CN110567510B - Atmospheric pollution monitoring method, system, computer equipment and storage medium - Google Patents
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Abstract
The application discloses an atmosphere pollution monitoring method, an atmosphere pollution monitoring system, computer equipment and a storage medium, and relates to the field of environment monitoring, wherein the method comprises the following steps: dividing a monitoring area into a plurality of area grids; acquiring pollutant concentration monitoring data in each regional grid; acquiring satellite remote sensing data and meteorological data of a monitoring area; and obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area. The method can utilize the big data technology and the artificial intelligence technology to be matched with the real-time pollutant monitoring data of regional meshing, can accurately position the source of pollution, track the pollution region which is continuously diffused, and display the real-time condition of atmospheric pollution, and can more comprehensively and accurately display the atmospheric pollution condition of the monitoring region compared with the traditional technology.
Description
Technical Field
The present application relates to the field of environmental monitoring, and in particular, to an atmospheric pollution monitoring method, system, computer device, and storage medium.
Background
Today, the technology is continuously developed and advanced, people living on the land are more and more concerned about environmental pollution, soil, water and atmosphere are more and more concerned about natural environments which are closely related to our lives, wherein the atmospheric pollution problem such as PM2.5 explosion meter is one of the most concerned hot spot problems in recent years, but unfortunately, the form of atmospheric pollution treatment is still very serious due to high cost and low efficiency of atmospheric pollution monitoring.
The traditional atmospheric pollution monitoring system is mainly used for monitoring atmospheric pollution by installing monitoring stations on the ground, the cost of the monitoring stations is high, the sources of pollution occurrence are single, discrete and difficult to accurately position, the continuously diffused pollution areas are difficult to track, and the real-time condition of the atmospheric pollution is more difficult to display.
Disclosure of Invention
In view of the above, the present application provides an atmospheric pollution monitoring method, system, computer device and storage medium that can precisely locate the source of pollution occurrence, track the continuously diffused pollution area, and exhibit the real-time status of atmospheric pollution.
According to a first aspect of the present application there is provided an atmospheric pollution monitoring method comprising:
dividing a monitoring area into a plurality of area grids;
acquiring pollutant concentration monitoring data in each regional grid;
acquiring satellite remote sensing data and meteorological data of a monitoring area;
and obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area.
According to a second aspect of the present application there is provided an atmospheric pollution monitoring system comprising:
grid dividing means for dividing the monitoring area into a plurality of area grids;
the monitoring equipment is arranged in each area grid of the monitoring area and is used for monitoring pollutant concentration data in each area grid;
the data acquisition device is used for acquiring satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area;
and the data processing device is used for obtaining the atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data in the grids of each area of the monitoring area.
According to a third aspect of the present application, there is provided a storage medium having stored thereon a computer program which when executed by a processor implements the above-described atmosphere pollution monitoring method.
According to a fourth aspect of the present application there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
dividing a monitoring area into a plurality of area grids;
acquiring pollutant concentration monitoring data in each regional grid;
acquiring satellite remote sensing data and meteorological data of a monitoring area;
and obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area.
According to the method, the system, the computer equipment and the storage medium for monitoring the atmospheric pollution, firstly, the monitoring area is subjected to gridding treatment, then, pollutant concentration monitoring data collected in real time in grids of each area are obtained, further, satellite remote sensing data and meteorological data of the monitoring area are obtained, and finally, according to the obtained various data, the atmospheric pollution monitoring result of the monitoring area is obtained through a pre-trained machine learning model. According to the atmosphere pollution monitoring method, system, computer equipment and storage medium, the source of pollution occurrence can be precisely positioned, the pollution area which is continuously diffused can be tracked, and the real-time condition of atmosphere pollution can be displayed.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 shows a schematic flow chart of an air pollution monitoring method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining an atmospheric pollution monitoring result of a monitoring area according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an air pollution monitoring system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another air pollution monitoring system according to an embodiment of the present application.
Detailed Description
The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
In order to solve the problem that the conventional air pollution monitoring device cannot comprehensively and accurately display the air pollution condition of the monitoring area, the embodiment of the application provides an air pollution monitoring method which can be applied to computer equipment, as shown in fig. 1, and comprises the following steps:
101. the monitoring area is divided into a plurality of area grids.
The monitoring area refers to an area where environmental monitoring is required, for example, the monitoring area may be one city or one province, or may be multiple cities or multiple provinces.
