CN114063197A - Method and device for predicting environmental pollution - Google Patents
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Abstract
The invention discloses a method and a device for predicting environmental pollution, which relate to the technical field of computers and are applied to the field of environmental protection, wherein the specific implementation mode of the method comprises the following steps: acquiring prediction data of meteorological factors in a first time range, determining regional meteorological prediction data corresponding to the meteorological factors in a preset region range in each unit time in a second time range from the prediction data, and predicting the environmental pollutant condition of the preset region range in the second time range by using an environmental quality numerical model based on the regional meteorological prediction data and the corresponding regional pollution data. The problems of large time range granularity, large space range granularity and low quantization of a prediction result in the conventional prediction method are solved, and the accuracy of environment pollution prediction is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for predicting environmental pollution.
Background
At present, with the increase of economic level, the increase of urban population and the expansion of traffic scale, the environmental pollution problem is more and more serious, and therefore, the environmental pollution problem becomes one of the problems to be solved urgently for improving the living environment of people.
In solving the problem of environmental pollution, the prediction of environmental pollution is an indispensable link, and the current method for predicting environmental pollution generally analyzes meteorological elements which have influences on air quality in a long time scale by utilizing an acquired prediction data set derived from a global climate model, and realizes the prediction of the environmental pollution condition in a time range (for example, 6 months in the future) set in the future of a regional scale by combining the influences of various meteorological elements on regional atmospheric pollution and the influences of regional meteorological conditions on pollutant generation and diffusion through artificial experience comprehensive analysis; however, the prediction results obtained by the conventional prediction method have the problems of large time range granularity, large space range granularity and low quantization.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for predicting environmental pollution, which are applied in the field of environmental protection, and are capable of obtaining prediction data of meteorological factors in a first time range, determining regional meteorological prediction data corresponding to the meteorological factors in a preset region range in each unit time in a second time range from the prediction data, and predicting an environmental pollutant condition of the preset region range in the second time range by using an environmental quality numerical model based on the regional meteorological prediction data and the corresponding regional pollution data. The problems of large time range granularity, large space range granularity and low quantization of a prediction result in the conventional prediction method are solved, and the accuracy of environment pollution prediction is improved.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of predicting environmental pollution, including: acquiring prediction data of meteorological factors in a first time range; in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in each unit time in the preset area range based on the prediction data, wherein the second time range belongs to the first time range; acquiring regional pollution data corresponding to the preset regional range; and predicting the environment pollutant condition of the preset area range in each unit time within the second time range by utilizing an environment quality numerical model based on the area weather prediction data and the area pollution data.
Optionally, the method of predicting environmental pollution,
the meteorological factors comprise any one or more of air temperature, precipitation, wind field, humidity, boundary layer height and solar radiation.
Optionally, the method of predicting environmental pollution,
obtaining forecast data of meteorological factors in a first time range, further comprising: obtaining historical data corresponding to the meteorological factors within a historical time range within the first time range; analyzing contemporaneous trends of the meteorological factors based on the predictive data and the historical data to adjust the predictive data.
Optionally, the method of predicting environmental pollution,
acquiring regional pollution data corresponding to the preset regional range, wherein the regional pollution data comprise: and collecting pollutant emission data corresponding to the preset area range, and analyzing the pollutant emission data from any one or more dimensions of space distribution, time distribution and material distribution to obtain the area pollution data corresponding to the preset area range.
Optionally, the method of predicting environmental pollution,
obtaining forecast data of meteorological factors in a first time range, further comprising: the predictive data is derived from a plurality of data sources; calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range based on the prediction data; predicting an environmental pollution condition within the first time range based on the weather diagnostic data.
Optionally, the method for predicting environmental pollution is characterized in that predicting environmental pollution conditions in the first time range based on the meteorological diagnosis data comprises: acquiring historical meteorological data in one or more historical time ranges corresponding to the first time range; comparing the weather diagnosis data corresponding to the first time range with the historical weather data; and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution.
