CN114564857A - Atmospheric pollution abnormal area identification method and device, storage medium and electronic equipment - Google Patents
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Abstract
The invention discloses a method and a device for identifying an atmosphere pollution abnormal area, a storage medium and electronic equipment, and belongs to the field of atmosphere monitoring. It comprises the following steps: carrying out grid division on an area to be monitored; calculating the hourly pollutant concentration of each grid, calculating the emission characteristics of each grid, and identifying a short-time peak value alarm grid according to the emission characteristics; for each hour of each grid, calculating the sum of the difference values of the pollutant concentration of the grid in the hour and the pollutant concentration of each adjacent grid in the surrounding area in the hour, and calculating the ratio of the sum of the difference values to the pollutant concentration of the grid in the hour to obtain the peak coefficient of the grid in the hour; accumulating the peak coefficients of all hours in a period of time of each grid to obtain the peak coefficient of the grid; and (4) taking grids with certain percentage from large peak coefficient to small peak coefficient to obtain the area peak alarm grid. The method realizes accurate and real-time identification of the atmospheric pollution abnormal area, and improves the scientification and the precision of atmospheric pollution identification.
Description
Technical Field
The invention relates to the field of atmospheric monitoring, in particular to a method and a device for identifying an atmospheric pollution abnormal area, a storage medium and electronic equipment.
Background
Atmospheric pollution refers to the phenomenon that the concentration of pollutants in the atmosphere reaches harmful degree, so that the ecological system and the normal living and developing conditions of human beings are damaged, and the harm is caused to human beings and objects. The causes of the fire are natural factors (such as volcanic eruption, forest disaster, rock weathering and the like) and artificial factors (such as industrial waste gas, fuel, automobile exhaust, nuclear explosion and the like). The influence of air pollution on human bodies is that the air pollution is uncomfortable to feel firstly, reversible reaction occurs physiologically later, and then acute harm symptoms occur further.
The harm of atmospheric pollution to industrial and agricultural production is very serious, and the harm can influence economic development and cause the loss of a large amount of manpower, material resources and financial resources. There are two main types of industrial hazards to atmospheric pollutants: firstly, acidic pollutants in the atmosphere, sulfur dioxide, nitrogen dioxide and the like corrode industrial materials, equipment and construction facilities; secondly, the fly ash increases the production, installation and debugging and the use of precision instruments and equipment brings adverse effect. The harm of air pollution to industrial production increases the production cost, improves the cost and shortens the service life of the product from the economic point of view. Air pollution also causes great harm to agricultural production. The acid rain can directly influence the normal growth of plants, and can also cause acidification of soil and water and dissolution of toxic components by infiltrating into the soil and entering the water, thereby generating toxicity to animals, plants and aquatic organisms. Severe acid rain can cause forest death and fish loss. Atmospheric pollutants can also affect weather and climate. The particulate matter reduces atmospheric visibility and reduces the amount of solar radiation reaching the ground.
The atmospheric environment protects the fundamental interests of the people concerned, and the economy concerned is continuously and healthily developed. At present, the atmospheric pollution situation in China is severe, the regional atmospheric environment problem taking inhalable particles (PM10) and fine particles (PM2.5) as characteristic pollutants is increasingly prominent, the health of people is damaged, and the social harmony and stability are influenced. With the deep promotion of industrialization and urbanization in China, the energy resource consumption is continuously increased, and the atmospheric pollution prevention pressure is continuously increased.
Therefore, atmospheric pollution monitoring is not slow, but the existing atmospheric pollution monitoring method cannot accurately and real-timely identify an atmospheric pollution abnormal area.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method, a device, a storage medium and electronic equipment for identifying an atmospheric pollution abnormal area, which realize accurate and real-time identification of the atmospheric pollution abnormal area and improve the scientification and the precision of atmospheric pollution identification.