Specifically, in order to facilitate accurate monitoring of the monitored area, the monitored area may be divided into a plurality of area grids according to a predetermined length value and a predetermined width value, for example, the monitored area may be divided into a plurality of area grids according to square units with the same size, and it should be noted that the divided area grids should not be too large, and the three-dimensional spatial distribution of the pollutants may not be accurately predicted if the area grids are too large, and for example, the Jing Ji area is taken as the monitored area, the area grids may be divided according to a size of 3km×3km, and the number of the divided area grids may be up to 36793.
102. Contaminant concentration monitoring data within each regional grid is acquired.
Wherein the pollutants refer to substances discharged into the atmosphere due to human activities or natural processes and having harmful effects on the environment or human, and mainly include one or more of sulfur-containing compounds (e.g., SO2, H2S, etc.), nitrogen-containing compounds (e.g., NO2, NH3, etc.), fine particles (PM 2.5), inhalable particles (PM 10), carbon monoxide (CO), ozone (O3), and Total Volatile organic compounds (Total Volatile OrganicCompounds, TVOC).
Specifically, the atmospheric pollutant monitoring device can be arranged in each area grid, the number of the monitoring devices can be one or more, and key monitoring points can be selected according to the positions where pollution is easy to occur or the positions where pollution sources are concentrated, wherein the monitoring device can monitor the pollutant concentration in real time, for example, data monitoring is carried out by adopting a minute level, and the monitored pollutant concentration data can be transmitted to the computer device in real time through the wireless communication module, so that the computer device can acquire the pollutant concentration monitoring data in each area grid in real time.
It can be understood that, according to practical situations, the pollutant concentration monitoring data in this embodiment may be concentration monitoring data of a certain pollutant, concentration monitoring data of a pollutant combination formed by multiple pollutants, or pollutant concentration monitoring data after normalizing units of different pollutants.
103. Satellite remote sensing data and meteorological data of a monitoring area are acquired.
Specifically, the server can directly acquire satellite remote sensing data and meteorological data of the monitoring area through a satellite observation database and a meteorological database disclosed by a network, and can also acquire satellite remote sensing data and meteorological data of the monitoring area through a satellite monitoring department or an environmental protection meteorological department. The satellite remote sensing data include, but are not limited to, satellite remote sensing image data such as Terra in the united states, MODIS data acquired on Aqua satellites, OMI data acquired on Aura satellites, sentinel data in europe, landsat8 data in the united states, himaware-8 satellite data in sunflower No. eight, and the like; meteorological data includes, but is not limited to, wind speed, wind direction, precipitation, relative humidity, and temperature data for each monitoring period of the monitored area.
104. And obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area.
Specifically, the server may preprocess the acquired satellite remote sensing data, weather data, and pollutant concentration monitoring data in each area grid, and then input the preprocessed satellite remote sensing data, weather data, and pollutant concentration monitoring data into a machine learning model trained in advance, so as to acquire an atmospheric pollution monitoring result in each period of the monitoring area. The training process of the machine learning model specifically comprises the following steps: firstly, constructing feature vectors with different dimensions according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in each regional grid in a past monitoring period, generating a multi-dimensional feature sample set according to the multi-dimensional feature vectors of a plurality of regional grids, selecting a part of data from the multi-dimensional feature sample set as a training set for model training, selecting another part of data as a testing set for testing a trained model, and finally performing feature learning such as deep learning, integrated learning, unsupervised learning and the like on the multi-dimensional feature sample set by adopting a deep learning model to obtain training model parameters, and determining a final relation model according to the model parameters. The relationship model can reflect the relationship among satellite remote sensing data, meteorological data and pollutant concentration monitoring data.
In one embodiment, as shown in fig. 2, according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitored area, an atmospheric pollution monitoring result of the monitored area is obtained through a pre-trained machine learning model, which specifically includes the following steps:
201. based on satellite remote sensing data, acquiring aerosol optical thickness and pollution source positioning data of a monitoring area.
Wherein the optical thickness (Aerosol Optical Depth, AOD) of the aerosol is the integral of the extinction coefficient of the medium in the vertical direction, which is an important parameter describing the attenuation of light by the aerosol, and is also an important physical quantity characterizing the degree of atmospheric turbidity.
