Method, device, equipment and storage medium for locating high-value areas of air pollution

Abstract

The present invention provides a method, device, equipment and storage medium for locating a high-value area of ​​atmospheric pollution. The method includes: determining a potential pollution area of ​​a target point based on historical monitoring data of a target point; dividing the potential pollution area into grids to obtain a plurality of subgrids; when the target point is in a pollution growth stage at time t, the target subgrid is also in a pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold, then determining that the target subgrid is in a high-value potential area; wherein the air quality data is obtained by monitoring at least two types of equipment among ground-based monitoring equipment, mobile monitoring equipment or high-altitude monitoring equipment; sorting the air quality data of all target subgrids in the high-value potential area to determine the high-value area affecting the target point. The present invention can quickly determine the high-value area affecting the target point.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring technology, and in particular to a method, device, equipment and storage medium for locating a high-value area of ​​air pollution. Background Art

[0002] With the continuous deepening of air pollution prevention and control measures, air quality has improved significantly. The quality of air reflects the degree of air pollution, which is judged by the concentration of pollutants in the air. Air pollution is a complex phenomenon. The concentration of air pollutants at a specific time and place is affected by many factors. The emission of man-made pollutants from fixed and mobile pollution sources is one of the most important factors affecting air quality. The development density of the city, topography and meteorology are also important factors affecting air quality.

[0003] Air pollution remains one of the main factors affecting air quality. However, it currently takes a long time to identify areas with high pollution values. It is impossible to locate high pollution areas in a timely manner during pollution periods, which has become a technical problem that urgently needs to be solved. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment and storage medium for locating an area with high atmospheric pollution value, so as to solve the problem that it takes a long time to locate the area with high atmospheric pollution value.

[0005] In a first aspect, an embodiment of the present invention provides a method for locating a high-value area of ​​air pollution, characterized by comprising:

[0006] Determine the potential contaminated area of ​​the target point based on the historical monitoring data of the target point;

[0007] Divide the potential pollution area into grids to obtain multiple sub-grids;

[0008] When the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage in the preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than the preset threshold, then it is determined that the target subgrid is in a high-value potential area; wherein the air quality data is obtained by monitoring at least two types of equipment among ground-based monitoring equipment, mobile monitoring equipment or high-altitude monitoring equipment, and the air quality data monitored by each type of monitoring equipment is the same type of data, and the air quality data monitored by different types of monitoring equipment are different types of data;

[0009] Sort the air quality data of all target subgrids within the high-value potential area to determine the high-value areas that affect the target points.

[0010] In a possible implementation, the air quality data of all target subgrids in the high-value potential area are sorted to determine the high-value area affecting the target point, including:

[0011] Sort the similar air quality data of all target subgrids in the high-value potential area, and number each target subgrid according to the sorting result;

[0012] Sum the numbers of each target subgrid under each type of monitoring equipment to determine the impact value of each target subgrid on the target point;

[0013] The target subgrid with the largest impact value is determined as the high-value area that affects the target point.

[0014] In a possible implementation, the numbers of each target subgrid under each type of monitoring equipment are summed to determine the impact value of each target subgrid on the target point, including:

[0015] Input the number of each target subgrid under each type of monitoring equipment into the pre-built evaluation model to obtain the impact value of each target subgrid on the target point;

[0016] Among them, the evaluation model P j for:

[0017] P j =R Dj *0.35+R Yj 0.35+R Gj *0.3;

[0018] Where j is the number of the target subgrid, R Dj is the number of the target subgrid determined by ground-based monitoring equipment, R Yj is the number of the target subgrid determined by the mobile monitoring device, R Gj It is the number of the target subgrid determined by the high-altitude monitoring equipment.

[0019] In a possible implementation, the same type of air quality data of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting result, including:

[0020] When there are multiple monitoring devices of the same type in the target subgrid within the high-value potential area, the air quality data monitored by each monitoring device of the same type are sorted and processed to determine the number of the target subgrid under the air quality data of this type;

[0021] The similar air quality data of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting result.

[0022] In a possible implementation, when a plurality of monitoring devices of the same type are provided in a target subgrid in a high-value potential area, the air quality data monitored by each monitoring device of the same type are sorted and processed to determine the number of the target subgrid under the air quality data of the same type, including:

[0023] Based on the air quality data monitored by each monitoring device of the same type in the target sub-grid and a preset sorting model, determine the number of the target sub-grid under the type of air quality data;

[0024] Among them, the preset sorting model R is:

[0025]

[0026] Among them, n is the total number of monitoring devices of the same type in the same target subgrid, Xi is the ranking of air quality data monitored by monitoring device i of the same type in the subgrid among similar air quality data, Xmax is the ranking of the maximum air quality data monitored by the monitoring device of the same type in the subgrid, and Xmin is the ranking of the minimum air quality data monitored by the monitoring device of the same type in the subgrid.

