CN109613179B - Method for determining cumulative high value area - Google Patents
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Abstract
The application provides a method for determining an accumulated high-value area, which comprises the following steps: acquiring numbered first pollutant concentration data of each grid in a first area within a first time period; obtaining a first set of grids comprising a first number of grids; calculating a first concentration difference; repeating the steps until the nth pollutant concentration data, the nth grid set and the nth concentration difference value corresponding to the nth time length are obtained; wherein the nth grid set includes an nth number of grids; determining a target grid set and the average value of each grid according to the numbers of the first to nth number grids; setting weight values for grids in the target grid set according to preset rules; and calculating the scores of the grids in the target grid set, and determining a high-value grid, namely accumulating a high-value area. Therefore, the accumulated high-value area is rapidly and accurately determined, the effectiveness of the determined accumulated high-value area is improved, the accuracy and the effectiveness of the whole environment monitoring work are improved, and a sufficient basis is provided for subsequent environment supervision.
Description
Technical Field
The application relates to the field of data processing, in particular to a method for determining a cumulative high-value area.
Background
With the rapid development of various industries, a large amount of harmful substances such as smoke, sulfur dioxide, nitrogen oxides, carbon monoxide, hydrocarbons, etc. are generated. The harmful substances are continuously discharged into the atmosphere, and when the content exceeds the limit of the environment, natural physical, chemical and ecological balance is destroyed, so that atmospheric pollution is formed, and life, work and health of people are endangered. With the advent of nationwide and wide range of haze weather, the term PM2.5 is coming into public view. PM2.5 refers to particulates having an ambient aerodynamic equivalent diameter of 2.5 microns or less. It can be suspended in air for a longer time, and the higher the content concentration of the suspension in the air is, the more serious the air pollution is.
With the rapid development of the economic society, environmental problems become one of important obstacle factors for the development of the society, and solving good environmental problems becomes an urgent problem for various countries.
One of the important bases for solving the environmental problems is to accurately grasp the current environmental situation, including which specific environmental problems exist, etc., and the environmental monitoring work is also the key for solving the environmental problems and knowing the current environmental situation in time, wherein the accuracy of the environmental monitoring data becomes the key point and the key link of the environmental monitoring work.
The environment monitoring data is the basis for formulating environment protection policies and measures, and is also the basis for environment management, law enforcement, statistics, information release and environment protection target responsibility system assessment. Therefore, whether the quality of the environment detection data is positive for the environment protection work.
The atmospheric pollution monitoring is to measure the type and concentration of pollutants in the atmospheric environment and observe the time-space distribution and change rule. The atmospheric pollution monitoring aims to identify pollutant in the atmosphere, master the distribution and diffusion rule of the pollutant, and monitor the emission and control conditions of an atmospheric pollution source. Because the monitoring area is large in range, manpower and material resources are limited, and difficulty is brought to atmospheric pollution monitoring.
Therefore, the monitoring area can be divided, and the divided area is monitored to determine the area with the exceeding pollutant. However, in the prior art, the monitoring area is generally divided according to the administrative unit, for example, the area a and the area B of a certain city are divided into one area, and after the pollutant concentration data of the two areas are obtained to exceed the standard, the pollutant concentrations of the area a and the area B are considered to exceed the standard. However, the division is unreasonable, for example, the people in the area A are rare, the population in the area B is high, the judgment result does not accord with the actual situation of the area A and the area B, the area A and the area B are obtained as out-of-standard areas through one monitoring, and the obtained result is not accurate.
Disclosure of Invention
An object of an embodiment of the present application is to provide a method for determining a cumulative high value zone, aiming at the defects existing in the prior art.
In order to solve the above-mentioned problems, in a first aspect, the present application provides a method of determining an accumulated high value zone, the method of determining an accumulated high value zone comprising:
acquiring the number of each grid and first pollutant concentration data of each grid in a first area in a first time period;
acquiring a first grid set from the grids of the first area according to the first pollutant concentration data; wherein the first set of grids includes a first number of grids;
respectively calculating first pollutant concentration data of each grid in the first number of grids and a first concentration difference value of a preset first pollutant concentration threshold value;
repeating the steps until the number of each grid in the first area and the nth pollutant concentration data of each grid are obtained in the nth time period; wherein n is an integer greater than 1;
acquiring an nth grid set from the grids of the first area according to the nth pollutant concentration data; wherein the nth grid set includes an nth number of grids;
respectively calculating nth pollutant concentration data of each grid in the nth number of grids and an nth concentration difference value of a preset nth pollutant concentration threshold value;
determining a target grid set according to the numbers from the first number of grids to the number of the nth number of grids;
determining the average value of the pollutant concentration of each grid in the target grid set according to the first time length, the nth time length, the first concentration difference value and the nth concentration difference value;
setting weight values for grids in the target grid set according to preset rules;
calculating the score of each grid in the target grid set according to the mean value of the pollutant concentration of each grid in the target grid set and the weight value of the mean value, and obtaining score information;
determining a high-value grid according to the score information; the area where the high-value grid is located is an accumulated high-value area.
