CN113777236A - Air quality monitoring method and device based on emission source - Google Patents
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Abstract
The present disclosure provides an air quality monitoring method based on an emission source, including: acquiring current emission source information sent by a target user side; analyzing the current emission source information, and determining the type, emission time, emission position and emission amount of pollutants emitted by an emission source; inputting corresponding pollutant information into a pre-trained single pollutant monitoring model to generate a first-stage monitoring result, wherein the first-stage monitoring result comprises concentration distribution conditions of single pollutants in different areas at different moments; inputting a plurality of first-stage monitoring results and external condition information into a pre-trained mixed pollutant monitoring model to generate second-stage monitoring results, wherein the second-stage monitoring results comprise concentration distribution conditions of a plurality of pollutants in different areas at different moments; and responding to an area with pollutant concentration exceeding a preset threshold in the secondary monitoring result, and sending alarm information to the target user side. In this way, a comprehensive and accurate monitoring of the contaminant concentration can be achieved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of environmental monitoring technology, and more particularly, to an air quality monitoring method and apparatus based on an emission source.
Background
In recent years, the problem of air pollution has become serious and has attracted much attention. Reasonable air quality prediction can help relevant departments to make corresponding decisions so as to limit artificial emission and guide the public to avoid pollution peak periods. The regional air quality numerical model is the main means of short-term approach and medium-term forecast.
The uncertainty of numerical prediction mainly comes from the uncertainty of the initial state of the atmosphere and the uncertainty of the prediction mode, and the nonlinear characteristics of atmospheric motion determine that very small errors whether from the initial field or from the mode are amplified in the mode integration process, so that the numerical prediction loses predictability after a certain time. The ensemble forecasting can reflect the propagation of uncertainty of mode initial conditions and input parameters in mode integration, so that forecasting uncertainty caused by the uncertainty can be known, and forecasting reliability and forecasting predictability information are provided.
The current ensemble forecasting method mainly considers forecasting of different modes, different areas and different timeliness as different modes, and because the forecasting performances of the single modes at a given site are different, in order to treat different mode forecasting results differently, the reliability or the weight of the mode on future forecasting values is evaluated based on the performances of the single modes in a historical period of time, and the forecasting results of the ensemble mode are obtained.
Aiming at the prior art, the current mainstream set mode algorithm can set the advantages and disadvantages of different single modes, but the influence of meteorological conditions such as temperature, humidity, wind speed, wind direction, precipitation, air pressure and other elements on the pollutant concentration and the influence of interaction among pollutants on the pollutant concentration are difficult to consider, so that the monitoring on the pollutant concentration is difficult to realize comprehensively and accurately.
Disclosure of Invention
According to the embodiment of the present disclosure, the influence of weather conditions on the pollutant concentration and the interaction between pollutants can be monitored on the pollutant concentration, so that the pollutant concentration can be comprehensively and accurately monitored.
In a first aspect of the present disclosure, there is provided an emission source-based air quality monitoring method, comprising:
acquiring current emission source information sent by a target user side;
analyzing the current emission source information, and determining the type, emission time, emission position and emission amount of pollutants emitted by an emission source;
inputting corresponding pollutant information into a pre-trained single pollutant monitoring model to generate a corresponding first-level monitoring result, wherein the first-level monitoring result comprises concentration distribution conditions of single pollutants in different areas at different moments;
inputting a plurality of first-stage monitoring results and external condition information into a pre-trained mixed pollutant monitoring model to generate second-stage monitoring results, wherein the second-stage monitoring results comprise concentration distribution conditions of a plurality of pollutants in different areas at different moments;
and responding to an area with pollutant concentration exceeding a preset threshold in the secondary monitoring result, and sending alarm information to the target user side.
In some embodiments, the analyzing the current emission source information to determine the type, emission time, and emission location of the pollutant includes:
analyzing the current emission source information, determining the number, size and content of files in the current emission source information, reading the corresponding files, and determining the type, emission time, emission position and emission amount of pollutants.
In some embodiments, after determining the type, discharge time, discharge location, and discharge amount of the pollutant, the method further comprises:
acquiring configuration information corresponding to the current emission source information, and determining whether a data format of data representing the type, emission time, emission position and emission amount of pollutants conforms to a preset format or not according to the configuration information;
and sending alarm information to the target user side in response to the fact that the data format of the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants does not conform to the preset format.