To achieve the above object, according to a second aspect of embodiments of the present invention, there is provided an apparatus for predicting environmental pollution, comprising: the system comprises a data acquisition and prediction module, an area data acquisition module and an environmental pollution prediction module; wherein,
the prediction data acquisition module is used for acquiring prediction data of the meteorological factors in a first time range; in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in each unit time in the preset area range based on the prediction data, wherein the second time range belongs to the first time range;
the pollution data acquisition module is used for acquiring regional pollution data corresponding to the preset regional range;
and the environment pollution predicting module is used for predicting the environment pollution condition of the preset area range in each unit time within the second time range by utilizing an environment quality numerical model based on the area meteorological prediction data and the area pollution data.
Optionally, the apparatus for predicting environmental pollution is characterized in that,
the meteorological factors comprise any one or more of air temperature, precipitation, wind field, humidity, boundary layer height and solar radiation.
Optionally, the apparatus for predicting environmental pollution is characterized in that,
obtaining forecast data of meteorological factors in a first time range, further comprising: obtaining historical data corresponding to the meteorological factors within a historical time range within the first time range; analyzing contemporaneous trends of the meteorological factors based on the predictive data and the historical data to adjust the predictive data.
Optionally, the apparatus for predicting environmental pollution is characterized in that,
acquiring regional pollution data corresponding to the preset regional range, wherein the regional pollution data comprise: and collecting pollutant emission data corresponding to the preset area range, and analyzing the pollutant emission data from any one or more dimensions of space distribution, time distribution and material distribution to obtain the area pollution data corresponding to the preset area range.
Optionally, the apparatus for predicting environmental pollution is characterized in that,
obtaining forecast data of meteorological factors in a first time range, further comprising: the predictive data is derived from a plurality of data sources; calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range based on the prediction data; predicting an environmental pollution condition within the first time range based on the weather diagnostic data.
Optionally, the apparatus for predicting environmental pollution is characterized in that, based on the meteorological diagnostic data, predicting environmental pollution conditions in the first time range includes: acquiring historical meteorological data in one or more historical time ranges corresponding to the first time range; comparing the weather diagnosis data corresponding to the first time range with the historical weather data; and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution.
To achieve the above object, according to a third aspect of the embodiments of the present invention, there is provided an electronic device for predicting environmental pollution, including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method as in any one of the methods for predicting environmental pollution described above.
To achieve the above object, according to a fourth aspect of the embodiments of the present invention, there is provided a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as in any one of the above methods of predicting environmental pollution.
One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of obtaining prediction data of meteorological factors in a first time range, determining regional meteorological prediction data corresponding to the meteorological factors in a preset region range in each unit time in a second time range from the prediction data, and predicting the environmental pollutant condition of the preset region range in the second time range by utilizing an environmental quality numerical model based on the regional meteorological prediction data and corresponding regional pollution data. The problems of large time range granularity, large space range granularity and low quantization of a prediction result in the conventional prediction method are solved, and the accuracy of environment pollution prediction is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic flow chart diagram of a method for predicting environmental pollution provided by an exemplary embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of a method for predicting environmental pollution provided by an exemplary embodiment of the present invention;
FIG. 3 is a block PM of a predefined block range provided by an exemplary embodiment of the present invention2.5A schematic diagram of the concentration daily average prediction condition;
FIG. 4 is a region O of a preset region range provided by an exemplary embodiment of the present invention3A concentration value is 8 hours, and the prediction situation is shown schematically;
FIG. 5 is a schematic diagram including east Asian summer wind index prediction data provided by exemplary embodiments of the present invention;
FIG. 6 is a schematic graph containing Western Pacific subtropical high pressure intensity index prediction data provided by exemplary embodiments of the present invention;
FIG. 7 is a schematic graph containing Western Pacific subtropical high-pressure ridge prediction data provided by an exemplary embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an apparatus for predicting environmental pollution according to an exemplary embodiment of the present invention;
FIG. 9 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
In recent years, with the increase of economic level, the dramatic increase of urban population, the expansion of traffic scale, and the environmental pollution problem begin to appear. In order to reduce the occurrence of environmental pollution (such as air pollution, water body pollution, soil pollution and the like), obtaining the prediction data of the environmental pollution becomes an important technical means for providing data support for environmental protection. The environmental pollution takes the atmospheric pollution as an example, the air quality of the future 6 months is predicted, the important significance is provided for the season-crossing air pollution prevention and control, and more time can be won for the regional air quality control work if the time range of the atmospheric pollution prediction (namely, the air quality prediction) is larger (namely, the time span is longer) and the accuracy is higher.