The technical scheme provided by the invention is as follows:
in a first aspect, the present invention provides a method for identifying an abnormal area of atmospheric pollution, the method including:
s1: carrying out grid division on a region to be monitored according to a certain size;
s2: calculating to obtain the hourly pollutant concentration of each grid;
s3: calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristics of each grid with a pre-established pollution emission characteristic information base, and identifying a short-time peak value alarm grid;
s4: for each hour of each grid, respectively calculating the difference value between the pollutant concentration of the grid in the hour and the pollutant concentration of each adjacent grid in the hour, summing all the difference values corresponding to the hour, and dividing the summation result by the pollutant concentration of the grid in the hour to obtain the peak value coefficient of the grid in the hour;
s5: for each grid, accumulating the peak coefficients of the grid for all hours within a period of time to obtain the peak coefficient of the grid;
s6: and sequencing the peak coefficients from large to small, and taking a certain percentage of grids in front of the peak coefficients to obtain the area peak alarm grid.
Further, the S2 includes:
and analyzing the hourly pollutant concentration of each grid by using the satellite remote sensing data, the meteorological data and the ground monitoring network data through a cognitive calculation method.
Further, the calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid includes:
and fitting according to the hourly pollutant concentration of each grid to obtain a pollution curve of each grid, and extracting according to the shape characteristic of the pollution curve of each grid to obtain the emission characteristic of each grid.
Further, after S4 and before S5, the method further includes:
s41: if the peak coefficient of the grid in the hour is E < -0.1,0.1 >, setting the peak coefficient of the grid in the hour to be 0;
s42: if the peak coefficient of the grid in the hour is 0, the maximum value of the peak coefficients of all adjacent grids in the hour around the grid is used for replacing the peak coefficient of the grid in the hour.
Further, the method further comprises:
s7: performing forward trajectory analysis on the short-time peak value alarm grid and the regional peak value alarm grid by using a diffusion model and combining meteorological data to obtain a potential pollution source region grid; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
In a second aspect, the present invention provides an atmosphere pollution abnormal area identification device, including:
the grid division module is used for carrying out grid division on the area to be monitored according to a certain size;
the pollutant concentration calculation module is used for calculating and obtaining the hourly pollutant concentration of each grid;
the short-time peak alarm grid identification module is used for calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristics of each grid with a pre-established pollution emission characteristic information base and identifying the short-time peak alarm grid;
an hourly peak coefficient calculation module, configured to calculate, for each hour of each grid, a difference between the hourly pollutant concentration of the grid and the hourly pollutant concentration of each neighboring grid, sum all differences corresponding to the hour, and divide the sum result by the hourly pollutant concentration of the grid to obtain a hourly peak coefficient of the grid;
the grid peak coefficient calculation module is used for accumulating the peak coefficients of all hours in a period of time of each grid to obtain the peak coefficient of the grid;
and the area peak alarm grid identification module is used for sequencing the peak coefficients from large to small, and obtaining the area peak alarm grid by taking the grid in a certain percentage before the peak coefficients.
Further, the contaminant concentration calculation module is configured to:
and analyzing by using satellite remote sensing data, meteorological data and ground monitoring network data through a cognitive computation method to obtain the hourly pollutant concentration of each grid.
Further, the calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid includes:
and fitting according to the hourly pollutant concentration of each grid to obtain a pollution curve of each grid, and extracting according to the shape characteristics of the pollution curve of each grid to obtain the emission characteristics of each grid.
Further, the apparatus further comprises:
a first substitution module, configured to set a peak coefficient of the grid for the hour to 0 if the peak coefficient of the grid for the hour belongs to [ -0.1,0.1 ];
and a second replacing module, configured to replace the crest factor of the hour of the grid with a maximum of crest factors of the hour of all neighboring grids around the grid if the crest factor of the hour of the grid is 0.
Further, the apparatus further comprises:
the potential pollution source early warning module is used for carrying out forward track analysis on the short-time peak value alarm grid and the regional peak value alarm grid by using a diffusion model and combining meteorological data to obtain a potential pollution source regional grid; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
In a third aspect, the present invention provides a computer-readable storage medium for identifying an abnormal area of atmospheric pollution, comprising a memory for storing processor-executable instructions, which when executed by the processor, implement the steps of the method for identifying an abnormal area of atmospheric pollution according to the first aspect.
