CN114624397A - Air quality monitoring method and device and computer readable storage medium - Google Patents
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Abstract
The invention discloses a method and a device for monitoring air quality and a computer readable storage medium, wherein the method comprises the following steps: acquiring the air quality of a first area, wherein the first area comprises at least one area to be monitored; determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored; determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region; and outputting the air quality grade. The invention can reduce the monitoring cost of the air quality.
Description
Technical Field
The present invention relates to the field of air quality monitoring technologies, and in particular, to a method and an apparatus for monitoring air quality, and a computer-readable storage medium.
Background
In the air quality monitoring, the main detection means is to arrange a plurality of air monitoring devices in the area to be monitored, and then determine the air quality of the area to be monitored based on the data returned by each air monitoring device, when the space range of the detection area is large, the number of required air quality detection devices is increased, and further the air quality monitoring cost is high.
Disclosure of Invention
The embodiment of the invention provides a method and a device for monitoring air quality and a computer readable storage medium, and aims to solve the technical problem of reducing the cost of monitoring the air quality.
The embodiment of the invention provides an air quality monitoring method, which comprises the following steps:
acquiring the air quality of a first area, wherein the first area comprises at least one area to be monitored;
determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored;
determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region;
and outputting the air quality grade.
In one embodiment, the step of determining a second zone associated with the first zone and determining the air quality of the second zone according to the air quality of the first zone comprises:
inputting the air quality of the first area into a pre-trained area air quality correlation model, wherein the area air quality correlation model determines a second area related to the first area, and determines the air quality of the second area according to the air quality of the first area to output.
In an embodiment, before the step of inputting the air quality of the first region into the pre-trained region air quality correlation model, the method further comprises:
obtaining a training set comprising a first air quality sample of the first region and a second air quality sample of the second region;
inputting the training set into a first neural network model to be trained;
and when the loss function of the first neural network model converges, using the first neural network model as the regional air quality correlation model.
In one embodiment, the step of acquiring the air quality of the first area comprises:
acquiring an image of the first region;
determining an air quality of the first region from the image.
In one embodiment, the step of determining the air quality of the first region from the image comprises:
and inputting the image as an input parameter into a preset trained air quality detection model, wherein the air quality detection model outputs the air quality of the first region according to the image.
In an embodiment, before the step of inputting the image as an input parameter into a pre-trained air quality detection model, the method further includes:
obtaining a training set, wherein the training set comprises a first sample image of the first region;
inputting the training set into a second neural network model to be trained;
and when the loss function of the second neural network model converges, using the second neural network model as the air quality detection model.
In one embodiment, the step of acquiring the air quality of the first area comprises:
acquiring visibility corresponding to the image;
and determining the air quality according to the visibility.
In an embodiment, the step of obtaining the visibility corresponding to the image includes:
acquiring a current image of a pre-calibrated reference region in the image, and acquiring a sample image corresponding to the preset reference region;
comparing the similarity of the sample image with the current image to obtain the image similarity;
and acquiring the image similarity to obtain the corresponding visibility so as to obtain the visibility corresponding to the image.
The embodiment of the invention also provides a device for monitoring the air quality, which comprises: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of monitoring air quality as described above when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the monitoring method for air quality as described above.
In the technical scheme of the embodiment, an air quality monitoring device acquires the air quality of a first area, wherein the first area comprises at least one area to be monitored; determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored; determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region; and outputting the air quality grade. Because the monitoring device of the air quality divides each area to be monitored, wherein the area comprises the first area and the second area, and because the first area and the second area are associated in advance, when the monitoring device of the air quality determines the air quality of the first area, the air quality of the second area can be obtained based on the air quality of the first area, no expensive air quality monitoring equipment is required to be arranged in the second area, and the monitoring cost of the air quality is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a hardware architecture diagram of an air quality monitoring apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the method for monitoring air quality according to the present invention;
FIG. 3 is a detailed flow chart of step S10 of the air quality monitoring method according to the second embodiment of the present invention;
fig. 4 is a detailed flow chart of step S12 of the third embodiment of the method for monitoring air quality according to the present invention.