Specifically, the computer device may obtain aerosol data by means of radiometric calibration, atmospheric correction, image stitching, image clipping, etc., obtain the aerosol optical thickness of the obtained aerosol data by means of extended dark pixel method, and finally perform humidity correction and vertical correction on the aerosol optical thickness, and calculate PM2.5 distribution condition of the monitoring area by means of statistical method. In addition, the server can also perform entity identification of the monitoring area through satellite remote sensing data, for example, the satellite remote sensing data is utilized to identify water bodies, farmlands, vegetation, traffic, densely populated areas and factory densely populated areas of the monitoring area, so that specific positions of pollution sources such as factories are positioned.
202. And outputting the pollutant space distribution data of the monitoring area through a pre-trained meteorological model and an air quality model according to the meteorological data.
The meteorological model is a mathematical physical model for reflecting the atmospheric motion state, and can output the space distribution condition of pollutants. The meteorological model may specifically include one or more of a CMAQ (community multiscale air quality modelingsystem) model, a CAMx (comprehensive air quality model with extensions) model, a WRF-CHEM (Weather Research Forecast-CHEMICAL) model. The air quality model is a model for calculating pollutant concentrations at different positions by using a mathematical equation, and the establishment of the model specifically comprises the following steps: firstly, calculating a solar altitude angle according to longitude and latitude of a regional grid and monitoring time, then calculating an atmospheric stability level according to meteorological data and the solar altitude angle by using a Turner method, and finally obtaining the relationship between the atmospheric stability level and the position of the maximum pollutant by using a turbulence statistics theory, thereby obtaining the position of the maximum pollutant concentration.
Specifically, the computer device may divide the meteorological conditions for the monitoring area according to the data such as the wind speed, the wind direction, the precipitation, the relative humidity, the temperature and the like of each monitoring period of the monitoring area, where different meteorological conditions correspond to different weather conditions, and the meteorological conditions may be specifically described by the meteorological parameters such as the wind speed, the wind direction, the air temperature, the air humidity, the atmospheric pressure and the like. Furthermore, by inputting the meteorological conditions of the monitoring area into the meteorological model and the air quality model, the pollutant space distribution data of the monitoring area and the position of the maximum pollutant concentration can be obtained. The spatial distribution of the pollutant concentration simulated for the same monitoring area is different under different meteorological conditions.
203. And outputting pollutant concentration calibration data through a pre-trained pollutant concentration calibration model according to the pollutant concentration monitoring data in each regional grid.
Specifically, the pollutant concentration monitoring data in each regional grid may be corrected using a pollutant concentration calibration model, wherein the pollutant concentration calibration model is selected from one or more of a linear function model, a logarithmic function model, a unitary quadratic, a unitary cubic model, a power function model, and an exponential function model. In the actual working process, because larger or smaller errors are necessarily present in the detection process, it is difficult to ensure that the pollutant concentration monitoring data and the standard monitoring data can be completely fitted, and a difference exists between the corrected pollutant concentration monitoring data and the standard monitoring data.
204. And generating an atmospheric pollution monitoring result of the monitoring area through a pre-trained atmospheric pollution monitoring model according to the preprocessed satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area.
Specifically, the server can fill the corrected pollutant concentration monitoring values into the machine learning model by utilizing an interpolation method according to PM2.5 distribution data obtained after the pretreatment of the three types of data, satellite images, pollutant space distribution under different meteorological conditions, and pollutant concentration distribution conditions of all position points in a monitoring area taking longitude and latitude as coordinates are obtained after certain weighting treatment. In this embodiment, the longitude of the area grid is the abscissa and the latitude of the area grid is the ordinate. The interpolation method can be one or more of a kriging interpolation algorithm, nearest neighbor interpolation and bilinear interpolation. It will be appreciated that the concentration profile of contaminants at the same location will be different for different periods of time and under different meteorological conditions.
Through a machine learning model, correlation functions among satellite remote sensing data, meteorological data and pollutant concentration data at the same time and at the same place can be obtained, so that an atmospheric pollution monitoring result of a monitoring area is obtained, wherein the atmospheric pollution monitoring result comprises one or more information of a source of atmospheric pollution, a hot spot area with serious atmospheric pollution, an atmospheric pollution transmission path and real-time conditions of the occurrence of atmospheric pollution.
In one embodiment, a method of determining a source of occurrence of atmospheric pollution may include: firstly, predicting a suspected pollution source area according to pollutant concentration data and satellite images, locking an area grid where the maximum pollutant concentration in the monitored area is located, planning a detection route by combining meteorological data of the suspected pollution source area, detecting pollutant concentration data at and around the position where the maximum pollutant concentration is located by using monitoring equipment, and finally determining the source of pollution.
