CN112132483B - Air quality numerical service forecast credibility evaluation method, device and storage medium - Google Patents
Air quality numerical service forecast credibility evaluation method, device and storage medium Download PDFInfo
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Abstract
The invention discloses an air quality numerical service forecast credibility assessment method, an air quality numerical service forecast credibility assessment device and a computer readable storage medium. The method for evaluating the credibility of the air quality numerical service forecast comprises the following steps: acquiring a plurality of nested layers and a plurality of time-efficient air quality simulation results through an air quality numerical service forecasting system; screening data from the air quality simulation result according to the nesting layer and/or the time effectiveness to obtain sample data of a simulated concentration value; calculating by using a confidence evaluation method according to the sample data to obtain uncertainty of the air quality simulation result; and determining the air quality numerical service forecast reliability according to the uncertainty of the air quality simulation result. According to the technical scheme, the samples of the calculated uncertainty are obtained through the simulation results of different nesting layers and different time effects, compared with the conventional method, the extra calculation overhead is obviously reduced, the uncertain range of the simulation results can be calculated at any time, and the calculation results have better indication significance for judging the performance of the forecasting system.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to an air quality numerical service forecast credibility assessment method, electronic equipment, a device and a storage medium.
Background
The air quality prediction model is a tool for air quality research, is established on the basis of scientific theory and hypothesis, describes the transmission, diffusion, chemical reaction and removal process of pollutants in the atmosphere by a numerical method, and obtains air quality data of a research area by inputting source emission, topographic and meteorological data and an operation mode of the area. The existing third-generation air quality prediction model comprises a nested grid air quality prediction mode NAQPMS, a universal multi-scale air quality CMAQ, an extended comprehensive air quality mode CAMx, a weather prediction-chemical mode WRF-chem and the like. In view of the complexity of the air quality prediction model, when the air quality prediction model performs air quality simulation, the model has uncertainty due to certain influence on the model simulation caused by parameters such as selection of a chemical mechanism, influence of an meteorological field, pollutant discharge amount, initial boundary conditions and the like. Furthermore, while some uncertainty may be caused by factors such as lack of sufficient knowledge of the system and the existence of some hypothetical simplifications, neglecting the uncertainty of the model may result in some deviation of the simulation results from the actual monitoring.
At present, uncertainty of a model is often ignored in research, so that the difference between a simulation result and a true value cannot be accurately estimated. The presence of uncertainty has led to a degree of restriction in the application of the air quality model. In the process of using the air quality model, the uncertainty analysis is ignored, so that the result lacks objectivity and scientificity, and only the uncertainty of the model is fully known and considered, so that the method has important scientific guiding significance for applying the simulation result to the reality, recognizing the air quality model and formulating the control measure of the air quality.
Many foreign researches on uncertainty analysis of air quality models are carried out, and the current foreign research trend is mainly reflected in the following aspects: 1) the method is not limited to a simple atmospheric diffusion model any more, and is turned to a photochemical model with multi-scale and complex area; 2) simultaneously considering the uncertainty of analyzing the common influence of a plurality of parameters; 3) attempts were made to study uncertainty transfer using methods other than the monte carlo method (e.g., the random response surface method SRSM); 4) quantitatively analyzing various uncertainty sources (meteorological factors, source lists, boundary conditions, chemical reaction rates, chemical reaction mechanisms and the like) from the aspects of data input, model mechanisms and the like; 5) starting to focus on the model structure itself, taking into account assumptions and simplifications in the modeling processUncertainty. For example, Latin hierarchical sampling technology is used for respectively researching different influence parameters on simulated PM2.5And ozone uncertainty, impact parameters including CACM chemistry, boundary conditions, emissions, and chemical reaction rates, among others.
In China, uncertainty studies on air quality models are just beginning to be started. Most of the research which is carried out at present is focused on a water quality model, and the related research is focused on identifying uncertainty sources or qualitative analysis, so that the uncertainty quantitative analysis cases based on the model are relatively few. The domestic uncertainty analysis related to the air quality model is very weak in method or example research. For example, a team has conducted uncertainty studies on emissions checklists. Another team began a study of the uncertainty in quantifying the Nested Air Quality Prediction mode System (NAQPMS for short).