In one possible implementation manner, the acquiring the number of each grid and the first pollutant concentration data of each grid in the first area within the first time period specifically includes:
dividing the first area to obtain each grid number in the first area;
receiving first pollutant concentration data of a first part of grids transmitted by grid-type monitoring equipment in the first part of grids in the first area; wherein each grid in the first region comprises a first partial grid and a second partial grid;
acquiring first pollutant concentration data of a second partial grid in a first area according to the first pollutant concentration data of the first partial grid;
and correlating the grid numbers in the first area, the first pollutant concentration data of the first part of grids and the first pollutant concentration data of the second part of grids to obtain the numbers of the grids in the first area and the first pollutant concentration data of the grids.
In one possible implementation manner, the acquiring a first grid set from the grids of the first area according to the first pollutant concentration data specifically includes:
comparing the first pollutant concentration data of each grid in the first area with a preset concentration threshold;
determining a grid with the first pollutant concentration data larger than a preset concentration threshold value as an original first grid set; wherein the original first set of grids includes an original first number of grids;
sorting the grids in the original first grid set according to the first pollutant concentration data;
acquiring a first grid set according to the sequencing result; wherein the first set of grids includes a first number of grids, the first number being no greater than the original first number.
In one possible implementation manner, the setting, according to a preset rule, a weight value for each grid in the target grid set specifically includes:
acquiring position information of a built-up area and position information of a non-built-up area in a first area;
acquiring position information of each grid in a target grid set;
matching the location information of the built-up area, the location information of the non-built-up area and the location information of each grid in the target grid set;
and setting a weight value for each grid in the target grid set according to the matching result.
In one possible implementation manner, the determining the high-value grid according to the score information specifically includes:
ranking each grid in the target grid set from high to low according to the high-low order of the score information;
and determining the grids ranked before the preset ranking as high-value grids.
In one possible implementation manner, the determining the high-value grid according to the score information specifically includes:
comparing the score information with a preset score threshold value;
and determining the grid with the score information larger than the preset score threshold value as a high-value grid.
In one possible implementation, the method further comprises thereafter:
and grading the high-value grids.
In a second aspect, the present application provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.
In a third aspect, the application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to any of the first aspects.
The method for determining the accumulated high-value area provided by the embodiment of the application comprises the following steps: acquiring the number of each grid and first pollutant concentration data of each grid in a first area in a first time period; acquiring a first grid set from the grids of the first area according to the first pollutant concentration data; wherein the first set of grids includes a first number of grids; respectively calculating first pollutant concentration data of each grid in the first number of grids and a first concentration difference value of a preset first pollutant concentration threshold value; repeating the steps until the number of each grid in the first area and the nth pollutant concentration data of each grid are obtained in the nth time period; wherein n is an integer greater than 1; acquiring an nth grid set from the grids of the first area according to the nth pollutant concentration data; wherein the nth grid set includes an nth number of grids; respectively calculating nth pollutant concentration data of each grid in the nth number of grids and an nth concentration difference value of a preset nth pollutant concentration threshold value; determining a target grid set according to the numbers from the first number of grids to the number of the nth number of grids; determining the average value of the pollutant concentration of each grid in the target grid set according to the first time length, the nth time length, the first concentration difference value and the nth concentration difference value; setting weight values for grids in the target grid set according to preset rules; calculating the score of each grid in the target grid set according to the mean value of the pollutant concentration of each grid in the target grid set and the weight value of the mean value, and obtaining score information; determining a high-value grid according to the score information; the area where the high-value grid is located is an accumulated high-value area. The method can quickly and accurately determine the accumulated high-value area, fully considers whether human activities frequently affect the accumulated high-value area, improves the effectiveness of the determined accumulated high-value area, and improves the accuracy and effectiveness of the whole environment monitoring work.
