CN116739191A

CN116739191A - Hot spot grid identification method and device, storage medium and electronic equipment

Info

Publication number: CN116739191A
Application number: CN202311020009.4A
Authority: CN
Inventors: 王洋; 吴冬; 蒋美合; 李亚林; 张晔华; 孙明生; 易志安; 马培翃; 秦东明
Original assignee: Beijing Zhongke Sanqing Environmental Technology Co ltd; 3Clear Technology Co Ltd
Current assignee: Beijing Zhongke Sanqing Environmental Technology Co ltd; 3Clear Technology Co Ltd
Priority date: 2023-08-14
Filing date: 2023-08-14
Publication date: 2023-09-12
Anticipated expiration: 2043-08-14
Also published as: CN116739191B

Abstract

The present disclosure relates to a hotspot grid identification method, a device, a storage medium, and an electronic apparatus, the hotspot grid identification method capable of identifying a plurality of historical hotspot grids having a maximum influence on a target air quality in a specified area by determining a target area in a plurality of weather periods in a plurality of simulation periods in a historical period; predicting a target hotspot grid of a future specified time period according to a plurality of historical hotspot grids, target weather background data of target spatial resolution of the future specified time period, a target meshed emission list and a target weather type set of the future specified time period; the method comprises the steps that target hot spot grids in a future time period can be effectively predicted based on a plurality of historical hot spot grids with the largest influence on target air quality in a designated area under each weather type in a plurality of simulation time periods in the historical time period; therefore, the accuracy of the prediction result can be effectively ensured while the prediction efficiency is improved, and the reliability of the target hot spot grid can be improved.

Description

Hot spot grid identification method and device, storage medium and electronic equipment

Technical Field

The disclosure relates to the technical field of environmental monitoring, in particular to a hot spot grid identification method, a hot spot grid identification device, a storage medium and electronic equipment.

Background

Along with the promotion of industrialization progress, in order to improve air quality, environmental supervision efficiency of key areas needs to be further improved, in order to discover and solve pollution problems in the first time, areas are generally thinned into grids with a certain size, and further refined environmental supervision is implemented on hot spot grids.

Disclosure of Invention

The invention aims to provide a hot spot grid identification method, a hot spot grid identification device, a storage medium and electronic equipment.

To achieve the above object, a first aspect of the present disclosure provides a hotspot grid identification method, the method including:

determining a plurality of historical hot spot grids of the target area, which have the greatest influence on the target air quality in the designated area under a plurality of weather types in a plurality of simulation time periods in the historical time period;

acquiring target weather background data of target spatial resolution of a future specified time period, a target grid emission list of the target spatial resolution and a target weather type set of the future specified time period;

and predicting the target hotspot grids of the future appointed time period according to the historical hotspot grids, the target weather background data, the target gridding emission list and the target weather type set, wherein the target hotspot grids are grids with the largest influence on the target air quality in the appointed area under each weather type in the target weather type set.

Optionally, the determining a plurality of historical hotspot grids of the target area with the greatest influence on the target air quality in the designated area under a plurality of weather types in a plurality of simulation time periods in the historical time period includes:

acquiring first meteorological background data of a first spatial resolution of a target area in a historical time period, a first grid emission list of the first spatial resolution and longitude and latitude information of each monitoring station in the target area;

determining a first contribution value of each first spatial resolution grid to the target air quality of a designated area in each weather type in a plurality of simulation time periods in the historical time period according to the first weather background data, the first grid emission list and the longitude and latitude information;

determining a standby grid with the largest first contribution value under each weather type in each simulation time period to obtain a plurality of standby grids under a plurality of weather types in a plurality of simulation time periods;

and according to the first meteorological background data and the first grid emission list, sequentially performing downscaling screening processing from the first spatial resolution to the target spatial resolution according to a preset amplitude to obtain historical hot spot grids of each weather type under the target spatial resolution, wherein the first spatial resolution is lower than or equal to the target spatial resolution.

Optionally, the predicting, according to the plurality of historical hotspot grids, the target weather background data, the target meshed emissions list and the target weather type set, the target hotspot grid for the future specified time period includes:

determining an alternative hotspot grid corresponding to the target weather type set according to the historical hotspot grids;

uniformly marking other grids except the alternative hot spot grids in the target area as first grid identifications, and marking different alternative hot spot grids by using different second grid identifications to obtain grid marking data comprising a plurality of different identifications;

inputting the grid marking data, the target weather background data and the target gridding emission list into a preset air quality mode to obtain a contribution value of each identified grid to the target air quality of the designated area;

and taking the grid with the largest contribution value to the target air quality of the designated area as the target hot spot grid.

Optionally, the determining, according to the plurality of historical hotspot grids, an alternative hotspot grid corresponding to the target weather type set includes:

Determining a plurality of first historical weather pattern sets of a plurality of specified simulation time periods under the historical time periods corresponding to the future specified time period, wherein each first historical weather pattern set comprises a historical weather pattern which appears in the specified simulation time period;

determining a plurality of target dormant grids corresponding to a first historical weather pattern set identical to the target weather pattern set from the plurality of historical hotspot grids under the condition that the target weather pattern set is determined to belong to one of the plurality of first historical weather pattern sets;

in the case that the target weather type set is determined not to belong to one of the plurality of first historical weather type sets, using the plurality of historical hot spot grids as a plurality of target standby grids corresponding to the target weather type set;

and determining the alternative hot spot grids according to the target standby grids.