At present, methods for processing and quantifying uncertainty of an air quality prediction model simulation result are more, and the methods generally comprise uncertainty quantitative analysis, input parameter uncertainty identification, uncertainty transmission methods and the like. The uncertainty quantitative analysis mainly utilizes a probability analysis method to quantitatively describe the uncertainty of the input of the calculation model and the uncertainty range value of the result finally output after the model is processed. The most common uncertainty analysis is the Monte Carlo method (MC for short), which is commonly used for uncertainty analysis of relatively simple models such as atmospheric diffusion models. The Monte Carlo method is also called a statistical simulation method and a statistical test method, is a numerical simulation method taking probability phenomena as a research object, is a calculation method for obtaining a statistical value according to a sampling survey method to estimate unknown characteristic quantity, and analyzes the influence of model input on model output uncertainty by performing a large amount of random sampling on parameters in a parameter space of the whole model input. However, the uncertainty of the output of the monte carlo simulation analysis model requires a huge number of samples, at least thousands of times or more, and several tens of thousands of times or more. Therefore, for a complex numerical computation model such as a nested grid air quality prediction model system, the monte carlo method requires relatively much computer resources and time, and thus the application of the monte carlo method in the uncertainty analysis of the complex environmental model is greatly limited.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, a device and a storage medium for evaluating the reliability of air quality numerical service prediction, so as to solve the problem in the prior art that a large number of samples are required for evaluating the reliability of air quality numerical service prediction.
Therefore, the embodiment of the invention provides the following technical scheme:
according to one aspect of the present invention, a method for evaluating reliability of an air quality numerical service forecast is provided, which includes the following steps:
acquiring a plurality of nested layers and a plurality of time-efficient air quality simulation results through an air quality numerical service forecasting system; screening data from the air quality simulation result according to the nesting layer and/or the time effectiveness to obtain sample data of a simulated concentration value; calculating by using a confidence evaluation method according to the sample data to obtain uncertainty of the air quality simulation result; and determining the air quality numerical service forecast reliability according to the uncertainty of the air quality simulation result.
Further, according to the sample data, after calculating by using a confidence evaluation method and obtaining uncertainty of the air quality simulation result, the method further comprises:
calculating the relative proportion of the simulated concentration value to the average value by using the uncertainty of the air quality simulation result to obtain the relative proportion of the uncertainty; calculating an uncertainty range value of the obtained simulated concentration value by using the simulated concentration value of the air quality simulation result and the relative proportion of the uncertainty; and using the uncertainty range value of the simulated concentration value to represent the air quality numerical service forecast credibility.
Further, obtaining an uncertainty of the air quality simulation result comprises:
the uncertainty is calculated by the following formula:
wherein u is the uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
Further, obtaining the relative fraction of uncertainty comprises:
calculating the relative ratio of said uncertainty by the following formula:
wherein P is the relative proportion of uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
Further, the uncertainty of the calculated simulated concentration valueThe formula for the range value is xp＝xi×P，
Wherein xpUncertainty range value, x, for the simulated concentration value at any timeiThe air quality simulation concentration value of the ith sample in the k groups of samples is shown, and P is the relative proportion of uncertainty.
Further, the selected significance level α selected by the calculating step using the confidence evaluation method is 0.05, and the corresponding confidence level is 95%.
Further, the air quality numerical service forecasting system adopts a NAQPMS mode.
According to another aspect of the present invention, there is provided an air quality numerical service forecast reliability evaluation device, including:
the simulation result acquisition module is configured to acquire a plurality of nested layers and a plurality of aged air quality simulation results through the air quality numerical service forecasting system; the simulated concentration value sample data acquisition module is configured to screen data from the air quality simulation result according to the nested layer and/or the time effectiveness to obtain simulated concentration value sample data; the simulation result uncertainty calculation module is configured to calculate by using a confidence evaluation method according to the sample data to obtain uncertainty of the air quality simulation result; the relative proportion calculation module is configured to calculate the relative proportion of the simulated concentration value to the average value by utilizing the uncertainty of the air quality simulation result to obtain the relative proportion of the uncertainty; and the uncertainty range value calculation module is configured to calculate an uncertainty range value for obtaining the simulated concentration value by using the simulated concentration value of the air quality simulation result and the relative proportion of the uncertainty.
According to another aspect of the present invention, there is provided an electronic apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform any one of the above methods for assessing air quality numerical service forecast reliability.