In a fourth aspect, the present invention provides an electronic device for identifying an abnormal area of atmospheric pollution, including at least one processor and a memory storing computer executable instructions, where the processor executes the instructions to implement the steps of the method for identifying an abnormal area of atmospheric pollution according to the first aspect.
The invention has the following beneficial effects:
the method comprises the steps of firstly, carrying out grid division on an area to be monitored, and obtaining the hourly pollutant concentration of each grid. And then obtaining a short-time peak value alarm grid according to the emission characteristics of each grid, obtaining an area peak value alarm grid according to the peak value coefficient of each grid, wherein the short-time peak value alarm grid and the area peak value alarm grid are atmospheric pollution abnormal areas. The method realizes accurate and real-time identification of the atmospheric pollution abnormal area, and improves the scientification and the precision of atmospheric pollution identification.
Drawings
FIG. 1 is a flow chart of the method for identifying an abnormal area of atmospheric pollution according to the present invention;
FIG. 2 is a schematic illustration of a potential pollution source warning;
fig. 3 is a schematic view of an atmosphere pollution abnormal area recognition device according to the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
the embodiment of the invention provides a method for identifying an atmosphere pollution abnormal area, which comprises the following steps of:
s1: and carrying out grid division on the area to be monitored according to a certain size.
According to the step, grids are divided according to a certain size according to the air pollution gridding supervision requirement. Taking Beijing as an example, 500m × 500m grid division is performed on the Beijing whole city, and a grid mapping relation coefficient data set is established, which comprises grid numbers, longitude and latitude information of grid four corners, grid positions, hot spot grid numbers, local counties, towns and towns to which the grids belong, satellite remote sensing data and pollution source distribution data sets in the grids, and the like.
S2: and calculating the hourly pollutant concentration of each grid.
Taking the pollutant as PM2.5 as an example, the hourly pollutant concentration of each 500m × 500m grid can be obtained by analyzing through a cognitive calculation method according to high-density PM2.5 monitoring data by utilizing satellite remote sensing data, meteorological data and ground monitoring network data.
S3: and calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristics of each grid with a pre-established pollution emission characteristic information base, and identifying the short-time peak value alarm grid.
One implementation of calculating the emission signature may be: and fitting according to the hourly pollutant concentration of each grid to obtain a pollution curve of each grid, and extracting according to the shape characteristic of the pollution curve of each grid to obtain the emission characteristic of each grid.
Specifically, the method comprises the following steps: based on the characteristics that local emission usually has short duration, a pollution curve is in a peak shape, the pollution transmission duration is relatively longer, and the pollution curve is in a highland shape, a local pollution emission characteristic information base is established.
The emission characteristics of each grid can be obtained according to the shape characteristics (such as the type, parameters and the like of the curve) of the pollution curve of the grid, and then the short-time peak alarm grid is identified through the emission characteristics of the grid and the matching with the pollution emission characteristic information base.
S4: for each hour of each grid, respectively calculating the difference value between the pollutant concentration of the grid in the hour and the pollutant concentration of each adjacent grid in the hour, summing all the difference values corresponding to the hour, and dividing the summation result by the pollutant concentration of the grid in the hour to obtain the peak coefficient of the grid in the hour.
Before the step begins, the non-built area grids such as mountain areas, water bodies and the like, which are main bodies of the grids, can be removed. And then calculating the difference value of the pollutant concentration of each hour between the grid and 8 adjacent grids at the periphery, summing the obtained difference value results according to each hour, and dividing the summation result by the pollutant concentration value of the grid at the hour to obtain the peak value coefficient of the grid at the hour.
The present invention may further comprise, after S4:
s41: and if the peak coefficient epsilon of the hour of the grid is [ -0.1,0.1], performing invalidation processing according to the coefficient, and setting the peak coefficient of the hour of the grid to be 0.
S42: if the peak coefficient of the grid in the hour is 0, the peak coefficient of the grid in the hour is replaced by the maximum value of the peak coefficients of all grids adjacent to the grid in the hour.
S5: for each grid, the crest factor of the grid is obtained by accumulating the crest factors of all hours in a period of time of the grid.
The step is to add and sum the peak coefficient values of the grid in each hour in a certain time (for example, one day or one week) to obtain the final peak coefficient of the grid.