In one embodiment, a method of determining a hot spot region where atmospheric pollution is severe may include: and comparing the pollutant concentration data of each regional grid with a preset value, wherein the regional grids with the pollutant concentration data being greater than or equal to the preset value can be determined as hot spot regional grids.
In one embodiment, a method of determining a path of an atmospheric pollution transfer may include: firstly, carrying out linear interpolation calculation by utilizing a plurality of wind speeds and wind directions of the positions of the grids of each area to obtain the vector speed of the movement of the pollutant particles, then determining the running time of the pollutant particles according to the acquisition time interval of the pollutant concentration data, and finally calculating the movement track of the pollutant particles according to the Lagrange track model and the integral of the position vectors of the Lagrange track model in time and space. Further, after the pollutant concentration data, the pollutant particle motion trail and the corresponding longitude and latitude and satellite images are subjected to superposition processing, the visual pollutant particle motion trail can be obtained, so that pollution tracing is realized, and the position of a pollution source is determined.
In one embodiment, a method of determining a real-time condition in which atmospheric pollution occurs may include: and updating and displaying the pollutant concentration data at regular time according to the pollutant concentration data, satellite remote sensing data and meteorological data which are monitored by the detection equipment in real time and the positions of the regional grids, and representing the regional grids with high pollutant concentration by special colors so as to display the real-time condition of the occurrence of atmospheric pollution.
In one embodiment, the computer device may display and transmit the atmospheric pollution monitoring results of the monitored area. Specifically, the computer device can display the atmosphere pollution monitoring condition of the monitoring area in real time through the display device such as a display, and marks information such as a source of the atmosphere pollution, a hot spot area with serious atmosphere pollution, a path of the atmosphere pollution transmission and the like by using special colors.
In one embodiment, the monitoring area is divided into a plurality of area grids according to a preset length value and a preset width value. Specifically, the monitoring area can be divided into a plurality of area grids according to the preset length value and the preset width value, for example, the monitoring area is divided into the area grids according to square units with the same size, and the fact that the divided area grids are not too large is needed, and the three-dimensional space distribution of pollutants cannot be accurately predicted if the area grids are too large. In this embodiment, each area grid is square, and the side length thereof may be between 500m and 3000m, and it is understood that the size of the area grid may be adjusted according to actual requirements, which is not limited thereto.
The embodiment provides an atmospheric pollution monitoring method, firstly, performing gridding treatment on a monitoring area, then acquiring pollutant concentration monitoring data acquired in real time in grids of each area, then acquiring satellite remote sensing data and meteorological data of the monitoring area, and finally acquiring an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to the acquired various data. According to the atmospheric pollution monitoring method, the big data technology and the artificial intelligence technology are matched with the real-time pollutant monitoring data of regional meshing, so that the pollution source can be accurately positioned, the pollution region which is continuously diffused is tracked, the real-time condition of atmospheric pollution is displayed, and compared with the traditional technology, the atmospheric pollution condition of the monitoring region can be displayed more comprehensively and accurately.
Further, as a specific implementation of the methods shown in fig. 1 and fig. 2, the embodiment provides an air pollution monitoring system, as shown in fig. 3, including: grid dividing means 31, monitoring device 32, data acquisition means 33, data processing means 34, wherein:
grid dividing means 31 for dividing the monitoring area into a plurality of area grids;
monitoring devices 32 disposed within each of the area grids of the monitoring area for monitoring contaminant concentration data within each of the area grids;
the data acquisition device 33 is used for acquiring satellite remote sensing data, meteorological data of the monitoring area and pollutant concentration monitoring data in grids of each area;
the data processing device 34 is configured to obtain an atmospheric pollution monitoring result of the monitored area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitored area.
In a specific application scenario, the data processing device 34 may be specifically configured to preprocess satellite remote sensing data, meteorological data of the monitored area, and pollutant concentration monitoring data in grids of each area; and generating an atmospheric pollution monitoring result of the monitoring area through a pre-trained atmospheric pollution monitoring model according to the preprocessed satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area.
In a specific application scenario, the data processing device 34 may be specifically configured to obtain, based on satellite remote sensing data, aerosol optical thickness and pollution source positioning data of the monitored area; outputting pollutant space distribution data of a monitoring area through a pre-trained meteorological model and an air quality model according to meteorological data; and outputting pollutant concentration calibration data through a pre-trained pollutant concentration calibration model according to the pollutant concentration monitoring data in each regional grid.