Detailed Description
In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The main solution of the invention is: the method comprises the steps that an air quality monitoring device acquires the air quality of a first area, wherein the first area comprises at least one area to be monitored; determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored; determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region; and outputting the air quality grade.
Because the monitoring device of the air quality divides each area to be monitored, wherein the area comprises the first area and the second area, and because the first area and the second area are associated in advance, when the monitoring device of the air quality determines the air quality of the first area, the air quality of the second area can be obtained based on the air quality of the first area, no expensive air quality monitoring equipment is required to be arranged in the second area, and the monitoring cost of the air quality is reduced.
As an implementation, the monitoring device of the air quality may be as shown in fig. 1.
The embodiment of the invention relates to an air quality monitoring device, which comprises: a processor 101, e.g. a CPU, a memory 102, a communication bus 103. Wherein a communication bus 103 is used for enabling the connection communication between these components.
The memory 102 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). As in fig. 1, a detection program may be included in the memory 103 as a computer-readable storage medium; and the processor 101 may be configured to call the detection program stored in the memory 102 and perform the following operations:
acquiring the air quality of a first area, wherein the first area comprises at least one area to be monitored;
determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored;
determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region;
and outputting the air quality grade.
In one embodiment, the processor 101 may be configured to invoke the detection program stored in the memory 102 and perform the following operations:
inputting the air quality of the first area into a pre-trained area air quality correlation model, wherein the area air quality correlation model determines a second area related to the first area, and determines the air quality of the second area according to the air quality of the first area to output.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
obtaining a training set comprising a first air quality sample of the first region and a second air quality sample of the second region;
inputting the training set into a first neural network model to be trained;
and when the loss function of the first neural network model converges, using the first neural network model as the regional air quality correlation model.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring an image of the first region;
determining an air quality of the first region from the image.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
and inputting the image as an input parameter into a preset trained air quality detection model, wherein the air quality detection model outputs the air quality of the first region according to the image.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
obtaining a training set, wherein the training set comprises a first sample image of the first region;
inputting the training set into a second neural network model to be trained;
and when the loss function of the second neural network model converges, using the second neural network model as the air quality detection model.
In one embodiment, the processor 101 may be configured to invoke the detection program stored in the memory 102 and perform the following operations:
acquiring visibility corresponding to the image;
and determining the air quality according to the visibility.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring a current image of a pre-calibrated reference region in the image, and acquiring a sample image corresponding to the preset reference region;
comparing the similarity of the sample image with the current image to obtain the image similarity;
and acquiring the image similarity to obtain the corresponding visibility so as to obtain the visibility corresponding to the image.
In the technical scheme of the embodiment, an air quality monitoring device acquires the air quality of a first area, wherein the first area comprises at least one area to be monitored; determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored; determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region; and outputting the air quality grade. Because the monitoring device of the air quality divides each area to be monitored, wherein the area comprises the first area and the second area, and because the first area and the second area are associated in advance, when the monitoring device of the air quality determines the air quality of the first area, the air quality of the second area can be obtained based on the air quality of the first area, no expensive air quality monitoring equipment is required to be arranged in the second area, and the monitoring cost of the air quality is reduced.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, fig. 2 is a first embodiment of the air quality monitoring method of the present invention, which includes the steps of:
step S10, acquiring an air quality of a first area, the first area including at least one area to be monitored.
The quality of Air quality (Air quality) reflects the Air pollution level, and is judged according to the concentration of pollutants in the Air. Air pollution is a complex phenomenon, and the concentration of air pollutants at a particular time and place is influenced by many factors. The magnitude of the emission of man-made pollutants from stationary and mobile sources is one of the most important factors affecting air quality, including exhaust gases from vehicles, ships, airplanes, industrial pollution, residential and heating, waste incineration, etc. The development density of cities, landforms, weather and the like are also important factors influencing the air quality.