S6: and sequencing the peak coefficients from large to small, and taking grids in a certain percentage (for example, the top 15%) before the peak coefficients to obtain the alarm grids of the area peak.
The method comprises the steps of firstly, carrying out grid division on an area to be monitored, and obtaining the hourly pollutant concentration of each grid. And then obtaining a short-time peak value alarm grid according to the emission characteristics of each grid, obtaining an area peak value alarm grid according to the peak value coefficient of each grid, wherein the short-time peak value alarm grid and the area peak value alarm grid are atmospheric pollution abnormal areas. The method is based on the fusion processing of multi-source data such as satellite remote sensing data, meteorological data, high-density monitoring network data and the like, the hot spot emission contribution grids are identified and screened, the accurate and real-time identification of the atmospheric pollution abnormal area is realized, and the scientification and the precision of atmospheric pollution identification are improved.
As an improvement of the embodiment of the present invention, the method of the present invention further comprises:
s7: performing forward trajectory analysis on the short-time peak alarm grids and the regional peak alarm grids by using a diffusion model and combining meteorological data to obtain potential pollution source region grids; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
The method is used for carrying out potential pollution source early warning, the potential pollution source early warning is to carry out alarm analysis by combining meteorological data and pollution source data, and aims to accurately trace the source of pollution, namely searching and positioning the source of high-concentration pollution at a certain position and early warning the emission flow direction of a certain pollution source.
The method adopts a diffusion model such as GAUSSIAN/CFD and the like, combines meteorological data such as wind temperature, humidity and pressure and the like to carry out forward trajectory analysis on pollutants in a high-concentration alarm grid (a short-time peak alarm grid and a regional peak alarm grid), and obtains a potential pollution source area grid on a small scale. And combining the acquired gridding pollution source information base, selecting a pollution source contained in the grid of the potential pollution source area to alarm, and reflecting the pollution source information and the alarm reason of the grid in alarm information.
The potential pollution area alarm is that according to a gridded pollution source information base, a large number of pollution sources are distributed in a wind direction area on a grid for real-time alarm (namely a short-time peak alarm grid) and accumulated alarm (namely an area peak alarm grid, and the wind direction in an accumulated alarm time period is relatively stable), and the potential pollution area alarm is carried out on the grid triggering the wind direction area on the alarm grid through a diffusion model algorithm by combining wind direction and wind speed data information, as shown in fig. 2.
Example 2:
an embodiment of the present invention provides an apparatus for identifying an abnormal area of atmospheric pollution, as shown in fig. 3, the apparatus includes:
and the meshing module 1 is used for meshing the area to be monitored according to a certain size.
And the pollutant concentration calculating module 2 is used for calculating and obtaining the pollutant concentration of each grid hour by hour.
And the short-time peak alarm grid identification module 3 is used for calculating the emission characteristic of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristic of each grid with a pre-established pollution emission characteristic information base and identifying the short-time peak alarm grid.
And the hourly peak coefficient calculation module 4 is configured to calculate, for each hour of each grid, a difference between the hourly pollutant concentration of the grid and the hourly pollutant concentration of each neighboring grid, sum all differences corresponding to the hour, and divide the sum result by the hourly pollutant concentration of the grid to obtain the hourly peak coefficient of the grid.
And the grid peak coefficient calculating module 5 is used for accumulating the peak coefficients of all hours in a period of time of each grid to obtain the peak coefficient of the grid.
And the area peak alarm grid identification module 6 is used for sequencing the peak coefficients from large to small, and taking a certain percentage of grids in front of the peak coefficients to obtain the area peak alarm grid.
The method comprises the steps of firstly, carrying out grid division on an area to be monitored, and obtaining the hourly pollutant concentration of each grid. And then obtaining a short-time peak value alarm grid according to the emission characteristics of each grid, obtaining an area peak value alarm grid according to the peak value coefficient of each grid, wherein the short-time peak value alarm grid and the area peak value alarm grid are atmospheric pollution abnormal areas. The method realizes accurate and real-time identification of the atmospheric pollution abnormal area, and improves the scientification and the precision of atmospheric pollution identification.