[0027] In a possible implementation, based on the historical monitoring data of the target point, the potential contaminated area of ​​the target point is determined, including:

[0028] Obtaining historical monitoring data of the target point, wherein the historical monitoring data includes environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes concentration values ​​of various pollutants arranged in time series, and the meteorological monitoring data includes meteorological data arranged in time series;

[0029] Based on the environmental monitoring data of the target points, extract the environmental monitoring data of the pollution growth stage and the high-value pollution points;

[0030] Determine the pollutant distribution map of the target point based on the environmental monitoring data of the pollution growth stage and the high pollution value points, and the meteorological monitoring data of the pollution growth stage and the high pollution value points;

[0031] Based on the pollutant distribution map of the target point, determine the potential pollution area of ​​the target point.

[0032] In a possible implementation, when the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold, then it is determined that the target subgrid is in a high value potential area, including:

[0033] Based on the hourly concentration data of pollutants at the target point, when the ratio of the air quality data at the target point at time t to the air quality data at time t-1 is greater than 100%, and the ratio of the air quality data at time t+1 to the air quality data at time t is greater than 100%, it is determined that the target point is in the pollution growth stage at time t;

[0034] Based on the pollutant hourly concentration data of the target subgrid, when the ratio of the air quality data of the target subgrid at time t-△t to the air quality data at time t-△t-1 is greater than 100%, and the ratio of the air quality data at time t-△t+1 to the air quality data at time t-△t is greater than 100%, it is determined that the target subgrid is in the pollution growth stage at time t-△t;

[0035] When the difference between the air quality data of the target point at time t and the air quality data of the target subgrid at time t, and the ratio of the air quality data of the target subgrid at time t are greater than a preset threshold, it is determined that the target subgrid is in a high value potential area.

[0036] In a second aspect, an embodiment of the present invention provides a device for locating a high-value area of ​​air pollution, including: a potential area determination module, for determining a potential pollution area of ​​a target point based on historical monitoring data of the target point;

[0037] A grid division module is used to divide the potential pollution area into grids to obtain multiple sub-grids;

[0038] A module for determining a high-value potential area is used to determine that the target subgrid is in a high-value potential area when the target point is in a pollution growth stage at time t, the target subgrid is also in a pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold; wherein the air quality data is obtained by monitoring at least two types of equipment among ground-based monitoring equipment, mobile monitoring equipment or high-altitude monitoring equipment, the air quality data monitored by each type of monitoring equipment is the same type of data, and the air quality data monitored by different types of monitoring equipment are different types of data;

[0039] The high-value determination module is used to sort the air quality data of all target subgrids within the high-value potential area and determine the high-value area that affects the target point.

[0040] In a possible implementation, a high value determination module is used to sort similar air quality data of all target subgrids in the high value potential area, and number each target subgrid according to the sorting result;

[0041] Sum the numbers of each target subgrid under each type of monitoring equipment to determine the impact value of each target subgrid on the target point;

[0042] The target subgrid with the largest impact value is determined as the high-value area that affects the target point.

[0043] In a possible implementation, a high-value determination module is used to input the serial number of each target subgrid under each type of monitoring equipment into a pre-built evaluation model to obtain the impact value of each target subgrid on the target point;

[0044] Among them, the evaluation model P j for:

[0045] P j =R Dj *0.35+R Yj 0.35+R Gj *0.3;

[0046] Where j is the number of the target subgrid, R Dj is the number of the target subgrid determined by ground-based monitoring equipment, R Yj is the number of the target subgrid determined by the mobile monitoring device, R Gj It is the number of the target subgrid determined by the high-altitude monitoring equipment.

[0047] In a possible implementation, the high-value determination module is used to, when a target subgrid in the high-value potential area is provided with a plurality of monitoring devices of the same type, sort the air quality data monitored by each monitoring device of the same type, and determine the number of the target subgrid under the air quality data of the same type;

[0048] The similar air quality data of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting result.

[0049] In a possible implementation, the high value determination module is used to determine the number of the target subgrid under the type of air quality data based on the air quality data monitored by each monitoring device of the same type in the target subgrid and a preset sorting model;

[0050] Among them, the preset sorting model R is:

[0051]

[0052] Among them, n is the total number of monitoring devices of the same type in the same target subgrid, Xi is the ranking of air quality data monitored by monitoring device i of the same type in the subgrid among similar air quality data, Xmax is the ranking of the maximum air quality data monitored by the monitoring device of the same type in the subgrid, and Xmin is the ranking of the minimum air quality data monitored by the monitoring device of the same type in the subgrid.

[0053] In a possible implementation, a potential zone determination module is used to obtain historical monitoring data of a target point, wherein the historical monitoring data includes environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes concentration values ​​of various pollutants arranged in time series, and the meteorological monitoring data includes meteorological data arranged in time series;

[0054] Based on the environmental monitoring data of the target points, extract the environmental monitoring data of the pollution growth stage and the high-value pollution points;

[0055] Determine the pollutant distribution map of the target point based on the environmental monitoring data of the pollution growth stage and the high pollution value points, and the meteorological monitoring data of the pollution growth stage and the high pollution value points;

[0056] Based on the pollutant distribution map of the target point, determine the potential pollution area of ​​the target point.

