Pollution level area proportion estimation method, device, equipment and medium

Abstract

The present disclosure provides a pollution level area proportion estimation method, device, equipment and medium. The pollution level area proportion estimation method of the present disclosure comprises: obtaining pollution grid point data of a target area according to the latitude and longitude of the target area, and an element in the pollution grid point data representing a pollution value at a corresponding grid latitude and longitude coordinate; generating a plurality of contour line regions using the pollution grid point data of the target area and a plurality of predetermined pollution level thresholds, each contour line region corresponding to a pollution level threshold and the pollution values of points in the contour line region being less than the pollution level threshold; dividing the target area using the plurality of contour line regions to obtain a plurality of polygon regions with different pollution levels, the pollution levels being determined by the plurality of pollution level thresholds; and calculating the area of each polygon region to obtain the pollution level area proportion of the target area. The present disclosure can effectively reduce the calculation complexity, improve the estimation efficiency, and also has high estimation accuracy and spatial-temporal resolution.

Description

Technical Field

[0001] This disclosure relates to the field of environmental monitoring and air pollution assessment technology, and in particular to a method, apparatus, equipment and medium for estimating the regional proportion of pollution levels. Background Technology

[0002] With rapid industrialization and urbanization, air pollution has become a significant factor affecting regional environmental quality, ecosystem health, and public living standards. To effectively formulate pollution prevention and control policies, implement early warning and emergency response, and conduct long-term environmental planning, it is necessary to conduct rapid and accurate quantitative assessments of the distribution of different pollution levels across large areas, such as entire cities, provinces, or specific geographical regions. The proportion of areas covered by each pollution level—e.g., excellent, good, lightly polluted, moderately polluted, heavily polluted, etc.—is a crucial evaluation indicator that directly reflects the spatial distribution pattern and overall severity of pollution.

[0003] However, related technologies suffer from problems such as complex calculation processes and low processing efficiency when estimating the regional proportion of air pollution levels over a large area with high timeliness and accuracy. They also have drawbacks such as strong dependence on infrastructure, insufficient spatial detail characterization, or limitations imposed by external observation conditions. Therefore, there is an urgent need for a pollution level regional proportion estimation scheme that can reduce computational complexity, improve estimation efficiency, and simultaneously balance estimation accuracy and spatiotemporal resolution, in order to meet the practical application requirements for rapid, accurate, and dynamic quantitative assessment of the spatial pattern of air pollution. Summary of the Invention

[0004] In view of this, this disclosure provides a method, apparatus, equipment and medium for estimating the proportion of pollution-level areas.

[0005] According to a first aspect of this disclosure, a method for estimating the proportion of pollution-level areas is provided, the method comprising:

[0006] The pollution grid data of the target area is obtained based on the latitude and longitude of the target area, and the elements in the pollution grid data represent the pollution value at the corresponding latitude and longitude coordinates of the grid. Multiple contour regions are generated using pollution grid data of the target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold, and the pollution value of each point within the contour region is less than the pollution level threshold. The target area is divided using the multiple contour lines to obtain multiple polygonal regions with different pollution levels, wherein the pollution level is determined by the multiple pollution level thresholds. Calculate the area of ​​each polygonal region to obtain the percentage of the target region with a pollution level.

[0007] In some embodiments of the first aspect of this disclosure, generating multiple contour regions using pollution grid data of a target area and predetermined multiple pollution level thresholds includes: extracting multiple contour lines from the pollution grid data of the target area using the value of each pollution level threshold as the contour line value, each contour line representing a set of sequentially arranged vertex coordinates; and performing the following processing on each contour line: determining its contour region using the vertex order of the contour line, wherein the pollution value of each point within the contour region is less than the value of the contour line.

[0008] In some embodiments of the first aspect of this disclosure, determining the isoline region using the vertex order of the isolines includes: determining whether the coordinates of the first and last vertices of the isolines are the same; if the coordinates of the first and last vertices of the isolines are the same, then the isolines are determined to be closed isolines; the vertex order of the isolines is used to determine whether the isolines are arranged clockwise or counterclockwise; if the isolines are arranged clockwise, then the region inside the isolines is determined to be an isoline region; if the isolines are arranged counterclockwise, then the isolines are connected to the boundary of the target region to form a closed polygon, which is the isoline region; if the coordinates of the first and last vertices of the isolines are different, then the isolines are determined to be open isolines; the vertex order of the isolines is used to determine the high-value side and the low-value side; the isolines are connected to the boundary of the target region with the low-value side to form a closed polygon, which is the isoline region.

