CN111400557B - Method and device for automatically identifying important areas of atmospheric pollution - Google Patents
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Abstract
A method and a device for automatically identifying an atmospheric pollution important area, wherein the method comprises the following steps: acquiring relevant interest points of air pollution; clustering the interest points to obtain a plurality of interest point clusters; obtaining the pollutant concentration of each interest point cluster from the monitoring equipment; and (3) arranging the pollutant concentrations of all the interest point clusters in a descending order, and taking a plurality of interest point clusters which are arranged at the front as an atmospheric pollution key area. The invention can obviously improve the working efficiency and the monitoring accuracy.
Description
Technical Field
The invention belongs to the technical field of environmental monitoring, and particularly relates to a method and a device for automatically identifying an atmospheric pollution important area.
Background
With the recent great improvement of air quality, the difficulty of further treatment is increased, the refined environment and pollution source management needs to be further enhanced, and the method is particularly important for identifying and treating the important areas of air pollution.
At present, two main methods exist for monitoring key areas, namely, key emission source verification based on a pollution source list; one is to artificially monitor and treat the delimited industrial area and the significantly high-row areas such as road traffic. Both methods need artificial subjective statistics and judgment, and the pollution source list is counted according to the maximum emission amount, so that the real-time emission intensity cannot be objectively obtained.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method and a device for automatically identifying an atmospheric pollution key area.
On one hand, the invention provides a method for automatically identifying an atmospheric pollution key area, which comprises the following steps:
acquiring relevant interest points of air pollution;
clustering the interest points to obtain a plurality of interest point clusters;
obtaining the pollutant concentration of each interest point cluster from the monitoring equipment;
and (3) arranging the pollutant concentrations of all the interest point clusters in a descending order, and taking a plurality of interest point clusters which are arranged at the front as an atmospheric pollution key area.
In some embodiments, the step of clustering the points of interest comprises:
numbering all the interest points, and connecting adjacent interest points with the distance within d to obtain a plurality of interest point clusters;
and further splitting the interest point cluster.
On the other hand, the invention provides a device for automatically identifying an atmospheric pollution important area, which comprises the following components:
a monitoring module including a plurality of monitoring devices for monitoring a concentration of a contaminant;
the data acquisition module is used for acquiring the relevant interest points of the air pollution and acquiring the pollutant concentration of each interest point cluster from the monitoring module;
the aggregation processing module is used for performing aggregation processing on the interest points to obtain a plurality of interest point clusters;
and the key region screening module is used for arranging the pollutant concentrations of all the interest point clusters in a descending order, and a plurality of interest point clusters which are arranged at the front are used as the atmospheric pollution key regions.
In some embodiments, the aggregation processing module numbers all the interest points, connects adjacent interest points with a distance within d to obtain a plurality of interest point clusters, and further splits the interest point clusters.
In some embodiments, the monitoring device is arranged as follows:
taking the interest point cluster as input to construct a convex polygon containing all the interest points, and taking the geometric center of the convex polygon as a monitoring equipment layout point position;
drawing a circle with the radius r by taking each vertex of the convex polygon as the circle center, and taking the outer common tangent of two adjacent circles as the shape boundary of the interest point cluster; dividing the shape boundary of the interest point cluster into 3-8 parts, and taking the dividing position as the layout point of the rest monitoring equipment.
In some embodiments, r ranges from 0.5-3km (e.g., 1km, 1.5km, 2km, or 2.5 km).
In some embodiments, the contaminant concentration of the point of interest cluster is an average of contaminant concentrations monitored by a plurality of monitoring devices of the point of interest cluster.
In some embodiments, the splitting step comprises:
(1) Connecting a plurality of interest points in each interest point cluster to form an undirected graph, and confirming attributes of each vertex in the undirected graph, wherein the attributes comprise adjacent vertices, a group and an attribution vertex, and the group and the attribution vertex are default to be empty;
(2) Executing the step (3) on the vertex x (i) if the attribution vertex is empty; if not, skipping to the next vertex x (i+1), wherein i is the vertex number;
(3) Obtaining each vertex x (n 1), x (n 2) … x (nm) in the adjacent vertex set R (i) of vertex x (i):
taking a neighboring vertex set R (n 1) of the vertex with the empty attribution vertex in each vertex of x (n 1), x (n 2) … x (nm), R (n 2) … R (nm);
removing vertices with non-null attribute of the attribution vertex in each set of R (i) and R (n 1), R (n 2) … R (nm);
taking intersection R (final) of each set of R (i) and R (n 1), R (n 2) … R (nm);
the aggregation of each vertex in the set R (final) is set as R (final), and the attribution vertex is set as x (i);
let i=i+1, proceed to step (2);
(4) The undirected graph is further split according to the aggregation.