In a specific application scenario, the atmospheric pollution monitoring result includes one or more information among a source of occurrence of atmospheric pollution, a hot spot area where the atmospheric pollution is serious, a path of atmospheric pollution transmission, and a real-time condition where the atmospheric pollution occurs.
In a specific application scenario, as shown in fig. 4, the system further includes: display means 35 and communication means 36, wherein:
a display device 35 for displaying the air pollution monitoring result of the monitoring area;
and the communication device 36 is used for sending the air pollution monitoring result of the monitoring area.
In a specific application scenario, the area grid dividing device 31 is further configured to divide the monitoring area into a plurality of area grids according to a preset length value and a preset width value.
It should be noted that, for other corresponding descriptions of each functional unit related to the air pollution monitoring system provided in this embodiment, reference may be made to corresponding descriptions in fig. 1 and fig. 2, and no further description is given here.
Based on the above-mentioned methods shown in fig. 1 and 2, correspondingly, the present embodiment further provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the above-mentioned method for monitoring air pollution shown in fig. 1 and 2.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, where the software product to be identified may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disc, a mobile hard disk, etc.), and include several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to execute the method described in the various implementation scenarios of the present application.
Based on the method shown in fig. 1 and fig. 2 and the system embodiments shown in fig. 3 and fig. 4, in order to achieve the above objects, the present embodiment further provides an entity device for monitoring air pollution, which may specifically be a personal computer, a server, a smart phone, a tablet computer, a smart watch, or other network devices, where the entity device includes a storage medium and a processor; a storage medium storing a computer program; a processor for executing a computer program to implement the method as shown in fig. 1 and 2.
Optionally, the physical device may further include a user interface, a network interface, a camera, radio Frequency (RF) circuitry, sensors, audio circuitry, WI-FI modules, and the like. The user interface may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
It will be appreciated by those skilled in the art that the structure of the physical device for monitoring the atmospheric pollution provided in this embodiment is not limited to the physical device, and may include more or fewer components, or may be combined with certain components, or may be arranged with different components.
The storage medium may also include an operating system, a network communication module. The operating system is a program for managing the entity equipment hardware and the software resources to be identified, and supports the operation of the information processing program and other software and/or programs to be identified. The network communication module is used for realizing communication among all components in the storage medium and communication with other hardware and software in the information processing entity equipment.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware. By applying the technical scheme of the application, the source of pollution can be precisely positioned, the pollution area which is continuously diffused can be tracked, and the real-time condition of atmospheric pollution can be displayed, so that compared with the traditional technology, the atmospheric pollution condition of the monitoring area can be displayed more comprehensively and accurately.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the application. Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the application, and the application is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the application.
Claims (10)
1. An atmosphere pollution monitoring method, the method comprising:
dividing a monitoring area into a plurality of area grids;
acquiring pollutant concentration monitoring data in each regional grid;
acquiring satellite remote sensing data and meteorological data of a monitoring area, wherein the meteorological data comprise, but are not limited to, wind speed, wind direction, precipitation, relative humidity and temperature of each monitoring period of the monitoring area;
according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area, obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model, wherein the atmospheric pollution monitoring result comprises an atmospheric pollution transmission path and/or an atmospheric pollution generation source;
the method for determining the path of the air pollution transmission comprises the following steps:
performing linear interpolation calculation by utilizing a plurality of wind speeds and wind directions of the positions of the grids of each area to obtain the vector speed of the movement of the pollutant particles; determining the running time of the pollutant particles according to the collection time interval of the pollutant concentration monitoring data; according to the Lagrange locus model, the moving locus of the pollutant particles is obtained through the integral calculation of vectors of the pollutant concentration monitoring data on time and space positions;
the method further comprises the steps of:
predicting a suspected pollution source area according to the pollutant concentration data and the satellite image; locking a regional grid where the maximum pollution concentration in the monitoring region is located; planning a detection route by combining with meteorological data of the suspected pollution source region, and finally determining a source of occurrence of atmospheric pollution by utilizing the position of a region grid where the maximum pollution concentration is and surrounding detection pollutant concentration data of monitoring equipment, wherein based on the satellite remote sensing data, acquiring aerosol optical thickness of the monitoring region and pollution source positioning data; dividing meteorological conditions for a monitoring area according to meteorological data of the monitoring area, inputting the meteorological conditions of the monitoring area into a meteorological model to obtain pollutant space distribution data of the monitoring area, inputting the meteorological conditions of the monitoring area into an air quality model to obtain the position of the maximum pollutant concentration of the monitoring area, wherein different meteorological conditions correspond to different weather conditions;
the step of establishing the air quality model comprises the following steps:
calculating a solar altitude according to longitude and latitude of the regional grid and the monitoring time; calculating the atmospheric stability level by utilizing a Turner method according to meteorological data and a solar altitude; and obtaining the relationship between the atmospheric stability level and the position of the maximum pollutant by using a turbulence statistics theory, thereby obtaining the position of the maximum pollutant concentration.