The pollutant concentration calculation module is used for:
and analyzing the hourly pollutant concentration of each grid by using the satellite remote sensing data, the meteorological data and the ground monitoring network data through a cognitive calculation method.
In the invention, the calculation of the emission characteristics of each grid according to the hourly pollutant concentration of each grid comprises the following steps:
and fitting according to the hourly pollutant concentration of each grid to obtain a pollution curve of each grid, and extracting according to the shape characteristic of the pollution curve of each grid to obtain the emission characteristic of each grid.
As an improvement of the embodiment of the present invention, the apparatus further comprises:
a first substitution module for setting the crest factor e [ -0.1,0.1] of the hour of the grid to 0 if the crest factor e [ -0.1,0.1] of the hour of the grid.
And a second replacing module, configured to replace the crest factor of the hour of the grid with a maximum of crest factors of the hour of all neighboring grids around the grid if the crest factor of the hour of the grid is 0.
As another improvement of the embodiment of the present invention, the apparatus further includes:
the potential pollution source early warning module is used for carrying out forward track analysis on the short-time peak value alarm grid and the regional peak value alarm grid by using a diffusion model and combining meteorological data to obtain a potential pollution source regional grid; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiment 1, and for the sake of brief description, reference may be made to the corresponding content in the method embodiment 1 where no mention is made in the device embodiment. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the apparatus and the unit described above may all refer to the corresponding processes in the above method embodiment 1, and are not described herein again.
Example 3:
the method provided by the present specification and described in the above embodiment 1 can implement business logic by a computer program and is recorded on a storage medium, and the storage medium can be read and executed by a computer, so as to achieve the effect of the solution described in embodiment 1 of the present specification. Accordingly, the present invention also provides a computer readable storage medium for atmospheric pollution anomaly area identification, comprising a memory for storing processor executable instructions, which when executed by a processor, implement the steps comprising the atmospheric pollution anomaly area identification method described in embodiment 1.
The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
The device described above may also include other implementations in accordance with the description of method embodiment 1. The specific implementation manner may refer to the description of the related method embodiment 1, and is not described in detail here.
Example 4:
the invention also provides an electronic device for identifying an atmosphere pollution abnormal area, which can be a single computer, and can also comprise an actual operation device and the like using one or more methods or one or more embodiment devices of the specification. The electronic device for identifying an atmosphere pollution abnormal area may include at least one processor and a memory storing computer-executable instructions, and when the processor executes the instructions, the steps of the atmosphere pollution abnormal area identification method in any one or more of the above embodiments 1 are implemented.
The description of the above-described device according to the method or apparatus embodiment may also include other embodiments, and for a specific implementation, reference may be made to the description of related method embodiment 1, which is not described in detail herein.
It should be noted that, the above-mentioned apparatus or system in this specification may also include other implementation manners according to the description of the related method embodiment, and a specific implementation manner may refer to the description of the method embodiment, which is not described herein in detail. All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program class, storage medium + program embodiment, since it is basically similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle human interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. However, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a plurality of sub-modules or sub-units, and so on. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and the actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.
As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
One or more embodiments of the specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above-mentioned embodiments are merely specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the technical solutions described in the foregoing embodiments or make equivalent substitutions for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An atmospheric pollution abnormal area identification method is characterized by comprising the following steps:
s1: carrying out grid division on the area to be monitored according to a certain size;
s2: calculating to obtain the hourly pollutant concentration of each grid;
s3: calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristics of each grid with a pre-established pollution emission characteristic information base, and identifying a short-time peak value alarm grid;
s4: for each hour of each grid, respectively calculating the difference value between the pollutant concentration of the grid in the hour and the pollutant concentration of each adjacent grid in the hour, summing all the difference values corresponding to the hour, and dividing the summation result by the pollutant concentration of the grid in the hour to obtain the peak value coefficient of the grid in the hour;
s5: for each grid, accumulating the peak coefficients of all hours in a period of time of the grid to obtain the peak coefficient of the grid;
s6: and sequencing the peak coefficients from large to small, and taking a certain percentage of grids in front of the peak coefficients to obtain the area peak alarm grid.
