CN109522603B - Vehicle-mounted Lagrange real-time atmospheric pollution source tracing system and method based on cloud platform - Google Patents
Vehicle-mounted Lagrange real-time atmospheric pollution source tracing system and method based on cloud platform Download PDFInfo
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Abstract
The invention provides a vehicle-mounted Lagrange real-time atmospheric pollution traceability system and method based on a cloud platform, and relates to the technical field of air pollution prevention and control. The vehicle-mounted Lagrange real-time atmospheric pollution traceability system and method based on the cloud platform are characterized in that a WRF meteorological model and a Lagrange particle release model which are based on high-performance cloud platform computing high accuracy are built, and a vehicle-mounted mobile rapid pollution traceability system is built by closely combining a design, a front-end calling platform and a GPS positioning system, so that the emergency handling capacity and the law enforcement rapidity and accuracy of relevant departments are effectively improved.
Description
Technical Field
The invention relates to the technical field of air pollution prevention and control, in particular to a Lagrange real-time atmospheric pollution source tracing system and method based on cloud platform high-performance computing and carried on a vehicle-mounted mobile platform.
Background
With the development of social economy, the influence of urban industry, traffic, electric power and resident life on urban air quality is increasingly emphasized. The air quality is more and more concerned by the masses of people, and the supervision of the urban atmospheric pollution by the relevant environmental departments is more and more intensive. Due to the development of urban industry and economy, the maximization of interest pursuit of enterprises, the repeated prohibition of unorganized pollutant discharge in consideration of the regulations of related laws, and the accurate, quick and real-time pollution source tracing technology is urgently needed by the work and law enforcement of related departments.
At present, the Lagrange pollution tracing common mode mainly comprises Hysplt developed by the American space agency and Flexpart developed by Norwegian atmospheric research institute, which are particle diffusion models based on the Lagrange principle, can research the diffusion and transmission of atmospheric pollution by calculating the track of released particles, has no complex chemical process, and realizes accurate and efficient simulation of atmospheric pollutants by parameterization only by considering meteorological factors. The method simulates the transmission track of the atmospheric pollutants by inputting meteorological element data and physical process parameter setting. The two methods are widely applied to atmospheric pollution transmission and diffusion research in different regions at home, but because real-time and accurate meteorological lattice point data in different regions are difficult to obtain, the Lagrange mode is less applied to the small-scale pollution real-time tracing aspect.
The existing pollution tracing technology and related platform have the defects that the law enforcement management department cannot timely, accurately and efficiently enforce law due to the fact that a plurality of nested models are provided, the process is complex, the operation speed is low, the spatial resolution is low, and the accuracy of the forecast result is low.
Disclosure of Invention
The invention aims to provide a vehicle-mounted Lagrange real-time atmospheric pollution traceability system and method based on a cloud platform, which can quickly and accurately find a pollution source.
In one aspect, the invention provides a vehicle-mounted lagrangian real-time atmospheric pollution traceability system based on a cloud platform, which comprises:
the data processing unit is used for collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data;
the meteorological model building unit is used for building a mesoscale WRF meteorological model on the cloud platform according to the processed data so as to obtain meteorological data;
the model building unit is used for building a Lagrangian particle release model on the cloud platform according to the meteorological data;
the information processing unit is used for receiving the space position information of the moving vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the polluted air mass in the source area at the current position and feeding the probability distribution back to the vehicle-mounted platform;
the planning unit is used for calculating contribution distribution of a potential pollution source area by combining a discharge list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking a discharge source;
and the statistical unit is used for counting the distribution rule of the pollution sources by combining the mobile observation of the historical pollution high value.
Optionally, the data collection unit is configured to:
warehousing the basic topographic data subjected to gridding treatment;
collecting global reanalysis data and extracting meteorological elements;
satellite underlying surface type data is processed.
Optionally, the meteorological pattern building unit is configured to:
constructing a cloud platform Linux operating environment;
configuring a cloud platform computing node resource environment;
designing WRF mode related parameter setting and running scripts;
and extracting and saving the output gridded four-dimensional data set.
Optionally, the model building unit is configured to:
converting the format of the meteorological data into a Lagrangian model driven ARL format;
setting the grid precision and range of a simulation area, and determining longitude and latitude information of a boundary;
establishing a simulation time scheme and setting a particle release amount, a particle release simulation duration and a backward stepping length;
and setting the layer top height, the vertical convection parameter and the output format of the Lagrange particle release model.