Drawings
Fig. 1 is a flowchart of a method for determining a cumulative high value area according to an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
In order to facilitate a better description of the method according to the application, the "high value zone" will be described first.
In order to achieve the purpose of fine control and management of regional atmospheric pollution, a target region is divided into different grids according to different monitoring requirements and environmental characteristics to carry out point location arrangement, and the concentration of relevant pollutants in each grid is monitored in real time, which is called gridding monitoring. The key pollution area divided by the urban meshing supervision work is called as a hot spot mesh. The high-density grid monitoring network is used for reasonably arranging various functional monitoring points in the area, can reflect the air quality change of the key polluted area, meets the requirement of area environment air monitoring, and objectively evaluates the air quality of the key polluted area.
The distribution condition of the pollutants can be evaluated according to the diffusion, migration and conversion rules of the local pollutants, and reasonable monitoring points can be determined by combining the feasibility of resources and economy, so that the obtained data is representative.
At the determined reasonable monitoring point location, a gridding monitoring device can be set. The gridding monitoring equipment is a detection method adopting light scattering, has small volume and light weight, and is used for continuously and automatically monitoring the pollutant condition in the ambient air.
At a site, there is typically one standard monitoring device (also referred to as a national control device or a provincial control device), within a certain range of the site, at least 3 meshed monitoring devices may be installed, each of the 3 meshed monitoring devices is referred to as a quality control device, and each quality control device is a quality control point.
Besides the quality control point, a plurality of point positions can be provided with grid monitoring equipment. After dividing a relatively large area into grids, the grids can be divided into a plurality of sub-grids, and the sub-grids can be divided into a plurality of small sub-grids so as to improve the accuracy of pollutant monitoring.
In one measurement, the grid may be referred to as a high value region when the concentration of contaminants in the grid exceeds a standard value or exceeds a set value.
It will be appreciated that the concept of a grid is relative. For example, the X market may be divided into 36 grids, and each of the 36 grids may be divided into 36 small grids. The grids involved in the present application may be the 36 grids or the 36 small grids, and in particular, should be determined in practical applications, which is not limited by the present application.
The following first, second and nth are for distinction only and are not actually meant.
Fig. 1 is a flowchart of a method for determining a cumulative high value area according to an embodiment of the present application. The application scenario of the method is a meshed monitoring network, the execution subject of the method may be a device with a computing function, for example, a computer, a mobile phone or a device for determining a cumulative high value area, etc., where the computer, the mobile phone or the device for determining a cumulative high value area may be connected to the meshed monitoring device, and the connection may be performed by a wireless or wired communication manner, which is not limited in this aspect of the application. As shown in fig. 1, the method comprises the steps of:
step 101, acquiring the number of each grid and the first pollutant concentration data of each grid in a first area in a first time period.
Specifically, after the meshed monitoring device is put in a fixed (may also be referred to as a preset) point location, the meshed monitoring device may acquire pollutant concentration data of the point location in real time, where the pollutant concentration data may include a type of pollutant and a concentration value of the pollutant under the type of pollutant. At this time, "real-time" may be set on the grid-type monitoring device, and may be set as needed, for example, but not limited to, 60 contaminant concentration data may be acquired in one minute, and the more data acquired in one minute, the more accurate contaminant concentration data at the subsequent first point location.
In order to determine the accumulated high-value area, the average value of the measured pollutant concentration data in a certain time period can be taken as the pollutant concentration data corresponding to the time period according to the requirement.
If the calculation is performed with 60 pieces of contaminant concentration data acquired in one minute, the contaminant concentration data acquired in one hour is 3600 pieces, and the average of the 3600 pieces of contaminant concentration data may be taken as the first contaminant concentration data. Therefore, not only is the manpower and financial resources saved, but also the accuracy of the pollutant concentration data is improved.
The first duration may be set as needed, and the first area may be divided into a certain number of grids, which is illustrated but not limited to, the first duration may be set to 1 hour, and the first grid may be divided into 10×10 grids.
Specifically, step 101 may be further divided into the following two examples.
In one example, assuming that each grid is provided with a grid monitoring device, dividing a first area, and after each grid number in the first area is obtained, receiving first pollutant concentration data sent by the grid monitoring devices in each grid in the first area; and correlating the numbers of the grids in the first area with the first pollutant concentration data to obtain the numbers of the grids in the first area and the first pollutant concentration data thereof.