2. The atmospheric pollution abnormal region identification method according to claim 1, wherein the S2 includes:
and analyzing the hourly pollutant concentration of each grid by using the satellite remote sensing data, the meteorological data and the ground monitoring network data through a cognitive calculation method.
3. The method for identifying an abnormal area of atmospheric pollution as claimed in claim 1, wherein the step of calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid comprises the following steps:
and fitting according to the hourly pollutant concentration of each grid to obtain a pollution curve of each grid, and extracting according to the shape characteristic of the pollution curve of each grid to obtain the emission characteristic of each grid.
4. The method for identifying an abnormal area of atmospheric pollution as recited in claim 1, further comprising, after S4 and before S5:
s41: if the peak coefficient of the grid in the hour is E < -0.1,0.1 >, setting the peak coefficient of the grid in the hour to be 0;
s42: if the peak coefficient of the grid in the hour is 0, the peak coefficient of the grid in the hour is replaced by the maximum value of the peak coefficients of all grids adjacent to the grid in the hour.
5. The method for identifying an abnormal area of atmospheric pollution as recited in any one of claims 1 to 4, further comprising:
s7: performing forward trajectory analysis on the short-time peak alarm grid and the regional peak alarm grid by using a diffusion model and combining meteorological data to obtain a potential pollution source region grid; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
6. An atmosphere pollution abnormal area recognition device, characterized in that the device includes:
the grid division module is used for carrying out grid division on the area to be monitored according to a certain size;
the pollutant concentration calculation module is used for calculating and obtaining the hourly pollutant concentration of each grid;
the short-time peak alarm grid identification module is used for calculating the emission characteristics of each grid according to the hourly pollutant concentration of each grid, matching the emission characteristics of each grid with a pre-established pollution emission characteristic information base and identifying the short-time peak alarm grid;
an hourly peak coefficient calculation module, configured to calculate, for each hour of each grid, a difference between the hourly pollutant concentration of the grid and the hourly pollutant concentration of each neighboring grid, sum all differences corresponding to the hour, and divide the sum result by the hourly pollutant concentration of the grid to obtain an hourly peak coefficient of the grid;
the grid peak coefficient calculation module is used for accumulating the peak coefficients of all hours in a period of time of each grid to obtain the peak coefficient of the grid;
and the area peak alarm grid identification module is used for sequencing the peak coefficients from large to small, and taking a certain percentage of grids before the peak coefficients to obtain the area peak alarm grid.
7. The atmospheric pollution abnormal region identification device as claimed in claim 6, wherein the device further comprises:
a first substitution module, configured to set a peak coefficient e [ -0.1,0.1] of the grid for the hour to 0;
and a second replacing module, configured to replace the crest factor of the hour of the grid with a maximum of crest factors of the hour of all neighboring grids around the grid if the crest factor of the hour of the grid is 0.
8. An atmospheric pollution anomaly area identification device as claimed in claim 6 or 7, further comprising:
the potential pollution source early warning module is used for carrying out forward trajectory analysis on the short-time peak value alarm grid and the regional peak value alarm grid by using a diffusion model and combining meteorological data to obtain a potential pollution source region grid; and selecting the pollution source contained in the potential pollution source area grid by combining with the gridding pollution source information base, and alarming.
9. A computer-readable storage medium for identifying an anomalous region of atmospheric pollution, comprising a memory for storing processor-executable instructions which, when executed by said processor, implement steps comprising the method of identifying an anomalous region of atmospheric pollution as claimed in any one of claims 1 to 5.
10. An electronic device for identifying an anomalous region of atmospheric pollution, comprising at least one processor and a memory storing computer-executable instructions, the processor implementing the steps of the method of identifying an anomalous region of atmospheric pollution as claimed in any one of claims 1 to 5 when executing said instructions.
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| CN117332357A (en) * | 2023-11-28 | 2024-01-02 | 北京市生态环境监测中心 | Multi-index fusion abnormal emission point location identification method |
| CN117332357B (en) * | 2023-11-28 | 2024-03-12 | 北京市生态环境监测中心 | Multi-index fusion abnormal emission point location identification method |
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