Optionally, the information processing unit is configured to:
receiving current position information sent by a vehicle-mounted platform to acquire a current moving path, position longitude and latitude and height information;
the Lagrange particle release model operates the model according to the longitude and latitude and the height information to obtain the probability distribution of the pollution air mass source area at the current position;
compressing the probability distribution of the polluted air mass coming source area into a data set, and sending the data set to a vehicle-mounted platform;
interpreting the data set, and obtaining the regional probability distribution of the polluted air mass source so as to display in real time and automatically update according to a preset time interval.
Optionally, the planning unit is configured to:
the method comprises the steps of sorting an atmospheric pollution source emission list to obtain emission characteristic information of different industries in a preset area;
calculating potential pollution source contribution percentages according to the probability distribution of the pollution air mass source area and the emission list;
and analyzing the air pollution data according to the contribution percentage of the potential pollution sources to adjust an air route to be close to a pollution emission source.
Optionally, the statistical unit is configured to:
acquiring position information at a high pollution period based on vehicle-mounted platform navigation observation data;
feeding back information of the high pollution period to a cloud server, and counting a pollution source distribution rule in a high pollution average state based on the mesoscale WRF meteorological model and the Lagrangian particle release model;
and establishing a heavy pollution event source tracing analysis library by combining an emission list and an environment checking method.
On the other hand, the invention provides a vehicle-mounted Lagrange real-time atmospheric pollution source tracing method based on a cloud platform, which comprises the following steps:
collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data;
constructing a mesoscale WRF meteorological model on a cloud platform according to the processed data to obtain meteorological data;
constructing a Lagrangian particle release model on the cloud platform according to the meteorological data;
receiving the space position information of the mobile vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the pollution air mass coming from the source area at the current position, and feeding the probability distribution back to the vehicle-mounted platform;
and calculating contribution distribution of potential pollution source regions by combining an emission list and the probability distribution of the source regions of the pollution air mass at the current position, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source regions, and locking an emission source.
Optionally, combining the moving observation of the historical pollution high value, and counting the distribution rule of the pollution sources.
Optionally, collecting data of a preset area and processing the data, wherein the data including meteorological grid point data and urban terrain data includes: warehousing the basic topographic data subjected to gridding treatment; collecting global reanalysis data and extracting meteorological elements; processing the type data of the underlying surface of the satellite;
constructing a mesoscale WRF meteorological model on a cloud platform according to the processed data to obtain meteorological data, wherein the mesoscale WRF meteorological model comprises the following steps: constructing a cloud platform Linux operating environment; configuring a cloud platform computing node resource environment; designing WRF mode related parameter setting and running scripts; extracting and storing the output gridding four-dimensional data set;
the method for constructing the Lagrangian particle release model on the cloud platform according to the meteorological data comprises the following steps: converting the format of the meteorological data into a Lagrangian model driven ARL format; setting the grid precision and range of a simulation area, and determining longitude and latitude information of a boundary; establishing a simulation time scheme and setting a particle release amount, a particle release simulation duration and a backward stepping length; setting the layer top height, the vertical convection parameter and the output format of the Lagrange particle release model;
the method comprises the following steps of receiving the space position information of the mobile vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the pollution air mass in the source area at the current position, and feeding the probability distribution back to the vehicle-mounted platform: receiving current position information sent by a vehicle-mounted platform to acquire a current moving path, position longitude and latitude and height information; the Lagrange particle release model operates the model according to the longitude and latitude and the height information to obtain the probability distribution of the pollution air mass source area at the current position; compressing the probability distribution of the polluted air mass coming source area into a data set, and sending the data set to a vehicle-mounted platform; interpreting the data set to obtain the regional probability distribution of the polluted air mass source so as to display in real time and automatically update according to a preset time interval;
calculating contribution distribution of a potential pollution source area by combining an emission list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking an emission source comprises the following steps: the method comprises the steps of sorting an atmospheric pollution source emission list to obtain emission characteristic information of different industries in a preset area; calculating potential pollution source contribution percentages according to the probability distribution of the pollution air mass source area and the emission list; analyzing the air pollution data according to the contribution percentage of the potential pollution sources to adjust an air route to be close to a pollution emission source;
combining with the moving observation of historical pollution high value, the statistical pollution source distribution rule comprises the following steps: acquiring position information at a high pollution period based on vehicle-mounted platform navigation observation data; feeding back information of the high pollution period to a cloud server, and counting a pollution source distribution rule in a high pollution average state based on the mesoscale WRF meteorological model and the Lagrangian particle release model; and establishing a heavy pollution event source tracing analysis library by combining an emission list and an environment checking method.
