CN113537563B - Pollution emergency management and control effect evaluation method and device - Google Patents
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Abstract
The embodiment of the disclosure provides a pollution emergency management and control effect evaluation method, device, equipment and computer readable storage medium. The method comprises the steps of determining a space-time range of air pollution emergency management and control effect evaluation; determining weather forecast data within the spatio-temporal range; determining emission source prediction data in the space-time range under different situations according to air pollution emergency control measures under different situations needing to be evaluated; inputting the weather forecast data and the emission source prediction data in the space-time range into a pre-trained air quality prediction model to obtain air quality prediction data in the space-time range under the corresponding situation; and comparing the air quality prediction data in the space-time range according to different situations. The pollution emergency control effect can be evaluated so as to select the most appropriate pollution emergency control measure and realize the balance of air quality and social production; the existing pollution emergency management and control measures can be adjusted through the evaluation result, so that the selection can be simpler and more effective in the later period.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of environmental monitoring, and more particularly, to a pollution emergency management and control effect evaluation method, device, apparatus, and computer-readable storage medium.
Background
Air quality is increasingly appreciated by governments and the public, and air pollution with PM2.5 as a main pollutant has become one of the current major environmental problems. In order to cope with this problem, relevant pollution control measures and heavy pollution emergency plans are established one after another in various places. In order to gradually improve the effectiveness of measures and plans, quantitative evaluation and optimization of pollution emergency control effects are an important link.
However, the currently common air quality prediction method mainly utilizes an air quality mode to systematize complex atmospheric physical and chemical modes, establish a model related to pollutant emission, weather and chemical reactions, and simulate the change of air quality. In addition to meteorological data, numerical forecasting requires more accurate pollutant emission data, detailed geographical environmental data, boundary conditions, etc., and requires extensive calculations. Meanwhile, due to the fact that pollutant emission dynamic change of the pollution source is large, accurate pollution source data are difficult to obtain, and therefore the current forecasting effect of numerical forecasting is often difficult to achieve an ideal effect, and therefore the corresponding pollution emergency control effect is also difficult to achieve required accuracy.
Disclosure of Invention
According to the embodiment of the disclosure, a pollution emergency management and control effect scheme is provided.
In a first aspect of the disclosure, a pollution emergency management and control effect evaluation method is provided, which includes determining a space-time range of air pollution emergency management and control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range; determining weather forecast data within the spatio-temporal range; determining emission source prediction data in the space-time range under different situations according to air pollution emergency management and control measures under different situations needing to be evaluated; inputting the weather forecast data and the emission source prediction data in the space-time range, and the weather data, the emission source data and the air quality data of m preset time intervals before the evaluation time range into a pre-trained air quality prediction model to obtain the air quality prediction data in the space-time range under the corresponding situation; wherein m is a positive integer greater than 1; and comparing the control effects of the air pollution emergency management and control measures in the space-time range under different situations according to the corresponding air quality prediction data in the space-time range under different situations.
The above-described aspect and any possible implementation further provide an implementation in which the geographic area includes one or more grid points, and the evaluation time range may be a time range from a current time or a time range from any time after the current time.
The above-described aspects and any possible implementation further provide an implementation in which inputting the weather forecast data, the emission source prediction data, and the weather data, the emission source data, and the air quality data at m preset time intervals before the evaluation time range into a pre-trained air quality prediction model includes: if the meteorological data, the emission source data and the air quality data of a plurality of time intervals in m preset time intervals before the evaluation time range are not obtained, obtaining corresponding meteorological forecast data, emission source prediction data and air quality prediction data.
The above aspects and any possible implementations further provide an implementation where the different scenarios include a scenario where no air pollution emergency management measures are taken.
The above aspect and any possible implementation manner further provide an implementation manner, and the determining, according to the air pollution emergency control measure in different situations to be evaluated, the emission source prediction data in the space-time range in different situations includes: and updating the interval-by-interval emission source prediction data within the evaluation time range at preset time intervals by using the pollutant emission reduction amount obtained after the air pollution emergency control measures are taken, and taking the updated interval-by-interval emission source prediction data as final emission source prediction data.
The above-described aspects and any possible implementation further provide an implementation in which obtaining air quality prediction data in a spatiotemporal range under a corresponding scenario includes: obtaining air quality prediction data of 1 to nth time interval in a space-time range under a corresponding situation; wherein n is a positive integer greater than 1.