Optionally, the determining the candidate hot-spot grid according to the target standby grids includes:

obtaining a first sum of first contribution values of the plurality of target inactive grids to the target air quality of the designated area, and a second sum of first contribution values of each first spatial resolution grid in the target area to the target air quality of the designated area;

And under the condition that the ratio of the first sum value to the second sum value is larger than or equal to a preset proportion threshold value, the target standby grids are used as the alternative hot spot grids.

Optionally, the determining the candidate hot-spot grid according to the target standby grids further includes:

and under the condition that the ratio of the first sum value to the second sum value is smaller than a preset proportion threshold value, determining a second contribution value of each target space resolution grid to the target air quality in the designated area according to the target weather background data and the target gridding emission list, and taking grids with the second contribution value being larger than or equal to a preset contribution value threshold value and the plurality of target standby grids as the candidate hot spot grids.

Optionally, the acquiring the first contribution value of each first spatial resolution grid of the target area to the target air quality of the designated area in a plurality of simulation time periods in the history time period includes:

And determining a first contribution value of each first spatial resolution grid to the target air quality of a designated area in a plurality of simulation time periods in the historical time period according to the first meteorological background data, the first grid emission list and the longitude and latitude information.

Optionally, the downscaling process includes: performing downscaling processing on the current spatial resolution to obtain updated current spatial resolution, and determining a third contribution value of each marking grid under the updated current spatial resolution to the target air quality in the designated area in the simulation time period under the condition that the updated current spatial resolution is determined to be not the target spatial resolution, and determining a second historical weather pattern set corresponding to the simulation time period, wherein the second historical weather pattern set comprises weather patterns appearing in the simulation time period; determining one or more designated grids with the largest third contribution value to the target air quality in the designated area under each weather type in the second historical weather type set, and taking the designated grids as standby grids updated in the simulation time period; and under the condition that the updated current spatial resolution is determined to be the target spatial resolution, using the standby grid as the historical hot spot grid.

Optionally, the determining one or more designated grids with the largest third contribution to the target air quality in the designated area for each weather type in the second set of historical weather types includes:

acquiring the contribution ratio of each current spatial resolution grid to the third contribution value of the target air quality under the updated current spatial resolution;

and taking the current spatial resolution grid with the contribution ratio being greater than or equal to a preset ratio threshold value as the specified grid.

A second aspect of the present disclosure provides a hotspot grid identification apparatus, the apparatus comprising:

the determining module is configured to determine a plurality of historical hot spot grids of the target area, which have the greatest influence on the target air quality in the designated area under a plurality of weather types in a plurality of simulation time periods in the historical time period;

an acquisition module configured to acquire target weather background data for a target spatial resolution for a future specified time period, a target grid-like emissions inventory for the target spatial resolution, and a target weather pattern set for the future specified time period;

the prediction module is configured to predict the target hotspot grids of the future designated time period according to the historical hotspot grids, the target weather background data, the target meshed emission list and the target weather type set, wherein the target hotspot grids are grids with the largest influence on the target air quality in the designated area under each weather type in the target weather type set.

Optionally, the determining module is configured to:

Optionally, the prediction module is configured to:

Optionally, the prediction module is further configured to:

Optionally, the determining module is configured to:

downscaling the current spatial resolution to obtain updated current spatial resolution, and determining a third contribution value of each marking grid under the updated current spatial resolution to the target air quality in the designated area in the simulation time period under the condition that the updated current spatial resolution is determined to be not the target spatial resolution, and determining a second historical weather pattern set corresponding to the simulation time period, wherein the second historical weather pattern set comprises weather patterns appearing in the simulation time period; determining one or more designated grids with the largest third contribution value to the target air quality in the designated area under each weather type in the second historical weather type set, and taking the designated grids as standby grids updated in the simulation time period; and under the condition that the updated current spatial resolution is determined to be the target spatial resolution, using the standby grid as the historical hot spot grid.

Optionally, the determining module is configured to:

A third aspect of the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect above.

A fourth aspect of the present disclosure provides an electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of the first aspect above.

According to the technical scheme, the plurality of historical hot spot grids with the greatest influence on the target air quality in the designated area in a plurality of weather types in a plurality of simulation time periods in the historical time period can be determined; acquiring target weather background data of target spatial resolution of a future specified time period, a target grid emission list of the target spatial resolution and a target weather type set of the future specified time period; according to the historical hotspot grids, the target weather background data, the target meshed emission list and the target weather type set predict target hotspot grids of the future appointed time period; the method has the advantages that the target hotspot grids in the future time period can be effectively predicted based on a plurality of historical hotspot grids with the largest influence on the target air quality in the designated area in each weather type in a plurality of simulation time periods in the historical time period, the prediction efficiency can be improved, meanwhile, the accuracy of a prediction result can be effectively ensured, the reliability of the target hotspot grids obtained by determination can be effectively improved, and reliable data basis can be provided for environmental pollution treatment.

Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.