In addition, the concentration of the "negative oxygen ions" is one of the signs of the quality of the air. According to the standards of the world health organization, the air is called "fresh air" when the concentration of negative oxygen ions in the air is higher than 1000-1500 per cubic centimeter.
Air polluting pollutants include smoke, total suspended particulate matter, respirable particulate matter (PM10), fine particulate matter (PM2.5), nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, volatile organic compounds, and the like.
In this embodiment, the region to be monitored may be a part of one total region, and the first region and the second region may be understood as regions in units of the region to be monitored. Wherein the air mass of the first zone is independent of the air mass of the second zone.
Step S20, determining a second area related to the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored.
In the present embodiment, the relationship between the first region and the second region is preset, and the air quality of the first region and the air quality of the second region are also corresponding, and the corresponding relationship is also preset.
Optionally, the air quality of the first area is input into a pre-trained area air quality correlation model, wherein the area air quality correlation model determines a second area associated with the first area, and determines the air quality of the second area according to the air quality of the first area to output.
Alternatively, the air quality correlation model may be a neural network model. The neural network model is a mathematical method for simulating the actual human neural network, and since the advent, people have been slowly used to directly refer to the artificial neural network as the neural network. The neural network has a wide and attractive prospect in the fields of system identification, pattern recognition, intelligent control and the like, particularly in intelligent control, people are particularly interested in the self-learning function of the neural network, and the important characteristic of the neural network is regarded as one of key keys for solving the problem of adaptability of the controller in automatic control.
Optionally, for training of the air quality correlation model, a training set may be obtained, where the training set includes a first air quality sample of the first region and a second air quality sample of the second region; inputting the training set into a first neural network model to be trained; and when the loss function of the first neural network model converges, using the first neural network model as the regional air quality correlation model.
Step S30, determining an air quality grade of each of the regions to be monitored according to the air quality of the first region and the air quality of the second region.
In this embodiment, the total air quality level of each area to be monitored is determined based on the air quality of the first area and the air quality of the second area.
And step S40, outputting the air quality grade.
In the technical scheme of this embodiment, since the monitoring device of the air quality divides each area to be monitored, including the first area and the second area, and since the first area and the second area are associated in advance, when the monitoring device of the air quality determines the air quality of the first area, the air quality of the second area can be obtained based on the air quality of the first area, and it is not necessary to set an expensive air quality monitoring device in the second area, thereby reducing the monitoring cost of the air quality.
Referring to fig. 3, fig. 3 is a second embodiment of the air quality monitoring method according to the present invention, and step S10 includes:
step S11, acquiring an image of the first region.
In the present embodiment, an image pickup device is disposed in the first region to pick up the above-described image.
Step S12, determining the air quality of the first region from the image.
In the present embodiment, the air quality of the first region is determined by a method of image recognition.
Optionally, the image is input into a preset trained air quality detection model as an input parameter, wherein the air quality detection model outputs the air quality of the first region according to the image. Wherein, the air quality detection model is a neural network model.
Optionally, for training of the air quality detection model, a training set may be acquired, the training set including a first sample image of the first region; inputting the training set into a second neural network model to be trained; and when the loss function of the second neural network model converges, using the second neural network model as the air quality detection model.
In the technical scheme of the embodiment, the visibility is determined through the image information, and the air quality of the first area is further determined, so that the cost of hardware equipment can be reduced.
Referring to fig. 4, fig. 4 is a third embodiment of the air quality monitoring method according to the present invention, and based on any one of the first to second embodiments, step S12 includes:
and step S121, acquiring visibility corresponding to the image.
