CN110567510A - Atmospheric pollution monitoring method, system, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses an atmospheric pollution monitoring method, a system, computer equipment and a storage medium, and relates to the field of environmental 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 area 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 the satellite remote sensing data and the meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid. The method can accurately position the source of pollution occurrence, track continuously diffused pollution areas and show the real-time condition of atmospheric pollution by utilizing a big data technology and an artificial intelligence technology to be matched with the real-time pollutant monitoring data of the area gridding.
Description
Technical Field
The invention relates to the field of environmental monitoring, in particular to an atmospheric pollution monitoring method, an atmospheric pollution monitoring system, computer equipment and a storage medium.
Background
Today with the continuous development and progress of science and technology, people living on the land pay more attention to the environmental pollution problem nearby and also pay more attention to natural environments, such as soil, water and atmosphere, which are closely related to our lives, wherein the air pollution problem, such as PM2.5 explosion surface, is one of the most concerned hot problems of people in recent years, but unfortunately, the form of air pollution abatement is still very severe due to the high cost and low efficiency of air pollution monitoring.
traditional atmosphere pollution monitoring system realizes the monitoring of atmosphere pollution mainly through installing the monitoring website on ground, and these monitoring websites not only are high in cost, and single, discrete and difficult accurate location pollution source of taking place moreover, are difficult to track the pollution area that constantly spreads, more difficult show the real-time situation of atmosphere pollution.
Disclosure of Invention
in view of the above, the present invention provides an atmospheric pollution monitoring method, system, computer device and storage medium, which can accurately locate the source of pollution occurrence, track the continuously diffused pollution area, and show the real-time status of atmospheric pollution.
according to a first aspect of the present invention, there is provided a method of atmospheric pollution monitoring, the method comprising:
dividing a monitoring area into a plurality of area grids;
Acquiring pollutant concentration monitoring data in each area 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 the satellite remote sensing data and the meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid.
according to a second aspect of the present invention there is provided an atmospheric pollution monitoring system, the system comprising:
The grid dividing device is used 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 used for monitoring pollutant concentration data in each area grid;
the data acquisition device is used for acquiring satellite remote sensing data and meteorological data of the monitored area and pollutant concentration monitoring data in each area grid;
And 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 the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data in each area grid of the monitoring area.
according to a third aspect of the present invention, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described atmospheric pollution monitoring method.
According to a fourth aspect of the present invention, 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 following steps when executing the program:
Dividing a monitoring area into a plurality of area grids;
Acquiring pollutant concentration monitoring data in each area 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 the satellite remote sensing data and the meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid.
According to the atmospheric pollution monitoring method, the system, the computer equipment and the storage medium, firstly, a monitoring area is subjected to gridding processing, then pollutant concentration monitoring data acquired in real time in each area grid are acquired, further satellite remote sensing data and meteorological data of the monitoring area are acquired, and finally, according to the acquired various data, an atmospheric pollution monitoring result of the monitoring area is acquired through a pre-trained machine learning model. According to the atmospheric pollution monitoring method, the atmospheric pollution monitoring system, the computer equipment and the storage medium, the big data technology and the artificial intelligence technology are matched with the real-time pollutant monitoring data of the area gridding, the source of pollution occurrence can be accurately positioned, the continuously diffused pollution area is tracked, the real-time condition of atmospheric pollution is displayed, and compared with the traditional technology, the atmospheric pollution condition of the monitored area can be displayed more comprehensively and accurately.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
Fig. 1 is a schematic flow chart of an atmospheric pollution monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process for obtaining an atmospheric pollution monitoring result of a monitored area according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating an atmospheric pollution monitoring system according to an embodiment of the present invention;
fig. 4 shows a schematic structural diagram of another atmospheric pollution monitoring system provided by the embodiment of the invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In order to solve the problem that the conventional atmospheric pollution monitoring equipment cannot comprehensively and accurately display the atmospheric pollution condition of a monitored area, the embodiment of the invention provides an atmospheric pollution monitoring method, which can be applied to computer equipment and comprises the following steps as shown in fig. 1:
101. the monitoring area is divided into a plurality of area grids.