In another example, it is assumed that a two-part grid is included in the grid, i.e., a first part grid in which the meshed monitoring device is provided and a second part grid in which the meshed monitoring device is not provided. Firstly, dividing a first area to obtain each grid number in the first area; then, receiving first pollutant concentration data of a first part of grids transmitted by the grid-type monitoring equipment in the first part of grids in the first area; next, acquiring first pollutant concentration data of a second partial grid in the first area according to the first pollutant concentration data of the first partial grid; and finally, correlating the grid numbers in the first area, the first pollutant concentration data of the first part of grids and the first pollutant concentration data of the second part of grids to obtain the numbers of the grids in the first area and the first pollutant concentration data of the grids.
Wherein the first contaminant concentration data of the second partial mesh in the first region may be obtained by interpolating the first contaminant concentration data of the first partial mesh.
By way of example and not limitation, the contaminants may be fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), nitrogen dioxide (NO 2 ) Sulfur dioxide (SO) 2 ) Carbon monoxide (CO), ozone (O) 3 ) And any one of total volatile organic compounds (Total Volatile Organic Compounds, TVOC).
It will be appreciated that in subsequent studies, the contaminants may be any combination of the above contaminants, and the units of different contaminants may be processed by normalization, whereby normalized contaminant concentration data is obtained, and after comprehensive determination of the normalized contaminant concentration data, a determination is made as to whether the grid is a high value grid.
Step 102, acquiring a first grid set from grids of a first area according to first pollutant concentration data; wherein the first set of grids includes a first number of grids.
Specifically, acquiring a first grid set from the grids of the first area according to the first pollutant concentration data specifically includes:
firstly, comparing first pollutant concentration data of each grid in a first area with a preset concentration threshold value; then, determining a grid with the first pollutant concentration data larger than a preset concentration threshold value as an original first grid set; wherein the original first grid set comprises an original first number of grids; then, sorting the grids in the original first grid set according to the first pollutant concentration data; finally, according to the sequencing result, a first grid set is obtained; wherein the first set of grids includes a first number of grids, the first number being no greater than the original first number.
The concentration threshold value may be set as needed, and the specific numerical value is not limited in the present application. In one example, the top 10 or top 15% of the grids in the order may be obtained from the original first grid set, constituting the first grid set.
Step 103, calculating first pollutant concentration data of each grid in the first number of grids and a first concentration difference value of a preset first pollutant concentration threshold value respectively.
By way of example and not limitation, the mean value of the first contaminant concentration data for the first region obtained from the national control point at the first time may be taken as the first contaminant concentration threshold.
Step 104, repeating steps 101 to 103 until the number of each grid and the n pollutant concentration data of each grid in the first area are obtained within the n-th time period; wherein n is an integer greater than 1.
In step 104, further including obtaining a number of each grid in the first area and second pollutant concentration data of each grid in the first duration; acquiring a second grid set from the grids of the first area according to the second pollutant concentration data; wherein the first set of grids includes a second number of grids; second contaminant concentration data and a second concentration difference value of a preset second contaminant concentration threshold value are calculated for each of the second number of grids, respectively.
The average value of the second pollutant concentration data of the first area obtained from the national control point in the second moment can be used as a second pollutant concentration threshold value.
To illustrate that the accumulated high value region is a high value region accumulated multiple times, here, n is substituted for the process of the loop described above.
It will be appreciated that the larger the value of n, the more the number of accumulations of the obtained accumulatively high value zone, i.e. the more accurate the value of the accumulatively high value zone.
Step 105, acquiring an nth grid set from grids of the first area according to the nth pollutant concentration data; wherein the nth grid set includes an nth number of grids.
The specific process of this step may refer to step 102, which is not described herein.
And 106, respectively calculating the nth pollutant concentration data of each grid in the nth number of grids and the nth concentration difference value of the preset nth pollutant concentration threshold value. The average value of the n-th pollutant concentration data of the first area obtained from the national control point in the n-th time can be used as the n-th pollutant concentration threshold value.
Step 107, determining the target grid set according to the numbers from the first number of grids to the nth number of grids.
Wherein, the union of the numbers of the first number of grids up to the number of the nth number of grids can be used as the target grid set.
And step 108, determining the average value of the pollutant concentration of each grid in the target grid set according to the first time length to the nth time length and the first concentration difference value to the nth concentration difference value.
Specifically, the mean value of the contaminant concentration of each grid in the target grid set may be determined by the method in the following example.