Drawings

The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:

FIG. 1 is a flowchart of a hotspot grid identification method, as shown in an exemplary embodiment of the present disclosure;

FIG. 2 is a flow chart of a hotspot grid identification method, shown in accordance with the embodiment of FIG. 1;

FIG. 3 is a flow chart of another hotspot grid identification method illustrated in accordance with the embodiment shown in FIG. 1;

FIG. 4 is a flowchart of a hotspot grid identification method, as shown in another example embodiment of the present disclosure;

FIG. 5 is a flow chart of a hotspot grid identification method illustrated in accordance with the embodiment shown in FIG. 4;

FIG. 6 is a block diagram of a hotspot grid identification apparatus, as shown in an exemplary embodiment of the present disclosure;

FIG. 7 is a block diagram of an electronic device, shown in accordance with an exemplary embodiment;

fig. 8 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment.

Detailed Description

Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.

It should be noted that, all actions for acquiring signals, information or data in the present disclosure are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.

Before describing embodiments of the present disclosure in detail, the following description is first made of an application scenario of the present disclosure, where the present disclosure may be applied to an environmental monitoring process, and in particular, to a determination scenario of a hot spot grid in environmental supervision, where the hot spot grid may be understood as a grid having a greater influence on air quality. In the related art, the hot spot grid determining method mainly comprises three methods: the first is to screen and identify a hot spot grid according to satellite inversion AOD (Aerosol Optical Depth, aerosol optical thickness) and high-resolution meteorological data; the second is to screen and identify the hot spot grid according to the monitoring data of the high-density air quality monitoring equipment and the high-resolution meteorological data; and thirdly, screening and identifying a hot spot grid according to an atmospheric emission source list and high-resolution meteorological data. However, the first method is to screen hot spot grids based on satellite inversion and based on micro-station monitoring data, mainly screen hot spot grids according to the pollutant concentration values of grids, wherein the grids with high pollutant concentration values are hot spot grids; the second type relies on a large number of monitoring devices; third, the hot spot grids are screened based on the atmosphere emission source list, and mainly the hot spot grids are screened and identified according to the emission amount of each grid. However, the grid with high concentration of contaminants and the grid with large emissions do not necessarily have a large impact on site monitoring concentration values. For example, a pollution source is located in the downwind direction of the urban prevailing wind direction, and even if the emission amount is large and the concentration value of the pollutant in the grid is high, the emitted pollutant diffuses along with the wind and has small influence on all sites of the city, so that the pollution source is not a pollution source which needs to be controlled in an important way in the actual environmental pollution control. In the actual atmospheric pollution treatment process, the grids with larger influence on the air quality of the stations are needed to be identified, the grids with larger influence on the air quality of the stations are used as hot spot grids, and corresponding pollution supervision and treatment measures are implemented for the hot spot grids so as to improve the urban air quality and promote the ranking of the air quality. In addition, the method for determining the hot spot grid in the related technology generally only aims at the analysis of existing pollution, and cannot predict the hot spot grid in the future time period, that is, the existing method for determining the hot spot grid has the problems of low accuracy, poor reliability and adverse effect on the improvement of the environmental pollution treatment efficiency, and cannot effectively predict the hot spot grid in the future time period, so that reliable data basis cannot be provided for the environmental pollution treatment.

In order to solve the technical problems, the present disclosure provides a method, an apparatus, a storage medium, and an electronic device for identifying a hotspot grid, where the method for identifying a hotspot grid can determine a plurality of historical hotspot grids with the greatest influence on target air quality in a designated area in a plurality of weather periods in a plurality of simulation periods in a historical period; acquiring target weather background data of target spatial resolution of a future specified time period, a target grid emission list of the target spatial resolution and a target weather type set of the future specified time period; according to the historical hotspot grids, the target weather background data, the target meshed emission list and the target weather type set predict target hotspot grids of the future appointed time period; the method has the advantages that the target hotspot grids in the future time period can be effectively predicted based on a plurality of historical hotspot grids with the largest influence on the target air quality in the designated area in each weather type in a plurality of simulation time periods in the historical time period, the prediction efficiency can be improved, meanwhile, the accuracy of a prediction result can be effectively ensured, the reliability of the target hotspot grids obtained by determination can be effectively improved, and reliable data basis can be provided for environmental pollution treatment.

FIG. 1 is a flowchart of a hotspot grid identification method, as shown in an exemplary embodiment of the present disclosure; as shown in fig. 1, the hotspot grid identification method may include:

step 101, determining a plurality of historical hot spot grids of the target area, which have the greatest influence on the target air quality in the designated area under a plurality of weather types in a plurality of simulation time periods in the historical time period.

Wherein the historical time period may be 3 years, 5 years, etc. of the history, and the simulated time period may be a time in units of weeks, months, or years, such as each month, year, etc. The designated area may be an area where a certain administrative area is located, for example, an area where a certain city is located, an area where a certain county (town) is located, or the like, or may be an area of a designated range, and the designated area may be a part of the target area, an area adjacent to the target area, or an area where the target area does not intersect. The target air quality is a parameter reflecting the air pollution level and can be expressed by the concentration of a certain pollutant. The weather pattern is used for representing the type of the weather situation, the weather pattern corresponding to a period of time can be obtained through a preset weather parting pattern in the prior art, for example, the altitude field and the sea level air pressure field which are several times a day can be selected from the historical meteorological data corresponding to a historical time period to be used as parting factor data to be input into the preset weather parting pattern, so that the daily weather pattern in the historical time period output by the preset weather parting pattern can be obtained.