The above-described aspects and any possible implementations further provide an implementation, and the method further includes: and if the number of the time intervals included in the evaluation time range is greater than n, circularly predicting forwards according to the obtained air quality prediction data of 1 to nth time intervals in the space-time range under the corresponding situation as the input of an air quality prediction model.
In a second aspect of the present disclosure, there is provided a pollution emergency control effect evaluation apparatus, including a space-time range determination module configured to determine a space-time range for air pollution emergency control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range; the weather forecast module is used for determining weather forecast data in the space-time range; the emission source prediction module is used for determining emission source prediction data in the space-time range under different situations according to air pollution emergency control measures under different situations needing to be evaluated; the air quality prediction module is used for inputting the meteorological forecast data and the emission source prediction data in the space-time range, and the meteorological data, the emission source data and the air quality data of m preset time intervals before the evaluation time range into a pre-trained air quality prediction model to obtain the air quality prediction data in the space-time range under the corresponding situation; wherein m is a positive integer greater than 1; and the effect evaluation module is used for comparing the control effects of the air pollution emergency management and control measures in the space-time range under different situations according to the corresponding air quality prediction data in the space-time range under different situations.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon 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 executed by a processor, implements a method as according to the first and/or second aspect of the present disclosure.
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.
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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 a pollution emergency management and control effect evaluation method according to an embodiment of the present disclosure;
fig. 2 shows a block diagram of a pollution emergency management and control effect evaluation device according to an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments 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.
Fig. 1 shows a flow chart of an air pollution emergency management and control effect evaluation method 100 according to an embodiment of the present disclosure.
At block 102, determining a space-time range of air pollution emergency management and control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range.
In some embodiments, the spatiotemporal scope includes an evaluation geographic area, an evaluation temporal scope. The geographic region includes one or more grid points, and the evaluation time range may be a time range from a current time or a time range starting at any time after the current time. In actual operation, the evaluation of the air pollution emergency control effect is generally performed in advance, for example, 1 day, 12 hours, and the like in advance.
In some embodiments, the evaluation time range is in days.
In some embodiments, the evaluation geographical area and the evaluation time range are geographical areas and time ranges for performing air pollution emergency management and control.
At block 104, determining weather forecast data within the spatio-temporal range;
in some embodiments, weather forecast data for each grid point within the geographic area is obtained for the evaluation time range; the weather forecast data may be forecasted at preset time intervals within the evaluation time range.
In some embodiments, the meteorological data and the current time at the current time or at a plurality of preset time intervals before the current time are input into a meteorological forecast prediction model established in advance, so as to obtain meteorological forecast data of 1 st to nth preset time intervals, wherein n is a positive integer greater than or equal to 1. Acquiring interval-by-interval weather forecast data at preset time intervals within an evaluation time range from the weather forecast data of the 1 st to the nth preset time intervals. By inputting the meteorological data at the current time or at the current time and a plurality of preset time intervals before the current time into the pre-established meteorological forecasting model, the meteorological forecasting can be more accurately performed compared with the method of only inputting the meteorological data at the current time.
In some embodiments, the method further includes obtaining weather forecast data or weather data of each grid point in the geographic area at m preset time intervals before the evaluation time range, where m is a positive integer greater than or equal to 1; that is, if there is already meteorological data, then meteorological data is used; if there is no weather data, weather forecast data is adopted, for example, 1 hour at preset time intervals, m is 24, if an air pollution emergency control measure after 12 hours is to be evaluated, historical weather data of 12 hours before the current time needs to be acquired, and the weather data of 12 hours from the current time and within the evaluation time range are forecasted to acquire corresponding weather forecast data.
In some embodiments, the meteorological data includes dew point, temperature, wind direction, wind speed, cumulative hourly snow and cumulative hourly rain, among other data.
At block 106, determining emission source prediction data in the space-time range under different situations according to air pollution emergency management and control measures under different situations needing to be evaluated;
in some embodiments, the air pollution emergency management measures include: surface source control measures and point source control measures.