According to the vehicle-mounted Lagrange real-time atmospheric pollution traceability system and method based on the cloud platform, a WRF meteorological model and a Lagrange particle release model which are based on high-performance cloud platform and calculate high precision are constructed, and a vehicle-mounted mobile rapid pollution traceability system is constructed by closely combining a design, a front-end calling platform and a GPS positioning system, so that the emergency handling capacity and the law enforcement rapidity and accuracy of relevant departments are effectively improved.
Furthermore, the vehicle-mounted Lagrange real-time atmospheric pollution tracing system (CRV-LPDM) based on the cloud platform can quickly and accurately track the pollutant source and analyze the pollution track source, is strong in real-time performance, is visual and quick, improves the quick judgment capability of a user on the result compared with other mode systems, and improves the scientificity and pertinence of air quality guarantee measures for the user. The traditional air pollution traceability system is low in response speed, low in resolution, poor in timeliness, incapable of tracking pollution source distribution in time and lacking in practical guiding significance for air quality guarantee and pollution control, and the CRV-LPDM air pollution rapid traceability system overcomes the defects on the basis of the traditional pollution traceability system.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a schematic flow chart of a cloud platform-based vehicle-mounted lagrangian real-time atmospheric pollution tracing method according to an embodiment of the present invention.
Detailed Description
The present embodiment is performed based on the technical solution of the present invention, and a specific operation method and a detailed calculation process are given, but the scope of the present invention is not limited to the following embodiments. Fig. 1 is a schematic flow chart of a cloud platform-based vehicle-mounted lagrangian real-time atmospheric pollution tracing method according to an embodiment of the present invention. As shown in figure 1 of the drawings, in which,
the basic flow of this embodiment is as follows:
1) collecting and processing meteorological grid point data and urban terrain data of a required area;
2) establishing a new generation of mesoscale WRF meteorological model on a cloud platform;
3) constructing a Lagrange particle release mode, wherein parameter settings comprise particle release height, simulation time period, backward-pushing time step length, output time step length and the like;
4) constructing a user interaction system, acquiring the space position information of the mobile vehicle in real time, and feeding back the source distribution characteristics of the polluted air mass in a second level;
5) planning and adjusting a navigation route by combining the emission list and the contribution distribution of the potential pollution source region, and further locking an emission source;
6) and (4) counting the distribution rule of the pollution sources by combining the moving observation of historical pollution high values.
Step 1) processing meteorological lattice point data and urban terrain data of a required area, comprising the following processes:
(1) and performing gridding processing on the basic terrain data, determining the terrain height characteristics of a simulation area, refining the terrain data of the grid points by difference values, and adjusting the resolution to be consistent with the application of a meteorological model and a Lagrange tracing model.
(2) The global reanalysis data arrangement and meteorological element extraction comprises observation data of wind speed, wind direction, temperature, humidity, air pressure and the like of a conventional meteorological station, and arrangement of background driving field data of a global forecasting field GFS, a European meteorological center ECMWF and the like.
(3) And (3) processing the type data of the satellite underlying surface, calculating a data set comprising land coverage, leaf area indexes, albedo, snow coverage and normalized vegetation index based on remote sensing parameter inversion and a data processing algorithm, and inputting the data set into an optimization forecasting model system.
Step 2) building a new generation of mesoscale WRF meteorological model on a cloud platform, which specifically comprises the following steps: constructing a cloud platform Linux operating environment; configuring a cloud platform computing node resource environment; designing WRF mode related parameter setting and running scripts; the construction of the preprocessing system (WPS system) of the WRF mode data mainly comprises three parts, namely the construction of an ungrid module, a metgrid module and a geogrid module. The specific process is as follows: defining a simulation area or a nested grid area range, determining map projection and grid point longitude and latitude parameters, interpolating corresponding meteorological data into a corresponding grid area, and interpolating underlying surface data of the simulation area into corresponding grid data. And extracting and saving the output gridded four-dimensional data set.