[0009] In some embodiments of the first aspect of this disclosure, the step of dividing the target region using the plurality of contour regions to obtain a plurality of polygon regions with different pollution levels includes: initializing a main region as the target region; calculating the difference between the main region and the contour region corresponding to the lowest pollution level threshold to obtain a first polygon region, wherein the pollution level of the first polygon region is determined by 0 and the lowest pollution level threshold; finding the intersection between the main region and the contour region corresponding to the lowest pollution level threshold and setting the main region as the polygon region obtained by the intersection; for each contour region whose pollution level threshold is greater than the lowest pollution level threshold and less than or equal to the highest pollution level threshold, performing the following processing sequentially in ascending order of its corresponding pollution level threshold to obtain one or more second polygon regions with different pollution levels: calculating the difference between the main region and the current contour region to obtain a second polygon region, wherein the pollution level of the second polygon region is determined by the previous pollution level threshold and the current pollution level threshold; finding the intersection between the main region and the current contour region and resetting the main region as the polygon region obtained by the intersection; and determining the main region as the third polygon region with the highest pollution level.

[0010] In some embodiments of the first aspect of this disclosure, each polygonal region is represented as a set of sequentially arranged vertex coordinates with the first and last vertex coordinates being the same; the step of calculating the area of ​​each polygonal region to obtain the proportion of the target region in terms of pollution level includes: determining the area of ​​the target region; calculating the area of ​​each polygonal region; and calculating the ratio of the area of ​​each polygonal region to the area of ​​the target region to obtain the proportion of the corresponding pollution level region.

[0011] 6. The method according to claim 1, wherein the pollution value is the target pollutant concentration or the ambient air quality index (AQI).

[0012] According to a second aspect of this disclosure, a device for estimating the proportion of pollution-level areas is provided, the device comprising: The data acquisition unit is used to acquire pollution grid data of the target area based on the latitude and longitude of the target area, wherein the elements in the pollution grid data represent the pollution value at the latitude and longitude coordinates of the corresponding grid center point; A contour region generation unit is used to generate multiple contour regions using pollution grid data of a target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold and the pollution value of each point in the contour region is less than the pollution level threshold. A pollution level area determination unit is used to divide the target area using the multiple contour lines to obtain multiple polygonal areas with different pollution levels, wherein the pollution level is determined by the multiple pollution level thresholds. The regional proportion calculation unit is used to calculate the area of ​​each polygonal region to obtain the regional proportion of pollution level of the target region.

[0013] In some embodiments of the second aspect of this disclosure, the contour region generation unit is specifically configured to: extract multiple contour lines from the pollution grid data of the target area using the values ​​of each pollution level threshold as contour lines, each contour line representing a set of sequentially arranged vertex coordinates; and perform the following processing for each contour line: determine its contour region using the vertex order of the contour line, wherein the pollution value of each point within the contour region is less than the value of the contour line.

[0014] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: one or more processors and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the methods described above.

[0015] According to a fourth aspect of this disclosure, a computer-readable storage medium storing a program, the program including instructions that, when executed by one or more processors, cause the processors to perform the methods described above.

[0016] As can be seen from the above technical solution, the embodiments of this disclosure generate contour regions based on the pollution grid data of the target area and the predetermined pollution level threshold. The target area is divided into multiple polygonal regions with different pollution levels using the contour regions. Finally, the proportion of the target area with pollution level is obtained by calculating the area of ​​these polygonal regions. Thus, the accurate assessment of the proportion of the target area with pollution level can be achieved by using a contour generation algorithm with low computational complexity and polygon Boolean operations. This can effectively reduce computational complexity, improve estimation efficiency, and also has high estimation accuracy and spatiotemporal resolution, which can meet the needs of rapid, accurate, and dynamic quantitative assessment of the spatial pattern of air pollution in practical applications. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the method for estimating the proportion of pollution-level areas provided in this embodiment of the disclosure; Figure 2 A schematic diagram of the structure of the pollution level area proportion estimation device provided in the embodiments of this disclosure; Figure 3 A schematic structural block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0019] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0020] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. The singular forms “a,” “the,” and “the” as used in the embodiments of this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0021] Depending on the context, words such as "if," "when," etc., used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrases "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] Figure 1 A flowchart illustrating a method for estimating the proportion of pollution-level areas according to an embodiment of this disclosure is shown. This method can be executed by the electronic device described below. See also Figure 1 The method for estimating the proportion of pollution-level areas in this disclosure may include the following steps: Step 101: Obtain pollution grid data of the target area based on the latitude and longitude of the target area. The elements in the pollution grid data represent the pollution values ​​at the corresponding latitude and longitude coordinates of the grid. Step 102: Generate multiple contour regions using the pollution grid data of the target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold and the pollution value of each point in the contour region is less than the pollution level threshold. Step 103: Divide the target area using multiple contour lines to obtain multiple polygonal areas with different pollution levels. The pollution level is determined by multiple pollution level thresholds. Step 104: Calculate the area of ​​each polygonal region to obtain the proportion of the target region with the highest pollution level.