In one example, first, a first sum of first concentration differences up to an nth concentration difference for each grid in a set of target grids is determined; then, determining a second sum of the first time period to the nth time period; and finally, calculating the quotient of the first sum value and the second sum value to obtain the average value of the pollutant concentration of each grid in the target grid set. Therefore, the average value of the pollutant concentration of each grid in the target grid set can be rapidly calculated, and the data processing speed is improved.
In another example, first, a first weight value is set for a first time period until an nth weight value is set for an nth time period; then, determining the product of the first weight value and the first concentration difference value to obtain a first product until the product of the nth weight value and the nth concentration difference value is determined to obtain an nth product; and finally, calculating the sum of the first product and the nth product to obtain the pollutant concentration average value of each grid in the target grid set. Therefore, the average value calculated by the weighted average method is more accurate, and the data processing precision is greatly improved.
Step 109, setting a weight value for each grid in the target grid set according to a preset rule.
Specifically, step 109 includes the steps of:
acquiring position information of a built-up area and position information of a non-built-up area in a first area;
acquiring position information of each grid in a target grid set;
matching the position information of the built-up area, the position information of the non-built-up area and the position information of each grid in the target grid set;
and setting a weight value for each grid in the target grid set according to the matching result.
Therein, by way of example and not limitation, a preset rule may be whether the grid is in a built-up area. The built-up area refers to the land which is collected in the urban area and is developed in actual construction and non-agricultural production construction areas, and comprises a part of centralized connection of the urban area and urban construction land (such as airports, railway marshalling stations, sewage treatment plants, communication stations and the like) which is distributed in suburban areas and has basically perfect municipal public facilities and has close relation with cities. The position information of the built-in area and the position information of the non-built-in area can be judged according to the previous basic data collection, the position information of the built-in area comprises longitude and latitude data of the built-in area, the number of the longitude and latitude data of the built-in area is related to the shape of the built-in area, when the built-in area is square, the longitude and latitude data of the built-in area comprises upper left longitude and latitude, lower left longitude and latitude, upper right longitude and latitude and lower right longitude and latitude, and when the built-in area is irregular square, the longitude and latitude data of the built-in area further comprises longitude and latitude of each irregular point on the upper basis. The location information of the non-built-up area includes longitude and latitude data of the non-built-up area, and the constitution of the longitude and latitude data of the non-built-up area is similar to that of the built-up area, and will not be repeated here.
When the first area is divided into grids, each grid has its position information, where the position information is longitude and latitude data, for example, one grid may include five longitude and latitude data, that is, upper left longitude and latitude, upper right longitude and latitude, lower left longitude and latitude, lower right longitude and latitude, and middle longitude and latitude.
The grid can be matched according to the longitude and latitude data of the built-up area, the longitude and latitude data of the non-built-up area and the longitude and latitude data of the grid, and whether the grid is in the built-up area or the non-built-up area is judged according to the matching result. A mesh is said to be in a built-up area when the mesh and the built-up area are successfully matched, a mesh is said to be in a non-built-up area when the mesh and the non-built-up area are successfully matched, or a mesh is said to be in a non-built-up area when the mesh and the built-up area are not matched. Since the built-up area is frequent for human life, the probability of causing a high-value area is high, and thus the set weight value is large compared with the mesh in a non-built-up area.
Step 110, calculating the score of each grid in the target grid set according to the mean value of the pollutant concentration of each grid in the target grid set and the weight value of the mean value, and obtaining score information.
The average value of the pollutant concentration of each grid in the target grid set and the weight value thereof can be multiplied to obtain the score of the grid.
Step 111, determining a high-value grid according to the score information; the area where the high-value grid is located is an accumulated high-value area.
In one example, the grids in the target grid set may be ranked from high to low according to the order of the score information; the grid ranked before the preset noun is determined to be a high value grid. For example, if the preset noun is the fifth, then the grids ranked 1-4 are all high value grids, i.e., the cumulative high value regions.
In another example, the score information may be compared to a preset score threshold; and determining the grid with the score information larger than the preset score threshold value as a high-value grid. For example, if the preset score threshold is 60, the grids with scores greater than 60 are all high-value grids, i.e. the accumulated high-value regions.
Further, after step 111, the method further comprises: and grading the high-value grids.
Wherein when there are at least two high-value grids, the high-value grids may be ranked. By way of example and not limitation, for a high value grid determined from ranking, a grid that is first ranked may be determined as a significantly high value region, a grid that is second ranked as a moderately high value region, and grids that are third and fourth ranked as generally high value regions.