The Lagrange particle release mode construction of the step 3) mainly comprises the following steps:
(1) the meteorological data format is converted into a lagrangian model-driven ARL format. Specifically, the method can realize outputting meteorological data of a specified area for 2 days in the future, factors such as wind speed, wind direction, temperature, humidity and air pressure of a meteorological field, and convert the output file format of a mesoscale mode into a data format which can be identified by a Lagrange particle release mode through MCIP.
(2) Setting the grid precision and range of a simulation area, setting a four-layer nested simulation scheme, covering most areas of China and eastern ocean areas on the outermost layer, and setting the resolution to be 35 km; the second layer covers the eastern region of China with a resolution of 12 km. The third layer is a designated city and surrounding areas, and the resolution is 3 km; the fourth layer is a designated city area, and the resolution is 1 km.
(3) The particle release amount is set to 100-1000, and a simulation time scheme, a particle release simulation duration and a backward step length are established. The time setting includes setting the push-back time to be 2 hours and the product output time step to be 1 hour.
(4) And setting the top height of the model layer, the vertical diffusion scheme and the output format. The simulated vertical stratification is 100m, 500m, 1000m, the mode top layer height is 10000 m. And outputting a gridding format file which comprises the starting time, the backward stepping length and the probability distribution information of the air mass source.
Step 4) constructing a user interaction system, acquiring the space position information of the moving vehicle in real time, and feeding back the source distribution characteristics of the polluted air mass in a second level, wherein the method specifically comprises the following processes:
(1) and starting the mobile vehicle to carry wireless transmission equipment, sending position information to the cloud server in real time, acquiring the current moving path, the longitude and latitude and the height information of the position, and storing the information to form a basic space moving information data set.
(2) And the cloud platform Lagrange traceability model operates the model to obtain the probability distribution of the pollution air mass source area at the current position according to the fed back longitude and latitude and height information. And giving longitude and latitude, tracing and deducing the probability distribution of the air pollution source for 2 hours, and calculating the residence time of the accumulated particles in each three-dimensional grid according to the actual situation. And extracting the residence time of the air mass below 100m, and performing sorting calculation by adopting a statistical method to obtain the spatial probability distribution of the released particles influencing ground pollution conveying.
(3) Compressing a pollution air mass source distribution data set, specifically, storing and backing up data information obtained by a data acquisition system according to spatial distribution data of residence time and quantity of particles in each grid of 500 × 500m, and sending the data information to a vehicle-mounted platform from a cloud server.
(4) And (3) interpreting the data set to obtain the distribution position information of the potential contamination source, displaying in real time based on a GIS platform, and automatically updating according to a set time interval. And converting the source data into an NC format file through a Fortran program, simultaneously obtaining a data list by the source data acquisition system, sorting out the NC format file of each pollution event, and obtaining a plurality of data packets of the NC format file by the source data acquisition system for carrying out pair analysis. The real-time tracing, manual positioning tracing, historical track query and pollution monitoring system construction can be realized under a terrain map mode.
And 5) planning and adjusting a navigation route by combining the emission list and the contribution distribution of the potential pollution source area, and further locking the emission source, wherein the method specifically comprises the following steps:
(1) and (4) arranging an atmospheric pollution source emission list, and acquiring emission characteristic information of different industries in the area. The method is divided into different areas, different resolutions and different industries according to actual conditions, and comprises five major types of source emissions of industry, agriculture, power plants, transportation and resident life.
(2) And calculating the contribution percentage of the potential pollution sources according to the probability distribution of the pollution air mass sources and the emission list. And establishing a potential pollutant source area database, reading database data, and calculating to obtain the simulated pollutant concentration and the contribution of each industry in the list to the pollutant concentration.
(3) The air pollution data of the air vehicle is analyzed, sources of different types of air pollution at specific time and specific places can be analyzed, and the contribution rates of other urban areas and other urban areas to the atmospheric environmental quality of heavily polluted areas, and the contribution rates of main pollutant types influencing the polluted atmosphere, such as PM2.5, raise dust, SO2 and the like, and the contribution rates of the main pollutant types influencing the polluted atmosphere, are output, SO that the pollution contribution households are analyzed and identified. And designing and adjusting a navigation route according to the peripheral emission condition and the Lagrange pollution tracing distribution to gradually approach a pollution emission source place.