Generally, existing methods for predicting atmospheric pollution include:
climate forecast data diagnostic analysis: the method comprises the steps of adopting a global climate model prediction data set, analyzing meteorological factors related to environmental pollution (atmospheric pollution) in a longer time range, such as temperature, wind speed, humidity, boundary layer height, precipitation and the like, combining various meteorological conditions of the following examples, and the influence of various meteorological factors on regional atmospheric pollution, combining artificial experience, comprehensively analyzing the influence of regional meteorological conditions on pollutant generation and diffusion, and realizing comprehensive prediction of pollutant generation and diffusion conditions of 6 months in the future of a regional scale, wherein the meteorological factors comprise: the meteorological conditions for the easy generation of ozone are for example: high temperature, low humidity, good lighting conditions (clear); the meteorological conditions liable to generate particulate matter are for example: a higher humidity; meteorological conditions that are not conducive to the spread of pollutants such as: low wind speed (not beneficial to horizontal diffusion), low boundary layer height (not beneficial to vertical diffusion) and the like; further climate factors associated with environmental pollution include: winter climate factors (mainly including tropical sea temperature anomaly, north (south) polar sea ice, siberian high pressure, highland snow accumulation, remote correlation and the like); summer climate factors (mainly including summer wind, snow accumulated on plateau, etc.); generally, particulate pollution is predominant in winter, O3 (ozone) pollution is predominant in summer, and the transition season is two seasons of spring and autumn.
Air mass numerical simulation method: regional weather prediction is carried out by driving a mesoscale meteorological numerical model through a global climate prediction data product, and then quantitative prediction of regional pollutant concentration in unit time (such as every day, every hour and the like) is carried out by driving an air quality model based on the data. The method has the advantage that the quantization of the prediction result can be improved by configuring the physical parameter configuration. However, when the method is used for predicting the air pollution condition of the area in a large event range, the accuracy of quantitative prediction data is greatly reduced due to factors such as prediction deviation (the larger the prediction time range is), pollution emission data deviation and deviation of a mode chemical mechanism. Further, since a tool (or software) for operating the weather and air quality numerical pattern is required depending on a large amount of computer hardware resources, there are problems in that the time cost for prediction and the computer cost are high.
In view of this, as shown in fig. 1, an embodiment of the present invention provides a method for predicting environmental pollution, which may include the following steps:
step S101: acquiring prediction data of meteorological factors in a first time range; in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in the preset area range in each unit time based on the prediction data, wherein the second time range belongs to the first time range.
Specifically, acquiring prediction data of meteorological factors in a first time range; wherein the first time range may be a predicted time range indicated by the prediction request, for example: 5 months into the future, 6 months into the future, 12 months into the future, etc.; meteorological factors include any one or more of temperature, precipitation, airflow, humidity, boundary layer height, solar radiation. Wherein the air temperature can be average air temperature, absolute air temperature and the like; the humidity may be relative humidity, average humidity, absolute humidity, or the like; the airflow may be horizontal (e.g., near-ground wind speed, seasonal wind type, etc.) or vertical; solar radiation is for example: ground solar radiant flux, etc. Further, the prediction data for the first time range may be derived from a global climate prediction data source; for example: CFS (Climate Forecast System) data, CAS-ESM (Earth System Model of Chinese Academy of Sciences) data, etc., to obtain data of various meteorological factors, such as: global prediction data of meteorological elements such as air temperature, air pressure, humidity, wind field, precipitation and the like are obtained.
