CN114280695A - Air pollutant monitoring and early warning method and cloud platform - Google Patents
Air pollutant monitoring and early warning method and cloud platform Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 59
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- 238000002310 reflectometry Methods 0.000 claims abstract description 20
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Abstract
The invention discloses an air pollutant monitoring and early warning method and a cloud platform, and the method comprises the following steps: setting a plurality of monitoring points; establishing a three-dimensional distribution grid, ground reflectivity distribution, a wind speed field and a temperature field of the flow channel; acquiring diffusion parameters and sedimentation parameters of pollutants, establishing a three-dimensional smoke packet track model, performing three-dimensional flow field simulation, correcting error data by fitting historical pollutant concentration and wind speed monitoring data, predicting the pollutant concentration of a certain fixed place in the future, and calculating an air pollution index; carrying out three-dimensional flow field simulation through a smoke group track model by using the pollutant concentration and wind speed monitoring data of the monitoring points so as to obtain plane distribution of the pollutant concentration and accurately track the pollution source; and regularly issuing an air quality detection report, a prediction report, a pollutant distribution diagram and a prediction pollution source position to the user terminal. The invention can intelligently process the pollutant concentration monitoring, and can accurately track the pollution source and predict the pollutant concentration of a certain fixed place in the future.
Description
The invention relates to a divisional application of an air pollutant monitoring and early warning method and a cloud platform, wherein the application number of a parent application is 201710322237.5, and the application date is 2017.05.09.
Technical Field
The invention relates to the technical field of pollutant monitoring, in particular to an air pollutant monitoring and early warning method and a cloud platform.
Background
With the rapid development of economy, the living standard of people is continuously improved, but the problem of environmental pollution is gradually highlighted. The inhalable particles and the toxic gas seriously threaten the life and health of people. In order to solve the problem, relevant departments develop a series of environmental monitoring standards, such as "environmental air quality standards", "environmental air quality monitoring specifications (trial implementation)", and "environmental air quality monitoring specifications (trial implementation)", etc. Mature detection technology, the vigorously developed internet and cloud computing technology provide comprehensive technical support for realizing real-time monitoring, wireless transmission, intelligent analysis, accurate early warning and timely release of air pollution.
However, the existing air pollution prediction mechanism and application software are full of information, but the results issued by the existing air pollution prediction mechanism and the application software are only list information of pollutant concentration monitoring results of fixed places, the prediction result is the pollutant concentration of a certain fixed area in the future, and the data utilization rate is low.
Disclosure of Invention
The invention aims to provide an air pollutant monitoring and early warning method and a cloud platform, which can detect the air quality in real time and carry out big data analysis on detection data so as to track a pollution source and predict the air quality, and then carry out release forenotice through a mobile terminal.
In order to achieve the purpose, the invention provides the following scheme:
an air pollutant monitoring and early warning method comprises the following steps:
setting a plurality of monitoring points; the monitoring points are used for collecting historical parameters of pollutant concentration, air temperature, wind direction and wind speed;
establishing a three-dimensional distribution grid of a flow channel according to the distribution of urban terrains and buildings;
measuring the ground reflectivity of different areas, and assigning values to each grid to obtain the ground reflectivity distribution;
establishing a wind speed field;
assigning the temperature data acquired by the monitoring points to each grid, and obtaining a temperature field by linear interpolation on the grids without the monitoring points;
establishing a three-dimensional smoke mass track model according to the three-dimensional distribution grid, the ground reflectivity distribution, the wind speed field, the temperature field and the diffusion parameters and the sedimentation parameters of pollutants;
based on a Hadoop platform, performing three-dimensional flow field simulation by adopting the three-dimensional smoke group track model, correcting error data by utilizing fitting historical pollutant concentration and wind speed monitoring data, predicting the pollutant concentration of a certain fixed place in the future, and calculating an air pollution index;
carrying out three-dimensional flow field simulation through a smoke group track model by using the pollutant concentration and wind speed monitoring data of the monitoring points so as to obtain plane distribution of the pollutant concentration and accurately track the pollution source;
and regularly issuing an air quality detection report, a prediction report, a pollutant distribution diagram and a prediction pollution source position to the user terminal.