[0057] In a possible implementation, a high-value potential area determination module is used to determine that the target point is in a pollution growth stage at time t based on the hourly concentration data of pollutants at the target point, when the ratio of the air quality data at time t to the air quality data at time t-1 is greater than 100%, and the ratio of the air quality data at time t+1 to the air quality data at time t is greater than 100%;

[0058] Based on the pollutant hourly concentration data of the target subgrid, when the ratio of the air quality data of the target subgrid at time t-△t to the air quality data at time t-△t-1 is greater than 100%, and the ratio of the air quality data at time t-△t+1 to the air quality data at time t-△t is greater than 100%, it is determined that the target subgrid is in the pollution growth stage at time t-△t;

[0059] When the difference between the air quality data of the target point at time t and the air quality data of the target subgrid at time t, and the ratio of the air quality data of the target subgrid at time t are greater than a preset threshold, it is determined that the target subgrid is in a high value potential area.

[0060] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method described in the first aspect or any possible implementation manner of the first aspect.

[0061] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0062] The embodiment of the present invention provides a method, device, equipment and storage medium for locating a high-value area of ​​atmospheric pollution. First, based on the historical monitoring data of the target point, the potential pollution area of ​​the target point is determined. Then, the potential pollution area is grid-divided to obtain multiple sub-grids; then, when the target point is in the pollution growth stage at time t, the target sub-grid is also in the pollution growth stage in the preset time period before time t, and the difference between the air quality data of the target sub-grid and the target point at time t is greater than the preset threshold, then it is determined that the target sub-grid is in the high-value potential area. Finally, the air quality data of all target sub-grids in the high-value potential area are sorted to determine the high-value area affecting the target point. Therefore, after the potential pollution area of ​​the target point is determined by analyzing the historical monitoring data of the target point, the high-value area affecting the target point can be quickly determined by analyzing the monitoring data of the various types of monitoring equipment deployed in the sub-grid of the potential pollution area during the pollution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0064] Figure 1 is a flow chart for implementing a method for locating a high-value area of ​​air pollution provided by an embodiment of the present invention;

[0065] Figure 2 is a schematic diagram of the structure of a device for locating a high-value area of ​​air pollution provided by an embodiment of the present invention;

[0066] Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0069] As described in the background technology, with the continuous deepening of air pollution prevention and control, timely positioning of high-value pollution areas is very important for environmental monitoring. However, the existing positioning methods of high-value areas take a long time to locate, which cannot meet the needs of rapid positioning.

[0070] In order to solve the problems of the prior art, the embodiments of the present invention provide a method, device, equipment and storage medium for locating a high-value area of ​​air pollution. The method for locating a high-value area of ​​air pollution provided by the embodiments of the present invention is first introduced below.

[0071] See also Figure 1 , which shows a flowchart of the method for locating high-value areas of air pollution provided by an embodiment of the present invention, and is described in detail as follows:

[0072] Step S110: Determine the potential contamination area of ​​the target point based on the historical monitoring data of the target point.

[0073] The target point can be a monitoring station where the pollutant concentration is higher than other monitoring stations, or a monitoring station where peak values ​​often occur.

[0074] In some embodiments, in order to determine the potential contamination area of ​​the target point, the historical monitoring data of the target point may be first obtained, and then the potential contamination area of ​​the target point may be finally determined based on the analysis of the historical monitoring data. Exemplarily, step S110 may include:

[0075] Step S1110: Obtain historical monitoring data of the target point.

[0076] In this embodiment, the historical monitoring data includes environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes concentration values ​​of a certain type of pollutants arranged in time series, and the meteorological monitoring data includes meteorological data arranged in time series.

[0077] Before positioning, you can first determine the type of pollutants that affect the target point, and select any type of pollutant for monitoring. Then extract the corresponding historical monitoring data for this type of pollutant.

[0078] The pollutant can be SO 2 、NO 2 ,CO,O 3 、PM 10 、PM 2.5 After selecting the pollutants to be monitored, the environmental monitoring data of such pollutants can be retrieved.

[0079] Meteorological monitoring data may include meteorological data such as wind speed, wind direction, temperature or humidity arranged in time series.

[0080] Step S1120: Based on the environmental monitoring data of the target point, extract the environmental monitoring data of the pollution growth stage and the high-value pollution point.

[0081] According to the changing trend of hourly concentration of pollutants, the entire pollution is divided into growth stage, decline stage and stable stage.

[0082] Among them, the growth stage refers to when the change rate of the pollutant concentration at time t and the pollutant concentration at time t-1 is greater than 100% and lasts for more than 2 hours, it means that the pollutant concentration is gradually increasing and the pollutant is in the growth stage. When the change rate of the pollutant concentration at time t and the pollutant concentration at time t-1 is less than 95%, it means that the pollutant concentration is gradually decreasing and is in the decline stage. When the change rate of the pollutant concentration at time t and the pollutant concentration at time t-1 is 100% ± 5%, it means that the pollutant concentration is in the stable stage.

[0083] Specifically, the calculation formula is:

[0084]

[0085] Among them, C t is the hourly / minute concentration of the pollutant at time t, in micrograms / cubic meter or milligrams / cubic meter, C t+1 is the hourly / minute concentration of the pollutant at time t+1.

[0086] When the pollutant concentration at the target point goes through growth, stability and decline stages according to the time series, it means that a pollution process has occurred.

