CN111339392B - Sky blue index determination method and system based on meteorological elements - Google Patents
Sky blue index determination method and system based on meteorological elements Download PDFInfo
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Abstract
The invention discloses a method and a system for determining sky blue index based on meteorological elements, wherein the method comprises the following steps: collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database; performing webpage search on the blue-sky keywords by adopting a semantic engine technology, downloading historical weather phenomenon data of a weather observation site by using a web crawler technology, and removing rain and snow dates from the blue-sky dates by using the historical weather phenomenon data; transmitting the blue-sky date to a meteorological element database for meteorological positioning, extracting meteorological elements corresponding to the blue-sky date, and selecting meteorological elements associated with the blue sky and the satisfied conditions of the meteorological elements by using a neural network model to obtain an alternative sky blue index; and selecting an optimal sky blue index from the alternative sky blue indexes by adopting a forecast quality inspection method and combining with urban air quality data to serve as an urban standard sky blue index. The method improves the accuracy of the sky blue index.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a sky blue index determining method and system based on meteorological elements.
Background
With the rapid development of Chinese economy and the acceleration of urbanization process, the problem of air pollution is more obvious. The weather bureau has defined the pollution phenomenon such as haze, is convenient for study heavy haze time, helps the government to make a decision and solve the pollution problem. However, with the development of "action plan for preventing and treating air pollution", blue sky is increasingly appearing in the visual field of people. The 'Beijing blue' rushes to the back of the red, which is a great direction for people to go to blue sky. Under the brand new idea of building beautiful China, after implementing a three-year action plan of the blue sky defense war, people desire a more direct and concise definition, which can include the double desires of the people for good weather and good air in the new period. For the government, in order to check the effectiveness of the "blue sky chinese" plan, reflect the relationship between the long-term change of the blue of china and the economic development and serve future policies, a direct blue index based on long-term records needs to be established.
When discussing blue days, the meteorologist only considers cloud cover, while the environmentst only concerns air quality. The pursued "blue sky" refers to not only sky blue but also a day with excellent air quality, and the influence of the two factors should be combined conceptually. However, compared with the polluted weather such as haze, blue is ignored by researchers as a probable event, and thus an accurate definition is always lacking.
Because only haze is defined at present, and accurate and unified definition of blue, namely sky blue index, is lacked, how to acquire the sky blue index and how to improve the accuracy of the sky blue index are problems to be solved urgently.
Disclosure of Invention
The invention aims to provide a method and a system for determining a sky blue index based on meteorological elements so as to improve the accuracy of the sky blue index.
In order to solve the technical problem, the invention provides a sky blue index determining method based on meteorological elements, which comprises the following steps:
collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by a web crawler technology, and removing rain and snow dates from the blue-sky dates by utilizing the historical weather phenomenon data;
transmitting the blue-sky date to a meteorological element database for meteorological positioning, extracting meteorological elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the meteorological elements, and selecting the meteorological elements associated with the blue sky and the meeting conditions of the meteorological elements by using a neural network model to obtain an alternative sky blue index;
and acquiring urban air quality data, and selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data to serve as an urban standard sky blue index.
Preferably, the obtaining of the meteorological element database after the quality control of the meteorological observation data includes:
performing quality control on meteorological observation data by using an R language, and converting visibility into dry visibility;
and preprocessing the meteorological observation data after quality control by utilizing matlab software, and establishing a meteorological element database.
Preferably, the downloading of the historical weather phenomenon data of the weather observation site by the web crawler technology, and the removing of the rain and snow date from the blue day date by using the historical weather phenomenon data, include:
and downloading historical weather phenomenon data of the weather observation site by using a Python crawler technology, finding out a date recorded as a sunny day and without rain and snow and using the date as a reference date, comparing a blue day date obtained from a webpage search result with the reference date, and using the date with the same blue day date as a standard blue day date.
Preferably, the acquiring city air quality data, selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the city air quality data as a city standard sky blue index, includes:
downloading urban air quality data of a meteorological observation site by using a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
and calculating the forecasting accuracy, the empty reporting rate and the missing reporting rate of each alternative sky blue index on the marked date by using a forecasting quality inspection method, and selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode to be used as a standard sky blue index.