[0023] Pollution grid data can be a two-dimensional matrix, with rows and columns representing latitude and longitude, respectively. Each element in the matrix corresponds to a grid cell, and the value of an element is equal to the pollution value at its corresponding latitude and longitude coordinates. The type of pollution value can be flexibly determined according to actual needs. For example, the pollution value can be, but is not limited to, the concentration of a target pollutant, the Air Quality Index (AQI), etc. Target pollutants can be, but are not limited to, PM2.5, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), etc.

[0024] In step 101, the pollution grid data can be converted from station monitoring data, or it can come from a dataset generated by combining monitoring station data with technologies such as satellite remote sensing and model simulation. Specifically, step 101 can obtain pollution grid data for the target area in the following two ways: 1) Based on the latitude and longitude of the target area, real-time monitoring data of the target area can be obtained from sources such as government data open platforms and third-party data providers. This monitoring data includes data from various fixed ground monitoring stations in the target area. The data for each monitoring station includes the station's name, code, latitude and longitude coordinates, AQI, data release time, primary pollutant, and real-time concentrations of various pollutants. Then, according to a preset grid interval (e.g., 1°×1°), the monitoring data of the target area is converted into pollution grid data using, for example, an interpolation algorithm.

[0025] 2) Pollution grid data of the target area can be extracted from high-resolution satellite inversion datasets or model fusion datasets based on the latitude and longitude of the target area.

[0026] Pollution level thresholds can be pre-configured. Pollution levels are typically determined by pollution level thresholds, and the thresholds for each pollution level usually follow relevant regulations.

[0027] In some examples, if the pollution value is an AQI, the pollution level threshold can be the AQI level threshold for each air quality pollution level specified in the "Ambient Air Quality Standard" (GB3095-2012) and its supporting "Technical Regulations for Ambient Air Quality Index (AQI)" (HJ 633-2012). For example, the air quality pollution levels, AQI ranges, and corresponding AQI level thresholds are shown in Table 1 below.

[0028]

[0029] Table 1 In some examples, if the pollution value is the target pollutant concentration, the pollution level threshold can be a predefined threshold for the target pollutant concentration at each pollution level.

[0030] Further, in step 102, multiple contour lines can be extracted from the pollution grid data of the target area using the value of each pollution level threshold as the contour line value. Each contour line is represented as a set of sequentially arranged vertex coordinates. Then, for each contour line, the following processing is performed to generate multiple contour line regions: the contour line region is determined by the vertex order of the contour line, and the pollution value of each point in the contour line region is less than the value of the contour line.

[0031] Taking Table 1 as an example, five AQI level thresholds can be used as contour line values. Five contour lines can be extracted using the pollution grid data of the target area. The values ​​of these five contour lines are 50, 100, 150, 200, and 300, respectively. Each contour line can be represented as a set of sequentially arranged vertex coordinates. These vertex coordinates are latitude and longitude coordinates, and the pollution value at that latitude and longitude coordinate is the value of the contour line. These contour lines may be closed or open.