[0087] By analyzing the environmental monitoring data of target points, the environmental monitoring data of pollution growth stages and high-value pollution points are extracted.

[0088] Step S1130: Determine the pollutant distribution map of the target point based on the environmental monitoring data at the pollution growth stage and the pollution high value points, and the meteorological monitoring data at the pollution growth stage and the pollution high value points.

[0089] Since there is a correlation between environmental monitoring data and meteorological monitoring data, the general pollution direction of pollutants can be inferred by using environmental monitoring data and meteorological monitoring data at pollution growth stages and high pollution points, and a pollutant distribution map of the target point can be drawn.

[0090] Step S1140: Determine a potential pollution area of ​​the target point based on the pollutant distribution map of the target point.

[0091] By obtaining the pollutant distribution map of the target point, it is possible to determine the direction from which the pollutants mainly come from the target point, thereby determining the potential pollution area of ​​the target point.

[0092] Through steps S1110 to S1140, by studying historical pollution data, the potential pollution area of ​​the target point can be first determined before pollution occurs, thereby providing a smaller screening range for the subsequent positioning process, and positioning can be performed more quickly and accurately.

[0093] Especially during heavy pollution weather, since the overall air quality in different areas does not differ much during heavy pollution, potential pollution areas can be identified before pollution occurs.

[0094] For example, after obtaining the pollutant distribution map of the target point, the area within 5 kilometers centered on the target point can be set as a potential pollution area by analyzing the pollutant distribution map.

[0095] Step S120: grid the potential pollution area to obtain a plurality of sub-grids.

[0096] After determining the potential pollution area, in order to facilitate the determination of high-value areas, it is necessary to divide the potential pollution area into grids and divide it into multiple sub-grids to facilitate the analysis of the monitoring data of each sub-grid.

[0097] For example, the potential pollution area is subdivided into 0.5*0.5 km grids, and a grid number is set for each grid.

[0098] Step S130: When the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage during the preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than the preset threshold, it is determined that the target subgrid is in a high value potential area.

[0099] After the potential pollution area is divided into grids, various types of monitoring equipment need to be deployed in each sub-grid, such as at least two types of equipment from ground-based monitoring equipment, mobile monitoring equipment, or high-altitude monitoring equipment. The air quality data monitored by each type of monitoring equipment is of the same type, and the air quality data monitored by different types of monitoring equipment is of different types.

[0100] Multiple monitoring devices of the same type can be deployed in each sub-grid, such as several different ground-based monitoring devices, or multiple different mobile monitoring devices, or multiple different high-altitude monitoring devices. There is no limitation here, and users can set it according to actual usage.

[0101] For example, ground-based monitoring equipment is mainly fixed on the ground, mainly micro-stations, and at least one fixed monitoring device is deployed in each sub-grid. Micro-stations are deployed in urban areas and enterprise boundaries, and focus on monitoring various conventional parameters and VOCs and other parameters.

[0102] Mobile monitoring equipment mainly refers to cruise monitoring equipment. When conducting mobile monitoring, the monitoring range covers every sub-grid in the potential pollution area, such as a cruise vehicle.

[0103] High-altitude monitoring equipment refers to drones, radars, satellite data, etc., which can monitor the entire potential pollution area.

[0104] It should be noted here that, as mentioned above, the present invention is aimed at monitoring a certain type of pollutants, so all monitoring devices arranged in each sub-grid can monitor the concentration of a certain type of pollutants.

[0105] Air quality data can be the concentration value of a certain type of pollutant.

[0106] In this embodiment, during the atmospheric pollution process, based on the hourly concentration data of pollutants at the target point, when the ratio of the air quality data of the target point at time t to the air quality data at time t-1 is greater than 100%, and the ratio of the air quality data at time t+1 to the air quality data at time t is greater than 100%, it is determined that the target point is in the pollution growth stage at time t.

[0107] Based on the hourly concentration data of pollutants of the target subgrid, when the ratio of the air quality data of the target subgrid at time t-△t to the air quality data at time t-△t-1 is greater than 100%, and the ratio of the air quality data at time t-△t+1 to the air quality data at time t-△t is greater than 100%, it is determined that the target subgrid is also in the pollution growth stage at time t-△t.

[0108] When the difference between the air quality data of the target point at time t and the air quality data of the target subgrid at time t, and the ratio of the air quality data of the target subgrid at time t are greater than a preset threshold, it is determined that the target subgrid is in a high value potential area.

[0109] The preset threshold is not limited here, and users can set it according to the actual usage scenario.

[0110] For example, the preset threshold may be set to 15%.

[0111] Step S140: sort the air quality data of all target subgrids in the high-value potential area to determine the high-value area that affects the target point.

[0112] After determining the high-value potential area, the air quality data monitored by various monitoring devices at time t can be distributed to all target subgrids within the high-value potential area, mainly based on the longitude and latitude information.

[0113] For example, ArcGIS analysis software can be used to divide the high-value potential area into 0.5km×0.5km grids or smaller grids. The data obtained is air quality data with longitude and latitude information. The ground data, mobile data and high-altitude data with longitude and latitude information are loaded into ArcGIS software respectively, and the data are matched to the corresponding grids through the spatial analysis tools in the software.