According to another aspect of the present invention, there is provided a computer-readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor to execute any one of the above-mentioned air quality numerical service forecast reliability evaluation methods.
According to the air quality numerical service forecast credibility assessment method, the device, the equipment and the storage medium, a large amount of sampling simulation is not needed, and time and computing resources can be saved. Of course, not all of the above-described advantages need to be achieved in the practice of any one product or method of the present invention.
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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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For purposes of illustrating and describing some portions of the present invention, corresponding parts may be exaggerated in the drawings, i.e., made larger relative to other components in an exemplary apparatus actually manufactured according to the present invention. In the drawings, the same or similar technical features or components will be denoted by the same or similar reference numerals.
Fig. 1 is a schematic diagram illustrating steps of an air quality numerical service forecast reliability evaluation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating steps of an air quality numerical service forecast reliability evaluation method according to another embodiment of the present invention;
fig. 3 is a schematic diagram showing an air quality simulation of an air quality numerical service forecast credibility assessment method according to an embodiment of the present invention;
fig. 4 is a diagram illustrating a part of simulation results of an air quality numerical service forecast reliability evaluation method according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a partial simulation result uncertainty range of an air quality numerical service forecast reliability evaluation method according to an embodiment of the present invention;
fig. 6 shows a schematic diagram of an air quality numerical service forecast credibility assessment apparatus according to one embodiment of the present invention;
fig. 7 shows a schematic diagram of an air quality numerical business forecast credibility assessment electronic device according to one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
According to one embodiment of the invention, an air quality numerical service forecast credibility evaluation method is provided. For convenience of explanation, fig. 1 is a schematic diagram illustrating steps of an air quality numerical service forecast reliability evaluation method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s1, obtaining air quality simulation results of a plurality of nested layers (d01, d02, d03 and d04 … …) and a plurality of aging layers (T01, T02, T03 and T04 … …) through an air quality numerical service forecasting system;
s2, screening data from the air quality simulation result according to the nesting layer and/or the aging to obtain sample data (X1, X2, X3 and X4 … …) of the simulated concentration value;
step S3, calculating by using a confidence evaluation method according to the sample data to obtain uncertainty u of the air quality simulation result;
and step S4, determining the business forecast credibility of the air quality value according to the uncertainty of the air quality simulation result.
It should be noted that the uncertainty u in step S3 is a percentage, and the air quality value service forecast reliability in step S4 is characterized by using uncertainty range values of the simulated concentration, and the like.
Compared with the prior art, the air quality numerical service forecast credibility assessment method can utilize forecast data of historical dates by acquiring air quality simulation results of a plurality of nested layers and a plurality of timeliness, does not need to perform a large amount of sampling simulation, and can save time and computing resources. Assuming that an air quality prediction service system adopts n layers of nested simulation areas, forecasting m days in the future every day, aiming at the area simultaneously covered by n layers of nests, the maximum number of samples which can be obtained is k which is n multiplied by m, the simulation uncertainty of the system can be rapidly calculated based on the samples without carrying out multiple groups of situation simulations again, and the uncertainty of the simulation effect can be evaluated by using the pollution forecast data of the historical date. And selecting sample data according to the simulated nesting layer or the simulated aging, and fully considering the influence of the resolution of the nesting layer and the simulated aging on the simulation result when selecting the sample data, so that the calculation result has better indication significance for forecasting system performance judgment.
In some embodiments, referring to fig. 2, after calculating by using a confidence evaluation method according to the sample data and obtaining uncertainty of the air quality simulation result, the method further includes:
step S5, calculating the relative proportion of the simulated concentration value to the average value by using the uncertainty of the air quality simulation result to obtain the relative proportion of the uncertainty;
step S6, calculating an uncertainty range value of the simulated concentration value by using the relative ratio of the simulated concentration value and the uncertainty of the air quality simulation result;
and step S7, using the uncertainty range value of the simulated concentration value to represent the business forecast credibility of the air quality numerical value.
By adopting the method, not only the uncertainty u of the simulation result can be obtained, but also the uncertainty range value of the simulation concentration value can be obtained.