In some embodiments, d ranges from 3-8km (e.g., 4km, 5km, 6km, or 7 km).
In yet another aspect, the present invention provides an electronic device, including:
the processor may be configured to perform the steps of,
a computer readable medium storing a computer program;
wherein the computer program, when executed by the processor, causes the processor to perform the method.
In yet another aspect, the invention proposes a computer readable medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the method.
Compared with the prior art, the method and the device have the following beneficial effects:
in the environment monitoring process, the related interest points of the atmospheric pollution with huge data volume can be related, when the key areas are determined by adopting the existing method, artificial subjective statistics and judgment are needed, and the pollution source list is counted according to the maximum emission amount, so that the real-time emission intensity can not be objectively obtained. The method and the device can automatically perform clustering, define the interest point aggregation area, screen the interest point aggregation area with abnormal pollution concentration in subsequent analysis and serve as a key treatment area.
Drawings
The following drawings are only for purposes of illustration and explanation of the present invention and are not intended to limit the scope of the invention. Wherein:
FIG. 1 is a flow chart of a method for automatically identifying an atmospheric pollution important area in an embodiment of the invention;
FIG. 2 is a diagram of an air pollution related interest point in an embodiment of the present invention;
FIG. 3 is a diagram of a point of interest cluster in an embodiment of the present invention;
FIG. 4 is a layout point of a monitoring device for a point of interest cluster in an embodiment of the present invention;
FIG. 5 is a block diagram of a device for automatically identifying an atmospheric pollution important area in an embodiment of the invention;
FIG. 6 is a distribution diagram of points of interest in the food and beverage industry in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
In the description of the present invention, reference to "one embodiment" means that a particular parameter or step or the like described in the embodiment is at least included in one embodiment according to the present invention. Thus, references to "one embodiment according to the present invention," "in an embodiment," and the like, in this specification are not intended to specify the presence of stated features but rather are intended to be included in particular embodiments, if they are used in the same sense. It will be appreciated by those of skill in the art that the specific parameters or steps, etc. disclosed in one or more of the embodiments of the invention may be combined in any suitable manner.
As shown in FIG. 1, the method for automatically identifying the atmospheric pollution key areas comprises the following steps:
acquiring relevant interest points of air pollution;
clustering the interest points to obtain a plurality of interest point clusters;
obtaining the pollutant concentration of each interest point cluster from the monitoring equipment;
and taking a plurality of interest point clusters with the pollutant concentrations ranked at the front as an atmospheric pollution key area.
According to one embodiment of the invention, when the interest points related to the atmospheric pollution are acquired, POI (Point of Information) text data with longitude and latitude can be acquired based on internet public information, survey data and the like, and interest points related to the atmospheric pollution, including but not limited to catering, industry, hospitals, gas stations, logistics and the like, in POI text can be screened based on common sense experience or historical monitoring data.
According to one embodiment of the present invention, the step of clustering the points of interest includes:
1, numbering all points of interest in the region, as shown in fig. 2:
2, setting the maximum distance d=5 km (artificial value) of adjacent interest points, connecting the adjacent interest points with the distance within D, and finally forming an interest point cluster (shown in fig. 3) containing a plurality of interest points connected with each other, wherein each interest point cluster forms an undirected graph, for example, a cluster (3) (undirected graph (3)) contains vertexes [4,5,6,7,8,9, 10], and edges [ B, C, D, E, F, G, H, I, J, K ].