[0114] If the air quality data is small, all the data in the high-value potential area can be expanded N times at the same time, and the value of N is not limited here.

[0115] In this embodiment, in order to facilitate the sorting of the air quality data of all target subgrids in the high-value potential area, it is necessary to first sort the same type of air quality data monitored by the same type of monitoring equipment in all target subgrids, and then determine the high-value area affecting the target point based on the sorting results of all types of air quality data. Exemplarily, step S140 may include:

[0116] Step S1410: sort the similar air quality data of all target sub-grids in the high-value potential area, and number each target sub-grid according to the sorting result.

[0117] When there is only one ground-based monitoring device, mobile-based monitoring device or high-altitude monitoring device in each target subgrid, there are three types of air quality data in the target subgrid.

[0118] For a certain type of air quality data, the air quality data of this type of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting result.

[0119] For example, there are a total of 8 target subgrids in the high-value potential area. The air quality data monitored by the ground-based monitoring equipment in these 8 target subgrids are sorted, and the 8 target subgrids are numbered according to the sorting results. For example, the largest data can be numbered 8 and the smallest data can be numbered 1. The specific numbering is not limited here. Then the air quality data monitored by the mobile and high-altitude monitoring equipment in these 8 target subgrids are also sorted separately, so that these 8 target subgrids have 3 numbers corresponding to these 3 types of monitoring equipment.

[0120] In some embodiments, if a target sub-grid is provided with multiple monitoring settings of the same type, such as two ground-type monitoring devices, then there will be two ground-type air quality data corresponding to the target sub-grid. These two ground-type air quality data need to be processed before they can be sorted with the ground-type air quality data in other target sub-grids.

[0121] First, the air quality data monitored by each monitoring device of the same type can be sorted to determine the number of the target subgrid under the air quality data of this type. The specific sorting process is:

[0122] First, the air quality data of each type of monitoring equipment of the same type in the target sub-grid are sorted respectively, and numbered according to the sorting results.

[0123] For example, there are four ground-based monitoring devices A, B, C, and D in the target subgrid. First, sort the air quality data monitored by devices A, B, C, and D. Number the device with the highest detected concentration as 4, and the device with the lowest as 1. The rest are sorted in order.

[0124] Then, the numbers of these ground-based monitoring devices A, B, C and D are respectively input into the preset sorting model to determine the number of the target subgrid under this type of air quality data.

[0125] Specifically, the preset sorting model R is:

[0126]

[0127] Among them, n is the total number of monitoring devices of the same type in the same target subgrid, Xi is the ranking of air quality data monitored by monitoring device i of the same type in the subgrid among similar air quality data, Xmax is the ranking of the maximum air quality data monitored by the monitoring device of the same type in the subgrid, and Xmin is the ranking of the minimum air quality data monitored by the monitoring device of the same type in the subgrid.

[0128] In the ground-type monitoring equipment A, B, C and D, n is 4, Xmin is 1, Xmax is 4, and Xi is the corresponding number of the ground-type monitoring equipment A, B, C and D.

[0129] Step S1420: sum the numbers of each target subgrid under each type of monitoring equipment to determine the impact value of each target subgrid on the target point.

[0130] After obtaining the number of each target subgrid under each type of monitoring equipment, the number of each target subgrid is input into the pre-built evaluation model to obtain the impact value of each target subgrid on the target point.

[0131] Among them, the evaluation model P j for:

[0132] P j =R Dj *0.35+R Yj 0.35+R Gj *0.3;

[0133] Where j is the number of the target subgrid, R Dj is the number of the target subgrid determined by ground-based monitoring equipment, R Yj is the number of the target subgrid determined by the mobile monitoring device, R Gj It is the number of the target subgrid determined by the high-altitude monitoring equipment.

[0134] By first sorting the same type of air quality data monitored by the same type of monitoring equipment in all target sub-grids, and then determining the high-value areas affecting the target points based on the sorting results of all types of air quality data, and thus setting multiple types of monitoring equipment in the target sub-grid to monitor a certain pollutant, the source of the pollutant can be determined more comprehensively and accurately.

[0135] The positioning method provided by the present invention first determines the potential pollution area of ​​the target point based on the historical monitoring data of the target point, then divides the potential pollution area into grids to obtain multiple subgrids; then, when the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage in the preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than the preset threshold, then it is determined that the target subgrid is in a high-value potential area. Finally, the air quality data of all target subgrids in the high-value potential area are sorted to determine the high-value area affecting the target point. Therefore, after determining the potential pollution area of ​​the target point by analyzing the historical monitoring data of the target point, the high-value area affecting the target point can be quickly determined by analyzing the monitoring data of the various types of monitoring equipment deployed in the subgrids of the potential pollution area during the pollution process.

[0136] After determining the potential pollution area of ​​the target point, the potential pollution area is divided into multiple sub-grids, and a three-dimensional monitoring system is deployed in each sub-grid, namely ground-based monitoring equipment, mobile monitoring equipment, and high-altitude monitoring equipment. The impact of the potential pollution area on air quality is used to analyze the high-value areas that affect the target point.