Optionally, the establishing a three-dimensional soot group trajectory model according to the three-dimensional distribution grid, the ground reflectivity distribution, the wind speed field, the temperature field, and diffusion parameters and sedimentation parameters of the pollutants specifically includes:
according to the formula
Determining an integral smoke mass model of pollutant diffusion;
in the formula, Cp(x,y,z,T)Represents the concentration, σ, of the p-th source at (x, y, z) at time Tx、σy、σzThe diffusion coefficients under different stabilities are shown, u represents the component of the flow field in the x direction, v represents the component of the flow field in the y direction, omega represents the component of the flow field in the z direction, H representseRepresents the effective height, V, of the p-th sourceSRepresents the settling velocity of pollutant particles, T represents the pollutant discharge time, QpRepresenting the intensity of the p-th point source, α1Representing the ground reflectivity.
Optionally, the air pollutant monitoring and early warning method further includes:
and sending the air pollution index to the user terminal.
Optionally, the establishing a wind speed field specifically includes:
wind field continuity equation:
u*＝u·ΔH(x·y),
v*＝v·ΔH(x·y),
ΔH(x,y)＝H(x,y)-h'
wherein u, v and w represent the wind field after coordinate adjustment, and sigma represents the vertical coordinate after adjustment;
hs'＝hs(x,y)+10
H(x,y)＝2000+hs(x,y)/2
wherein z represents the vertical height of the coordinate, hs(x, y) represents terrain height, H (x, y) represents mode upper boundary;
in order to facilitate interpolation of the wind field on x, y and sigma coordinates to satisfy a wind field continuous equation and simultaneously minimize the wind field change value, the problem is solved as the minimum value of the functional:
wherein: e (U, V, omega, lambda) represents the value of the fanfold at any point,
representing the initial wind field, λ representing the Lagrangian product, a1Indicating a horizontal observation error, a2Indicating the error of observation in the vertical direction,
representing the observation error variance;
initializing a ground wind field:
respectively inserting the wind speed measured values of each observation point on the ground into each ground grid point of the mode according to the u and v components by using a weighted interpolation method to obtain a ground initial field;
wherein U represents a component of U, v, i, j represents a (i, j) grid point, R represents an average distance from each observation point K to the (i, j) grid point, n represents the number of detected points, R representsERepresenting the radius of influence;
initializing an upper wind field:
the upper wind field is obtained by utilizing extrapolation of wind speed power law and wind-turning data, and specifically comprises the following steps: the wind speed changes from the ground to the layers of 200m without changing the wind direction, and the change of the wind speed follows:
wherein: u represents wind speed, U10Showing groundObserving the wind speed by a 10-meter wind vane; the wind fields of the heights above 200m are obtained by interpolating the wind field lines of 200m layers of the upper layer turning wind and the ground extrapolation.
In order to achieve the purpose, the invention also provides the following technical scheme:
an air pollutant monitoring and early warning cloud platform, comprising:
the data acquisition module is used for acquiring historical parameters of pollutant concentration, air temperature, wind direction and wind speed;
the data processing platform is used for acquiring diffusion parameters and sedimentation parameters of each pollutant;
establishing a three-dimensional cigarette packet track model: in uneven fluency, pollutant diffusion employs an integral plume model:
wherein, Cp(x,y,z,T)Represents the concentration, σ, of the p-th source at (x, y, z) at time Tx、σy、σzThe diffusion coefficients under different stabilities are shown, u represents the component of the flow field in the x direction, v represents the component of the flow field in the y direction, omega represents the component of the flow field in the z direction, H representseRepresents the effective height, V, of the p-th sourceSRepresents the settling velocity of pollutant particles, T represents the pollutant discharge time, QpRepresenting the intensity of the p-th point source, α1Representing the ground reflectivity;
predicting a pollution source, adjusting the position and intensity discharge time of the pollution source, fitting the obtained pollutant concentration data of each monitoring point, wherein the fitting precision reaches more than 80%, bringing detection data deviating from the calculation result into an in-doubt database, checking and maintaining the monitoring points, taking the position, intensity and discharge time of the pollution source reaching the fitting precision as the prediction result of the pollution source, completing a fitted three-dimensional smoke cluster track model, and obtaining a three-dimensional distribution field diagram reflecting pollutants;
the data processing platform is also used for predicting the concentration distribution of future pollutants by increasing time steps and calculating an air pollution index;
the management system is connected with the data processing platform through an optical fiber and is used for acquiring a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, future pollutant concentration distribution and an air pollution index;
the user terminal is connected with the data processing platform through a GPRS transmission module and is used for periodically receiving a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, future pollutant concentration distribution and an air pollution index; or the data processing platform is used for outputting a request to the data processing platform and then receiving a three-dimensional distribution field diagram reflecting the pollutants and a future pollutant concentration distribution and air pollution index output by the data processing platform.