3, constructing an attribute table for each vertex in each undirected graph, wherein the attribute comprises adjacent edges, adjacent vertices (including self), groups and attribution vertices, and the groups and attribution vertices default to be empty:
| vertex point | Adjacent edge | Adjacent vertex (with self) | Aggregation group | Ascription vertex |
| 4 | B，C | 4，5，6 | Without any means for | Without any means for |
| 5 | B，D，E | 5，4，6，7 | Without any means for | Without any means for |
| 6 | C，D，L，F | 6，4，5，7，8 | Without any means for | Without any means for |
| 7 | E，L，G，H，I | 7，5，6，8，9，10 | Without any means for | Without any means for |
| 8 | F，G，J | 8，6，7，9 | Without any means for | Without any means for |
| 9 | H，J.K | 9，7，8，10 | Without any means for | Without any means for |
| 10 | I，K | 10，7，9 | Without any means for | Without any means for |
4, executing the following operations on each vertex in the undirected graph (namely, each row in the attribute table in sequence) in the following order, and finally, further subdividing the undirected graph into a plurality of smaller undirected graphs, wherein the vertices are connected pairwise;
4.1, executing 4.2 on the vertex x (i) if the attribution vertex is empty; if not, skip to the next vertex x (i+1), where i is the vertex number.
4.2 for each vertex x (n 1), x (n 2) … x (nm) in the adjacent vertex set R (i) of vertex x (i), the following is performed:
taking a neighboring vertex set R (n 1) of the vertex with the empty attribution vertex in each vertex of x (n 1), x (n 2) … x (nm), R (n 2) … R (nm);
removing vertices of R (i) and R (n 1), R (n 2) … R (nm) with non-null attribute of the belonging vertex;
taking intersection R (final) of each set of R (i) and R (n 1), R (n 2) … R (nm);
the aggregation of each vertex in the set R (final) is set as R (final), and the attribution vertex is set as x (i);
let i=i+1 and return to 2.4.1;
taking vertex 4 as an example, the adjacent vertex set R (4) includes vertices 4,5, and 6, all of the attributed vertices of vertices 4,5, and 6 default to be empty, and the adjacent vertex set of vertices 4,5, and 6 is taken: r (4) comprises 4,5,6; r (5) includes 5,4,6,7; r (6) includes 6,4,5,7,8; since the attributed vertex of each vertex defaults to empty, no removal is required; taking R (4), R (5) and R (6) intersection to obtain R (final), wherein R (final) comprises 4,5 and 6; the group of vertices 4,5, and 6 in R (final) is defined as R (final), and all of the vertices are vertex 4, as shown in the following table.
For vertex 7, its adjacent vertex set includes vertices 7,5,6,8,9, 10, where the attributed vertex of vertex 5,6 is not null, thus taking the adjacent set of vertices 7,8,9, 10; removing vertexes with non-empty attribution vertexes in adjacent point sets of the vertexes, wherein R (7) comprises vertexes 7,8,9, 10, R (8) comprises vertexes 8,7,9, R (9) comprises 9,7,8, 10, R (10) comprises 10,7,9, taking intersection of R (7), R (8), R (9) and R (10) to obtain R (final), and R (final) comprises 7,9; the group of vertices 7 and 9 in R (final) is set to R (final), and its home vertex is set to 7, as shown in the following table.
And executing the operation on the rest vertexes to obtain the group and the attribution vertex of each vertex.
And 4.3, finally, further splitting the undirected graph according to the aggregation, and finally, completing the aggregation of the interest points. For example, vertices 4,5,6 are a split cluster of points of interest; the vertexes 7 and 9 are a split interest point cluster; vertices 8 and 10 are each a split cluster of points of interest, as shown in the table below.
According to one embodiment of the invention, when monitoring the pollutant concentration of each interest point cluster, the monitoring equipment points are arranged in the following manner:
as shown in fig. 4, firstly, a convex polygon containing all interest points is constructed by taking the interest point cluster as input, and the geometric center of the convex polygon is taken as a point location 1 of the monitoring equipment layout;
then, drawing a circle with a radius of 1km (the numerical value is determined manually) by taking each vertex of the constructed convex polygon as a circle center, and drawing external public tangents of two adjacent circles to form an interest point cluster shape boundary; and then, quartering the shape boundary of the interest point cluster, taking the halving position (the first halving position is randomly selected) as the monitoring equipment layout points 2,3,4 and 5, and finally determining 5 monitoring equipment layout points.