[0032] Specifically, for each contour line, the process of determining its contour region using the vertex order of the contour line can include the following steps a1 to a3: Step a1: Determine whether the coordinates of the first and last vertices of the contour lines are the same; Step a2: If the coordinates of the first and last vertices of the contour lines are the same, then the contour lines are determined to be closed contour lines. The order of the vertices of the contour lines is used to determine whether the contour lines are arranged clockwise or counterclockwise. If the contour lines are arranged clockwise, then the area inside the contour lines is determined as the contour line region. If the contour lines are arranged counterclockwise, then the contour lines are connected to the boundary of the target area to form a closed polygon. The closed polygon is the contour line region. Specifically, when the contour lines are closed contour lines, the order of their vertices determines whether they are arranged clockwise or counterclockwise. Following the principle that "as you proceed along the vertex order, the values ​​in the left-hand region are always greater than the contour line values," if the contour lines are arranged clockwise, the contamination values ​​at points inside the contour lines are less than the contour line values, thus defining the region inside the contour lines as the contour line region. Similarly, following the same principle, if the contour lines are arranged clockwise, the contamination values ​​at points inside the contour lines are greater than the contour line values, while the contamination values ​​at points outside the contour lines are less than the contour line values. Connecting the contour lines to the boundary of the target area forms a closed polygon, which is the contour line region.

[0033] Step a3: If the coordinates of the first and last vertices of the contour line are different, then the contour line is determined to be an open contour line. The high-value side and the low-value side are determined by the vertex order of the contour line. The contour line is connected to the boundary of the target area on its low-value side to form a closed polygon. The closed polygon is the contour line region.

[0034] Specifically, when the contour lines are open contour lines, the order of the contour line vertices can be used to determine which side of the contour line is the high-value side and which side is the low-value side according to the rule of left-high and right-low. In this embodiment of the disclosure, the closed polygon formed by connecting the contour line and the boundary of the target area on its low-value side is regarded as the contour line region.

[0035] In practical applications, step 102 can generate the contour region by calling the `Contourf` function from the Matplotlib library. Alternatively, step 102 can extract the aforementioned multiple contour lines by calling the `contour` function from the Matplotlib library, and then use these contour lines to determine the contour region.

[0036] Furthermore, step 103 may include the following steps b1 to b3: Step b1: Initialize the main region as the target region. Calculate the difference between the main region and the contour area corresponding to the lowest pollution level threshold (e.g., the contour area corresponding to the aforementioned AQI level threshold of 50) to obtain a first polygonal region (e.g., a first-level excellent region). The pollution level of the first polygonal region is determined by 0 and the lowest pollution level threshold (e.g., first-level excellent). Find the intersection between the main region and the contour area corresponding to the lowest pollution level threshold, and set the main region as the polygonal region obtained from the intersection. Step b3: For each contour region whose pollution level threshold is greater than the lowest pollution level threshold and less than or equal to the highest pollution level threshold (e.g., 100, 150, 200, and 300 in the aforementioned AQI level thresholds), perform the following processing sequentially in ascending order of its corresponding pollution level threshold to obtain one or more second polygon regions with different pollution levels (e.g., Level 2 Good, Level 3 Lightly Polluted, Level 4 Moderately Polluted, and Level 5 Heavyly Polluted): Calculate the difference between the main region and the current contour region to obtain a second polygon region. The pollution level of the second polygon region is determined by the previous pollution level threshold and the current pollution level threshold (e.g., Level 2 Good, Level 3 Lightly Polluted, Level 4 Moderately Polluted, and Level 5 Heavyly Polluted); Find the intersection between the main region and the current contour region and reset the main region to the polygon region obtained from the intersection. Step b3: The main area is identified as the third polygonal area with the highest pollution level (e.g., a level VI severely polluted area).

[0037] As shown above, polygon Boolean operations can be used to accurately and efficiently locate the corresponding areas of each pollution level in the target area.

[0038] By generating contour areas in step 102 and dividing the target area using contour areas in step 103, the precise location of each pollution level area in the target area was achieved. In other words, by using multiple contour lines with values ​​equal to the pollution level threshold to create holes in the target area, the various pollution level areas in the target area were efficiently and accurately located.