The invention also provides a system for determining sky blue index based on meteorological elements, which is used for realizing the method and comprises the following steps:
the pretreatment system is used for collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
the semantic selection system is used for setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by using a web crawler technology, and removing rain and snow dates from the blue-sky dates by using the historical weather phenomenon data;
the weather positioning system is used for transmitting the blue-sky date to the weather element database for weather positioning, extracting weather elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the weather elements, and selecting the weather elements associated with the blue sky and the meeting conditions of the weather elements by using the neural network model to obtain a candidate sky blue index;
and the verification system is used for acquiring urban air quality data, and selecting an optimal sky blue index from the alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data as an urban standard sky blue index.
Preferably, the pretreatment system includes:
the collection module is used for collecting meteorological observation data of a meteorological observation station;
the conversion module is used for performing quality control on meteorological observation data by utilizing an R language and converting visibility into dry visibility;
and the establishing module is used for preprocessing the meteorological observation data after the quality control by utilizing matlab software and establishing a meteorological element database.
Preferably, the semantic selection system includes:
the setting module is used for setting blue sky keywords, performing webpage search on the blue sky keywords by adopting a semantic engine technology, and acquiring blue sky dates from webpage search results;
the downloading module is used for downloading historical weather phenomenon data of the weather observation site by utilizing a Python crawler technology, and finding out a date recorded as sunny and no rain or snow and using the date as a reference date;
and the comparison module is used for comparing the blue-sky date acquired from the webpage search result with the reference date and taking the date in which the blue-sky date is the same as the reference date as the standard blue-sky date.
Preferably, the authentication system includes:
the download module is used for downloading urban air quality data of the meteorological observation site by utilizing a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
and the calculation module is used for calculating the forecast accuracy, the empty report rate and the missing report rate of each alternative sky blue index on the marked date by using a forecast quality inspection method, selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode, and using the optimal alternative sky blue index as a standard sky blue index.
The invention provides a method and a system for determining sky blue index based on meteorological elements, which are characterized in that meteorological observation data of a meteorological observation station are collected, and the quality of the meteorological observation data is controlled to obtain a meteorological element database; setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by a web crawler technology, and removing rain and snow dates from the blue-sky dates by utilizing the historical weather phenomenon data; transmitting the blue-sky date to a meteorological element database for meteorological positioning, extracting meteorological elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the meteorological elements, and selecting the meteorological elements associated with the blue sky and the satisfied conditions of the meteorological elements by using a neural network model to obtain an alternative sky blue index; and acquiring urban air quality data, and selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data to serve as an urban standard sky blue index. Therefore, according to the existing meteorological observation stations and the air quality data and the historical weather phenomenon data published by the environmental protection bureau, the blue index is defined based on the comprehensive assessment of various stations by utilizing multiple factors, the sky blue index which is most suitable for the local can be defined according to the long-term meteorological monitoring condition and the air pollution evolution condition of various stations, the urban sky blue model and the long-term blue data set can be obtained, and the accuracy of the sky blue index is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining sky blue index based on meteorological elements according to the present invention;
FIG. 2 is a schematic flow chart of a single station blue implementation system;
FIG. 3 is a schematic flow diagram of a regional/national blue implementation system;
FIG. 4 is a schematic flow diagram of a blue motion detection system;
FIG. 5 is a distribution diagram of China weather observation sites;
FIG. 6 is a schematic diagram of a historical weather phenomenon and air quality data recording website;
FIG. 7 is a diagram illustrating a raw format of meteorological data;
fig. 8 is a schematic structural diagram of a sky blue index determination system based on meteorological elements according to the present invention.
Detailed Description
The core of the invention is to provide a method and a system for determining the sky blue index based on meteorological elements so as to improve the accuracy of the sky blue index.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for determining a sky blue index based on meteorological elements according to the present invention, the method including the following steps:
s11: collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
s12: setting blue sky keywords, performing webpage search on the blue sky keywords by adopting a semantic engine technology, acquiring blue sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by using a web crawler technology, and removing rain and snow dates from the blue sky dates by using the historical weather phenomenon data;
s13: transmitting the blue-sky date to a meteorological element database for meteorological positioning, extracting meteorological elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the meteorological elements, and selecting the meteorological elements associated with the blue sky and the satisfied conditions of the meteorological elements by using a neural network model to obtain an alternative sky blue index;
s14: and acquiring urban air quality data, and selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data to serve as an urban standard sky blue index.
Therefore, in the method, the blue index is defined based on the multi-factor comprehensive evaluation of each station according to the air quality data and the historical weather phenomenon data published by the existing meteorological observation stations and the environmental protection bureau, the sky blue index which is most suitable for the local is defined according to the long-term meteorological monitoring condition and the air pollution evolution condition of each station, the urban sky blue model and the long-term blue data set are obtained, and the accuracy of the sky blue index is improved.