2. The method for monitoring the atmospheric pollution according to claim 1, wherein the obtaining the atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in each area grid of the monitoring area comprises:
preprocessing satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area;
and generating an atmosphere pollution monitoring result of the monitoring area through a pre-trained atmosphere pollution monitoring model according to the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data in the grids of each area of the preprocessed monitoring area.
3. The method of claim 2, wherein preprocessing the satellite remote sensing data, the weather data, and the pollutant concentration monitoring data in each regional grid of the monitored region comprises:
and outputting pollutant concentration calibration data through a pre-trained pollutant concentration calibration model according to the pollutant concentration monitoring data in each regional grid.
4. The method of claim 3, wherein the atmospheric pollution monitoring result further comprises one or more of information of a hot spot area where atmospheric pollution is serious and a real-time condition where atmospheric pollution occurs.
5. The method of any one of claims 1-4, further comprising:
and displaying and/or transmitting the air pollution monitoring result of the monitoring area.
6. The method of any one of claims 1-4, wherein dividing the monitored area into a plurality of area grids comprises:
dividing the monitoring area into a plurality of area grids according to a preset length value and a preset width value.
7. An atmospheric pollution monitoring system, the system comprising:
grid dividing means for dividing the monitoring area into a plurality of area grids;
the monitoring equipment is arranged in each area grid of the monitoring area and is used for monitoring pollutant concentration data in each area grid;
the data acquisition device is used for acquiring satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area, wherein the meteorological data comprise, but are not limited to, wind speed, wind direction, precipitation, relative humidity and temperature of each monitoring period of the monitoring area;
the data processing device is used for obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to satellite remote sensing data, meteorological data and pollutant concentration monitoring data in grids of each area of the monitoring area, wherein the atmospheric pollution monitoring result comprises an atmospheric pollution transmission path and/or an atmospheric pollution generation source;
the data processing device is specifically used for performing linear interpolation calculation by utilizing a plurality of wind speed and wind direction data of the positions of the grids in each area to obtain the vector speed of the pollutant particle motion; determining the running time of the pollutant particles according to the collection time interval of the pollutant concentration monitoring data; according to the Lagrange locus model, the moving locus of the pollutant particles is obtained through the integral calculation of vectors of the pollutant concentration monitoring data on time and space positions;
the data processing device is specifically used for predicting a suspected pollution source area according to pollutant concentration data and satellite images; locking a regional grid where the maximum pollution concentration in the monitoring region is located; planning a detection route by combining with meteorological data of the suspected pollution source region, and finally determining a source of occurrence of atmospheric pollution by utilizing the position of a region grid where the maximum pollution concentration is and surrounding detection pollutant concentration data of monitoring equipment, wherein based on the satellite remote sensing data, acquiring aerosol optical thickness of the monitoring region and pollution source positioning data; dividing meteorological conditions for a monitoring area according to meteorological data of the monitoring area, inputting the meteorological conditions of the monitoring area into a meteorological model to obtain pollutant space distribution data of the monitoring area, inputting the meteorological conditions of the monitoring area into an air quality model to obtain the position of the maximum pollutant concentration of the monitoring area, wherein different meteorological conditions correspond to different weather conditions; the step of establishing the air quality model comprises the following steps: calculating a solar altitude according to longitude and latitude of the regional grid and the monitoring time; calculating the atmospheric stability level by utilizing a Turner method according to meteorological data and a solar altitude; and obtaining the relationship between the atmospheric stability level and the position of the maximum pollutant by using a turbulence statistics theory, thereby obtaining the position of the maximum pollutant concentration.
8. The atmosphere pollution monitoring system of claim 7, wherein the system further comprises:
the display device is used for displaying the air pollution monitoring result of the monitoring area;
and the communication device is used for sending the air pollution monitoring result of the monitoring area.
9. A storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program when executed by the processor implements the steps of the method according to any one of claims 1 to 6.
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