Therefore, by applying the method for determining the cumulative high value area provided by the embodiment of the application, the cumulative high value area can be determined rapidly and accurately, the influence of human activities on the cumulative high value area is fully considered, the effectiveness of the determined cumulative high value area is improved, the accuracy and the effectiveness of the whole environment monitoring work are improved, and a sufficient basis is provided for subsequent environment supervision.
The method of determining the cumulative high value zone in the present application will be specifically described below with reference to a specific example using PM2.5 as an example.
In the first step, the area a is divided into 10×10 grids, and the divided grids are shown in table 1.
| 1 | 11 | 21 | 31 | 41 | 51 | 61 | 71 | 81 | 91 |
| 2 | 12 | 22 | 32 | 42 | 52 | 62 | 72 | 82 | 92 |
| 3 | 13 | 23 | 33 | 43 | 53 | 63 | 73 | 83 | 93 |
| 4 | 14 | 24 | 34 | 44 | 54 | 64 | 74 | 84 | 94 |
| 5 | 15 | 25 | 35 | 45 | 55 | 65 | 75 | 85 | 95 |
| 6 | 16 | 26 | 36 | 46 | 56 | 66 | 76 | 86 | 96 |
| 7 | 17 | 27 | 37 | 47 | 57 | 67 | 77 | 87 | 97 |
| 8 | 18 | 28 | 38 | 48 | 58 | 68 | 78 | 88 | 98 |
| 9 | 19 | 29 | 39 | 49 | 59 | 69 | 79 | 89 | 99 |
| 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
TABLE 1
And secondly, acquiring pollutant concentration data of the grid.
If monitoring points exist in all grids, wherein the points are provided with grid monitoring equipment, the pollutant concentration data of the grids are the data monitored by the grid monitoring equipment.
If the monitoring point positions exist in the first part of grids, and the monitoring point positions do not exist in the second part of grids, interpolating the pollutant concentration data in the second part of grids from the pollutant concentration data of the first part of grids. As shown in tables 2 and 3, table 2 is the contaminant concentration data before interpolation, and table 3 is the contaminant concentration data after interpolation.
TABLE 2
| 75 | 60 | 58 | 28 | 30 | 40 | 38 | 32 | 27 | 19 |
| 64 | 57 | 53 | 30 | 35 | 37 | 39 | 28 | 23 | 28 |
| 19 | 30 | 48 | 40 | 51 | 55 | 65 | 40 | 34 | 32 |
| 15 | 20 | 35 | 67 | 60 | 51 | 43 | 44 | 23 | 12 |
| 20 | 23 | 52 | 55 | 67 | 68 | 72 | 52 | 41 | 20 |
| 25 | 30 | 54 | 75 | 74 | 51 | 41 | 64 | 15 | 48 |
| 21 | 64 | 19 | 24 | 26 | 25 | 30 | 35 | 61 | 36 |
| 30 | 25 | 28 | 13 | 14 | 17 | 19 | 34 | 41 | 40 |
| 28 | 80 | 54 | 30 | 55 | 76 | 14 | 24 | 80 | 68 |
| 40 | 50 | 49 | 20 | 47 | 65 | 10 | 32 | 73 | 74 |
TABLE 3 Table 3
Third, ranking the grids with the pollutant concentration data higher than 35 mug/m once in 1 hour, marking the top 15% of grids as a high value once, and simultaneously calculating a first concentration difference delta c between the pollutant concentration data of the high-value grids and the mean concentration (also called a first pollutant concentration threshold) of the A region at the same time 1 。
Contaminant concentration data was higher than 35. Mu.g/m 3 In total, 53 grids and 8 grids of the first 15% in total, the average concentration of the region at this time being 41. Mu.g/m 3 The high value grid information at this time is shown in table 4 below.
TABLE 4 Table 4
Fourth step, 2 h, for contaminant concentration data higher than 35. Mu.g/m 3 Ranking the grids once, recording the top 15% of grids as a high value, and calculating a second concentration difference delta c between the pollutant concentration data of the high value grids and the average concentration (also called a second pollutant concentration threshold) of the area A at the same time 2 ；
Contaminant concentration data was higher than 35. Mu.g/m 3 In total 58, 9 in total of the first 15% of small grids, at which the average concentration of the region is 44. Mu.g/m 3 The high-value grid information at this time is shown in table 5.