Preferably, the obtaining of the prediction data of the meteorological factors in the first time range further comprises: obtaining historical data corresponding to the meteorological factors within a historical time range within the first time range; analyzing contemporaneous trends of the meteorological factors based on the predictive data and the historical data to adjust the predictive data. For example: the first time range is the future six months (months 6-11), historical data within a historical time range (e.g.: months 6-11 of the past year, months 6-11 of the past 5 years, months 6-11 of the past 10 years, months 6-11 of the past 30 years, etc.) corresponding to the future six months is further obtained, and contemporaneous trends of the meteorological factors are analyzed based on the forecast data and the historical data, such as: for the average air temperature, a numerical statistic is calculated based on numerical values of the average air temperature of each of the history data and the prediction data, for example: average value, median and the like to adjust the prediction data, namely, fine adjustment is carried out on the prediction data; it can be understood that the predictive data are adjusted by analyzing the rule and the contemporaneous trend of the meteorological data by utilizing the historical data, so that the rationality and the stability of the predictive data are improved.
Further, in response to an environmental pollution prediction request for a preset area range, on the basis of the prediction data, determining regional weather prediction data corresponding to the weather factors in a second time range, which is attributed to the first time range, in each unit time in the preset area range. For example, the environmental pollution prediction request is to predict the environmental pollution of one or more preset area ranges in the city ABC, for example, the preset area ranges are one or more area ranges with a spatial resolution corresponding to a numerical value of not less than 9 km; a second time range (shorter than the first time range, e.g. 30 days) belonging to the first time range (longer time range, e.g. 5 months), the second time range being e.g. 30 days, 45 days; the unit time is, for example, 1 hour, and in each unit time in the second time range, namely, in each hour in 30 days or 45 days, based on the prediction data, the wrf (weather Research and weather modeling) mesoscale meteorological model is driven to obtain regional meteorological prediction data of one or more regions with 9-27 km corresponding to the spatial resolution in each hour in 30 days or 45 in the future. Therefore, by obtaining the regional meteorological prediction data of each hour corresponding to the preset region, the granularity of the prediction data is reduced, and the accuracy and the quantification of prediction are improved.
Step S102: and acquiring regional pollution data corresponding to the preset regional range.
Specifically, the area pollution data corresponding to the preset area range described in step S101 is obtained, and the method for obtaining the area pollution data corresponding to the preset area range may be: the method comprises the steps of collecting pollutant emission data corresponding to a preset area range, and further analyzing the pollutant emission data from any one or more dimensions of space distribution, time distribution and material distribution based on the collected pollutant emission data to obtain the area pollution data corresponding to the preset area range.
The space distribution dimensionality is used for analyzing pollutant discharge data according to a space position or a space range, for example, pollution sources corresponding to the pollutant discharge data comprise a heating source, an electric power source, a residential source (catering oil fume, agricultural incineration and the like), a moving source (vehicle discharge) and the like, and the weight of the space distribution dimensionality is determined (the weight value of each pollution source corresponding to the space distribution dimensionality can be further determined) by analyzing factors related to the space distribution dimensionality in a preset area range.
The time allocation dimension is the allocation of values of pollutant emission data to units of time, for example: hours, a manifest input model in hours is generated. Thereby processing the pollutant discharge number based on the monthly distribution coefficient, the weekly distribution coefficient and the daily distribution coefficient according to the time distribution dimension. The pollutant emission data of the pollutant sources contained in the preset area range has a correlation with time, such as: generally, the pollutant emissions of industrial and electric power sources are slightly higher in the next half year than in the first half year. Heating source emissions are typically during 11 months to 3 months of the following year, etc.; the daily change characteristics of the mobile source are obvious, the emission amount in the morning and evening peak periods is large, and the like.
The material distribution dimension is the correlation of a particular material with the pollutant emission data, adapted to the chemical mechanisms of the air quality model, such as: analyzing data such as chemical species (e.g., area oxide);
and analyzing the pollutant emission data from any one or more dimensions of space distribution, time distribution and material distribution, and acquiring the regional pollution data corresponding to the preset regional range through multi-dimensional comprehensive analysis, so that the accuracy and flexibility of acquiring the regional pollution data are improved.