[0039] Step 104 may include: determining the area of ​​the target region, calculating the area of ​​each polygonal region, and calculating the ratio of the area of ​​each polygonal region to the area of ​​the target region to obtain the proportion of areas with corresponding pollution levels. Taking Table 1 as an example, the proportion of Level 1 Excellent areas can be obtained by calculating the ratio of the area of ​​Level 1 Excellent areas to the area of ​​the target region; the proportion of Level 2 Good areas can be obtained by calculating the ratio of the area of ​​Level 2 Good areas to the area of ​​the target region; the proportion of Level 3 Slightly Polluted areas can be obtained by calculating the ratio of the area of ​​Level 3 Lightly Polluted areas to the area of ​​the target region; the proportion of Level 4 Moderately Polluted areas can be obtained by calculating the ratio of the area of ​​Level 4 Moderately Polluted areas to the area of ​​the target region; the proportion of Level 5 Severely Polluted areas can be obtained by calculating the ratio of the area of ​​Level 5 Severely Polluted areas to the area of ​​the target region; and the proportion of Level 6 Severely Polluted areas can be obtained by calculating the ratio of the area of ​​Level 6 Severely Polluted areas to the area of ​​the target region.

[0040] Each polygonal region obtained in step 103 can be represented as a set of sequentially arranged vertex coordinates with the first and last vertices having the same coordinates. Therefore, in step 104, the area of ​​each polygonal region can be calculated using the following shoelace formula.

[0041] Suppose the polygonal region has n vertices, whose vertices are arranged in a wraparound order as (x1, y1), (x2, y2), (x3, y3)...(x... n ,y n The directed area A of the polygonal region can be calculated using the following formula:

[0042] Here, it is agreed that the vertex indexes are cyclically closed, that is, when hour, , .

[0043] Similarly, the area of ​​the target region can be calculated using the aforementioned shoelace formula, and the boundary points of the target region can be extracted from the contaminated grid data of the target region. Alternatively, the area of ​​the target region can be read from a public data center.

[0044] As described above, this embodiment uses pollution grid data of the target area to generate contour regions corresponding to each pollution level threshold. These contour regions are then used to extract holes from the target area to accurately locate each pollution level region within the target area. Finally, the area of ​​each pollution level region is determined by calculating the area of ​​the polygon region, ultimately obtaining the proportion of each pollution level region in the target area. It is evident that this embodiment achieves accurate assessment of the proportion of pollution level regions in the target area using a contour generation algorithm with low computational complexity and polygon Boolean operations. This effectively reduces computational complexity, improves estimation efficiency, and also possesses high estimation accuracy and spatiotemporal resolution, meeting the needs of rapid, accurate, and dynamic quantitative assessment of atmospheric pollution spatial patterns in practical applications.

[0045] Figure 2 A schematic diagram of the pollution level area proportion estimation device provided in an embodiment of this disclosure is shown. See also Figure 2 The pollution level area proportion estimation device 200 of this disclosure embodiment may include: The data acquisition unit 201 is used to acquire pollution grid data of the target area based on the latitude and longitude of the target area. The elements in the pollution grid data represent the pollution value at the latitude and longitude coordinates of the corresponding grid center point. The contour region generation unit 202 is used to generate multiple contour regions using the pollution grid data of the target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold and the pollution value of each point in the contour region is less than the pollution level threshold. The pollution level area determination unit 203 is used to divide the target area using multiple contour lines to obtain multiple polygonal areas with different pollution levels. The pollution level is determined by multiple pollution level thresholds. The area proportion calculation unit 204 is used to calculate the area of ​​each polygonal region to obtain the pollution level area proportion of the target region.

[0046] In some implementations, the contour region generation unit 202 may specifically be used to: extract multiple contour lines from the pollution grid data of the target area using the values ​​of each pollution level threshold as contour lines, each contour line being represented as a set of sequentially arranged vertex coordinates; and to perform the following processing for each contour line: determine its contour region using the vertex order of the contour line, wherein the pollution value of each point within the contour region is less than the value of the contour line.

[0047] In some implementations, the contour region generation unit 202 can specifically be used to: determine whether the coordinates of the first and last vertices of the contour line are the same; if the coordinates of the first and last vertices of the contour line are the same, then the contour line is determined to be a closed contour line, and the order of the vertices of the contour line is used to determine whether the contour line is arranged clockwise or counterclockwise; if the contour line is arranged clockwise, then the area inside the contour line is determined to be a contour region; if the contour line is arranged counterclockwise, then the contour line is connected to the boundary of the target area to form a closed polygon, which is the contour region; if the coordinates of the first and last vertices of the contour line are different, then the contour line is determined to be an open contour line, and the order of the vertices of the contour line is used to determine the high-value side and the low-value side, and the contour line is connected to the boundary of the target area with its low-value side to form a closed polygon, which is the contour region.