In some embodiments, the emission source prediction data may be interval-by-interval emission source prediction data for each grid point in the evaluation geographic area at preset time intervals within an evaluation time range, including interval-by-interval emission source prediction data for different pollutants of different industries at preset time intervals within an evaluation time range for each grid point in the evaluation geographic area. The contaminants include one or more of sulfur dioxide, nitrogen oxides, ozone, or suspended particulate matter.
The non-point source control measures comprise reduction of pollutants in a certain area (for example, emergency emission reduction measures are adopted to achieve emission reduction proportion of pollutants such as primary particulate matters, SO2, NOx and VOCs). The point source control measures comprise a combination of control measures taken for different point sources in different regions.
In some embodiments, the different scenarios include scenarios where air pollution emergency management measures are not taken. According to the air quality prediction result, comprehensively considering the air pollution degree and duration, and dividing the air heavy pollution early warning into 4 grades (from light to heavy) of blue, yellow, orange and red; similarly, the air pollution emergency management and control measures are divided into a plurality of situations, and different specific management and control measures are adopted in different situations, such as motor vehicle stop, enterprise production stop and production limit.
In some embodiments, the emission source data at the current time or at the current time and a plurality of preset time intervals before the current time are input into a pre-established emission source data prediction model, so that the emission source prediction data of 1 st to nth preset time intervals can be obtained, wherein n is a positive integer greater than or equal to 1. By inputting the emission source data at the current time or at a plurality of preset time intervals before the current time into the pre-established emission source data prediction model, the emission source prediction can be more accurately performed compared with the case that only the emission source data at the current time is input.
In some embodiments, emission parameter monitoring data is obtained from an emission source provided with monitoring equipment, a training sample is generated according to the emission parameter monitoring data and corresponding time (including information such as month, working day, time (different time in each day) and the like), a preset neural network model is trained, and an emission source data prediction model is established; the emission source data prediction model can reflect the rule that the emission parameters of the emission sources of the area to be subjected to air quality prediction change along with time.
In some embodiments, the emission source data at the current time or at the current time and a plurality of preset time intervals before the current time and the current time are input into a pre-established emission source data prediction model, so as to obtain the emission source prediction data of the 1 st to nth first preset time intervals, wherein n is a positive integer greater than or equal to 1. By inputting the emission source data at the current time or at a plurality of preset time intervals before the current time into the pre-established emission source data prediction model, the emission source prediction can be more accurately performed compared with the case that only the emission source data at the current time is input.
In some embodiments, the rules for the variation of the emission parameters with time include rules that vary according to time such as quarterly, month, week (working day), day (hour), and the like.
In some embodiments, interval-by-interval emission source prediction data at preset time intervals within the evaluation time range is obtained from the emission source prediction data of the 1 st to n-th preset time intervals.
In some embodiments, emission source data for m preset time intervals before the evaluation time range of each grid point in the geographic area is obtained, wherein m is a positive integer greater than or equal to 1; if the emission source data already exists, adopting the emission source data; if the emission source data does not exist, adopting emission source prediction data, for example, if the preset time interval is 1 hour, m is 24, and if the air pollution emergency control measures after 12 hours are to be evaluated, historical emission source data of 12 hours before the current time needs to be obtained, and predicting the emission source data of 12 hours from the current time and within the evaluation time range to obtain corresponding emission source prediction data.
In some embodiments, the pollutant emission reduction amount obtained after the air pollution emergency management and control measure is taken updates the interval-by-interval emission source prediction data within the evaluation time range at preset time intervals, and the updated emission source prediction data is used as the final emission source prediction data. And updating the interval-by-interval emission source prediction data at preset time intervals within the evaluation time range through pollutant emission reduction amounts obtained through air pollution emergency control measures under different situations, and finally obtaining the interval-by-interval emission source prediction data at the preset time intervals within the evaluation time ranges corresponding to the different situations.
At block 108, the weather forecast data, the emission source forecast data, and the weather forecast data, the emission source data, and the air quality data at m preset time intervals before the evaluation time range are input into a pre-trained air quality prediction model to obtain the air quality forecast data in the space-time range under the corresponding situation.
In some embodiments, if the number of time intervals included in the evaluation time range is greater than n, the air quality prediction data of the 1 st to nth time intervals in the space-time range under the corresponding situation is used as the input of the air quality prediction model, and the prediction is performed circularly and forwards until the air quality prediction data of all the time intervals in the space-time range under the corresponding situation is obtained.