The monitoring area refers to an area where environmental monitoring is needed, for example, the monitoring area may be one city or one province, or may be multiple cities or multiple provinces, and the like.
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 predetermined length values and width values, for example, the area grids of the monitored area are divided according to square units with the same size, it should be noted that the divided area grids are not too large, and the three-dimensional spatial distribution of the pollutants cannot be accurately predicted if the area grids are too large, for example, the jingji area is taken as the monitored area, the area grids may be divided according to the size of 3km × 3km, and the number of the divided area grids may reach 36793 in total.
102. And acquiring pollutant concentration monitoring data in each area grid.
The pollutants refer to substances that are emitted into the atmosphere due to human activities or natural processes and have harmful effects on the environment or human beings, and mainly include one or more of sulfur-containing compounds (such as SO2, H2S, etc.), nitrogen-containing compounds (such as NO, NO2, NH3, etc.), fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), Ozone (O3), and Total Volatile Organic Compounds (TVOC).
Specifically, atmosphere pollutant monitoring devices can be arranged in each regional grid, the number of the monitoring devices can be one or more, and key monitoring points can be selected according to the position where pollution easily occurs or the concentrated position of a pollution source, wherein the monitoring devices can monitor the pollutant concentration in real time, if data monitoring is carried out by adopting a minute level, the monitored pollutant concentration data can be transmitted to computer equipment in real time through a wireless communication module, and the computer equipment can acquire the pollutant concentration monitoring data in each regional grid in real time.
It can be understood that, according to the actual situation, the pollutant concentration monitoring data referred to in this embodiment may be concentration monitoring data of a certain pollutant, concentration monitoring data of a pollutant combination composed of multiple pollutants, or pollutant concentration monitoring data obtained by performing normalization processing on units of different pollutants.
103. satellite remote sensing data and meteorological data of a monitored area are obtained.
Specifically, the server can directly acquire the satellite remote sensing data and meteorological data of the monitored area through a satellite observation database and a meteorological database which are disclosed by a network, and can also acquire the satellite remote sensing data and meteorological data of the monitored area through a satellite monitoring department or an environmental protection meteorological department. The satellite remote sensing data comprises satellite remote sensing image data such as but not limited to Terra data of the United states, MODIS data acquired from Aqua satellites, OMI data acquired from Aura satellites, European Sentinel data, United states Landsat8 data, Japanese sunflower eight Himapari-8 satellite data and the like; the meteorological data includes, but is not limited to, wind speed, wind direction, precipitation, relative humidity, and temperature data for each monitoring period of the monitoring area.
104. And obtaining an atmospheric pollution monitoring result of the monitoring area through a pre-trained machine learning model according to the satellite remote sensing data and the meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid.
Specifically, the server can preprocess the acquired satellite remote sensing data, meteorological data and pollutant concentration monitoring data in each area grid, and then input the preprocessed data into a machine learning model trained in advance so as to acquire an atmospheric pollution monitoring result in each time 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 the regional grids, then selecting one part of data from the multi-dimensional feature sample set as a training set to perform model training, selecting the other part of data as a testing set to test a trained model, finally performing feature learning such as deep learning, ensemble learning and unsupervised learning on the multi-dimensional feature sample set by adopting a deep learning model to obtain trained model parameters, and further determining a final relation model according to the model parameters. The relational model can reflect the relationship among the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data.