In some embodiments, the determining whether a data format of the data representing the type, the discharge time, the discharge location, and the discharge amount of the pollutant conforms to a preset format includes:
and comparing the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants with the data representing the type, the discharge time, the discharge position and the discharge amount of the discharge source in the configuration information, and determining whether the data format of the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants conforms to a preset format.
In some embodiments, the single contaminant monitoring model is obtained by:
taking a preset amount of emission source information as a training sample set, marking training samples in the training sample set, and marking corresponding distribution conditions of pollutants in different areas at different moments of the training samples;
inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolution layer of the neural network model when the difference between the distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the distribution conditions of the pollutants in the different areas at different moments corresponding to the marked training samples is greater than a preset threshold;
and repeating the process until the difference degree between the distribution conditions of the pollutants in different areas at different moments in the output training samples and the distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value.
In some embodiments, the mixed contaminant monitoring model is obtained by:
taking a preset amount of mixed distribution information with external condition information as a training sample set, marking training samples in the training sample set, and marking out the corresponding mixed distribution conditions of pollutants in different areas at different moments of the training samples;
inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the mixed distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolutional layers of the neural network model when the difference between the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the labeled training samples is greater than a preset threshold;
and repeating the process until the difference degree between the mixed distribution conditions of the pollutants in different areas at different moments in the output training samples and the mixed distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value.
In some embodiments, further comprising:
and constructing a three-dimensional space coordinate system, mapping the distribution conditions of the multiple pollutants in different areas at different moments included in the secondary monitoring result into the three-dimensional space coordinate system, and dividing the three-dimensional space coordinate system into superposition of multiple irregular stereograms in the horizontal direction and the vertical direction by using the comprehensive indexes of the multiple pollutants.
In a second aspect of the present disclosure, there is provided an emission source-based air quality monitoring apparatus, comprising:
the emission source information acquisition module is used for acquiring current emission source information sent by a target user side;
the emission source information analysis module is used for analyzing the current emission source information and determining the type, emission time, emission position and emission amount of pollutants;
the single pollutant monitoring module is used for inputting the corresponding pollutants into a pre-trained single pollutant monitoring model to generate corresponding first-level monitoring results, and the first-level monitoring results comprise the distribution conditions of the single pollutants in different areas at different moments;
the mixed pollutant monitoring module is used for inputting a plurality of primary monitoring results into a pre-trained mixed pollutant monitoring model to generate secondary monitoring results, and the secondary monitoring results comprise the distribution conditions of a plurality of pollutants in different areas at different moments;
and the alarm module is used for responding to an area with pollutant concentration exceeding a preset threshold value in the secondary monitoring result and sending alarm information to the target user side.
In a third aspect of the present disclosure, an electronic device is provided, comprising a memory having stored thereon a computer program and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method as set forth above.
Through the air quality monitoring method based on the emission source, the pollutant concentration can be monitored according to the influence of meteorological conditions on the pollutant concentration and the interaction between pollutants, so that the pollutant concentration can be comprehensively and accurately monitored.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
fig. 1 shows a flow chart of an emission source based air quality monitoring method according to a first embodiment of the disclosure;
FIG. 2 is a flow chart illustrating an emission source based air quality monitoring method according to a second embodiment of the disclosure;
FIG. 3 is a schematic structural diagram of an air quality monitoring device based on an emission source according to a third embodiment of the disclosure;
fig. 4 shows a schematic structural diagram of an air quality monitoring apparatus based on an emission source according to a fourth embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The air quality monitoring method based on the emission source can determine the change of the distribution state of the corresponding pollutants along with time according to the emission time, the emission position and the emission amount of the single pollutants, and further realize comprehensive and accurate monitoring of the pollutant concentration according to the interaction among the pollutants and the change of the distribution state of the pollutants under the action of external conditions (such as climate or terrain factors). Specifically, as shown in fig. 1, it is a flowchart of an air quality monitoring method based on an emission source according to a first embodiment of the present disclosure. As can be seen from fig. 1, the method for monitoring air quality based on emission source of the present embodiment may include the following steps:
s101: and acquiring current emission source information sent by a target user side.
The method of the embodiment of the disclosure can be applied to a pollutant emission monitoring platform, and when a monitored party needs to emit pollutants, information (namely, emission source information) of the pollutants needing to be emitted can be sent to the monitoring platform through a target user side (namely, the user side of the monitored party), and the emission source information includes information of one or more pollutants.
S102: and analyzing the current emission source information, and determining the type, emission time, emission position and emission amount of pollutants emitted by the emission source.