In some embodiments, the air quality data of m preset time intervals before the evaluation time range is air quality data or air quality prediction data, wherein m is a positive integer greater than or equal to 1; that is, if there is already air quality data, then the air quality data is taken; if there is no air quality data, adopting air quality prediction data, for example, if the preset time interval is 1 hour, m is 24, and if an air pollution emergency control measure after 12 hours is to be evaluated, historical air quality data of 12 hours before the current time needs to be acquired, and predicting the air quality data of 12 hours from the current time and within the evaluation time range to acquire corresponding air quality prediction data. Likewise, the emission data of m preset time intervals before the evaluation time range is emission source data or emission source prediction data.
By comprehensively considering the local emission source prediction data and the meteorological forecast data, the influence of the local emission source and regional transmission (for example, under the influence of meteorological data such as wind direction and wind speed, pollutants in other regions can be diffused to the local to reduce the local air quality, or the local pollutants can be diffused to other regions to improve the local air quality) on the air quality can be better reflected, and the air quality prediction precision is improved.
In some embodiments, the air quality prediction model is trained by:
generating a training set according to historical emission source data, historical meteorological data and historical air quality data; the historical emission source data is emission parameter monitoring data and other statistical data acquired from an emission source provided with monitoring equipment; the historical meteorological data is meteorological monitoring data; historical air quality data is air quality monitoring data, the historical emission source data and the historical meteorological data are used as samples, the air quality monitoring data are used as labels, and a training data set is produced; after the air quality prediction model is trained, the meteorological data and the emission source (or meteorological forecast data and emission source prediction data) data can be input into the trained air quality prediction model, and then the corresponding air quality prediction data can be obtained.
In some embodiments, the training set comprises samples and annotations, the samples comprising: emission source data, meteorological data for 1 to n preset time intervals from a first time instant, the tagging comprising: air quality data for 1 st to nth preset time intervals from the first time.
In some embodiments, the training samples comprise: emission source data, meteorological data, air quality data for m preset time intervals before the first time, emission source data, meteorological data for 1 to n preset time intervals from the first time, the labeling comprising: air quality data for 1 st to n th preset time intervals from the first time. By adding the discharge source data, the meteorological data and the air quality data of m preset time intervals before the first moment into the training sample, the change rule of the air quality can be better embodied.
Training a preset neural network model through the training sample; wherein the neural network model may be an RNN recurrent neural network model or an LSTM long-short term memory model.
In some embodiments, the air quality data for 1 st to nth preset time intervals from the first time instant may be directly predicted. Or the air quality prediction data of the 1 st preset time interval from the first time point can be predicted firstly, then the air quality prediction data of the 2 nd preset time interval from the first time point is predicted on the basis, and the like, so that the prediction accuracy of the air quality prediction data is further improved. The air quality prediction data obtained by predicting in the above manner and the air quality prediction data obtained by directly predicting the air quality prediction data of the 1 st to n th preset time intervals from the first time may be subjected to weighted summation to be the final air quality prediction data.
The existing air quality prediction method is influenced by the prediction result at the previous moment, so that the prediction is easy to have hysteresis, and the accuracy of air quality prediction is reduced. The concrete expression is as follows: (1) hysteresis at the onset of heavy fouling: for example, when a heavy contamination process is started, the forecast may not capture the contamination because the observed value of the day before the heavy contamination is started is low, and the predicted value of the day may still be low. (2) Hysteresis at the end of heavy fouling: when the heavy contamination process is over, the predicted value for the day may indicate that contamination is still occurring for that day due to the effect of the high concentration observations on the day before the end of heavy contamination. Therefore, it is necessary to combine the current air quality monitoring data and the current meteorological data to avoid the occurrence of hysteresis in prediction.
In some embodiments, because a large amount of computing resources and computing time are consumed for air quality prediction with a large data volume, air quality prediction may be performed at a large preset time interval, and a prediction result is determined. Firstly, training the air quality prediction model by adopting a large number of samples at a first preset time interval to obtain a trained first preset time interval air quality prediction model; then, the trained first preset time interval air quality prediction model parameters are transferred to a new second preset time interval air quality prediction model to help the first preset time interval air quality prediction model training, and a small amount of updated samples of the second preset time interval are adopted to perform transfer learning on the trained first preset time interval air quality prediction model, so that the air quality prediction model adapting to the updated preset time interval can be obtained.