Step 6) combining the mobile observation of historical pollution high values, counting the distribution rule of pollution sources, and mainly comprising the following steps:
(1) the method comprises the steps of obtaining position information of a high-pollution time period based on vehicle-mounted platform navigation observation data, and selecting characteristic pollutants, wherein the high-pollution time period is set if fine particles exceed a certain standard range.
(2) Selecting feedback information of a high-pollution time period to the cloud server, retrieving and inquiring historical patrol traceability data based on a WRF gridding meteorological and Lagrange real-time gridding traceability data set of the cloud server, and counting a pollution source distribution rule under a high-pollution average state.
(3) Integrating site monitoring and mobile monitoring data by combining an emission list and an environment troubleshooting method, wherein the data acquisition system comprises an acquisition public meteorological monitoring point and other available acquisition terminals and is used for summarizing pollution source data and reporting the pollution source data to a cloud platform; the acquisition terminal and the cloud platform adopt wireless communication to transmit information; after the mobile monitoring is connected with the GPS, the system can display the position of the mobile monitoring equipment and the monitoring air quality data information. And further establishing a heavy pollution event traceability analysis library, acquiring observation data in real time, verifying distortion, sorting and analyzing, simulating observation and comparison, further optimizing and adjusting source data lists of different regions, calling stored data of the cloud platform, and updating the pollution traceability important factor library in time.
The system and the method realize analysis and calculation of the source contribution percentage of the medium and small-scale air pollution sources by constructing a medium-scale WRF meteorological mode and a Lagrange particle release mode based on a high-performance cloud computing platform and combining a GPS positioning system and a front-end interaction platform, and are mainly applied to important activity guarantee cruise pollution source tracing and medium and small-scale pollution cause source analysis.
Furthermore, the vehicle-mounted Lagrange real-time atmospheric pollution tracing system (CRV-LPDM) based on the cloud platform can quickly and accurately track the pollutant source and analyze the pollution track source, is strong in real-time performance, is visual and quick, improves the quick judgment capability of a user on the result compared with other mode systems, and improves the scientificity and pertinence of air quality guarantee measures for the user. The traditional air pollution traceability system is low in response speed, low in resolution, poor in timeliness, incapable of tracking pollution source distribution in time and lacking in practical guiding significance for air quality guarantee and pollution control, and the CRV-LPDM air pollution rapid traceability system overcomes the defects on the basis of the traditional pollution traceability system.
The CRV-LPDM model is able to give the area of origin of the air mass at the receptor site, while giving the percentage contribution. The method can be used for determining the contribution condition of the regions such as cities, towns, parks and the like to the pollution of receptor sites, thereby providing a basis for adopting regional pollution control measures, avoiding the condition that the environmental air quality of sensitive regions at special periods is guaranteed by blindly carrying out industrial production limit or production stop measures in a large regional range, and reducing the implementation cost of the air quality guarantee measures while ensuring the effectiveness of the air quality guarantee measures.
On the other hand, the invention also provides a vehicle-mounted Lagrange real-time atmospheric pollution traceability system based on the cloud platform, which comprises the following steps:
the data processing unit is used for collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data;
the meteorological model building unit is used for building a mesoscale WRF meteorological model on the cloud platform according to the processed data so as to obtain meteorological data;
the model building unit is used for building a Lagrangian particle release model on the cloud platform according to the meteorological data;
the information processing unit is used for receiving the space position information of the moving vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the polluted air mass in the source area at the current position and feeding the probability distribution back to the vehicle-mounted platform;
the planning unit is used for calculating contribution distribution of a potential pollution source area by combining a discharge list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking a discharge source;
and the statistical unit is used for counting the distribution rule of the pollution sources by combining the mobile observation of the historical pollution high value.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (9)
1. A vehicle-mounted Lagrange real-time atmospheric pollution traceability system based on a cloud platform is characterized by comprising: the data processing unit is used for collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data; the meteorological model building unit is used for building a mesoscale WRF meteorological model on the cloud platform according to the processed data so as to obtain meteorological data; the model building unit is used for building a Lagrangian particle release model on the cloud platform according to the meteorological data; the information processing unit is used for receiving the space position information of the moving vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the polluted air mass in the source area at the current position and feeding the probability distribution back to the vehicle-mounted platform; the planning unit is used for calculating contribution distribution of a potential pollution source area by combining a discharge list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking a discharge source; the statistical unit is used for counting the distribution rule of the pollution sources by combining the mobile observation historical pollution high value;
collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data, and the data comprises the following steps: warehousing the basic topographic data subjected to gridding treatment; collecting global reanalysis data and extracting meteorological elements; processing the type data of the underlying surface of the satellite; constructing a mesoscale WRF meteorological model on a cloud platform according to the processed data to obtain meteorological data, wherein the mesoscale WRF meteorological model comprises the following steps: constructing a cloud platform Linux operating environment; configuring a cloud platform computing node resource environment; designing WRF mode related parameter setting and running scripts; extracting and storing the output gridding four-dimensional data set; the method for constructing the Lagrangian particle release model on the cloud platform according to the meteorological data comprises the following steps: converting the format of the meteorological data into a Lagrangian model driven ARL format; setting the grid precision and range of a simulation area, and determining longitude and latitude information of a boundary; establishing a simulation time scheme and setting a particle release amount, a particle release simulation duration and a backward stepping length; setting the layer top height, the vertical convection parameter and the output format of the Lagrange particle release model; the method comprises the following steps of receiving the space position information of the mobile vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the pollution air mass in the source area at the current position, and feeding the probability distribution back to the vehicle-mounted platform: receiving current position information sent by a vehicle-mounted platform to acquire a current moving path, position longitude and latitude and height information; the Lagrange particle release model operates the model according to the longitude and latitude and the height information to obtain the probability distribution of the pollution air mass source area at the current position; compressing the probability distribution of the polluted air mass coming source area into a data set, and sending the data set to a vehicle-mounted platform; interpreting the data set to obtain the regional probability distribution of the polluted air mass source so as to display in real time and automatically update according to a preset time interval; calculating contribution distribution of a potential pollution source area by combining an emission list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking an emission source comprises the following steps: the method comprises the steps of sorting an atmospheric pollution source emission list to obtain emission characteristic information of different industries in a preset area; calculating potential pollution source contribution percentages according to the probability distribution of the pollution air mass source area and the emission list; analyzing the air pollution data according to the contribution percentage of the potential pollution sources to adjust an air route to be close to a pollution emission source; combining with the moving observation of historical pollution high value, the statistical pollution source distribution rule comprises the following steps: acquiring position information at a high pollution period based on vehicle-mounted platform navigation observation data; feeding back information of the high pollution period to a cloud server, and counting a pollution source distribution rule in a high pollution average state based on the mesoscale WRF meteorological model and the Lagrangian particle release model; and establishing a heavy pollution event source tracing analysis library by combining an emission list and an environment checking method.
2. The real-time atmospheric pollution traceability system of claim 1, wherein the data processing unit is configured to: warehousing the basic topographic data subjected to gridding treatment; collecting global reanalysis data and extracting meteorological elements; satellite underlying surface type data is processed.
3. The real-time atmospheric pollution traceability system of claim 1, wherein the meteorological pattern building unit is configured to: constructing a cloud platform Linux operating environment; configuring a cloud platform computing node resource environment; designing WRF mode related parameter setting and running scripts; and extracting and saving the output gridded four-dimensional data set.
4. The real-time atmospheric pollution traceability system of claim 1, wherein the model building unit is configured to: converting the format of the meteorological data into a Lagrangian model driven ARL format; setting the grid precision and range of a simulation area, and determining longitude and latitude information of a boundary; establishing a simulation time scheme and setting a particle release amount, a particle release simulation duration and a backward stepping length; and setting the layer top height, the vertical convection parameter and the output format of the Lagrange particle release model.
5. The real-time atmospheric pollution traceability system of claim 1, wherein the information processing unit is configured to: receiving current position information sent by a vehicle-mounted platform to acquire a current moving path, position longitude and latitude and height information; the Lagrange particle release model operates the model according to the longitude and latitude and the height information to obtain the probability distribution of the pollution air mass source area at the current position; compressing the probability distribution of the polluted air mass coming source area into a data set, and sending the data set to a vehicle-mounted platform; interpreting the data set, and obtaining the regional probability distribution of the polluted air mass source so as to display in real time and automatically update according to a preset time interval.