Step S103: and predicting the environment pollutant condition of the preset area range in each unit time within the second time range by utilizing an environment quality numerical model based on the area weather prediction data and the area pollution data.
Specifically, the environmental pollution includes: atmospheric pollution, water pollution, marine pollution, soil pollution, and the like;
and predicting the environmental pollutant conditions of the preset area range in each unit time (for example, each hour) in the second time range (for example, 30 days) by utilizing an environmental quality numerical model based on the regional meteorological predicted data described in the step S101 and the regional pollution data described in the step S102. The environmental pollution is exemplified by atmospheric pollution, the environmental quality numerical model may be an air quality numerical model, such as NAQPMS, CMAQ, CAMx, and the like, and the environmental pollutant situation of the preset area range in each unit time within the second time range is predicted by quantitatively predicting the area pollutant concentration of the preset area range (for example, 9 to 20 kilometers) corresponding to the spatial resolution for each hour of the future 30 days of the city ABC through the air quality numerical model. The environmental contaminant condition may include, among others: the environmental pollution condition is predicted through one or more concentration values according to the concentrations of PM2.5, PM10, SO2, NO2, CO and O3 and conditions (namely the environmental pollution condition) such as excellent, good and light pollution obtained by comparing each pollutant data with historical data.
Further, a time range (for example: one day, 8 hours) can be set and a chart showing statistical data in the time range can be obtained based on the environmental pollutant condition in unit time (for example: every hour); for example, take fig. 3 and 4 as an example: FIG. 3 shows the prediction of the daily average value of PM2.5 concentration in a predetermined region range included in a certain region within a certain time range; fig. 4 shows the prediction of the concentration value of the region O3 in the range of the preset region included in a certain time range and a certain region for 8 hours. The corresponding environmental pollution situation can be judged and predicted by the data indicated by the graphs of fig. 3 and 4.
As shown in fig. 2, an embodiment of the present invention provides a process for predicting environmental pollution, which may include the following steps:
step S201: obtaining forecast data of meteorological factors in a first time range, further comprising: and calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range based on the prediction data. In particular, the forecast data of the meteorological factors in the first time range is derived from a plurality of data sources (e.g., NCEP, American weather Environment forecasting center reanalysis data, CAS-EAM-C data, etc.).
The method for obtaining the first time range, the weather factors, and the forecast data of the weather factors in the first time range is consistent with the description of step S101, and will not be described herein again.
Further, the present invention can predict the environmental pollution condition in each unit time of the second time range (included in the first time range), and then perform the following steps on a larger time range (i.e. the first time range), for example: and predicting the environmental pollution condition in the future 6 months and 12 months. The method comprises the following steps: and calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale meteorological elements in the first time range based on the prediction data. Among these, climate factors, such as: ENSO (a term for Hercino and southern billow) index, arctic sea ice, climate and remote correlation relationship, etc.; large scale weather systems, such as siberian hyperbaric pressure, subtropical hyperbaric pressure, winter/summer monsoon, etc., regional scale meteorological factor changes, such as precipitation, temperature, humidity, etc; and calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range according to the factors. As illustrated in fig. 5-7 below; for example: the current time period is 4 months (represented by Apr in the figure), and the correlation between the meteorological diagnosis data of a predicted first time range (for example: 5-8 months, represented by May, Aug and the like in the figure) and the Ranina phenomenon (namely the abnormal cold condition of the east seawater in the Pacific ocean) can be obtained according to the current time; based on the incidence relation, weather factors, large-scale weather systems and weather diagnosis data corresponding to regional scale weather factor changes can be calculated based on the obtained re-analysis data of the NCEP (American weather environmental forecast center) and the data of the CAS-EAM-C (namely, the prediction data is from various data sources); FIG. 5 shows a schematic chart of the chronological real-time status and forecast data for the east Asian summer wind index (EASM) resulting from this step (showing weather diagnostic data corresponding to the east Asian summer wind index); similarly, fig. 6 shows a schematic graph of the current chronological-based status of the pacific subtropical high pressure intensity (WPSH intensity) index and the prediction data (showing weather diagnostic data corresponding to the pacific subtropical high pressure intensity index); fig. 7 shows a schematic graph of the current situation of the pacific subtropical high-voltage ridge (WPSH ridge line) index based on the chronological order and the prediction data (showing weather diagnosis data corresponding to the pacific subtropical high-voltage ridge index).