[0048] In some implementations, the pollution level area determination unit 203 may specifically be used to: initialize the main area as the target area; calculate the difference between the main area and the contour area corresponding to the lowest pollution level threshold to obtain a first polygonal area, the pollution level of the first polygonal area being determined by 0 and the lowest pollution level threshold; calculate the intersection between the main area and the contour area corresponding to the lowest pollution level threshold and set the main area as the polygonal area obtained by the intersection; for each contour area whose pollution level threshold is greater than the lowest pollution level threshold and less than or equal to the highest pollution level threshold, perform the following processing sequentially according to the pollution level threshold in ascending order to obtain one or more second polygonal areas with different pollution levels: calculate the difference between the main area and the current contour area to obtain a second polygonal area, the pollution level of the second polygonal area being determined by the previous pollution level threshold and the current pollution level threshold; calculate the intersection between the main area and the current contour area and reset the main area as the polygonal area obtained by the intersection; and determine the main area as the third polygonal area with the highest pollution level.

[0049] In some implementations, the area proportion calculation unit 204 can be specifically used to: determine the area of ​​the target area, calculate the area of ​​each polygonal region, and calculate the ratio of the area of ​​each polygonal region to the area of ​​the target area to obtain the area proportion of the corresponding pollution level.

[0050] In practical applications, the pollution level area proportion estimation device 200 can be implemented through software, hardware, or a combination of both. For example, the pollution level area proportion estimation device 200 can be implemented as software running in the electronic device 300 described below.

[0051] In addition, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program thereon, the program including instructions that, when executed by one or more processors, implement the steps of the aforementioned method for estimating the proportion of pollution-level areas.

[0052] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure is shown. See also... Figure 3 The electronic device 300 may include one or more processors 301, and a memory 302 storing one or more programs. The programs in the memory are executed by the one or more processors 301 to implement the method flow and / or program units corresponding to each unit in the apparatus shown in the above embodiments of this disclosure.

[0053] Processor 301 may include one or more single-core or multi-core processors. Processor 301 may include any combination of general-purpose processors or special-purpose processors.

[0054] Memory 302 is the computer-readable storage medium provided in this disclosure, which can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as those in the embodiments of this disclosure. Figure 1 The program instructions / units corresponding to the pollution level area proportion estimation method shown are as follows. Processor 301 executes non-transient software programs, instructions, and units stored in memory 302, thereby performing operations such as those described in the above method embodiments. Figure 1 The program, instructions, and units corresponding to the method for estimating the proportion of pollution level areas are shown.

[0055] See Figure 3 The electronic device 300 may further include a communication component 303, which can be used to communicate with external devices. For example, the aforementioned pollution grid data or site monitoring data can be acquired through the communication component 303. The processor 301, memory 302, and communication component 303 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0056] Furthermore, the electronic device 300 may include any other suitable components, which are not limited in this embodiment.

[0057] The aforementioned programs (also known as software, software applications, or code) include machine instructions for a programmable processor and can be implemented using object-oriented programming languages, assembly language, or machine language.

[0058] With the development of time and technology, the meaning of "medium" has become increasingly broad. The dissemination of computer programs is no longer limited to tangible media; they can also be downloaded directly from the network. Any combination of one or more computer-readable storage media can be used. Computer-readable storage media can be, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0059] In a specific implementation, the electronic device 300 can be implemented as a computer, a server, or a cluster thereof. This disclosure does not limit the specific implementation of the electronic device 300.

[0060] The technical solutions provided in this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. Furthermore, those skilled in the art will recognize that, based on the ideas of this disclosure, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

[0061] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications or equivalent substitutions made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for estimating the proportion of pollution-level areas, characterized in that, The method includes: The pollution grid data of the target area is obtained based on the latitude and longitude of the target area, and the elements in the pollution grid data represent the pollution value at the corresponding latitude and longitude coordinates of the grid. Multiple contour regions are generated using pollution grid data of the target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold, and the pollution value of each point within the contour region is less than the pollution level threshold. The target area is divided using the multiple contour lines to obtain multiple polygonal regions with different pollution levels, wherein the pollution level is determined by the multiple pollution level thresholds. Calculate the area of ​​each polygonal region to obtain the percentage of the target region with a pollution level.