[0137] The following is a detailed description of the method for locating high-value areas of air pollution provided by the present invention using a specific embodiment:

[0138] First, a monitoring station in Shijiazhuang is set as the target point, and the SO 2 Historical monitoring data, including SO at the target point 2 Concentration values ​​and meteorological monitoring data. It should be noted here that the NO 2 ,CO,O 3 、PM 10 、PM 2.5 And other pollutants, users can select the pollutants to be monitored according to their needs.

[0139] Then, according to the SO of the target point 2 Concentration value, extracting SO in the pollution growth stage and pollution high value point 2 Concentration value. According to the pollution growth stage and the SO 2 The concentration value, as well as the meteorological monitoring data corresponding to the pollution growth stage and the high-value pollution points, determine the pollutant distribution map of the target point. Based on the pollutant distribution map, the potential pollution area of ​​the target point can be determined. For example, the potential pollution area can be set within a 5-kilometer radius centered on the target point.

[0140] Next, the potential pollution area is divided into grids, such as subdividing the potential pollution area into 0.5*0.5 km subgrids, and each subgrid is assigned a grid number. Three types of monitoring equipment are set in each subgrid, namely ground monitoring equipment, mobile monitoring equipment, and high-altitude monitoring equipment. Each subgrid has at least one ground monitoring device, and mobile monitoring equipment and high-altitude monitoring equipment can monitor all subgrids in the potential pollution area, thereby establishing a three-dimensional monitoring system.

[0141] Secondly, by analyzing the hourly concentration data of pollutants monitored by various monitoring equipment, when the target point is at the SO 2 Concentration value and SO at time t-1 2 The ratio of the concentration values ​​is greater than 100%, and the SO 2 Concentration value and SO at time t 2 When the ratio of the concentration values ​​is greater than 100%, it is determined that the target point is in the pollution growth stage at time t. 2 Concentration value and SO at time t-△t-1 2 The ratio of the concentration values ​​is greater than 100%, and the SO2 concentration value at time t-△t+1 is greater than the SO2 concentration value at time t-△t. 2 When the ratio of the concentration values ​​is greater than 100%, it is determined that the target subgrid is in the pollution growth stage at time t-△t. 2 Concentration value and SO of target subgrid at time t 2 When the difference of the concentration values ​​and the ratio of the SO2 concentration value of the target subgrid at time t are greater than a preset threshold, the target subgrid is determined to be in a high value potential area. The preset threshold here can be set to 15%, which is not limited here.

[0142] After that, the SO of all target subgrids in the high potential area at time t is obtained. 2 Concentration value. Since ground, mobile and high-altitude monitoring equipment are deployed in each target subgrid, each target subgrid will have three types of SO at time t. 2 Concentration value, ground-type SO 2 Concentration value, mobile SO 2 Concentration values ​​and high-altitude SO 2 Concentration value.

[0143] When there are multiple monitoring devices of the same type in a target subgrid, it is necessary to first process the data monitored by the multiple monitoring devices of the same type in the target subgrid, and finally obtain the monitoring data of one monitoring device of this type. For example, if there are 4 ground-type monitoring devices A, B, C and D in the target subgrid, first sort the air quality data monitored by devices A, B, C and D, number the device with the highest concentration as 4, the device with the lowest concentration as 1, and the rest are sorted in order. The numbers of these ground-type monitoring devices A, B, C and D are respectively input into the preset sorting model.

[0144]

[0145] n is 4, Xmin is 1, Xmax is 4, and Xi is the corresponding number of ground-type monitoring equipment A, B, C, and D. The final value of R is the SO of the ground-type of the target subgrid. 2 Concentration value.

[0146] The same type of air quality data of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting results. After obtaining the number of each target subgrid under each type of monitoring equipment, the number of each target subgrid is input into the pre-built evaluation model to obtain the impact value of each target subgrid on the target point.

[0147] Among them, the evaluation model P j for:

[0148] P j =R Dj *0.35+R Yj 0.35+R Gj *0.3;

[0149] Where j is the number of the target subgrid, R Dj is the number of the target subgrid determined by ground-based monitoring equipment, R Yj is the number of the target subgrid determined by the mobile monitoring device, R Gj It is the number of the target subgrid determined by the high-altitude monitoring equipment.

[0150] Finally, according to the influence value of each target sub-grid on the target point, the target sub-grid with the largest influence value is determined as the high-value area affecting the target point.

[0151] Therefore, before pollution occurs, the potential pollution area of ​​the target point is first determined. Then, when pollution occurs, a three-dimensional monitoring system is deployed in the sub-grid of the potential pollution area to comprehensively monitor the potential pollution area. Then, according to the pollutant concentration values ​​of each sub-grid at a certain moment in the pollution growth stage, the high-value potential area can be further determined, thereby further narrowing the scope of data processing. Finally, according to the sorting of the pollutant concentration values ​​of each target sub-grid in the high-value potential area, the high-value area affecting the target point can be finally determined quickly and accurately.

[0152] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0153] Based on the method for locating a high-value air pollution area provided in the above embodiment, the present invention also provides a specific implementation of a device for locating a high-value air pollution area applied to the method for locating a high-value air pollution area. Please refer to the following embodiment.