After receiving the emission source information sent by the target user side, the supervision platform may parse the current emission source information, for example, the supervision platform may utilize a third-party library of python to call datatime, subpacess, os, and sys to parse the current emission source information, determine the number of files, the size of the files, and the content of the files in the current emission source information, and further determine the type, the emission time, the emission position, and the emission amount of pollutants. The type of the pollutant may be, for example, sulfur dioxide, nitrogen dioxide, PM2.5, PM10, ozone, etc., the discharge time may be, for example, a discharge start time point and a discharge end time point of each type of pollutant, or may also be a discharge start time point and a discharge duration, the discharge position may be, for example, the latitude and longitude of the discharged pollutant, and the discharge amount may be the number of the corresponding pollutant in metering units, such as volume, weight, etc.
S103: and inputting the corresponding pollutant information into a pre-trained single pollutant monitoring model to generate a corresponding first-level monitoring result, wherein the first-level monitoring result comprises the concentration distribution conditions of the single pollutants in different areas at different moments.
In this embodiment, after determining the type, the emission time, the emission location, and the emission amount of the pollutant emitted by the emission source, the diffusion condition of the single pollutant in the current emission source information may be predicted by using a pre-trained single pollutant monitoring model, and the concentration change of the single pollutant emitted by the emission source in different areas over time, for example, the different concentrations of PM2.5 emitted by the emission source in different areas over time are determined. When the pollutants emitted by the emission source include multiple types, the diffusion condition of the corresponding pollutants can be predicted by using the single pollutant monitoring model according to the information of the corresponding pollutants in the emission source information.
The single pollutant monitoring model of the embodiment is a neural network model, and the model is obtained by training in the following way:
and taking a preset amount of emission source information as a training sample set, marking the training samples in the training sample set, and marking the corresponding distribution conditions of pollutants in different areas at different moments of the training samples. Wherein, the distribution condition of a single pollutant in the emission source information in different areas at different moments is marked.
Inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolution layer of the neural network model when the difference between the distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the distribution conditions of the pollutants in the different areas at different moments corresponding to the marked training samples is greater than a preset threshold;
and repeating the process until the difference between the distribution conditions of the pollutants in different areas at different moments in the output training samples and the distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value, thereby completing the training of the single pollutant monitoring model.
S104: and inputting a plurality of primary monitoring results and external condition information into a pre-trained mixed pollutant monitoring model to generate a secondary monitoring result, wherein the secondary monitoring result comprises the concentration distribution conditions of various pollutants in different areas at different moments.
In this embodiment, after the distribution conditions of each single pollutant in the emission source information corresponding to different areas at different times are generated, the distribution conditions of each single pollutant corresponding to different areas at different times and the external condition information of the corresponding area may be input into a pre-trained mixed pollutant monitoring model, and the mixed pollutant monitoring model is used to obtain the concentration distribution conditions of multiple pollutants in different areas at different times under the influence of external conditions and the interaction between pollutants.
The external condition information in this embodiment may include temperature, wind direction, wind speed, humidity, weather conditions, and the like, and these external conditions may affect the diffusion of the pollutants, and at the same time, the pollutants may also affect each other, for example, nitrogen dioxide and ozone may reduce their concentrations due to chemical reactions, and the wind direction and wind speed may affect the diffusion speed of the pollutants, and the like. The secondary monitoring result in this embodiment includes the concentration distribution of a plurality of pollutants in different areas at different times, for example, the distribution of different pollutants in different vertical height ranges in the same horizontal area.
The mixed pollutant monitoring model in the implementation can be obtained by training in the following way:
and taking a preset amount of mixed distribution information with external condition information as a training sample set, marking the training samples in the training sample set, and marking the corresponding mixed distribution conditions of pollutants in different areas at different moments of the training samples.
Inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the mixed distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolution layer of the neural network model when the difference between the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the labeled training samples is greater than a preset threshold value.
And repeating the process until the difference degree between the mixed distribution conditions of the pollutants in different areas at different moments in the output training samples and the mixed distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value.
S105: and responding to an area with pollutant concentration exceeding a preset threshold in the secondary monitoring result, and sending alarm information to the target user side.
In this embodiment, when an area with a pollutant concentration exceeding a preset threshold exists in the secondary monitoring result, alarm information is sent to a target user side.
The air quality monitoring method based on the emission source can monitor the pollutant concentration according to the influence of meteorological conditions on the pollutant concentration and the interaction between pollutants, thereby realizing comprehensive and accurate monitoring of the pollutant concentration.