2. The method according to claim 1, characterized in that, The process of generating multiple contour lines using pollution grid data of the target area and predetermined pollution level thresholds includes: Multiple contour lines are extracted from the pollution grid data of the target area using the values ​​of each pollution level threshold as contour lines. Each contour line is represented as a set of sequentially arranged vertex coordinates. For each contour line, the following process is performed: the contour region is determined by the order of the vertices of the contour line, and the pollution value of each point within the contour region is less than the value of the contour line.

3. The method according to claim 2, characterized in that, The step of determining the contour region using the vertex order of the contour lines includes: Determine whether the coordinates of the first and last vertices of the contour lines are the same; If the coordinates of the first and last vertices of the contour lines are the same, then the contour lines are determined to be closed contour lines. The order of the vertices of the contour lines determines whether the contour lines are arranged clockwise or counterclockwise. If the contour lines are arranged clockwise, then the area inside the contour lines is determined as the contour line region. If the contour lines are arranged counterclockwise, then the contour lines are connected to the boundary of the target area to form a closed polygon, which is the contour line region. If the coordinates of the first and last vertices of the contour line are different, the contour line is determined to be an open contour line. The high-value side and the low-value side are determined by the vertex order of the contour line. The boundary of the target area is connected to the contour line and its low-value side to form a closed polygon. The closed polygon is the contour line region.

4. The method according to claim 1, characterized in that, The method of dividing the target area using the multiple contour lines to obtain multiple polygonal regions with different pollution levels includes: The main region is initialized as the target region. The difference between the main region and the contour region corresponding to the lowest pollution level threshold is calculated to obtain a first polygonal region. The pollution level of the first polygonal region is determined by 0 and the lowest pollution level threshold. The intersection between the main region and the contour region corresponding to the lowest pollution level threshold is calculated, and the main region is set as the polygonal region obtained by the intersection. For each contour region whose pollution level threshold is greater than the lowest pollution level threshold and less than or equal to the highest pollution level threshold, the following processing is performed sequentially according to the corresponding pollution level threshold from low to high to obtain one or more second polygon regions with different pollution levels: the difference between the main region and the current contour region is calculated to obtain a second polygon region, the pollution level of the second polygon region being determined by the previous pollution level threshold and the current pollution level threshold; the intersection of the main region and the current contour region is calculated, and the main region is reset to the polygon region obtained by the intersection calculation; The main area was determined to be the third polygonal area with the highest pollution level.

5. The method according to claim 1, characterized in that, Each polygonal region is represented by a set of sequentially arranged vertex coordinates with the first and last vertices having the same coordinates; The calculation of the area of ​​each polygonal region to obtain the proportion of the target region with a pollution level includes: Determine the area of ​​the target region; Calculate the area of ​​each of the polygonal regions; Calculate the ratio of the area of ​​each polygonal region to the area of ​​the target region to obtain the proportion of the region with the corresponding pollution level.

6. The method according to claim 1, characterized in that, The pollution value is the target pollutant concentration or the ambient air quality index (AQI).

7. A device for estimating the proportion of pollution-level areas, characterized in that, The pollution level area proportion estimation device includes: The data acquisition unit is used to acquire pollution grid data of the target area based on the latitude and longitude of the target area, wherein the elements in the pollution grid data represent the pollution value at the latitude and longitude coordinates of the corresponding grid center point; A contour region generation unit is used to generate multiple contour regions using pollution grid data of a target area and multiple predetermined pollution level thresholds. Each contour region corresponds to a pollution level threshold and the pollution value of each point in the contour region is less than the pollution level threshold. A pollution level area determination unit is used to divide the target area using the multiple contour lines to obtain multiple polygonal areas with different pollution levels, wherein the pollution level is determined by the multiple pollution level thresholds. The regional proportion calculation unit is used to calculate the area of ​​each polygonal region to obtain the regional proportion of pollution level of the target region.

8. The apparatus according to claim 7, characterized in that, The contour region generation unit is specifically used to: extract multiple contour lines from the pollution grid data of the target area using the values ​​of each pollution level threshold as contour lines, each contour line representing a set of sequentially arranged vertex coordinates; and perform the following processing for each contour line: determine its contour region using the vertex order of the contour line, wherein the pollution value of each point within the contour region is less than the value of the contour line.

9. An electronic device, characterized in that, include: A memory for storing one or more processors and programs, the programs comprising instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a program, the program comprising instructions that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1 to 6.

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