The implementation of this step may be as shown in fig. 2, where fig. 2 is a flowchart of a method for identifying a hotspot grid according to the embodiment shown in fig. 1; step 101 in fig. 1 may include:

step 1011, acquiring first weather background data of a first spatial resolution of a target area in a historical time period, a first grid emission list of the first spatial resolution, and longitude and latitude information of each monitoring site in the target area.

In this step, the method for acquiring the first weather background data may include: acquiring meteorological data of a target area in a historical time period, wherein the meteorological data can comprise air temperature, air humidity, wind direction, wind speed, weather and snow, precipitation, special disastrous weather and the like; the grid resolution may be set to a first spatial resolution in WRF (regional aerodynamic) or other meteorological modes, and then the meteorological data for a historical period of time (e.g., three years of history) may be simulated to generate the first meteorological background data.

In addition, the method for acquiring the first meshed emission list may include: acquiring an atmospheric pollution source emission list of a target area, and generating a first grid emission list with a first spatial resolution by using SMOKE (a pollution source processing system) or other pollution source processing software so as to obtain the first grid emission list; for example, the grid is first set to a resolution of 9km by 9km, and then the meteorological data and the atmospheric pollution source emissions list are simulated using the pollution source processing system to obtain the first meteorological background data and the first grid emissions list.

Step 1012, determining a first contribution value of each first spatial resolution grid to the target air quality of the designated area in each weather type in a plurality of simulation time periods in the history time period according to the first weather background data, the first grid emission list and the longitude and latitude information.

Wherein the first spatial resolution grid is a grid with a resolution of the first spatial resolution.

In this step, the first weather background data, the first grid emission list and the latitude and longitude information may be input into a preset air quality mode, so as to obtain a contribution value of each first spatial resolution grid to the target air quality of the designated area every day in the historical time period output by the preset air quality mode. The weather pattern per day may be determined, and the mean of the contribution value under each weather pattern in each simulation period (e.g., each month of three years of history) may be calculated from the weather pattern per day to obtain the contribution value under each weather pattern in the simulation period. The preset air quality mode can be a third-generation air quality mode such as a CAMQ (complex numerical model for describing air quality of air pollutants), a CAMx (comprehensive air quality mode), a NAQPMS (nested grid air quality prediction system) and the like.

For example, if the first spatial resolution may be 9km×9km, a first grid emission list with 9km×9km resolution is obtained, and first meteorological background data with 9km×9km resolution is obtained, the historical period is within three years of the history, the simulation period is each month (may be every two months, every half month, or every day) of the three years of the history, the first meteorological background data, the first grid emission list and longitude and latitude information may be input into a preset air quality mode, and a contribution value of each first spatial resolution grid to a target air quality of a specified area in each unit period (for example, each hour) is obtained; acquiring a weather pattern for each unit time period (for example, may be daily) by a preset weather parting pattern; the weather patterns of each day in each month can be counted, and if the weather pattern 1 is 1-5 and 15-18 in 5 months, the average value of the contribution values of each first spatial resolution grid in 1-5 and 15-18 in 5 months to the target air quality of the designated area can be obtained to obtain the contribution value of the weather pattern 1 in 5 months.

It should be noted that, the contribution value of each first spatial resolution grid to the target air quality of the specified area in each month may be obtained by calculating the average value of the contribution values of each first spatial resolution grid to the target air quality of the specified area in each month, and the contribution value of each first spatial resolution grid to the target air quality of the specified area in each day may be determined by calculating the average value of the contribution values of each first spatial resolution grid to the target air quality of the specified area in each hour in each month. In addition, when a plurality of environment monitoring sites are included in the designated area, a contribution value of the first spatial resolution grid to each environment monitoring site may be obtained, and then a mean value (or a sum value) of the contribution values of each first spatial resolution grid to the plurality of environment monitoring sites in the designated area may be obtained, so as to obtain a contribution value of the first spatial resolution grid to the designated area.

Step 1013, determining a standby grid with the largest first contribution value under each weather type in each simulation time period, so as to obtain a plurality of standby grids under a plurality of weather types in a plurality of simulation time periods.

In this step, one or more inactive grids with the largest first contribution value may be determined from first contribution values of each first spatial resolution grid for the target air quality of the designated area in each weather type in the multiple simulation time periods, where, when determining the one or more inactive grids with the largest first contribution value, a contribution value threshold may be preset, a first spatial resolution grid corresponding to a first contribution value greater than the preset contribution value threshold may be used as the inactive grid, a contribution proportion threshold may also be preset, and a first spatial resolution grid with a contribution proportion greater than the preset contribution proportion threshold may be used as the inactive grid.