In an embodiment, as shown in fig. 2, obtaining an atmospheric pollution monitoring result of a monitored area through a pre-trained machine learning model according to satellite remote sensing data of the monitored area, meteorological data and pollutant concentration monitoring data in each area grid may specifically include the following steps:
201. and acquiring the optical thickness of the aerosol and the positioning data of the pollution source in the monitoring area based on the satellite remote sensing data.
among them, the Aerosol Optical Depth (AOD) is the integral of the extinction coefficient of the medium in the vertical direction, and is an important parameter describing the attenuation effect of the Aerosol on light, and is also an important physical quantity for representing the degree of atmospheric turbidity.
specifically, the computer device may first obtain aerosol data by radiation calibration, atmospheric correction, image stitching, image clipping, and the like, then obtain aerosol optical thickness from the obtained aerosol data by an extended dark pixel method, and finally calculate PM2.5 distribution in the monitoring area by a statistical method after humidity correction and vertical correction are performed on the aerosol optical thickness. In addition, the server can also perform entity identification of the monitored area through satellite remote sensing data, for example, water, farmlands, vegetation, traffic, densely populated areas and factory-dense areas of the monitored area are identified by using the satellite remote sensing data, so that specific positions of pollution sources such as factories and the like are positioned.
202. And outputting pollutant spatial 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 used for reflecting the atmospheric motion state, and can output the spatial distribution condition of pollutants. The meteorological model may specifically include one or more of a CMAQ (community multiple air quality modeling system) model, a CAMx (comprehensive air quality model with extensions) model, and a WRF-CHEM (Weather Research Forecast-CHEMICAL) model. The air quality model is a model for calculating the pollutant concentration at different positions by using a mathematical equation, and the establishment of the model specifically comprises the following steps: the method comprises the steps of firstly calculating a solar altitude according to longitude and latitude of a regional grid and monitoring time, then calculating an atmospheric stability grade according to meteorological data and the solar altitude by using a Turner method, and finally obtaining the relation between the atmospheric stability grade and the position of the maximum pollutant by using a turbulence statistical theory so as to obtain the position of the maximum pollutant concentration.
specifically, the computer device may divide meteorological conditions for the monitoring area according to data such as wind speed, wind direction, precipitation, relative humidity, and temperature of each monitoring period of the monitoring area, wherein different meteorological conditions correspond to different weather conditions, and the meteorological conditions may be described specifically by meteorological parameters such as wind speed, wind direction, air temperature, air humidity, and atmospheric pressure. Further, by inputting the meteorological conditions of the monitored area into the meteorological model and the air quality model, the pollutant spatial distribution data of the monitored area and the position of the maximum pollutant concentration can be obtained. It should be noted that the simulated spatial distribution of contaminant concentrations for the same monitored area is not the same 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 area grid.
Specifically, the pollutant concentration monitoring data in each area grid may be corrected by 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 model, a unitary cubic model, a power function model and an exponential function model. In the actual working process, because the detection process inevitably has larger or smaller errors, it is difficult to ensure that the pollutant concentration monitoring data and the standard monitoring data can be completely fitted, and a difference value exists between the corrected pollutant concentration monitoring data and the standard monitoring data, in this embodiment, according to the actual working condition, after the pollutant concentration monitoring data is corrected, the obtained average difference value between the corrected data and the standard monitoring data slightly floats within a threshold (for example, the threshold is 1) range, which is allowed in the actual operating process.
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 of the monitoring area, meteorological data and pollutant concentration monitoring data in each area grid.
Specifically, the server may fill the PM2.5 distribution data, the satellite images, the spatial distribution of the pollutants under different meteorological conditions, and the corrected pollutant concentration monitoring values obtained by preprocessing the three types of data into the machine learning model by using an interpolation method, and obtain the pollutant concentration distribution condition of each position point in the monitoring area with the longitude and latitude as the coordinate after certain weighting processing. 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 contaminant concentration profile at the same location will be different for different time periods 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 the same place can be obtained, and accordingly atmospheric pollution monitoring results of a monitoring area are obtained, wherein the atmospheric pollution monitoring results comprise one or more of the source of atmospheric pollution, a hot spot area with serious atmospheric pollution, an atmospheric pollution transmission path and the real-time condition of atmospheric pollution.
in one embodiment, the method for determining the source of the occurrence of atmospheric pollution may include: firstly, predicting a suspected pollution source area according to pollutant concentration data and a satellite image, locking an area grid where the maximum pollution concentration is located in a monitored area, planning a detection route by combining meteorological data of the suspected pollution source area, detecting the pollutant concentration data at the position where the maximum pollution concentration is located and around by using monitoring equipment, and finally determining a pollution source.
