, T1 is the temperature of the previous day, obtained according to the step a, T2 is the temperature of the current day, obtained according to the step a, Tx is the parameter of the cold air flow, and determined according to the historical data average value.
Calculating a combustion index value: and performing cross calculation by combining the index K obtained by the condition calculation and the calculation result of the administrative division adjacent to the administrative division to obtain the final combustion index value S. Calculating the distance between the villages and the towns, calculating the distance between the longitude and latitude coordinates of the central points of the 2 villages by using an gis map distance measuring tool to obtain a distance index value L, wherein the distance index value is 0 when the distance exceeds 50KM, and the distance index value is 1 when the distance is between 30-50 KM. When the distance is within 30KM, the distance index value is 2.
Wherein S is a combustion index value, N is a natural number greater than 0 and is determined by the number of adjacent villages, K0 is a current village pollutant diffusion index, L is a distance parameter, Kn is a pollutant diffusion index value of an adjacent village, and N is determined by the number of adjacent villages.
Obtaining a prediction result: and judging whether the burning-out work can be carried out or not according to the size of the index value.
If the combustion index value is larger than 0.6, judging that the fuel cannot be burnt out; the burning index value is more than or equal to 0.4 and less than or equal to 0.6, and the burning can be carried out as appropriate; if the combustion index value is less than 0.4, judging that the fuel can be burnt out; and carrying out color marking on the colors of the target prediction area on the gis map according to the prediction result values, wherein the non-burnable areas are marked by red, the burnable areas are marked by yellow as appropriate, and the burnable areas are marked by green. This allows the situation to be visually seen on the gis map, predicted.
The use of this model is described below with village a as a specific example.
Firstly, calling a data interface of an environment-friendly weather monitoring station to acquire weather forecast wind power, wind level, wind direction, temperature and rain, snow and clear data of 72 hours in the future of an administrative district where village A is located, cleaning a series of data through de-overlapping combination and the like, and dividing the data into groups according to dates as keys for subsequent use.
And calling a data interface of the environment-friendly weather monitoring station to acquire air quality data of 72 hours in the future of the administrative district where the village A is located, and performing grouping division according to the coordinate position of the weather monitoring station through a series of data cleaning processes such as de-duplication combination and the like for subsequent use.
And calling a Goodpasture map API according to the name of the A village to acquire the longitude and latitude coordinates of the A village on an gis map. And marking the A village on a map.
And (3) acquiring a coordinate point set of the A village on the map by using a java language built-in class library Path2D and a mathematic circular boundary calculation formula in combination with a system preset prediction radius, drawing the position of the A village on the map gis, filling colors, and marking as a predicted area.
And (3) calculating a pollutant diffusion index K of the village A by using the weather data obtained in the step a and the step 2 and applying a model.
And c, calculating the wind direction change of the A village by using the wind direction data acquired in the step a.
And c, calculating whether the cold air flow exists in the A village or not by using the temperature information acquired in the step a.
Firstly operating gis map distance measuring tool to calculate the distance between village A and the adjacent villages B, C, D and E, and then calculating the combustion index S by using the calculation formula in the model.
And judging whether the A village can be burnt or not according to the value of S.
And marking the prediction result of the A village into an gis map for convenient viewing.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (10)
1. A model prediction method for influence of straw burning on air quality is characterized by comprising the following steps: the model prediction method comprises the following steps:
step a, acquiring weather forecast data;
step b, acquiring air quality forecast data;
step c, obtaining the position of the predicted village and town;
step d, calculating a prediction range;
step e, calculating a pollutant diffusion index;
step f, calculating the diffusion direction of the pollutants;
step g, calculating a superimposed pollutant diffusion index;
step h, calculating a combustion index value;
and step l, obtaining a prediction result and judging whether the burning-out work can be carried out.
2. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: and a, acquiring weather forecast data of 72 hours in the future by calling a data interface of the environment-friendly weather monitoring station, acquiring weather information including wind power, wind level, wind direction, temperature, rain, snow and clear, and grouping according to the date of province, city and county as key.
3. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: and b, acquiring air quality data of 72 hours in the future by calling a data interface of the environmental protection weather monitoring station, and grouping according to weather monitoring station areas.
4. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: and c, dynamically acquiring the name of the village to be predicted according to a preset value of the system, calling a Goodpasture map API (application program interface) to acquire longitude and latitude coordinates (x, y) of the predicted village, determining the predicted position, and marking the point position into an gis map.
5. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: in the step d, a java language built-in class library Path2D is used to combine with a system preset prediction radius to calculate boundary coordinate values of the area to be predicted, and the boundary coordinate values are marked as a predicted area; the formula of any point on the circle is:
x1＝x0+r*cos(a0*π/180)；
y1＝y0+r*sin(a0*π/180)；
wherein x0 and y0 are coordinates of the center of a circle, r is a radius, a0 is an angle, and a0 x pi/180 converts the angle a0 into radian.
6. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: step e, calculating a pollutant diffusion index of the marked area according to the weather information obtained in the step a, wherein a formula is as follows:
wherein K is a diffusion index; r1 is a wind level index, R1 is a decimal number which is larger than 0 and smaller than 1, and the decimal number is determined according to the average value of historical data; fx is the current wind level and is obtained through the step a; fn is the maximum value of the wind power level, and an expected value is set through a system; r2 is the air quality index, R2 is a decimal number which is more than 0 and less than 1, and is determined according to the average value of historical data; ax is the current air quality api value and is determined according to the step a; an is the AQI historical maximum.
7. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: and f, calculating the diffusion direction of the pollutants according to the wind direction information obtained in the step a, wherein the diffusion direction is divided into eight direction indexes of east, west, south, north, southeast, northeast, southwest and northwest, and the eight direction indexes are respectively marked in the predicted area of the gis map.
8. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: step g, combining the temperature obtained in the step a, continuously superposing and calculating a diffusion index, judging whether the temperature reduction amplitude is more than 8 ℃, and subtracting 0.3 from the diffusion index if the temperature reduction amplitude is more than 8 ℃;
wherein, T0 is the index of the cold air flow temperature, T1 is the temperature of the previous day, obtained according to the step a, T2 is the temperature of the current day, obtained according to the step a, Tx is the parameter of the cold air flow, and determined according to the historical data average value.
9. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: e, performing cross calculation on the index K obtained by calculation in the step e and the calculation result of the administrative district adjacent to the administrative district to obtain a final combustion index value S, calculating the distance between a village and the town, calculating the distance between longitude and latitude coordinates of the central points of the two villages and the town by using an gis map distance measuring tool to obtain a distance index value L, wherein the distance index value is 0 when the distance exceeds 50KM, the distance index value is 1 when the distance is between 30-50KM, and the distance index value is 2 when the distance is within 30 KM;
wherein S is a combustion index value, N is a natural number greater than 0 and is determined by the number of adjacent villages, K0 is a current village pollutant diffusion index, L is a distance parameter, Kn is a pollutant diffusion index value of an adjacent village, and N is determined by the number of adjacent villages.
10. The model prediction method of the influence of straw burning on air quality according to claim 1, characterized in that: step l, judging whether the burn-out operation can be carried out or not according to the size of the combustion index value in step h, wherein the combustion index value is more than 0.6, and judging that the burn-out operation cannot be carried out; the burning index value is more than or equal to 0.4 and less than or equal to 0.6, and the burning can be carried out as appropriate; if the combustion index value is less than 0.4, judging that the fuel can be burnt out; and marking colors of the target prediction area on an gis map according to the prediction result value, and visually observing the target prediction area on a gis map to predict the situation.
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