According to one embodiment of the invention, when screening an important treatment area, the average value of five monitoring devices in each interest point cluster area is used as the evaluation concentration of the monitoring area, the evaluation concentrations are arranged in descending order, and the first n% (n is 0-100 and is optionally used as the important treatment area).
As shown in fig. 5, according to an embodiment of the present invention, the present invention provides an apparatus for automatically identifying an atmospheric pollution important area, including:
a monitoring module including a plurality of monitoring devices for monitoring a concentration of a contaminant;
the data acquisition module is used for acquiring the relevant interest points of the air pollution and acquiring the pollutant concentration of each interest point cluster from the monitoring module;
the aggregation processing module is used for performing aggregation processing on the interest points to obtain a plurality of interest point clusters;
and the key region screening module is used for arranging the pollutant concentrations of all the interest point clusters in a descending order, and taking the interest point clusters which are arranged at the front as the atmospheric pollution key regions.
In some embodiments, the aggregation processing module numbers all the interest points, connects adjacent interest points with a distance within d to obtain a plurality of interest point clusters, and further splits the interest point clusters. Preferably, d ranges from 3-8km (e.g., 4km, 5km, 6km, or 7 km).
In some embodiments, the monitoring device is arranged as follows:
taking the interest point cluster as input to construct a convex polygon containing all the interest points, and taking the geometric center of the convex polygon as a monitoring equipment layout point position;
drawing a circle with the radius r by taking each vertex of the convex polygon as the circle center, and taking the outer common tangent of two adjacent circles as the shape boundary of the interest point cluster; dividing the shape boundary of the interest point cluster into 3-8 parts, and taking the dividing position as the layout point of the rest monitoring equipment. Preferably, r ranges from 0.5-3km (e.g., 1km, 1.5km, 2km, or 2.5 km).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above.
According to an embodiment of the invention, the method flow according to the invention may be implemented as a computer software program. For example, embodiments of the present invention include an electronic device comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the above-described methods. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the system of the embodiment of the present invention are performed when the computer program is executed by a processor. According to an embodiment of the invention, the apparatus may be implemented by means of computer program modules.
The present invention also provides a computer-readable storage medium that may be included in the apparatus in the above embodiment; or may be present alone without being fitted into the device. The computer-readable storage medium carries one or more computer programs which, when executed, implement methods in accordance with embodiments of the present invention.
According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the invention, the computer-readable storage medium may include ROM and/or RAM and/or one or more memories other than ROM and RAM as described above.
In one embodiment, according to the requirements of environmental management work, pollution sources such as catering oil smoke and the like which are not in the traditional pollution source emission list also need to be included in the supervision category, and the monitoring of the heavy point area before mainly has two methods, namely, the important emission source check based on the pollution source elimination list; one is to artificially monitor and treat the delimited industrial area and the significantly high-row areas such as road traffic. But such as restaurant sites, are very data intensive and difficult to handle in this manner.
For example, in one embodiment, the area is defined by clustering 91962 points of interest in the restaurant industry in Beijing as shown in FIG. 6, if the major area of air pollution is determined by adopting a traditional method, a lot of manpower is consumed and the objectivity of the area definition is difficult to ensure, so that the method and the device can hardly be completed in actual work, and the method and the device can automatically perform clustering to define the aggregate area of the restaurant industry, screen the aggregate area of the restaurant industry with abnormal pollution concentration in subsequent analysis, and use the aggregate area as the major treatment area, thereby remarkably improving the working efficiency and the monitoring accuracy.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the invention thereto, but to limit the invention thereto, and any modifications, equivalents, improvements and equivalents thereof may be made without departing from the spirit and principles of the invention.