Therefore, the accuracy of the regional weather forecast data is improved by acquiring the forecast data of various data sources, calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and regional scale weather factor changes and combining the weather factors related to the weather factor changes and the forecast data of the large-scale weather system.
Step S202: acquiring one or more historical time ranges corresponding to the first time range; comparing the weather diagnosis data corresponding to the first time range with historical weather data in the historical time range; and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution.
Specifically, for example, the first time range is 5 months (5-9 months) into the future, and the corresponding one or more historical time ranges, for example: acquiring historical meteorological data within one or more historical time ranges in the last 5-9 months and in the last 5-9 months, and further comparing the meteorological diagnosis data with the historical meteorological data; and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution. For example, Table 1 shows predicted environmental pollution conditions for months 5-9 (i.e., the first time frame) comparing the weather diagnostic data for months 5-9 with historical weather data, such as: environmental pollution conditions (atmospheric pollution conditions) are indicated by the diffusion conditions (e.g. substantial leveling, preference, etc.) of the pollutants PM2.5 and O3.
TABLE 1
Therefore, the embodiment of the invention adopts a mode of combining a climate prediction data diagnosis analysis method and an air quality numerical simulation method to predict the regional environmental pollution condition in a longer time range (for example, 5-6 months in the future), namely, the unit time (for example, every day and every hour in 1 month in the future) in a shorter time range can be predicted, so that the daily quantitative prediction data can be realized, the granularity of the prediction time range is refined, the accuracy of the prediction data is improved, the regional environmental pollution condition in a longer time range (for example, 5-6 months in the future) can be predicted, and the labor cost and the consumption of computing resources are reduced; in addition, the reliability and the accuracy of the prediction result are improved by adopting a plurality of meteorological prediction data sources to carry out air quality diagnosis and analysis compared with the single prediction data.
As shown in fig. 8, an embodiment of the present invention provides an apparatus 800 for predicting environmental pollution, including: an acquisition prediction data module 801, an acquisition region data module 802 and a prediction environmental pollution module 803; wherein,
the obtain prediction data module 801 is configured to obtain prediction data of the meteorological factors in a first time range; in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in each unit time in the preset area range based on the prediction data, wherein the second time range belongs to the first time range;
the pollution data acquiring module 802 is configured to acquire regional pollution data corresponding to the preset regional range;
the environmental pollution predicting module 803 is configured to predict the environmental pollution condition of the preset area range in each unit time within the second time range by using an environmental quality numerical model based on the regional meteorological prediction data and the regional pollution data.
Optionally, the module 801 for obtaining prediction data includes: the meteorological factors comprise any one or more of air temperature, precipitation, wind field, humidity, boundary layer height and solar radiation.
Optionally, the obtain forecast data module 801 is further configured to obtain historical data corresponding to the meteorological factors in a historical time range in the first time range; analyzing contemporaneous trends of the meteorological factors based on the predictive data and the historical data to adjust the predictive data.
Optionally, the pollution data obtaining module 802 is further configured to collect pollutant emission data corresponding to the preset area range, and analyze the pollutant emission data from any one or more dimensions of space distribution, time distribution, and material distribution to obtain the area pollution data corresponding to the preset area range.
Optionally, the obtain prediction data module 801 is further configured to obtain prediction data of meteorological factors in a first time range, and further includes: the predictive data is derived from a plurality of data sources; calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range based on the prediction data; predicting an environmental pollution condition within the first time range based on the weather diagnostic data.