6. The real-time atmospheric pollution traceability system of claim 1, wherein the planning unit is configured to: the method comprises the steps of sorting an atmospheric pollution source emission list to obtain emission characteristic information of different industries in a preset area; calculating potential pollution source contribution percentages according to the probability distribution of the pollution air mass source area and the emission list; and analyzing the air pollution data according to the contribution percentage of the potential pollution sources to adjust an air route to be close to a pollution emission source.
7. The real-time atmospheric pollution traceability system of claim 1, wherein the statistical unit is configured to: acquiring position information at a high pollution period based on vehicle-mounted platform navigation observation data; feeding back information of the high pollution period to a cloud server, and counting a pollution source distribution rule in a high pollution average state based on the mesoscale WRF meteorological model and the Lagrangian particle release model; and establishing a heavy pollution event source tracing analysis library by combining an emission list and an environment checking method.
8. A vehicle-mounted Lagrange real-time atmospheric pollution source tracing method based on a cloud platform is characterized by comprising the following steps: collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data; constructing a mesoscale WRF meteorological model on a cloud platform according to the processed data to obtain meteorological data; constructing a Lagrangian particle release model on the cloud platform according to the meteorological data; receiving the space position information of the mobile vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the pollution air mass coming from the source area at the current position, and feeding the probability distribution back to the vehicle-mounted platform; calculating contribution distribution of a potential pollution source area by combining an emission list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking an emission source;
collecting data of a preset area and processing the data, wherein the data comprises meteorological lattice point data and urban terrain data, and the data comprises the following steps: warehousing the basic topographic data subjected to gridding treatment; collecting global reanalysis data and extracting meteorological elements; processing the type data of the underlying surface of the satellite; constructing a mesoscale WRF meteorological model on a cloud platform according to the processed data to obtain meteorological data, wherein the mesoscale WRF meteorological model comprises the following steps: constructing a cloud platform Linux operating environment; configuring a cloud platform computing node resource environment; designing WRF mode related parameter setting and running scripts; extracting and storing the output gridding four-dimensional data set; the method for constructing the Lagrangian particle release model on the cloud platform according to the meteorological data comprises the following steps: converting the format of the meteorological data into a Lagrangian model driven ARL format; setting the grid precision and range of a simulation area, and determining longitude and latitude information of a boundary; establishing a simulation time scheme and setting a particle release amount, a particle release simulation duration and a backward stepping length; setting the layer top height, the vertical convection parameter and the output format of the Lagrange particle release model; the method comprises the following steps of receiving the space position information of the mobile vehicle transmitted by the user interaction system in real time to obtain the probability distribution of the pollution air mass in the source area at the current position, and feeding the probability distribution back to the vehicle-mounted platform: receiving current position information sent by a vehicle-mounted platform to acquire a current moving path, position longitude and latitude and height information; the Lagrange particle release model operates the model according to the longitude and latitude and the height information to obtain the probability distribution of the pollution air mass source area at the current position; compressing the probability distribution of the polluted air mass coming source area into a data set, and sending the data set to a vehicle-mounted platform; interpreting the data set to obtain the regional probability distribution of the polluted air mass source so as to display in real time and automatically update according to a preset time interval; calculating contribution distribution of a potential pollution source area by combining an emission list and the probability distribution of the source area of the current position pollution air mass, planning and adjusting a navigation route according to the contribution distribution of the potential pollution source area, and locking an emission source comprises the following steps: the method comprises the steps of sorting an atmospheric pollution source emission list to obtain emission characteristic information of different industries in a preset area; calculating potential pollution source contribution percentages according to the probability distribution of the pollution air mass source area and the emission list; analyzing the air pollution data according to the contribution percentage of the potential pollution sources to adjust an air route to be close to a pollution emission source; combining with the moving observation of historical pollution high value, the statistical pollution source distribution rule comprises the following steps: acquiring position information at a high pollution period based on vehicle-mounted platform navigation observation data; feeding back information of the high pollution period to a cloud server, and counting a pollution source distribution rule in a high pollution average state based on the mesoscale WRF meteorological model and the Lagrangian particle release model; and establishing a heavy pollution event source tracing analysis library by combining an emission list and an environment checking method.
9. The real-time atmospheric pollution tracing method of claim 8, further comprising: and (4) counting the distribution rule of the pollution sources by combining the moving observation of historical pollution high values.
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