Claims (8)
1. The method for automatically identifying the atmospheric pollution key area is characterized by comprising the following steps of:
acquiring relevant interest points of air pollution;
clustering the interest points to obtain a plurality of interest point clusters;
obtaining the pollutant concentration of each interest point cluster from the monitoring equipment;
arranging the pollutant concentrations of all the interest point clusters in a descending order, and taking a plurality of interest point clusters which are arranged at the front as an atmospheric pollution key area; wherein the method comprises the steps of
The aggregation treatment of the interest points comprises the following steps:
numbering all the interest points, and connecting the adjacent interest points with the distance within d to obtain a plurality of interest point clusters, wherein the range of d is 3-8km;
further splitting the interest point cluster;
the splitting includes:
(1) Connecting a plurality of interest points in each interest point cluster to form an undirected graph, and confirming attributes of each vertex in the undirected graph, wherein the attributes comprise adjacent vertices, a group and an attribution vertex, and the group and the attribution vertex are default to be empty;
(2) Executing the step (3) on the vertex x (i) if the attribution vertex is empty; if not, skipping to the next vertex x (i+1), wherein i is the vertex number;
(3) Obtaining each vertex x (n 1), x (n 2) … x (nm) in the adjacent vertex set R (i) of vertex x (i):
taking a neighboring vertex set R (n 1) of the vertex with the empty attribution vertex in each vertex of x (n 1), x (n 2) … x (nm), R (n 2) … R (nm);
removing vertices with non-null attribute of the attribution vertex in each set of R (i) and R (n 1), R (n 2) … R (nm);
taking intersection R (final) of each set of R (i) and R (n 1), R (n 2) … R (nm);
the aggregation of each vertex in the set R (final) is set as R (final), and the attribution vertex is set as x (i);
let i=i+1, proceed to step (2);
(4) The undirected graph is further split according to the aggregation.
2. The method of claim 1, wherein the d comprises 4km, 5km, 6km, or 7km.
3. The method of claim 1, wherein the contaminant concentration of the point of interest cluster is an average of contaminant concentrations monitored by a plurality of monitoring devices of the point of interest cluster.
4. An apparatus for automatically identifying an atmospheric pollution important area, comprising:
a monitoring module including a plurality of monitoring devices for monitoring a concentration of a contaminant;
the data acquisition module is used for acquiring the relevant interest points of the air pollution and acquiring the pollutant concentration of each interest point cluster from the monitoring module;
the aggregation processing module is used for performing aggregation processing on the interest points to obtain a plurality of interest point clusters;
the key region screening module is used for arranging the pollutant concentrations of all the interest point clusters in a descending order, and a plurality of interest point clusters which are arranged at the front are used as the atmospheric pollution key regions; wherein,,
the agglomeration processing module is further configured to:
numbering all the interest points, and connecting adjacent interest points with the distance within d to obtain a plurality of interest point clusters;
further splitting the interest point cluster;
the splitting includes:
(1) Connecting a plurality of interest points in each interest point cluster to form an undirected graph, and confirming attributes of each vertex in the undirected graph, wherein the attributes comprise adjacent vertices, a group and an attribution vertex, and the group and the attribution vertex are default to be empty;
(2) Executing the step (3) on the vertex x (i) if the attribution vertex is empty; if not, skipping to the next vertex x (i+1), wherein i is the vertex number;
(3) Obtaining each vertex x (n 1), x (n 2) … x (nm) in the adjacent vertex set R (i) of vertex x (i):
taking a neighboring vertex set R (n 1) of the vertex with the empty attribution vertex in each vertex of x (n 1), x (n 2) … x (nm), R (n 2) … R (nm);
removing vertices with non-null attribute of the attribution vertex in each set of R (i) and R (n 1), R (n 2) … R (nm);
taking intersection R (final) of each set of R (i) and R (n 1), R (n 2) … R (nm);
the aggregation of each vertex in the set R (final) is set as R (final), and the attribution vertex is set as x (i);
let i=i+1, proceed to step (2);
(4) The undirected graph is further split according to the aggregation.
5. The apparatus of claim 4, wherein the monitoring device is arranged in the following manner:
taking the interest point cluster as input to construct a convex polygon containing all the interest points, and taking the geometric center of the convex polygon as a monitoring equipment layout point position;
drawing a circle with the radius r by taking each vertex of the convex polygon as the circle center, and taking the outer common tangent of two adjacent circles as the shape boundary of the interest point cluster; dividing the shape boundary of the interest point cluster into 3-8 parts, taking the dividing position as the layout point of the rest monitoring equipment, and setting the r range to be 0.5-3km.
6. The apparatus of claim 5, wherein the r comprises 1km, 1.5km, 2km, or 2.5km.
7. An electronic device, comprising:
the processor may be configured to perform the steps of,
a computer readable medium storing a computer program;
wherein the computer program, when executed by the processor, causes the processor to perform the method of any of claims 1-3.
8. A computer readable medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform the method of any of claims 1-3.
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