Optionally, the data acquisition module includes a flow regulator, a PLC controller, a memory, and a sensor;
wherein the sensor comprises a sulfur dioxide, nitrogen dioxide, carbon monoxide, ozone, PM10, PM2.5, total suspended particulate matter, nitrogen oxides, lead, and benzopyrene contaminant concentration detector.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
setting a plurality of monitoring points to acquire historical parameters of pollutant concentration, air temperature, wind direction and wind speed; establishing an urban air flow channel, a ground reflectivity, a wind speed field and a temperature field; obtaining diffusion parameters and sedimentation parameters of each pollutant, and then establishing a three-dimensional smoke mass track model; predicting a pollution source according to the three-dimensional smoke group track model, adjusting the position, intensity and discharge time of the pollution source, and fitting the acquired pollutant concentration data of each monitoring point; if the position, the intensity and the emission time of the pollution source reach the fitting accuracy, taking the position, the intensity and the emission time of the pollution source as the prediction result of the pollution source, and obtaining a three-dimensional distribution field map reflecting pollutants according to the fitted three-dimensional smoke mass track model; and regularly issuing an air quality detection report, a prediction report, a pollutant distribution diagram and a prediction pollution source position to the user terminal. The invention realizes real-time detection of air quality, and carries out big data analysis on detection data, thereby tracking pollution sources and predicting air quality, and further issuing forenotice through the mobile terminal.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other structural schematic diagrams according to these drawings without inventive labor.
FIG. 1 is a platform topology of the present invention;
FIG. 2 is a block diagram of a data acquisition module of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, 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 invention.
Example 1
As shown in fig. 1 and fig. 2, the present embodiment provides an air pollutant monitoring and early warning method, including:
(1) setting a plurality of monitoring points, and acquiring historical parameters of pollutant concentration, air temperature, wind direction and wind speed of each monitoring point; pollutant concentration, air temperature, wind direction and wind speed are used as model input parameters.
(2) The method comprises the following steps of establishing an urban air flow channel, ground reflectivity, wind speed and temperature distribution three-dimensional field, and specifically comprises the following steps:
1) flow channel
And establishing a three-dimensional distribution grid of the flow channel according to the distribution of urban terrains and buildings.
2) Distribution of ground reflectivity
And measuring the ground reflectivity of different areas, and assigning values to each grid to obtain the ground reflectivity distribution.
3) Wind velocity field
In order to accurately describe the distribution of pollutant concentration, a wind speed field is established, and the pollutant distribution condition under the changing wind field can be predicted by combining the point source smoke mass track model.
a. Wind field continuity equation:
u*＝u·ΔH(x·y),
v*＝v·ΔH(x·y),
ΔH(x,y)＝H(x,y)-h'
in the formula: u, v and w represent the wind field after coordinate adjustment, and m/s; σ represents the adjusted vertical coordinate, m.
hs'＝hs(x,y)+10
H(x,y)＝2000+hs(x,y)/2
In the formula: z represents the vertical height of the coordinate, m; h iss(x, y) represents terrain height, m; h (x, y) denotes the mode upper bound, m.
In order to facilitate interpolation of the wind field on x, y and sigma coordinates to satisfy a wind field continuous equation and simultaneously minimize the wind field change value, the problem is solved as the minimum value of the functional:
in the formula, E (U, V, omega, lambda) represents the value of the function of any point,
it is shown that the initial wind field is,m/s; λ represents the Lagrangian multiplication value, a1Indicating a horizontal observation error, a2Indicating the error of observation in the vertical direction,
indicating the observed error variance.
b. Initializing a ground wind field:
and respectively inserting the actual wind speed measurement values of all observation points on the ground into all ground grid points of the mode according to the u and v components by using a weighted interpolation method to obtain a ground initial field.