Fig. 2 is a flowchart of an air quality monitoring method based on an emission source according to a second embodiment of the disclosure. The air quality monitoring method based on the emission source comprises the following steps:
s201: and acquiring current emission source information sent by a target user side.
S202: and analyzing the current emission source information, and determining the type, emission time, emission position and emission amount of pollutants emitted by the emission source.
S203: it is determined whether a data format of data representing the type, discharge time, discharge position, and discharge amount of the pollutant conforms to a preset format.
In this embodiment, after analyzing the current emission source information, and determining the type, the emission time, the emission position, and the emission amount of the pollutant emitted by the emission source, the configuration information corresponding to the current emission source information may be obtained, and according to the configuration information, it is determined whether the data format of the data representing the type, the emission time, the emission position, and the emission amount of the pollutant conforms to the preset format, specifically, the data representing the type, the emission time, the emission position, and the emission amount of the pollutant may be compared with the data representing the type, the emission time, the emission position, and the emission amount of the emission source in the configuration information, and it is determined whether the data format of the data representing the type, the emission time, the emission position, and the emission amount of the pollutant conforms to the preset format.
S204: and sending alarm information to the target user side in response to the fact that the data format of the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants does not conform to the preset format.
S205: and inputting the corresponding pollutant information into a pre-trained single pollutant monitoring model to generate a corresponding first-level monitoring result, wherein the first-level monitoring result comprises the concentration distribution conditions of the single pollutants in different areas at different moments.
And S206, inputting a plurality of primary monitoring results and external condition information into a pre-trained mixed pollutant monitoring model to generate secondary monitoring results, wherein the secondary monitoring results comprise concentration distribution conditions of a plurality of pollutants in different areas at different moments.
And S207, constructing a three-dimensional space coordinate system, mapping the distribution conditions of the multiple pollutants in different areas at different moments included in the secondary monitoring result into the three-dimensional space coordinate system, and dividing the three-dimensional space coordinate system into superposition of multiple irregular stereograms in the horizontal direction and the vertical direction according to the comprehensive indexes of the multiple pollutants.
In this embodiment, in order to visually display the secondary monitoring result, before or after the secondary monitoring result is generated, a three-dimensional space coordinate system may be constructed, distribution conditions of multiple pollutants included in the secondary monitoring result in different areas at different times are mapped into the three-dimensional space coordinate system, and the three-dimensional space coordinate system is divided into a plurality of overlays of irregular stereograms in a horizontal direction and a vertical direction by using a comprehensive index of the multiple pollutants, that is, the distribution areas of the multiple pollutants in the secondary monitoring result at different times are represented by the three-dimensional space coordinate system.
S208: and responding to an area with pollutant concentration exceeding a preset threshold in the secondary monitoring result, and sending alarm information to the target user side.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily essential to the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
As shown in fig. 3, a schematic structural diagram of an air quality monitoring device based on an emission source according to a third embodiment of the present disclosure is shown, where the air quality monitoring device based on an emission source of the present embodiment includes:
an emission source information obtaining module 301, configured to obtain current emission source information sent by a target user side.
And an emission source information analyzing module 302, configured to analyze the current emission source information, and determine a type, an emission time, an emission location, and an emission amount of the pollutant.
And the single pollutant monitoring module 303 is configured to input the corresponding pollutant into a pre-trained single pollutant monitoring model, and generate a corresponding first-level monitoring result, where the first-level monitoring result includes distribution conditions of the single pollutant in different areas at different times.
And the mixed pollutant monitoring module 304 is configured to input a plurality of the primary monitoring results into a pre-trained mixed pollutant monitoring model to generate a secondary monitoring result, where the secondary monitoring result includes distribution conditions of the plurality of pollutants in different areas at different times.
And the alarm module 305 is configured to send alarm information to the target user side in response to an area where the concentration of the pollutant exceeds a preset threshold in the secondary monitoring result.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement embodiments of the present disclosure. As shown, device 400 includes a processing unit (CPU)401 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)402 or loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. An air quality monitoring method based on an emission source, comprising:
acquiring current emission source information sent by a target user side;
analyzing the current emission source information, and determining the type, emission time, emission position and emission amount of pollutants emitted by an emission source;
inputting corresponding pollutant information into a pre-trained single pollutant monitoring model to generate a corresponding first-level monitoring result, wherein the first-level monitoring result comprises concentration distribution conditions of single pollutants in different areas at different moments;
inputting a plurality of first-stage monitoring results and external condition information into a pre-trained mixed pollutant monitoring model to generate second-stage monitoring results, wherein the second-stage monitoring results comprise concentration distribution conditions of a plurality of pollutants in different areas at different moments;
and responding to an area with pollutant concentration exceeding a preset threshold in the secondary monitoring result, and sending alarm information to the target user side.