In one embodiment, the method for determining the hot spot area with serious atmospheric pollution can comprise the following steps: and comparing the pollutant concentration data of each area grid with a preset value, wherein the area grid of which the pollutant concentration data is greater than or equal to the preset value can be determined as a hotspot area grid.
In one embodiment, the method for determining the path of the atmospheric pollution transmission can comprise the following steps: the method comprises the steps of firstly, carrying out linear interpolation calculation by utilizing a plurality of wind speed and wind direction data of the positions of grids of each region to obtain the vector velocity of the movement of pollutant particles, then determining the running time of the pollutant particles according to the acquisition time interval of pollutant concentration data, and finally calculating the movement track of the pollutant particles through the integral of position vectors of the pollutant particles in time and space according to a Lagrange track model. Further, after the pollutant concentration data, the pollutant particle motion track, the corresponding longitude and latitude and the satellite image are superposed, the visual pollutant particle motion track can be obtained, so that pollution tracing is realized, and the position of the pollution source is determined.
In one embodiment, the method for determining the real-time condition of the occurrence of atmospheric pollution may comprise: according to pollutant concentration data monitored by the detection equipment in real time, satellite remote sensing data and meteorological data, updating and displaying the pollutant concentration data at regular time according to the position of the area grid, and expressing the area grid with high pollutant concentration by using special colors to show the real-time condition of atmospheric pollution.
In one embodiment, the computer device may display and transmit results of atmospheric pollution monitoring of the monitored area. Specifically, the computer equipment can display the atmospheric pollution monitoring condition of the monitoring area in real time through display equipment such as a display, and mark information such as a source of atmospheric pollution, a hot spot area with serious atmospheric pollution, a path of atmospheric pollution transmission and the like with special colors, and further, the computer equipment can also send the atmospheric pollution monitoring result to other computer equipment such as mobile phones and other mobile terminals in a wired and wireless communication mode so as to facilitate real-time checking of users.
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 may be divided into a plurality of area grids according to the predetermined length value and the predetermined width value, and if the area grids of the monitoring area are divided according to the square units with the same size, it should be noted that the divided area grids are not too large, and the three-dimensional spatial distribution of the 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, it can be understood that the size of the area grid may be adjusted according to actual requirements, and the embodiment is not limited thereto.
The embodiment provides an atmospheric pollution monitoring method, which includes firstly performing gridding processing on a monitoring area, then acquiring pollutant concentration monitoring data acquired in real time in each area grid, 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 used for being matched with the real-time pollutant monitoring data of the regional grid, the source of pollution can be accurately positioned, the continuously diffused pollution region 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 method shown in fig. 1 and fig. 2, the embodiment provides an atmospheric pollution monitoring system, as shown in fig. 3, the system includes: meshing device 31, monitoring equipment 32, data acquisition device 33, data processing device 34, wherein:
A mesh dividing means 31 for dividing the monitoring area into a plurality of area meshes;
The monitoring equipment 32 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 33 is used for acquiring satellite remote sensing data of the monitored area, meteorological data and pollutant concentration monitoring data in each area grid;
And the data processing device 34 is used for obtaining an 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 each area grid of the monitoring area.
In a specific application scenario, the data processing device 34 may be specifically configured to pre-process satellite remote sensing data and meteorological data of a monitored area and pollutant concentration monitoring data in each area grid; 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 of the monitoring area, meteorological data and pollutant concentration monitoring data in each area grid.
in a specific application scenario, the data processing device 34 is specifically configured to obtain optical aerosol thickness and pollution source positioning data in a monitoring area based on satellite remote sensing data; outputting pollutant spatial 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 area grid.
In a specific application scenario, the atmospheric pollution monitoring result includes one or more information of a source of atmospheric pollution, a hot spot area with serious atmospheric pollution, an atmospheric pollution transmission path and a real-time condition of atmospheric pollution.