In some embodiments, the air quality prediction is periodically performed again, the air quality prediction data is updated, and the period of periodically performing the air quality prediction again can be set according to the requirement on the prediction accuracy, for example, if higher accuracy is required, the data can be updated at preset time intervals; if less precision is required, the data may be updated again after every other predetermined time interval.
Through this step, the previous air quality prediction data can be periodically updated to provide more accurate air quality prediction data.
At block 110, the control effects of the air pollution emergency management and control measures within the space-time range under different situations are compared according to the corresponding air quality prediction data within the space-time range under different situations.
In some embodiments, the air quality prediction data corresponding to the air pollution emergency control measures in different situations is compared with the air quality prediction data corresponding to the air pollution emergency control measures which are not taken, the effect of the air pollution emergency control measures in different situations is determined, and the air pollution emergency control measures in proper scenes are selected according to the expected air quality.
In some embodiments, if the air quality prediction data corresponding to the air pollution emergency management and control measure does not meet the expectation, the corresponding air pollution emergency management and control measure is adjusted and the prediction is performed again until the corresponding air quality prediction data meets the expectation. In some embodiments, a surface source control measure and a point source control measure in the air pollution emergency management and control measures are adjusted; in some embodiments, the execution time of the air pollution emergency management and control measure is adjusted, for example, when the corresponding air quality prediction data does not reach the expectation, that is, the pollutant index is higher, the air pollution emergency management and control measure may be executed in advance.
According to the embodiment of the disclosure, the following technical effects are achieved:
based on the emission source prediction data and the meteorological forecast data, the influence of local pollutant emission and the influence of pollutant migration in other areas are considered (for example, under the influence of meteorological data such as wind direction and wind speed, pollutants in other areas can be diffused to the local, the local air quality is reduced, and the local pollutants can be diffused to other areas, the local air quality is improved), and the air quality forecast can be more accurately carried out.
By the embodiment, the pollution emergency control effect can be evaluated so as to select the most appropriate pollution emergency control measure and realize the balance between the air quality and social production; the existing pollution emergency management and control measures can be adjusted through the evaluation result, so that the selection can be simpler and more effective in the later period.
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 the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 2 shows a block diagram of a pollution emergency management and control effect evaluation device 200 according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus 200 includes:
the space-time range determining module 202 is used for determining a space-time range of air pollution emergency management and control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range;
a weather forecast module 204 for determining weather forecast data within said spatio-temporal range;
an emission source prediction module 206, configured to determine, according to air pollution emergency management and control measures in different situations that need to be evaluated, emission source prediction data in the space-time range in the different situations;
the air quality prediction module 208 is configured to input the weather forecast data and the emission source prediction data in the space-time range, and the weather data, the emission source data and the air quality data at m preset time intervals before the evaluation time range into a pre-trained air quality prediction model to obtain air quality prediction data in the space-time range under a corresponding situation; wherein m is a positive integer greater than 1;
and the effect evaluation module 210 is configured to compare the control effects of the air pollution emergency management and control measures in the space-time range under different situations according to the corresponding air quality prediction data in the space-time range under different situations.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
FIG. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. As shown, the device 300 includes a CPU301 that can perform various appropriate actions and processes according to computer program instructions stored in a ROM302 or loaded from a storage unit 308 into a RAM 303. In the RAM303, various programs and data necessary for the operation of the device 300 can also be stored. The CPU301, ROM302, and RAM303 are connected to each other via a bus 304. An I/O interface 305 is also connected to bus 304.
Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, or the like; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The CPU301 executes the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 300 via ROM302 and/or communication unit 309. When the computer program is loaded into RAM303 and executed by CPU301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the CPU301 may be configured to perform the method 100 by any other suitable means (e.g., by way of firmware).