[0154] like Figure 2 As shown, a device 200 for locating high-value air pollution areas is provided, the device comprising:

[0155] A potential area determination module 210 is used to determine a potential contaminated area of ​​a target point based on historical monitoring data of the target point;

[0156] A grid division module 220 is used to divide the potential pollution area into grids to obtain multiple sub-grids;

[0157] The module 230 for determining a high-value potential area is used to determine that the target subgrid is in a high-value potential area when the target point is in a pollution growth stage at time t, the target subgrid is also in a pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold; wherein the air quality data is obtained by monitoring at least two types of equipment including ground-based monitoring equipment, mobile monitoring equipment, or high-altitude monitoring equipment, and the air quality data monitored by each type of monitoring equipment is the same type of data, and the air quality data monitored by different types of monitoring equipment are different types of data;

[0158] The high value determination module 240 is used to sort the air quality data of all target subgrids in the high value potential area to determine the high value area affecting the target point.

[0159] In a possible implementation, the high value determination module 240 is used to sort the similar air quality data of all target subgrids in the high value potential area, and number each target subgrid according to the sorting result;

[0160] Sum the numbers of each target subgrid under each type of monitoring equipment to determine the impact value of each target subgrid on the target point;

[0161] The target subgrid with the largest impact value is determined as the high-value area that affects the target point.

[0162] In a possible implementation, the high value determination module 240 is used to input the serial number of each target subgrid under each type of monitoring equipment into a pre-built evaluation model to obtain the impact value of each target subgrid on the target point;

[0163] Among them, the evaluation model P j for:

[0164] P j =R Dj *0.35+R Yj 0.35+R Gj *0.3;

[0165] Where j is the number of the target subgrid, R Dj is the number of the target subgrid determined by ground-based monitoring equipment, R Yj is the number of the target subgrid determined by the mobile monitoring device, R Gj It is the number of the target subgrid determined by the high-altitude monitoring equipment.

[0166] In a possible implementation, the high value determination module 240 is used to determine the number of the target subgrid under the type of air quality data by sorting the air quality data monitored by each monitoring device of the same type when a plurality of monitoring devices of the same type are provided in the target subgrid within the high value potential area;

[0167] The similar air quality data of all target subgrids in the high-value potential area are sorted, and each target subgrid is numbered according to the sorting result.

[0168] In a possible implementation, the high value determination module 240 is used to determine the number of the target subgrid under the type of air quality data based on the air quality data monitored by each monitoring device of the same type in the target subgrid and a preset sorting model;

[0169] Among them, the preset sorting model R is:

[0170]

[0171] Among them, n is the total number of monitoring devices of the same type in the same target subgrid, Xi is the ranking of air quality data monitored by monitoring device i of the same type in the subgrid among similar air quality data, Xmax is the ranking of the maximum air quality data monitored by the monitoring device of the same type in the subgrid, and Xmin is the ranking of the minimum air quality data monitored by the monitoring device of the same type in the subgrid.

[0172] In a possible implementation, the potential area determination module 210 is used to obtain historical monitoring data of the target point, wherein the historical monitoring data includes environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes concentration values ​​of various pollutants arranged in time series, and the meteorological monitoring data includes meteorological data arranged in time series;

[0173] Based on the environmental monitoring data of the target points, extract the environmental monitoring data of the pollution growth stage and the high-value pollution points;

[0174] Determine the pollutant distribution map of the target point based on the environmental monitoring data of the pollution growth stage and the high pollution value points, and the meteorological monitoring data of the pollution growth stage and the high pollution value points;

[0175] Based on the pollutant distribution map of the target point, determine the potential pollution area of ​​the target point.

[0176] In a possible implementation, the high-value potential area determination module 230 is used to determine that the target point is in a pollution growth stage at time t when the ratio of the air quality data of the target point at time t to the air quality data at time t-1 is greater than 100%, and the ratio of the air quality data at time t+1 to the air quality data at time t is greater than 100% based on the hourly concentration data of pollutants at the target point;

[0177] Based on the pollutant hourly concentration data of the target subgrid, when the ratio of the air quality data of the target subgrid at time t-△t to the air quality data at time t-△t-1 is greater than 100%, and the ratio of the air quality data at time t-△t+1 to the air quality data at time t-△t is greater than 100%, it is determined that the target subgrid is in the pollution growth stage at time t-△t;

[0178] When the difference between the air quality data of the target point at time t and the air quality data of the target subgrid at time t, and the ratio of the air quality data of the target subgrid at time t are greater than a preset threshold, it is determined that the target subgrid is in a high value potential area.

[0179] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, the electronic device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned various air pollution high value area positioning method embodiments are implemented, such as Figure 3 Alternatively, when the processor 30 executes the computer program 32, the functions of each module in the above-mentioned device embodiments are realized, for example, Figure 2 The functions of modules 210 to 240 are shown.

[0180] Exemplarily, the computer program 32 may be divided into one or more modules, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may be divided into Figure 2 Modules 210 to 240 are shown.