For example, in one embodiment, the weather patterns of 1 month each year in the history of 5 years may be determined, if two weather patterns (weather pattern 1 and weather pattern 3) occur in the first 1 month of the history, three weather patterns (weather pattern 1, weather pattern 2 and weather pattern 3) occur in the second 1 month of the history, two weather patterns (weather pattern 2 and weather pattern 5) occur in the third 1 month of the history, two weather patterns (weather pattern 1 and weather pattern 3) occur in the fourth 1 month of the history, and if two weather patterns (weather pattern 3 and weather pattern 4) occur in the fifth 1 month of the history, one or more standby grids corresponding to each weather pattern in each 1 month may be determined, thereby obtaining a plurality of standby grids corresponding to each weather pattern in 5 1 months. In another embodiment, a part of the plurality of standby grids corresponding to each weather type in 5 months 1 may be screened as the plurality of standby grids to be finally determined, for example, 4 groups of weather types (i.e., 4 weather type sets, the weather type sets of 1 month in two years are the same in 5 years) may be present altogether, and 4 groups of grids with larger influence may be screened as the standby grids. In still another embodiment, 5 weather patterns may appear in total in 1 month in 5 years, and a standby grid with the largest first contribution value under each weather pattern in 5 months may be counted, so as to obtain 5 weather patterns in 5 months corresponding to a plurality of standby grids respectively.

Step 1014, performing downscaling filtering processing sequentially according to a preset amplitude from the first spatial resolution to the target spatial resolution according to the first meteorological background data and the first grid emission list, so as to obtain historical hotspot grids of each weather type under the target spatial resolution, wherein the first spatial resolution is lower than or equal to the target spatial resolution.

Wherein the downscaling screening process comprises: downscaling the current spatial resolution to obtain updated current spatial resolution, and determining a third contribution value of each marking grid under the updated current spatial resolution to the target air quality in the designated area in the simulation time period under the condition that the updated current spatial resolution is determined to be not the target spatial resolution, and determining a second historical weather pattern set corresponding to the simulation time period, wherein the second historical weather pattern set comprises weather patterns appearing in the simulation time period; determining one or more designated grids with the largest third contribution value to the target air quality in the designated area under each weather type in the second historical weather type set, and taking the designated grids as standby grids updated in the simulation time period; and under the condition that the updated current spatial resolution is determined to be the target spatial resolution, using the standby grid as the historical hot spot grid.

Thus, through the above steps 1011 to 1014, it is possible to effectively acquire a plurality of historical hot spot grids of the target area having the greatest influence on the target air quality in the specified area in a plurality of weather patterns in a plurality of simulation periods within the historical period.

Step 102, acquiring target weather background data of target spatial resolution of a future specified time period, a target grid emission list of the target spatial resolution and a target weather type set of the future specified time period.

It should be noted that, weather data of the target area in a specified time period in the future may be acquired, the grid resolution is set to the target spatial resolution in the WRF or other weather modes, and then the weather data of the specified time period in the future is simulated to generate the target weather background data. In addition, an atmospheric pollution source emission list of the target area can be obtained, and a SMOKE (a pollution source processing system) or other pollution source processing software is used for generating a grid emission list with target spatial resolution, so that the target grid emission list is obtained; for example, if the target spatial resolution is 3km×3km, when determining the target weather background data of the target spatial resolution and the target grid emission list of the target spatial resolution, the grid may be set to a resolution of 3km×3km first, and then the weather data of a specified time period in the future and the atmospheric pollution source emission list are simulated by using the pollution source processing system to obtain the target weather background data of 3km×3km and the grid emission list (i.e., the target grid emission list) at the resolution of 3km×3 km. The target weather type set may be obtained by obtaining forecast weather data of the future specified time period, where the forecast weather data includes parting factor data (e.g. altitude field, sea level air pressure field, etc. several times a day), and the parting factor data belongs to a preset weather parting model, so as to obtain the target weather type set output by the preset weather parting model.

Step 103, predicting the target hotspot grids of the future designated time period according to the historical hotspot grids, the target weather background data, the target meshed emission list and the target weather type set.

The target hotspot grid is the grid with the largest influence on the target air quality in the designated area under each weather type in the target weather type set.

By way of example, if the specified time period in the future is 1 month of the year, the history time period is 5 years, the simulation time period is each month, taking 1 month of the year as an example, two weather types (weather type 1 and weather type 3) appear in the first month of the history, three weather types (weather type 1, weather type 2 and weather type 3) appear in the second month of the history, two weather types (weather type 2 and weather type 5) appear in the third month of the history, two weather types (weather type 1 and weather type 3) appear in the fourth month of the history, two weather types (weather type 3 and weather type 4) appear in the fifth month of the history, it is necessary to determine whether the target weather type set of 1 month of the year is one of 5 sets of weather types in the history 5 years, if it is determined that the target weather pattern set of 1 month in tomorrow is one of 5 sets of weather patterns within 5 years of history (for example, the target weather pattern set of 1 month in tomorrow is weather pattern 2 and weather pattern 5, which are the same as the weather pattern occurring in 1 month of history, the target hotspot grid of 1 month in tomorrow may be determined from the standby grids corresponding to weather pattern 2 and weather pattern 5 in 5 months of history (the standby grid corresponding to weather pattern 2 in 1 month of history and the standby grid corresponding to weather pattern 2 and weather pattern 5 of third year 1), if it is determined that the target weather pattern set of 1 month in tomorrow does not belong to one of 5 sets of weather patterns within 5 years of history, the target hotspot grid of 1 month in tomorrow may be determined from the standby grid of each weather pattern in 5 sets of weather patterns.