Wherein U represents a component of U, v, m/s; i, j represents (i, j) grid point, r represents the average distance from each observation point K to (i, j) grid point, m; n represents the number of detection points, REDenotes the radius of influence, m.
c. Initializing an upper wind field:
the upper wind field is obtained by utilizing the extrapolation of the wind speed power law and the wind-turning data.
Specifically, the wind speed changes from the ground to the layers of 200m without changing the wind direction, and the change in wind speed follows:
in the formula: u represents wind speed, m/s; u shape10Representing the observation wind speed of a 10-meter wind vane on the ground, m/s; the wind fields of the heights above 200m are obtained by interpolating the wind field lines of 200m layers of the upper layer turning wind and the ground extrapolation.
4) Temperature field
And assigning the temperature data obtained at each detection point to a three-dimensional grid, and obtaining a temperature field by linear interpolation of grids without the detection points.
(3) And inputting diffusion parameters and sedimentation parameters of each pollutant.
(4) Establishing a three-dimensional cigarette packet track model:
in uneven fluency, pollutant diffusion employs an integral plume model:
in the formula, Cp(x,y,z,T)Represents the concentration of the p-th source at (x, y, z) at time T, mg/m3；σx、σy、σzDenotes the diffusion coefficient at different degrees of stability, m2S; u represents the component of the flow field in the x direction, m/s; v represents the component of the flow field in the y direction, m/s; omega represents the component of the flow field in the z direction, m/s; heRepresents the effective height of the p-th source, m; vSRepresenting the settling velocity of pollutant particles, the gaseous pollutant is 0, m/s; t represents the pollutant discharge time, hr; qpDenotes the intensity of the p-th point source, mg/s, α1Representing the ground reflectivity.
Predicting a pollution source in an area with higher pollutant concentration, adjusting the position and the intensity discharge time of the pollution source, fitting the obtained historical parameters (concentration data of each monitoring point), leading the historical fitting precision to reach more than 80 percent, bringing the detection data deviating from the calculation result into an in-doubt database, and checking and maintaining the monitoring points. And the position, intensity and discharge time of the pollution source reaching the fitting precision are used as the prediction result of the pollution source. And finishing the fitted three-dimensional smoke group track model to obtain a three-dimensional distribution field diagram accurately reflecting pollutants.
(5) The embodiment further includes calculating an air pollution index by increasing the time step to predict a future pollutant concentration distribution.
(6) The embodiment also comprises the step of periodically issuing an air quality detection report, a prediction report, a pollutant distribution diagram and a pollution source position prediction to the user terminal.
Example 2
The embodiment provides an air pollutant monitoring and early warning cloud platform, includes:
and the data acquisition module is used for acquiring historical parameters of pollutant concentration, air temperature, wind direction and wind speed.
And the data processing platform is used for acquiring diffusion parameters and sedimentation parameters of each pollutant.
Establishing a three-dimensional cigarette packet track model: in uneven fluency, pollutant diffusion employs an integral plume model:
wherein, Cp(x,y,z,T)Represents the concentration of the p-th source at (x, y, z) at time T, mg/m3；σx、σy、σzDenotes the diffusion coefficient at different degrees of stability, m2S; u represents the component of the flow field in the x direction, m/s; v represents the component of the flow field in the y direction, m/s; omega represents the component of the flow field in the z direction, m/s; heRepresents the effective height of the p-th source, m; vSRepresenting the settling velocity of pollutant particles, the gaseous pollutant is 0, m/s; t represents the pollutant discharge time, hr; qpDenotes the intensity of the p-th point source, mg/s, α1Representing the ground reflectivity.
Predicting a pollution source, adjusting the position and the intensity discharge time of the pollution source, fitting the obtained pollutant concentration data of each monitoring point, wherein the fitting precision reaches more than 80%, bringing the detection data deviating from the calculation result into an in-doubt database, checking and maintaining the monitoring points, taking the position, the intensity and the discharge time of the pollution source reaching the fitting precision as the prediction result of the pollution source, completing a fitted three-dimensional smoke cluster track model, and obtaining a three-dimensional distribution field diagram reflecting pollutants.
And the data processing platform is also used for predicting the concentration distribution of the future pollutants by increasing the time step and calculating the air pollution index.
And the management system is in communication connection with the data processing platform through optical fibers and is used for acquiring a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, the future pollutant concentration distribution and the air pollution index.