2. The emission source-based air quality monitoring method according to claim 1, wherein the analyzing the current emission source information to determine the type, emission time and emission location of the pollutant comprises:
analyzing the current emission source information, determining the number, size and content of files in the current emission source information, reading the corresponding files, and determining the type, emission time, emission position and emission amount of pollutants.
3. The emission source-based air quality monitoring method of claim 2,
after determining the type, discharge time, discharge location, and discharge amount of the pollutant, the method further comprises:
acquiring configuration information corresponding to the current emission source information, and determining whether a data format of data representing the type, emission time, emission position and emission amount of pollutants conforms to a preset format or not according to the configuration information;
and sending alarm information to the target user side in response to the fact that the data format of the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants does not conform to the preset format.
4. The emission source-based air quality monitoring method according to claim 3, wherein the determining whether a data format of the data representing the type, emission time, emission location, and emission amount of the pollutant conforms to a preset format includes:
and comparing the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants with the data representing the type, the discharge time, the discharge position and the discharge amount of the discharge source in the configuration information, and determining whether the data format of the data representing the type, the discharge time, the discharge position and the discharge amount of the pollutants conforms to a preset format.
5. The emission source-based air quality monitoring method of claim 1, wherein the single pollutant monitoring model is obtained by:
taking a preset amount of emission source information as a training sample set, marking training samples in the training sample set, and marking corresponding distribution conditions of pollutants in different areas at different moments of the training samples;
inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolution layer of the neural network model when the difference between the distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the distribution conditions of the pollutants in the different areas at different moments corresponding to the marked training samples is greater than a preset threshold;
and repeating the process until the difference degree between the distribution conditions of the pollutants in different areas at different moments in the output training samples and the distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value.
6. The emission source-based air quality monitoring method of claim 1, wherein the mixed pollutant monitoring model is obtained by:
taking a preset amount of mixed distribution information with external condition information as a training sample set, marking training samples in the training sample set, and marking out the corresponding mixed distribution conditions of pollutants in different areas at different moments of the training samples;
inputting the training samples in the training sample set into a pre-established neural network model, learning the training samples in the training sample set, outputting the mixed distribution conditions of pollutants in different areas at different moments corresponding to the training samples, and correcting the parameters of the convolutional layers of the neural network model when the difference between the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the output training samples and the mixed distribution conditions of the pollutants in the different areas at different moments corresponding to the labeled training samples is greater than a preset threshold;
and repeating the process until the difference degree between the mixed distribution conditions of the pollutants in different areas at different moments in the output training samples and the mixed distribution conditions of the pollutants in different areas at different moments corresponding to the marked training samples is less than a preset threshold value.
7. The emission source-based air quality monitoring method of claim 1, further comprising:
and constructing a three-dimensional space coordinate system, mapping the distribution conditions of the multiple pollutants in different areas at different moments included in the secondary monitoring result into the three-dimensional space coordinate system, and dividing the three-dimensional space coordinate system into superposition of multiple irregular stereograms in the horizontal direction and the vertical direction by using the comprehensive indexes of the multiple pollutants.
8. An air quality monitoring device based on an emission source, comprising:
the emission source information acquisition module is used for acquiring current emission source information sent by a target user side;
the emission source information analysis module is used for analyzing the current emission source information and determining the type, emission time, emission position and emission amount of pollutants;
the single pollutant monitoring module is used for inputting the corresponding pollutants into a pre-trained single pollutant monitoring model to generate corresponding first-level monitoring results, and the first-level monitoring results comprise the distribution conditions of the single pollutants in different areas at different moments;
the mixed pollutant monitoring module is used for inputting a plurality of primary monitoring results into a pre-trained mixed pollutant monitoring model to generate secondary monitoring results, and the secondary monitoring results comprise the distribution conditions of a plurality of pollutants in different areas at different moments;
and the alarm module is used for responding to an area with pollutant concentration exceeding a preset threshold value in the secondary monitoring result and sending alarm information to the target user side.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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