According to the technical scheme, the target hot spot grids in the future time period can be effectively predicted based on the plurality of historical hot spot grids with the largest influence on the target air quality in the designated area in each weather type in the plurality of simulation time periods, the prediction efficiency can be improved, the accuracy of the prediction result can be effectively ensured, the reliability of the target hot spot grids obtained by determination can be effectively improved, and reliable data basis can be provided for environmental pollution treatment.

FIG. 3 is a flow chart of another hotspot grid identification method illustrated in accordance with the embodiment shown in FIG. 1; as shown in fig. 3, the predicting, according to the plurality of historical hotspot grids in step 103 in fig. 1, the target weather background data, the target meshed emissions list and the target weather type set, the target hotspot grid for the future specified period of time may include:

step 1031, determining an alternative hotspot grid corresponding to the target weather type set according to the historical hotspot grids.

In this step, a plurality of first historical weather pattern sets of a plurality of specified simulation time periods under the historical time period corresponding to the future specified time period may be determined, and each first historical weather pattern set includes a historical weather pattern that appears in one of the specified simulation time periods; determining a plurality of target dormant grids corresponding to a first historical weather pattern set identical to the target weather pattern set from the plurality of dormant grids in the case that the target weather pattern set is determined to belong to one of the plurality of first historical weather pattern sets; in the case that the target weather type set is determined not to belong to one of the plurality of first historical weather type sets, using the plurality of standby grids as a plurality of target standby grids corresponding to the target weather type set; obtaining a first sum of first contribution values of the plurality of inactive grids to the target air quality of the designated area, and a second sum of first contribution values of each first spatial resolution grid in the target area to the target air quality of the designated area; and under the condition that the ratio of the first sum value to the second sum value is larger than or equal to a preset proportion threshold value, the plurality of standby grids are used as the alternative hot spot grids.

For example, if the future specified period is 1 month of the year, the historical period is 5 years, the specified simulation period is 1 month of the year, two weather types occur in the first month of the history (i.e., the first set of weather types includes weather type 1 and weather type 3), three weather types occur in the second month of the history 1 (the first set of weather types includes weather type 1, weather type 2 and weather type 3), two weather types occur in the third month of the history 1 (the first set of weather types includes weather type 2 and weather type 5), two weather types occur in the fourth month of the history 1 (the first set of weather types includes weather type 1 and weather type 3), two weather types occur in the fifth month of the history 1 (the first set of weather types includes weather type 3 and weather type 4), and if the target set of weather types of the year 1 is determined to be weather type 2 and weather type 5, a plurality of weather patterns corresponding to weather patterns 2 and 5 in the 5 months of the history 5 can be used as the target type grid for standby weather patterns; namely, taking a standby grid corresponding to weather type 2 in 1 month of the second year of history and a standby grid corresponding to weather type 2 and weather type 5 of 1 month of the third year as a target standby grid of 1 month of the tomorrow; if it is determined that the target weather type set of 1 month of tomorrow does not belong to one of 5 sets of weather types within 5 years of history, a dormant grid according to each of the 5 sets of weather types (i.e., the 5 first historical weather type sets) may be used as the target dormant grid of the target weather type set.

It should be noted that, when the ratio of the first sum value to the second sum value is determined to be smaller than the preset ratio threshold, the reliability of the candidate hotspot grids can be represented to be lower, so that the number of the candidate hotspot grids needs to be expanded to expand the screening range of the target hotspot grids, and the effect of improving the accuracy of the determination result of the target hotspot grids is achieved; therefore, in this step, when it is determined that the ratio of the first sum value to the second sum value is smaller than the preset proportion threshold, a second contribution value of each target spatial resolution grid to the target air quality in the designated area may be determined according to the target weather background data and the target meshed emission list, and a grid with the second contribution value being greater than or equal to the preset contribution value threshold may also be used as the candidate hotspot grid.

Step 1032, uniformly marking other grids except the alternative hot spot grids in the target area as first grid identifications, and marking different alternative hot spot grids with different second grid identifications to obtain grid marking data comprising a plurality of different identifications.

Wherein the first grid identification and the second grid identification may be different IDs or different codes.

For example, if the second spatial resolution is 3km×3km, an ArcGIS (embedded GIS (Geography Information System, geographic information system) component may be used to perform ID marking (different grids are marked as different IDs) on the selected candidate hot spot grids (3 km×3km grids), and the grids other than the candidate hot spot grids are marked as one ID, so that the number of grids to be marked can be effectively reduced without affecting the accuracy of the simulation result, computing resources are saved, and processing efficiency is improved.

And step 1033, inputting the grid marking data, the target weather background data and the target grid discharge list into a preset air quality mode to obtain a contribution value of each identified grid to the target air quality of the designated area.

Step 1034, using a grid with the largest contribution value to the target air quality of the designated area in the grids with different identifications as the target hot spot grid.

Through the steps 1031 to 1034, the target hotspot grid of the future designated time period can be effectively predicted, the computing resources required by determining the target hotspot grid can be effectively saved, and the target hotspot grid determining efficiency is improved.

FIG. 4 is a flowchart of a hotspot grid identification method, as shown in another example embodiment of the present disclosure; as shown in fig. 4, the hotspot grid identification method may include:

step 301, acquiring first weather background data of a first spatial resolution of a target area in a historical time period, a first grid emission list of the first spatial resolution, and longitude and latitude information of each monitoring site in the target area.