In this embodiment, the relationship between the image and the visibility is a preset setting, wherein the visibility refers to the maximum distance that a person with normal vision can recognize the target object from the background. That is, the sky near the horizon is used as the background in the daytime, the outline of a dark target object on the ground with a visual angle larger than 20 degrees can be clearly seen, the object can be identified, and the luminous point of the target lamp can be clearly seen at night. In m (meters). The amount of visibility is mainly determined by two factors: the luminance difference between the object and the background against which it is set off. The larger (smaller) the difference, the larger (smaller) the visible distance. But this difference in brightness typically does not vary much. ② atmospheric transparency. The air layer between the observer and the target can reduce the aforementioned brightness difference. The poorer (better) the atmospheric transparency, the smaller (greater) the visible distance. The visibility changes are mainly dependent on how well the atmosphere is transparent. And the weather phenomena such as fog, smoke, sand and dust, heavy snow, rough rain and the like can make the atmosphere turbid and the transparency small.
And S122, determining the air quality according to the visibility.
In this embodiment, the correlation between the air quality and the visibility is a numerical correlation, and the corresponding relationship is a preset setting.
In the technical scheme of this embodiment, visibility can be determined through image information, and then the air quality of the first region can be determined, that is to say, the existing image acquisition device can be utilized to monitor the air quality, and a targeted air quality monitoring device is not required, so that the cost of air monitoring is further reduced.
In order to achieve the above object, an embodiment of the present invention further provides an air quality monitoring device, where the air quality monitoring device includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the air quality monitoring method as described above when executing the computer program.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the air quality monitoring method as described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program embodied on one or more computer-usable computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is 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.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for monitoring air quality, comprising:
acquiring the air quality of a first area, wherein the first area comprises at least one area to be monitored;
determining a second area associated with the first area, and determining the air quality of the second area according to the air quality of the first area, wherein the second area comprises at least one area to be monitored;
determining the air quality grade of each region to be monitored according to the air quality of the first region and the air quality of the second region;
and outputting the air quality grade.
2. The method of claim 1, wherein the step of determining a second zone associated with the first zone and determining the air quality of the second zone based on the air quality of the first zone comprises:
inputting the air quality of the first area into a pre-trained area air quality correlation model, wherein the area air quality correlation model determines a second area related to the first area, and determines the air quality of the second area according to the air quality of the first area to output.
3. The method of monitoring air quality of claim 2, wherein prior to the step of inputting the air quality of the first zone into a pre-trained zone air quality correlation model, the method further comprises:
obtaining a training set comprising a first air quality sample of the first region and a second air quality sample of the second region;
inputting the training set into a first neural network model to be trained;
and when the loss function of the first neural network model converges, using the first neural network model as the regional air quality correlation model.
4. The method of monitoring air quality of claim 1, wherein the step of obtaining the air quality of the first zone comprises:
acquiring an image of the first region;
determining an air quality of the first region from the image.
5. The method of monitoring air quality of claim 4, wherein the step of determining the air quality of the first region from the image comprises:
and inputting the image as an input parameter into a preset trained air quality detection model, wherein the air quality detection model outputs the air quality of the first region according to the image.
6. The method for monitoring air quality according to claim 5, wherein before the step of inputting the image as an input parameter into a pre-trained air quality detection model, the method further comprises:
obtaining a training set, wherein the training set comprises a first sample image of the first region;
inputting the training set into a second neural network model to be trained;
and when the loss function of the second neural network model converges, using the second neural network model as the air quality detection model.
7. The method of monitoring air quality of claim 4, wherein the step of obtaining the air quality of the first zone comprises:
acquiring visibility corresponding to the image;
and determining the air quality according to the visibility.
8. The method for monitoring the quality of the air as claimed in claim 7, wherein the step of obtaining the visibility corresponding to the image comprises:
acquiring a current image of a pre-calibrated reference region in the image, and acquiring a sample image corresponding to the preset reference region;
comparing the similarity of the sample image with the current image to obtain image similarity;
and acquiring the image similarity to obtain the corresponding visibility so as to obtain the visibility corresponding to the image.
9. An air quality monitoring device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of monitoring air quality as claimed in any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the method for monitoring air quality according to any one of claims 1 to 8.
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