In a specific application scenario, as shown in fig. 4, the system further includes: a display device 35 and a communication device 36, wherein:
the display device 35 is used for displaying the atmospheric pollution monitoring result of the monitoring area;
And the communication device 36 is used for sending the atmospheric 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 other corresponding descriptions of the functional units related to the atmospheric pollution monitoring system provided in this embodiment may refer to the corresponding descriptions in fig. 1 and fig. 2, and are not repeated herein.
Based on the methods shown in fig. 1 and fig. 2, correspondingly, the present embodiment further provides a storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the atmospheric pollution monitoring method shown in fig. 1 and fig. 2.
based on such understanding, the technical solution of the present application may be embodied in the form of a software product, and the software product to be identified may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, or the like), and include several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the 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 object, the present embodiment further provides an entity device for monitoring atmospheric pollution, which may specifically be a personal computer, a server, a smart phone, a tablet computer, a smart watch, or other network devices, and the entity device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing the computer program to implement the above-mentioned methods as shown in fig. 1 and fig. 2.
Optionally, the entity device may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface may include a Display screen (Display), an input unit such as a keypad (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 understood by those skilled in the art that the physical device structure for monitoring atmospheric pollution provided by the present embodiment does not constitute a limitation to the physical device, and may include more or less components, or combine some components, or arrange different components.
The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware of the above-mentioned entity device 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 components in the storage medium and communication with other hardware and software in the information processing entity device.
through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware. Through using the technical scheme of this application, utilize big data technology and artificial intelligence technique cooperation regional meshing's real-time pollutant monitoring data, the source that can accurate location pollution take place tracks the pollution area that constantly spreads to show the real-time situation of atmosphere pollution, compare traditional technique, can be more comprehensive and accurate show monitoring area's atmosphere pollution condition.
those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. 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 application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. an atmospheric pollution monitoring method, characterized in that the method comprises:
Dividing a monitoring area into a plurality of area grids;
Acquiring pollutant concentration monitoring data in each area 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 the satellite remote sensing data and the meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid.
2. The atmospheric pollution monitoring method according to claim 1, wherein the obtaining of the atmospheric pollution monitoring result of the monitored 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 each area grid of the monitored area comprises:
Preprocessing the satellite remote sensing data and meteorological data of the monitoring area and the pollutant concentration monitoring data in each area grid;
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 of the monitoring area, meteorological data and pollutant concentration monitoring data in each area grid.
3. The atmospheric pollution monitoring method according to claim 2, wherein the preprocessing of the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data in each regional grid of the monitoring region comprises:
acquiring the optical thickness of the aerosol and the positioning data of the pollution source in the monitoring area based on the satellite remote sensing data;
Outputting pollutant spatial distribution data of a monitoring area through a pre-trained meteorological model and an air quality model according to the 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 area grid.
4. The atmospheric pollution monitoring method as claimed in claim 3, wherein the atmospheric pollution monitoring result includes one or more of information of a source of the atmospheric pollution occurrence, a hot spot area with serious atmospheric pollution, a transmission path of the atmospheric pollution and a real-time condition of the atmospheric pollution occurrence.
5. The atmospheric pollution monitoring method as claimed in any one of claims 1 to 4, further comprising:
and displaying and/or sending the atmospheric pollution monitoring result of the monitoring area.
6. The atmospheric pollution monitoring method as claimed in any one of claims 1 to 4, wherein said dividing the monitoring area into a plurality of area grids comprises:
And 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:
The grid dividing device is used 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 and meteorological data of the monitored area and pollutant concentration monitoring data in each area grid;
and 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 the satellite remote sensing data, the meteorological data and the pollutant concentration monitoring data in each area grid of the monitoring area.
8. the atmospheric pollution monitoring system of claim 6, further comprising:
the display device is used for displaying the atmospheric pollution monitoring result of the monitoring area;
And the communication device is used for sending the atmospheric pollution monitoring result of the monitoring area.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 6.
10. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by the processor.
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