[0181] The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0182] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0183] The memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 31 is used to store the computer program and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0184] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0185] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0186] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0187] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0188] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0190] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned positioning method embodiments of each high-value area of ​​air pollution can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0191] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for locating high-value areas of air pollution, characterized in that: include: Obtain historical monitoring data of target points, and extract environmental monitoring data of pollution growth stages and high-value pollution points based on the environmental monitoring data of the target points; Based on the environmental monitoring data at the pollution growth stage and the pollution high value point, and the meteorological monitoring data at the pollution growth stage and the pollution high value point, determine the pollutant distribution map of the target point; based on the pollutant distribution map of the target point, determine the potential pollution area of ​​the target point; wherein the historical monitoring data includes the environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes the concentration value of a certain type of pollutant arranged in time series, and the meteorological monitoring data includes the meteorological data arranged in time series; Dividing the potential pollution area into grids to obtain a plurality of sub-grids; When the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage in the preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than the preset threshold, then it is determined that the target subgrid is in a high-value potential area; wherein the air quality data is obtained by monitoring ground-based monitoring equipment, mobile monitoring equipment and high-altitude monitoring equipment, the air quality data monitored by each type of monitoring equipment is the same type of data, and the air quality data monitored by different types of monitoring equipment are different types of data; wherein the target subgrid is any subgrid; Sorting the same type of air quality data of all target subgrids in the high-value potential area, and numbering each target subgrid according to the sorting result; inputting the number of each target subgrid under each type of monitoring equipment into a pre-built evaluation model to obtain the impact value of each target subgrid on the target point; determining the target subgrid with the largest impact value as the high-value area affecting the target point; Among them, the evaluation model for: ； Where j is the number of the target subgrid, is the number of the target subgrid determined by ground-based monitoring equipment. is the number of the target subgrid determined by the mobile monitoring device. It is the number of the target subgrid determined by the high-altitude monitoring equipment.

2. The positioning method according to claim 1, characterized in that: The step of sorting the similar air quality data of all target subgrids in the high-value potential area and numbering each target subgrid according to the sorting result includes: When a plurality of monitoring devices of the same type are provided in the target subgrid in the high-value potential area, the air quality data monitored by each monitoring device of the same type are sorted and processed to determine the number of the target subgrid under the air quality data of this type; The similar air quality data of all target sub-grids in the high-value potential area are sorted, and each target sub-grid is numbered according to the sorting result.

3. The positioning method according to claim 1, characterized in that: When the target point is in the pollution growth stage at time t, the target subgrid is also in the pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold, then determining that the target subgrid is in a high value potential area includes: Based on the hourly concentration data of pollutants at the target point, when the ratio of the air quality data at the target point at time t to the air quality data at time t-1 is greater than 100%, and the ratio of the air quality data at time t+1 to the air quality data at time t is greater than 100%, it is determined that the target point is in the pollution growth stage at time t; Based on the pollutant hourly concentration data of the target subgrid, when the ratio of the air quality data of the target subgrid at time t-△t to the air quality data at time t-△t-1 is greater than 100%, and the ratio of the air quality data at time t-△t+1 to the air quality data at time t-△t is greater than 100%, it is determined that the target subgrid is in the pollution growth stage at time t-△t; When the difference between the air quality data of the target subgrid at time t and the air quality data of the target point at time t, and the ratio of the air quality data of the target subgrid at time t are greater than a preset threshold, it is determined that the target subgrid is in a high value potential area.

4. A device for locating high-value areas of air pollution, characterized in that: include: A potential area determination module is used to obtain historical monitoring data of a target point, extract environmental monitoring data of a pollution growth stage and a pollution high value point based on the environmental monitoring data of the target point; determine a pollutant distribution map of the target point based on the environmental monitoring data of the pollution growth stage and the pollution high value point, and the meteorological monitoring data of the pollution growth stage and the pollution high value point; determine a potential pollution area of ​​the target point based on the pollutant distribution map of the target point; wherein the historical monitoring data includes environmental monitoring data and meteorological monitoring data of the target point, the environmental monitoring data includes concentration values ​​of a certain type of pollutants arranged in time series, and the meteorological monitoring data includes meteorological data arranged in time series; A grid division module is used to divide the potential pollution area into grids to obtain multiple sub-grids; A module for determining a high-value potential area is used to determine that the target subgrid is in a high-value potential area when the target point is in a pollution growth stage at time t, the target subgrid is also in a pollution growth stage in a preset time period before time t, and the difference between the air quality data of the target subgrid and the target point at time t is greater than a preset threshold; wherein the air quality data is obtained by monitoring ground-based monitoring equipment, mobile monitoring equipment, and high-altitude monitoring equipment, the air quality data monitored by each type of monitoring equipment is the same type of data, and the air quality data monitored by different types of monitoring equipment are different types of data; A high-value determination module is used to sort the similar air quality data of all target subgrids in the high-value potential area, and number each target subgrid according to the sorting result; input the number of each target subgrid under each type of monitoring equipment into a pre-built evaluation model to obtain the impact value of each target subgrid on the target point; determine the target subgrid with the largest impact value as the high-value area affecting the target point; Among them, the evaluation model for: ； Where j is the number of the target subgrid, is the number of the target subgrid determined by ground-based monitoring equipment. is the number of the target subgrid determined by the mobile monitoring device. It is the number of the target subgrid determined by the high-altitude monitoring equipment.

5. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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