TABLE 5
Fifthly, solving the concentration of the super-peripheral average value: Δc= (Δc1+Δc) 2+ & -. DELTA.c i +···+Δc n ) K, where k is the number of grid high values, Δc n Is the nth concentration difference at the nth time.
The mean value of the concentration of the contaminants in each grid is shown in Table 6.
| Grid numbering | 1 | 19 | 36 | 46 | 59 | 65 | 89 | 90 | 100 |
| Δc1/μg·m-3 | 34 | 39 | 34 | 33 | 35 | -- | 39 | 32 | 33 |
| Δc2/μg·m-3 | 31 | 41 | 35 | 34 | 37 | 33 | 40 | 30 | 35 |
| Δc/μg·m-3 | 32.5 | 40 | 34.5 | 33.5 | 36 | 33 | 39.5 | 31 | 34 |
| High row duration/h | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 |
TABLE 6
Sixth, setting the weight of the built-up area as w 1 Non-built-up area weight w 2 (w 1 >w 2 ). By way of example and not limitation, in w 1 Is 1, w 2 An example of 0.8 is illustrated. Table 7 is the weight values set for the grid.
| Grid numbering | 1 | 19 | 36 | 46 | 59 | 65 | 89 | 90 | 100 |
| Δc1/μg·m-3 | 34 | 39 | 34 | 33 | 35 | -- | 39 | 32 | 33 |
| Δc2/μg·m-3 | 31 | 41 | 35 | 34 | 37 | 33 | 40 | 30 | 35 |
| Δc/μg·m-3 | 32.5 | 40 | 34.5 | 33.5 | 36 | 33 | 39.5 | 31 | 34 |
| High row duration/h | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 |
| Whether or not to be a built-up area | Whether or not | Whether or not | Is that | Is that | Is that | Is that | Whether or not | Whether or not | Whether or not |
| w | 0.8 | 0.8 | 1 | 1 | 1 | 1 | 0.8 | 0.8 | 0.8 |
TABLE 7
And seventh, scoring grids according to the formula score = T multiplied by delta c multiplied by w, wherein the score and ranking of each obtained grid are shown in table 8, and T is the high-value area duration.
| Grid numbering | 1 | 19 | 36 | 46 | 59 | 65 | 89 | 90 | 100 |
| Δc1/μg·m-3 | 34 | 39 | 34 | 33 | 35 | -- | 39 | 32 | 33 |
| Δc2/μg·m-3 | 31 | 41 | 35 | 34 | 37 | 33 | 40 | 30 | 35 |
| Δc/μg·m-3 | 32.5 | 40 | 34.5 | 33.5 | 36 | 33 | 39.5 | 31 | 34 |
| High row duration/h | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 |
| Whether or not to be a built-up area | Whether or not | Whether or not | Is that | Is that | Is that | Is that | Whether or not | Whether or not | Whether or not |
| w | 0.8 | 0.8 | 1 | 1 | 1 | 1 | 0.8 | 0.8 | 0.8 |
| Score of | 52 | 64 | 69 | 67 | 72 | 33 | 63.2 | 49.6 | 54.4 |
| Ranking | 7 | 4 | 2 | 3 | 1 | 9 | 5 | 8 | 6 |
TABLE 8
And eighth, ranking according to the scores, wherein 15% of grids at the top of ranking are determined to be significant high-value regions, 16% -30% of grids at the bottom of ranking are determined to be medium high-value regions, and 31% -45% of grids at the bottom of ranking are determined to be general high-value regions. I.e. grid 59 is a significantly high value region, grid 36 is a moderately high value region, and grids 19 and 46 are generally high value regions. Therefore, the high-value area is classified, different countermeasures are adopted according to the grades, and the subsequent processing efficiency is improved.
The second embodiment of the application provides a device, which comprises a memory and a processor, wherein the memory is used for storing programs, and the memory can be connected with the processor through a bus. The memory may be non-volatile memory, such as a hard disk drive and flash memory, in which software programs and device drivers are stored. The software program can execute various functions of the above method provided in the first embodiment of the present application; the device driver may be a network and interface driver. The processor is configured to execute a software program, where the software program is executed to implement the method provided in the first embodiment of the present application.
A third embodiment of the present application provides a computer program product containing instructions, which when executed on a computer, cause the computer to perform the method provided by the first embodiment of the present application.
The fourth embodiment of the present application provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method provided in the first embodiment of the present application is implemented.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing detailed description of the application has been presented for purposes of illustration and description, and it should be understood that the application is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the application.