Step 302, determining a first contribution value of each first spatial resolution grid to the target air quality of the designated area in each weather type in a plurality of simulation time periods in the historical time period according to the first weather background data, the first grid emission list and the longitude and latitude information.

The duration of the simulation time period is a single month duration.

Step 303, determining a standby grid with the largest first contribution value under each weather type in each simulation time period, so as to obtain a plurality of standby grids under a plurality of weather types in a plurality of simulation time periods.

It should be noted that, the embodiments of the above steps 301 to 303 may refer to the embodiments of the steps 101 to 102 in fig. 1, and the disclosure is not repeated herein.

Step 304, a target spatial resolution is obtained.

The target spatial resolution may be 3km×3km or 1km×1km, or 0.5km×0.5km, or other preset spatial resolutions, or spatial resolutions input by the user through a preset input interface.

Step 305, performing downscaling filtering processing sequentially according to a preset amplitude from the first spatial resolution to the target spatial resolution according to the first weather background data and the first grid emission list, so as to obtain a historical hotspot grid of each weather type under the target spatial resolution.

Wherein the preset amplitude may be S times, and if the first spatial resolution is mxn, downscaling is performed once according to the preset amplitude, thereby obtaining×/>M, N is an unnatural number, and S is a number greater than 1.

In addition, the downscaling filtering process may include the steps shown in fig. 5, and fig. 5 is a flowchart of a hotspot grid identification method according to the embodiment shown in fig. 4; as shown in fig. 5, the downscaling process may include:

s1, downscaling is carried out on the current spatial resolution so as to obtain updated current spatial resolution.

In this step, the current spatial resolution grid may be matched with the grid after the downscaling process, for example, the current spatial resolution may be 9km×9km, one 9km×9km grid may be downscaled to 9 grids of 3km×3km, one 3km×3km grid may be downscaled to 9 grids of 1km×1km if the current spatial resolution is 3km×3km, and one grid of 1km×1km may be downscaled to 4 grids of 0.5km×0.5km if the current spatial resolution is 1km×1 km.

S2, determining whether the updated current spatial resolution is the target spatial resolution.

In the step, under the condition that the updated current spatial resolution is determined to be not the target spatial resolution, executing S3; and in case that the updated current spatial resolution is determined to be the target spatial resolution, executing S7.

S3, acquiring the updated current gridding emission list under the current spatial resolution, the updated current weather background data under the current spatial resolution and the updated current grid marking data under the current spatial resolution.

In this step, the embodiment of obtaining the updated current grid marking data under the current spatial resolution may include: uniformly marking other grids except the multiple standby grids in the target area as designated grid identifications; and marking the plurality of standby grids by adopting different third grid identifications to obtain the current grid marking data.

And S4, determining a third contribution value of each marked grid in the simulation time period under the updated current spatial resolution to the target air quality in the designated area according to the current grid marked data, the current grid emission list and the current weather background data.

In this step, the current grid marking data, the current grid emission list and the current weather background data may be input into a preset air quality mode, so as to obtain a third contribution value of each marking grid under the updated current spatial resolution to the target air quality in the specified area.

S5, determining a second historical weather type set corresponding to the simulation time period, wherein the second historical weather type set comprises weather types which appear in the simulation time period.

For example, if the historical time period is 5 years of history and the simulation time period is each month, a second set of historical weather patterns corresponding to each month may be obtained. For example, two types of weather types that occur in 1 month of the first year of history are weather type 1 and weather type 3, i.e., one second set of historical weather types is weather type 1 and weather type 3, three types of weather types that occur in 2 months of the first year of history are weather type 1, weather type 2, and weather type 3, i.e., another second set of historical weather types is weather type 1, weather type 2, and weather type 3; similarly, a second set of historical weather patterns for each month over 5 years of history may be determined.

And S6, determining one or more designated grids with the largest third contribution value to the target air quality in the designated area under each weather type in the second historical weather type set, and taking the designated grids as standby grids updated in the simulation time period.

For example, if the second historical weather type set of a month in the historical time period includes weather type 1 and weather type 2, where No. 1-5 and No. 15-18 of the month are weather type 1, and the remaining time is weather type 2, one or more specified grids of the maximum third contribution value of each marked grid of the weather type 1 to the target air quality in the specified area in the multiple days of the month need to be determined, one or more specified grids of the maximum third contribution value of each marked grid of the weather type 2 to the target air quality in the specified area in the multiple days of the month need to be determined, and the specified grid corresponding to the weather type 1 and the specified grid corresponding to the weather type 2 are taken as standby grids after updating in the month.

It should be noted that, a contribution ratio of each current spatial resolution grid to the third contribution value of the target air quality under the updated current spatial resolution may be obtained; and taking the current spatial resolution grid with the contribution ratio being greater than or equal to a preset ratio threshold value as the specified grid.

For example, if the preset proportion threshold value is 95%, the current spatial resolution grid with the contribution proportion of the third contribution value to the target air quality being greater than or equal to 95% is taken as the specified grid.

And S7, taking the standby grid corresponding to the current spatial resolution as the historical hot spot grid.