The user terminal is in communication connection with the data processing platform through the GPRS transmission module and is used for periodically receiving a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, the concentration distribution of the pollutants in the future and an air pollution index; or the system is used for outputting a request to the data processing platform, and receiving the three-dimensional distribution field diagram reflecting the pollutants and the future pollutant concentration distribution and air pollution index output by the data processing platform.
The air pollution detector is connected with the data processing platform through a GPRS transmission module and an optical fiber; the data processing platform is connected with the management system through optical fibers and is connected with the user terminal through a GPRS transmission module. The GPRS transmission module is used for a data processing platform obtained by the data acquisition module under normal working conditions. The optical fiber transmission is a data processing platform obtained by the data acquisition module under the condition that the GPRS transmission module fails.
The data processing platform is based on a Hadoop platform, a smoke group track model is adopted to carry out three-dimensional flow field simulation, the fitting historical pollutant concentration and wind speed monitoring data are utilized to correct error data, the pollutant concentration of a certain fixed place in the future is predicted, and an air pollution index is calculated; monitoring data of pollutant concentration and wind speed at each monitoring point are utilized, and three-dimensional flow field simulation is carried out through a smoke group track model to obtain a pollutant concentration plane distribution accurate tracking pollution source; and periodically issuing an air quality detection report, a prediction report, a pollutant distribution equivalent diagram and a possible pollution source position to the user terminal.
Compared with the prior art, the invention has the following advantages:
(1) the invention can intelligently process pollutant concentration monitoring, data transmission, analysis and release.
(2) The method can accurately track the pollution source and predict the pollutant concentration of a certain fixed place in the future.
(3) The invention can issue an accurate pollutant distribution map.
(4) The invention combines the Internet technology and the cloud computing technology to fully utilize the detection data, can accurately track the pollution source in time and predict the concentration of the future pollutants.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. An air pollutant monitoring and early warning method is characterized by comprising the following steps:
setting a plurality of monitoring points; the monitoring points are used for collecting historical parameters of pollutant concentration, air temperature, wind direction and wind speed;
establishing a three-dimensional distribution grid of a flow channel according to the distribution of urban terrains and buildings;
measuring the ground reflectivity of different areas, and assigning values to each grid to obtain the ground reflectivity distribution;
establishing a wind speed field;
assigning the temperature data acquired by the monitoring points to each grid, and obtaining a temperature field by linear interpolation on the grids without the monitoring points;
establishing a three-dimensional smoke mass track model according to the three-dimensional distribution grid, the ground reflectivity distribution, the wind speed field, the temperature field and the diffusion parameters and the sedimentation parameters of pollutants;
based on a Hadoop platform, performing three-dimensional flow field simulation by adopting the three-dimensional smoke group track model, correcting error data by utilizing fitting historical pollutant concentration and wind speed monitoring data, predicting the pollutant concentration of a certain fixed place in the future, and calculating an air pollution index;
carrying out three-dimensional flow field simulation through a smoke group track model by using the pollutant concentration and wind speed monitoring data of the monitoring points so as to obtain plane distribution of the pollutant concentration and accurately track the pollution source;
and regularly issuing an air quality detection report, a prediction report, a pollutant distribution diagram and a prediction pollution source position to the user terminal.
2. The air pollutant monitoring and early warning method according to claim 1, wherein the establishing of the three-dimensional soot trajectory model according to the three-dimensional distribution grid, the ground reflectivity distribution, the wind speed field, the temperature field, and the diffusion parameter and the sedimentation parameter of the pollutant specifically comprises:
according to the formula
Determining an integral smoke mass model of pollutant diffusion;
in the formula, Cp(x,y,z,T)Represents the concentration, σ, of the p-th source at (x, y, z) at time Tx、σy、σzThe diffusion coefficients under different stabilities are shown, u represents the component of the flow field in the x direction, v represents the component of the flow field in the y direction, omega represents the component of the flow field in the z direction, H representseRepresents the effective height, V, of the p-th sourceSRepresents the settling velocity of pollutant particles, T represents the pollutant discharge time, QpRepresenting the intensity of the p-th point source, α1Representing the ground reflectivity.
3. The air pollutant monitoring and early warning method according to claim 1, further comprising:
and sending the air pollution index to the user terminal.