In some embodiments, the uncertainty is calculated by the formula
Wherein u is the uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
In some embodiments, the relative proportion of uncertainty is calculated by the formula
Wherein P is the relative proportion of uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
In some embodiments, the formula for calculating the uncertainty range value for the simulated concentration value is xp＝xi×P，
Wherein xpUncertainty range value, x, for the simulated concentration value at any timeiThe air quality simulation concentration value of the ith sample in the k groups of samples is shown, and P is the relative proportion of uncertainty.
In some embodiments, the calculation step using the confidence evaluation method selects a significance level α of 0.05 with a corresponding confidence level of 95%. In other embodiments, the significance level α can also be selected from other acceptable values for specific scenarios, and the corresponding bilateral threshold value Z of the standard normal distribution can be obtained accordinglyα/2。
In some embodiments, the air quality numerical traffic prediction system employs a NAQPMS mode (nested mesh air quality prediction mode). In other embodiments, the Air quality numerical service forecasting system may adopt other third-generation Air quality numerical service forecasting systems based on an Air quality numerical mode, such as a universal multi-scale Air quality mode cmaq (community multiple Air quality requirement); extended integrated air quality mode camx (community antenna model); weather forecast-chemical model WRF-CHEM.
In order to better explain the air quality numerical service forecast credibility assessment method provided by the invention, a specific example of credibility assessment of a plurality of urban air quality simulation and prediction models in a Yu-forming region is further explained below.
Referring to fig. 1-5, due to the complex geographical conditions of the Yu-forming areas, in order to reduce the influence of terrain errors on the simulation result, the pattern grid configuration with the resolution of 27-9-3-1km (fig. 3) is adopted in the embodiment, the high-precision grid with the spatial resolution of 1km is configured in the metropolis and the surrounding areas for simulation, and four layers of simulation nests (d01, d02, d03 and d04) are set to forecast the aging for 10 days. Forming regional PM from 12 months 16 days in 2017 to 1 month 4 days in 20182.5And (4) taking a pollution process as an example, applying a simulation effect confidence evaluation method, and finally calculating an uncertainty range value of a simulation result. Six cities of city, Chongqing, Germany Yang, Meishan mountain, Mianyang and Yibin are selected in the Yu-forming area, and confidence evaluation (prediction model confidence evaluation) is carried out on simulation results of the cities.Firstly, an air quality numerical service forecasting system is utilized to carry out air quality model simulation to obtain PM in the time range2.5And (5) simulating the result. FIG. 4 shows the benchmark simulation results (d04, 24h aging) of 6 selected cities, then for each day of the research period, selecting simulation data of a plurality of nested layers (4 layers, d01, d02, d03, d04) and different forecast aging (10 days) from the simulation results to obtain 40 sample data, calculating the uncertainty of the air quality simulation result, the relative proportion of the simulation concentration value to the average value (relative proportion of uncertainty), the uncertainty range value of the simulation concentration value according to the simulation effect confidence evaluation method, and finally calculating to obtain the PM of each day in the research period2.5Uncertainty range of simulated concentration. As shown in fig. 5, the uncertainty range of the concentration of the Chongqing city is shown, and the result shows the uncertainty range of the concentration of each day in the study period: the uncertainty ranges of Chengdu, Chongqing, Deyang, Meishan, Mianyang and Yibin are 12-38%, 4-20%, 6-30%, 4-26%, 7-29% and 4-33% in sequence; the concentration confidence intervals of Chengdu, Chongqing, Deyang, Meishan, Mianyang and Yibin are 2.9-18.8 mu g/m in sequence3、3.5-27.4μg/m3、5.0-20.2μg/m3、6.1-30.9μg/m3、3.4-20.8μg/m3And 4.4-36.9. mu.g/m3。
According to the air quality numerical service forecast credibility evaluation method, samples for calculating uncertainty are obtained through a plurality of nested layers and a plurality of time-efficient simulation results, compared with a conventional method, extra calculation overhead is remarkably reduced, and the uncertainty range of the simulation results can be calculated at any time; the influence of the resolution and the simulation aging of the nested layer on the simulation result is fully considered, so that the calculation result has better indication significance for forecasting system performance judgment.
According to one embodiment of the invention, an air quality numerical service forecast credibility assessment device is provided.