Optionally, the obtain prediction data module 801 is further configured to obtain historical meteorological data in one or more historical time ranges corresponding to the first time range; comparing the weather diagnosis data corresponding to the first time range with the historical weather data; and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution.
An exemplary embodiment of the present invention also provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program, when executed by the at least one processor, is for causing the electronic device to perform a method according to an embodiment of the invention.
Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is operable to cause the computer to perform a method according to an embodiment of the present invention.
Exemplary embodiments of the present invention also provide a computer program product comprising a computer program, wherein the computer program is operative, when executed by a processor of a computer, to cause the computer to perform a method according to an embodiment of the present invention.
Referring to fig. 9, a block diagram of a structure of an electronic device 900, which may be a server or a client of the present invention, which is an example of a hardware device that may be applied to aspects of the present invention, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 9, the electronic apparatus 900 includes a computing unit 901, which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 may be any type of device capable of inputting information to the electronic device 900, and the input unit 906 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. Output unit 907 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 908 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and/or chipsets, such as bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 901 performs the respective methods and processes described above. For example, in some embodiments, the method of predicting environmental pollution may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 900 via the ROM 902 and/or the communication unit 909. In some embodiments, the computing unit 901 may be configured to perform the method of predicting environmental pollution by any other suitable means (e.g. by means of firmware).
Program code for implementing the methods of the present invention 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 the present invention, 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.
As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
Claims (9)
1. A method of predicting environmental pollution, comprising:
acquiring prediction data of meteorological factors in a first time range;
in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in each unit time in the preset area range based on the prediction data, wherein the second time range belongs to the first time range;
acquiring regional pollution data corresponding to the preset regional range;
and predicting the environment pollutant condition of the preset area range in each unit time within the second time range by utilizing an environment quality numerical model based on the area weather prediction data and the area pollution data.
2. The method of claim 1,
the meteorological factors comprise any one or more of air temperature, precipitation, wind field, humidity, boundary layer height and solar radiation.
3. The method of claim 1,
obtaining forecast data of meteorological factors in a first time range, further comprising:
obtaining historical data corresponding to the meteorological factors within a historical time range within the first time range;
analyzing contemporaneous trends of the meteorological factors based on the predictive data and the historical data to adjust the predictive data.
4. The method of claim 1,
acquiring regional pollution data corresponding to the preset regional range, wherein the regional pollution data comprise:
and collecting pollutant emission data corresponding to the preset area range, and analyzing the pollutant emission data from any one or more dimensions of space distribution, time distribution and material distribution to obtain the area pollution data corresponding to the preset area range.
5. The method of claim 1,
obtaining forecast data of meteorological factors in a first time range, further comprising:
the predictive data is derived from a plurality of data sources;
calculating weather diagnosis data corresponding to the weather factors, the large-scale weather system and the regional scale weather factor change in the first time range based on the prediction data;
predicting an environmental pollution condition within the first time range based on the weather diagnostic data.
6. The method of claim 5,
predicting an environmental pollution condition within the first time range based on the weather diagnostic data, comprising:
acquiring historical meteorological data in one or more historical time ranges corresponding to the first time range;
comparing the weather diagnosis data corresponding to the first time range with the historical weather data;
and predicting the environmental pollution condition in the first time range according to the comparison result and the correlation between the meteorological factors and the environmental pollution.
7. An apparatus for predicting environmental pollution, comprising: the system comprises a data acquisition and prediction module, an area data acquisition module and an environmental pollution prediction module; wherein,
the prediction data acquisition module is used for acquiring prediction data of the meteorological factors in a first time range; in response to an environmental pollution prediction request for a preset area range, determining regional weather prediction data corresponding to the weather factors in a second time range in each unit time in the preset area range based on the prediction data, wherein the second time range belongs to the first time range;
the pollution data acquisition module is used for acquiring regional pollution data corresponding to the preset regional range;
and the environment pollution predicting module is used for predicting the environment pollution condition of the preset area range in each unit time within the second time range by utilizing an environment quality numerical model based on the area meteorological prediction data and the area pollution data.
8. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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