4. The air pollutant monitoring and early warning method according to claim 1, wherein the establishing of the wind speed field specifically comprises:
wind field continuity equation:
u*＝u·ΔH(x·y),
v*＝v·ΔH(x·y),
ΔH(x,y)＝H(x,y)-h'
wherein u, v and w represent the wind field after coordinate adjustment, and sigma represents the vertical coordinate after adjustment;
h's＝hs(x,y)+10
H(x,y)＝2000+hs(x,y)/2
wherein z represents the vertical height of the coordinate, hs(x, y) represents terrain height, H (x, y) represents mode upper boundary;
in order to facilitate interpolation of the wind field on x, y and sigma coordinates to satisfy a wind field continuous equation and simultaneously minimize the wind field change value, the problem is solved as the minimum value of the functional:
wherein: e (U, V, omega, lambda) represents the value of the fanfold at any point,
representing the initial wind field, λ representing the Lagrangian product, a1Indicating a horizontal observation error, a2Indicating the error of observation in the vertical direction,
representing the observation error variance;
initializing a ground wind field:
respectively inserting the wind speed measured values of each observation point on the ground into each ground grid point of the mode according to the u and v components by using a weighted interpolation method to obtain a ground initial field;
wherein U represents a component of U, v, i, j represents a (i, j) grid point, R represents an average distance from each observation point K to the (i, j) grid point, n represents the number of detected points, R representsERepresenting the radius of influence;
initializing an upper wind field:
the upper wind field is obtained by utilizing extrapolation of wind speed power law and wind-turning data, and specifically comprises the following steps: the wind speed changes from the ground to the layers of 200m without changing the wind direction, and the change of the wind speed follows:
wherein: u represents wind speed, U10Representing the observed wind speed of a 10-meter wind vane on the ground; the wind fields of the heights above 200m are obtained by interpolating the wind field lines of 200m layers of the upper layer turning wind and the ground extrapolation.
5. The utility model provides an air pollution monitoring early warning cloud platform which characterized in that, air pollution monitoring early warning cloud platform includes:
the data acquisition module is used for acquiring historical parameters of pollutant concentration, air temperature, wind direction and wind speed;
the data processing platform is used for acquiring diffusion parameters and sedimentation parameters of each pollutant;
establishing a three-dimensional cigarette packet track model: in uneven fluency, pollutant diffusion employs an integral plume model:
wherein, Cp(x,y,z,T)Represents the concentration, σ, of the p-th source at (x, y, z) at time Tx、σy、σzThe diffusion coefficients under different stabilities are shown, u represents the component of the flow field in the x direction, v represents the component of the flow field in the y direction, omega represents the component of the flow field in the z direction, H representseRepresents the effective height, V, of the p-th sourceSRepresents the settling velocity of pollutant particles, T represents the pollutant discharge time, QpRepresenting the intensity of the p-th point source, α1Representing the ground reflectivity;
predicting a pollution source, adjusting the position and intensity discharge time of the pollution source, fitting the obtained pollutant concentration data of each monitoring point, wherein the fitting precision reaches more than 80%, bringing detection data deviating from the calculation result into an in-doubt database, checking and maintaining the monitoring points, taking the position, intensity and discharge time of the pollution source reaching the fitting precision as the prediction result of the pollution source, completing a fitted three-dimensional smoke cluster track model, and obtaining a three-dimensional distribution field diagram reflecting pollutants;
the data processing platform is also used for predicting the concentration distribution of future pollutants by increasing time steps and calculating an air pollution index;
the management system is connected with the data processing platform through an optical fiber and is used for acquiring a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, future pollutant concentration distribution and an air pollution index;
the user terminal is connected with the data processing platform through a GPRS transmission module and is used for periodically receiving a three-dimensional distribution field diagram reflecting pollutants output by the data processing platform, future pollutant concentration distribution and an air pollution index; or the data processing platform is used for outputting a request to the data processing platform and then receiving a three-dimensional distribution field diagram reflecting the pollutants and a future pollutant concentration distribution and air pollution index output by the data processing platform.
6. The air pollutant monitoring and early warning cloud platform of claim 5, wherein the data acquisition module comprises a flow regulator, a PLC controller, a memory and a sensor;
wherein the sensor comprises a sulfur dioxide, nitrogen dioxide, carbon monoxide, ozone, PM10, PM2.5, total suspended particulate matter, nitrogen oxides, lead, and benzopyrene contaminant concentration detector.
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