In some embodiments, referring to fig. 6, the air quality numerical service forecast credibility assessment apparatus includes:
the simulation result acquisition module is configured to acquire a plurality of nested layers and a plurality of aged air quality simulation results through the air quality numerical service forecasting system;
the simulated concentration value sample data acquisition module is configured to screen data from the air quality simulation result according to the influence parameters to obtain simulated concentration value sample data;
the simulation result uncertainty calculation module is configured to calculate by using a confidence evaluation method to obtain uncertainty of the air quality simulation result;
and the reliability determining module is configured to determine the air quality value service forecast reliability according to the uncertainty of the air quality simulation result.
Further, the air quality numerical service forecast reliability evaluation device may further include: the relative proportion calculation module is configured to calculate the relative proportion of the simulated concentration value to the average value by using the uncertainty to obtain the relative proportion of the uncertainty;
further, the air quality numerical service forecast reliability evaluation device may further include: and the uncertainty range value calculating module is configured to calculate an uncertainty range value for obtaining the simulated concentration value by using the relative ratio of the simulated concentration value and the uncertainty of the air quality simulation result.
In specific implementation, the modules and units may be implemented as independent entities, or may be combined arbitrarily and implemented as one or several entities. The specific implementation of each module and unit can refer to the foregoing method embodiments, and is not described herein again.
According to the air quality numerical service forecast credibility evaluation device, samples for calculating uncertainty are obtained through a plurality of nested layers and a plurality of time-efficient simulation results, compared with a conventional method, extra calculation overhead is remarkably reduced, and the uncertainty range of the simulation results can be calculated at any time; the influence of the resolution and the simulation aging of the nested layer on the simulation result is fully considered, so that the calculation result has better indication significance for forecasting system performance judgment.
According to one embodiment of the invention, the electronic equipment for evaluating the reliability of the air quality numerical service forecast is provided. Referring to fig. 7, the electronic device 400 includes at least one processor 401; and a memory 402 communicatively coupled to the at least one processor 401; the memory 402 stores instructions executable by the at least one processor 401, and the instructions are executed by the at least one processor 401, so that the at least one processor 401 executes any one of the above methods for evaluating reliability of air quality numerical service forecast. The electronic device can be a smart phone, a tablet computer and the like. In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to processes of one or more application programs into the memory 402, and the processor 401 runs the application program stored in the memory 402, thereby implementing any of the above steps of the method for evaluating the reliability of the air quality value service forecast.
Since the instructions stored in the memory 402 can execute the steps of any one of the methods for evaluating the reliability of air quality numerical service forecast provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods for evaluating the reliability of air quality numerical service forecast provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again. The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor. To this end, the present application provides a storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps of any one of the methods for evaluating reliability of an air quality numerical service forecast provided in the present application.
Wherein the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and more specifically, may include a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), and the like.
Since the instructions stored in the storage medium can execute the steps of any one of the methods for evaluating the reliability of air quality numerical service forecast provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods for evaluating the reliability of air quality numerical service forecast provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again. The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
According to the above description, the technical effects of the present invention are: the method and the device for evaluating the air quality numerical service forecast credibility and the computer readable storage medium are provided, samples of uncertainty are obtained through a plurality of nested layers and a plurality of time-efficient simulation results, compared with a conventional method, extra calculation overhead is obviously reduced, and the uncertainty range of the simulation results can be calculated at any time; the influence of the resolution and the simulation aging of the nested layer on the simulation result is fully considered, so that the calculation result has better indication significance for forecasting system performance judgment.
The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present application should be included in the scope of the present application.
Those skilled in the art will appreciate that the above description is not meant to be limiting of the apparatus and may include more or less components, or combinations of certain components, or different arrangements of components.
It should be noted that the terms "first" and "second" in the description of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising" is used to specify the presence of stated elements, but not to preclude the presence or addition of additional like elements in a process, method, article, or apparatus that comprises the stated elements. All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and electronic apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points. The above description is only an example of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention. In the foregoing description of specific embodiments of the invention, features described and/or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, in combination with or instead of the features of the other embodiments.