Claims (9)
1. A method of determining a cumulative high value zone, the method comprising: acquiring the number of each grid and first pollutant concentration data of each grid in a first area in a first time period; the acquiring the number of each grid and the first pollutant concentration data of each grid in the first area in the first time period comprises the following steps: according to the diffusion, migration and conversion rules of pollutants in the first area, estimating the distribution condition of the pollutants, combining the feasibility of resources and economy, determining reasonable monitoring points, setting grid monitoring equipment at the reasonable monitoring points, and taking the average value of the measured pollutant concentration data in the first time period as first pollutant concentration data;
acquiring a first grid set from the grids of the first area according to the first pollutant concentration data; wherein the first set of grids includes a first number of grids; respectively calculating first pollutant concentration data of each grid in the first number of grids and a first concentration difference value of a preset first pollutant concentration threshold value; wherein obtaining the preset first pollutant concentration threshold value comprises: the method comprises the steps of taking a mean value of pollutant concentration data obtained from a national control point in a first area as a first pollutant concentration threshold value within a first time period;
repeating the steps until the number of each grid in the first area and the nth pollutant concentration data of each grid are obtained in the nth time period; wherein n is an integer greater than 1; acquiring an nth grid set from the grids of the first area according to the nth pollutant concentration data; wherein the nth grid set includes an nth number of grids; respectively calculating nth pollutant concentration data of each grid in the nth number of grids and an nth concentration difference value of a preset nth pollutant concentration threshold value; determining a target grid set according to the numbers from the first number of grids to the number of the nth number of grids; determining the average value of the pollutant concentration of each grid in the target grid set according to the first time length, the nth time length, the first concentration difference value and the nth concentration difference value; setting weight values for grids in the target grid set according to preset rules; calculating the score of each grid in the target grid set according to the mean value of the pollutant concentration of each grid in the target grid set and the weight value of the mean value, and obtaining score information; determining a high-value grid according to the score information; the area where the high-value grid is located is an accumulated high-value area.
2. The method for determining a cumulative high value zone according to claim 1, wherein said acquiring the number of each grid and the first contaminant concentration data of each grid in the first area within the first time period specifically comprises: dividing the first area to obtain each grid number in the first area; receiving first pollutant concentration data of a first part of grids transmitted by grid-type monitoring equipment in the first part of grids in the first area; wherein each grid in the first region comprises a first partial grid and a second partial grid; acquiring first pollutant concentration data of a second partial grid in a first area according to the first pollutant concentration data of the first partial grid; and correlating the grid numbers in the first area, the first pollutant concentration data of the first part of grids and the first pollutant concentration data of the second part of grids to obtain the numbers of the grids in the first area and the first pollutant concentration data of the grids.
3. The method of determining a cumulative high value zone according to claim 1, characterized in that said obtaining a first grid set from the grids of said first area based on said first pollutant concentration data specifically comprises: comparing the first pollutant concentration data of each grid in the first area with a preset concentration threshold; determining a grid with the first pollutant concentration data larger than a preset concentration threshold value as an original first grid set; wherein the original first set of grids includes an original first number of grids; sorting the grids in the original first grid set according to the first pollutant concentration data; acquiring a first grid set according to the sequencing result; wherein the first set of grids includes a first number of grids, the first number being no greater than the original first number.
4. The method for determining the cumulative high value area according to claim 1, wherein said setting weight values for each grid in said target grid set according to a preset rule specifically includes: acquiring position information of a built-up area and position information of a non-built-up area in a first area; acquiring position information of each grid in a target grid set; matching the location information of the built-up area, the location information of the non-built-up area and the location information of each grid in the target grid set; and setting a weight value for each grid in the target grid set according to the matching result.
5. The method for determining a cumulative high value area according to claim 1, wherein said determining a high value grid based on said score information specifically comprises:
ranking each grid in the target grid set from high to low according to the high-low order of the score information; and determining the grids ranked before the preset ranking as high-value grids.
6. The method for determining a cumulative high value area according to claim 1, wherein said determining a high value grid based on said score information specifically comprises: comparing the score information with a preset score threshold value; and determining the grid with the score information larger than the preset score threshold value as a high-value grid.
7. The method of determining a cumulative high value area of claim 1, further comprising grading said high value grid.
8. An apparatus comprising a memory for storing a program and a processor for performing the method of any of claims 1-7.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1-7.
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