After the historical hotspot grid is obtained, the historical hotspot grid may be output to show the user a grid with a larger influence on the contribution value of each weather type to the designated area every month.

According to the technical scheme, coarse grids with larger influence can be screened firstly and fine grids with larger influence can be screened secondly through a downscaling method, and grids with larger influence under each weather type in each month can be screened respectively, so that the problems that more grids need to be marked and the historical hot spot grid prediction efficiency are influenced due to the difference of weather conditions in each month are solved.

FIG. 6 is a block diagram of a hotspot grid identification apparatus, as shown in an exemplary embodiment of the present disclosure; as shown in fig. 6, the apparatus may include:

a determining module 601 configured to determine a plurality of historical hotspot grids of the target area having a greatest impact on the target air quality in the designated area under a plurality of weather types in a plurality of simulated time periods within the historical time period;

An acquisition module 602 configured to acquire target weather background data for a target spatial resolution for a future specified time period, a target grid-like emissions inventory for the target spatial resolution, and a target weather pattern set for the future specified time period;

the prediction module 603 is configured to predict, according to the plurality of historical hotspot grids, the target weather background data, the target meshed emission list and the target weather type set, a target hotspot grid for the future specified time period, where the target hotspot grid is a grid with the greatest influence on the target air quality in the specified area under each weather type in the target weather type set.

Optionally, the determining module 601 is configured to:

Optionally, the prediction module 603 is configured to:

Optionally, the prediction module 603 is further configured to:

Optionally, the determining module 601 is configured to:

According to the technical scheme, coarse grids with larger influence can be screened firstly and fine grids with larger influence can be screened secondly through a downscaling method, and grids with larger influence in each month can be screened respectively, so that the problems that more grids need to be marked and the prediction efficiency of historical hot spot grids is influenced due to the difference of meteorological conditions in each month are solved.

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

Fig. 7 is a block diagram of an electronic device, according to an example embodiment. As shown in fig. 7, the first electronic device 700 may include: a first processor 701, a first memory 702. The first electronic device 700 may also include one or more of a multimedia component 703, a first input/output interface 704, and a first communication component 705.

The first processor 701 is configured to control the overall operation of the first electronic device 700, so as to complete all or part of the steps in the hot spot grid identification method described above. The first memory 702 is used to store various types of data to support operation at the first electronic device 700, which may include, for example, instructions for any application or method operating on the first electronic device 700, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The first Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the first memory 702 or transmitted through the first communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The first input/output interface 704 provides an interface between the first processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The first communication component 705 is configured to perform wired or wireless communication between the first electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding first communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.

In an exemplary embodiment, the first electronic device 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (Digital Signal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic components for performing the hotspot grid identification method described above.

In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the hotspot grid identification method described above. For example, the computer readable storage medium may be the first memory 702 described above including program instructions executable by the first processor 701 of the first electronic device 700 to perform the hotspot grid identification method described above.

Fig. 8 is a block diagram of another electronic device, shown in accordance with an exemplary embodiment. For example, the second electronic device 800 may be provided as a server. Referring to fig. 8, the second electronic device 800 includes a second processor 822, which may be one or more in number, and a second memory 832 for storing a computer program executable by the second processor 822. The computer program stored in the second memory 832 may include one or more modules each corresponding to a set of instructions. Further, the second processor 822 may be configured to execute the computer program to perform the hotspot grid identification method described above.

In addition, the second electronic device 800 may further include a power component 826 and a second communication component 850, the power component 826 may be configured to perform power management of the second electronic device 800, and the second communication component 850 may be configured to enable communication, e.g., wired or wireless communication, of the second electronic device 800. In addition, the second electronic device 800 may also include a second input/output interface 858. The second electronic device 800 may operate based on an operating system stored in the second memory 832.

In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the hotspot grid identification method described above. For example, the non-transitory computer readable storage medium may be the second memory 832 described above that includes program instructions executable by the second processor 822 of the second electronic device 800 to perform the hotspot grid identification method described above.

In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described hotspot grid identification method when executed by the programmable apparatus.

The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.

In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.

Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.

Claims

1. A hotspot grid identification method, the method comprising:

2. The method of claim 1, wherein determining a plurality of historical hotspot grids for the target area that have a greatest impact on the target air quality in the designated area for a plurality of weather types over a plurality of simulation time periods over the historical time period comprises:

3. The method of claim 1, wherein the predicting the target hotspot grid for the future specified period of time from the plurality of historical hotspot grids, the target weather background data, the target meshed emissions inventory, and the target weather type set comprises:

4. The method of claim 3, wherein the determining, from the plurality of historical hotspot grids, an alternative hotspot grid corresponding to the target weather pattern set comprises:

5. The method of claim 4, wherein the determining the alternative hotspot grid from the plurality of target dormant grids comprises:

6. The method of claim 5, wherein the determining the alternative hotspot grid from the plurality of target dormant grids further comprises:

7. The method of claim 2, wherein the downscaling process comprises:

8. The method of claim 7, wherein the determining one or more designated grids having a greatest third contribution to the target air quality in the designated area for each weather type within the second set of historical weather types comprises:

9. A hotspot grid identification apparatus, the apparatus comprising:

10. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-8.

11. An electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-8.