In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not set forth in detail in order to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components. The terms "a," "an," "two," "1," "2," "n-" and the like, as they relate to ordinal numbers, do not necessarily denote the order of execution or importance of the features, elements, steps, or components identified by the terms, but are used merely for identification among the features, elements, steps, or components for clarity of description.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (8)
1. A method for evaluating reliability of air quality numerical service forecast is characterized by comprising the following steps:
acquiring a plurality of nested layers and a plurality of time-efficient air quality simulation results through an air quality numerical service forecasting system;
screening data from the air quality simulation result according to the nesting layer and/or the time effectiveness to obtain sample data of a simulated concentration value;
calculating by using a confidence evaluation method according to the sample data to obtain uncertainty of the air quality simulation result;
determining the air quality numerical service forecast reliability according to the uncertainty of the air quality simulation result;
wherein, according to the sample data, the calculation is carried out by utilizing a confidence evaluation method, and after the uncertainty of the air quality simulation result is obtained, the method further comprises the following steps:
calculating the relative proportion of the simulated concentration value to the average value by using the uncertainty of the air quality simulation result to obtain the relative proportion of the uncertainty;
calculating an uncertainty range value of the obtained simulated concentration value by using the simulated concentration value of the air quality simulation result and the relative proportion of the uncertainty;
using the uncertainty range value of the simulated concentration value to represent the air quality numerical service forecast credibility;
wherein obtaining uncertainty of the air quality simulation result comprises:
the uncertainty is calculated by the following formula:
wherein u is the uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
2. The air quality numerical service forecast reliability evaluation method of claim 1, wherein obtaining the relative proportion of uncertainty comprises:
calculating the relative ratio of said uncertainty by the following formula:
wherein P is the relative proportion of uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Which are the corresponding bilateral thresholds of the standard normal distribution.
3. The method according to claim 2, wherein the formula for calculating the uncertainty range value of the simulated concentration value is as follows: x is the number ofp＝xi×P，
Wherein xpUncertainty range value, x, for the simulated concentration value at any timeiThe air quality simulation concentration value of the ith sample in the k groups of samples is shown, and P is the relative proportion of uncertainty.
4. The method according to claim 1, wherein the significance level α selected in the calculating step using the confidence evaluation method is 0.05, and the corresponding confidence level is 95%.
5. The air quality numerical service prediction credibility assessment method according to any one of claims 1 to 4, wherein the air quality numerical service prediction system adopts a NAQPMS mode.
6. An air quality numerical service forecast reliability evaluation device, comprising:
the simulation result acquisition module is configured to acquire a plurality of nested layers and a plurality of aged air quality simulation results through the air quality numerical service forecasting system;
the simulated concentration value sample data acquisition module is configured to screen data from the air quality simulation result according to the nested layer and/or the time effectiveness to obtain simulated concentration value sample data;
the simulation result uncertainty calculation module is configured to calculate by using a confidence evaluation method according to the sample data to obtain uncertainty of the air quality simulation result; wherein obtaining uncertainty of the air quality simulation result comprises:
the uncertainty is calculated by the following formula:
wherein u is the uncertainty,
k is the number of samples selected,
the average value of the air quality simulation concentration in k groups of samples is taken as x, the value of the air quality simulation concentration in k groups of samples is taken as xiSimulating a concentration value for the air quality of the ith sample in the k groups of samples, wherein sigma is the standard deviation of the selected sample data, alpha is the selected significance level, and Zα/2Bilateral critical values of the corresponding standard normal distribution;
the reliability determining module is configured to determine the air quality numerical service forecast reliability according to the uncertainty of the air quality simulation result; after the uncertainty of the air quality simulation result is obtained, calculating the relative proportion of the simulated concentration value to the average value by using the uncertainty of the air quality simulation result to obtain the relative proportion of the uncertainty; calculating an uncertainty range value of the obtained simulated concentration value by using the simulated concentration value of the air quality simulation result and the relative proportion of the uncertainty; and using the uncertainty range value of the simulated concentration value to represent the air quality numerical service forecast credibility.
7. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the air quality numerical service forecast reliability assessment method of any of claims 1-5 above.
8. A computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor to perform the air quality numerical service forecast reliability assessment method of any one of claims 1 to 5.
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Application publication date: 20201225 Assignee: Beijing Zhongke Sanqing Environmental Technology Co.,Ltd. Assignor: 3CLEAR TECHNOLOGY Co.,Ltd. Contract record no.: X2022980012305 Denomination of invention: Air quality numerical business forecast reliability assessment method, device and storage medium Granted publication date: 20210706 License type: Common License Record date: 20220815 |