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: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), 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 RAM, a ROM, an EPROM, an optical fiber, a 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 (9)
1. A pollution emergency management and control effect evaluation method is characterized by comprising the following steps:
determining a space-time range of air pollution emergency management and control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range;
determining weather forecast data within the spatio-temporal range;
determining emission source prediction data in the space-time range under different situations according to air pollution emergency control measures under different situations needing to be evaluated; the different situations comprise situations without air pollution emergency control measures, the air pollution emergency control measures are divided into a plurality of situations according to the air quality prediction result and the comprehensive consideration of the air pollution degree and duration, and different specific control measures are adopted in different situations;
the emission source prediction data is interval-by-interval emission source prediction data of each grid point in the evaluation geographical area at preset time intervals within an evaluation time range, and the interval-by-interval emission source prediction data comprises interval-by-interval emission source prediction data of different pollutants of different industries within the evaluation geographical area at preset time intervals within the evaluation time range, wherein the pollutants comprise one or more of sulfur dioxide, nitrogen oxides, ozone or suspended particulate matters;
inputting the weather forecast data and the emission source prediction data in the space-time range, and the weather data, the emission source data and the air quality data of m preset time intervals before the evaluation time range into a pre-trained air quality prediction model to obtain the air quality prediction data in the space-time range under the corresponding situation; wherein m is a positive integer greater than 1;
and comparing the control effects of the air pollution emergency management and control measures in the space-time range under different situations according to the corresponding air quality prediction data in the space-time range under different situations.
2. The method of claim 1,
the geographic region includes one or more grid points, and the evaluation time range is a time range starting from a current time or a time range starting at any time after the current time.
3. The method of claim 2, wherein inputting the weather forecast data, the emission source forecast data, the weather data, the emission source data, and the air quality data at m preset time intervals before the evaluation time range into a pre-trained air quality prediction model comprises:
if the meteorological data, the emission source data and the air quality data of a plurality of time intervals in m preset time intervals before the evaluation time range are not obtained, obtaining corresponding meteorological forecast data, emission source prediction data and air quality prediction data.
4. The method of claim 3, wherein determining the prediction data of the emission source in the spatiotemporal range under different situations according to the air pollution emergency management and control measures under different situations needing to be evaluated comprises:
and updating the interval-by-interval emission source prediction data within the evaluation time range at preset time intervals by using the pollutant emission reduction amount obtained after the air pollution emergency control measures are taken, and taking the updated interval-by-interval emission source prediction data as final emission source prediction data.
5. The method of claim 3, wherein obtaining air quality prediction data over a spatiotemporal range at corresponding contexts comprises:
obtaining air quality prediction data of 1 st to nth time intervals in a space-time range under a corresponding situation; wherein n is a positive integer greater than 1.
6. The method of claim 5, further comprising:
and if the number of the time intervals included in the evaluation time range is greater than n, circularly predicting forwards according to the obtained air quality prediction data of the 1 st to the nth time intervals in the space-time range under the corresponding situation as the input of an air quality prediction model.
7. The utility model provides an emergent management and control effect evaluation device of pollution which characterized in that includes:
the space-time range determining module is used for determining a space-time range of air pollution emergency management and control effect evaluation; wherein the space-time range comprises an evaluation geographic area and an evaluation time range;
the weather forecast module is used for determining weather forecast data in the space-time range;
the emission source prediction module is used for determining emission source prediction data in the space-time range under different situations according to air pollution emergency management and control measures under different situations needing to be evaluated; the different situations comprise situations without air pollution emergency control measures, the air pollution emergency control measures are divided into a plurality of situations according to the air quality prediction result and the comprehensive consideration of the air pollution degree and duration, and different specific control measures are adopted in different situations;
the emission source prediction data is interval-by-interval emission source prediction data of each grid point in the evaluation geographical area at preset time intervals within an evaluation time range, and the interval-by-interval emission source prediction data comprises interval-by-interval emission source prediction data of different pollutants of different industries within the evaluation geographical area at preset time intervals within the evaluation time range, wherein the pollutants comprise one or more of sulfur dioxide, nitrogen oxides, ozone or suspended particulate matters;
the air quality prediction module is used for inputting the weather forecast data, the emission source prediction data, the weather forecast data of m preset time intervals before the evaluation time range, the emission source prediction data and the air quality data in the space-time range into a pre-trained air quality prediction model to obtain the air quality prediction data in the space-time range under the corresponding situation; wherein m is a positive integer greater than 1;
and the effect evaluation module is used for comparing the control effects of the air pollution emergency management and control measures in the space-time range under different situations according to the corresponding air quality prediction data in the space-time range under different situations.
8. 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 1-6.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 6.
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