Based on the above method, further, in step S11, the process of obtaining the meteorological element database after performing quality control on the meteorological observation data includes the following steps:
s21: performing quality control on meteorological observation data by using an R language, and converting visibility into dry visibility;
s22: and preprocessing the meteorological observation data after quality control by utilizing matlab software, and establishing a meteorological element database.
Further, in step S12, the historical weather phenomenon data of the weather observation site is downloaded by using the web crawler technology, and the process of removing the rainy and snowy date from the blue day date by using the historical weather phenomenon data specifically includes: and downloading historical weather phenomenon data of the weather observation site by using a Python crawler technology, finding out a date recorded as a sunny day and without rain and snow and using the date as a reference date, comparing a blue day date obtained from a webpage search result with the reference date, and using the date with the same blue day date as a standard blue day date.
Further, the process of step S14 specifically includes the following steps:
s31: downloading urban air quality data of a meteorological observation site by using a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
s32: and calculating the forecasting accuracy, the empty reporting rate and the missing reporting rate of each alternative sky blue index on the marked date by using a forecasting quality inspection method, and selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode to be used as a standard sky blue index.
Further, after step S14, the following steps are also included:
s15: and acquiring real-time forecast meteorological data, analyzing the real-time forecast meteorological data according to the standard sky blue index to obtain a judgment result of the sky blue, and issuing the judgment result.
Further, after step S15, the method further includes the following steps:
s16: and for each city, establishing a long-term blue-day record database according to the sky blue index, updating the sky blue judgment result of each city in real time according to the daily meteorological elements, and uploading the sky blue judgment result to a webpage.
In this way, the web pages in the world blue space-time distribution are updated in real time in step S16.
In order to meet the requirements of long-term research and real-time forecast on the blue, the blue index is defined by utilizing multi-factor comprehensive evaluation based on each station according to air quality data and historical weather phenomenon data published by the existing weather observation stations and the environmental protection bureau. Meanwhile, because the air quality observation time is too short, in order to obtain the blue long-term time evolution, visibility data is adopted to replace air quality data to replace good air.
Due to the limitation of the existing records, the website records the historical weather phenomenon of the Chinese city only after 2011, the air quality monitoring is carried out nationwide after 2013, and the long-time actual observation record of sunny day and air quality data is lacked, so that the method mainly selects meteorological elements by analyzing the existing blue events when establishing a long-term model, can still judge the blue accurately and clearly, and has operability.
The method can dynamically judge the real-time blue-sky condition of each city based on ground meteorological observation data, air quality monitoring data and related technologies and algorithms, establish blue models and long-term data sets of each city, and realize the demand selection of sites.
In the method, the executor in step S11 is a preprocessing system, the executor in step S12 is a semantic selection system, the executor in step S13 is a weather positioning system, the executor in step S14 is a verification system, and the executor in step S15 is a real-time forecasting system.
Referring to fig. 2, fig. 2 is a schematic flow chart of a single-station bluetooth implementation system. For a single station blue implementation system, the determination of the index is mainly to satisfy the definition of "good weather" and "good air". The processing method mainly comprises a preprocessing system, a semantic selection system, a meteorological positioning system, a verification system and a real-time forecasting system.
In the preprocessing system, based on step S11, specifically, the system is first docked with the relevant meteorological department to collect data of national ground meteorological observation stations, including common data such as visibility, cloud cover, temperature, precipitation, sunshine hours, and relative humidity. After the data is subjected to quality control by using the R language, the visibility is converted into dry visibility so as to eliminate the influence of humidity on visibility observation data. Preprocessing the data by utilizing processing software such as matlab and the like, and establishing a meteorological element database based on a single site.
In the semantic selection system, based on step S12, specifically, a semantic engine technology is used to perform a web search by using keywords such as "XX (i.e., city name) blue sky", "XX blue", "XX good weather", and the like, find out related news reports, microblog information, and the like, analyze and refine data by using an artificial intelligence system, and determine a date recorded as blue. And meanwhile, downloading historical weather phenomenon data of the site by utilizing a Python crawler technology, positioning the date which is recorded as 'fine' and has no rain or snow, comparing the date with the date on which blue appears, and determining that the date on which blue is extracted has reliability. The system can fully reflect and meet the expectations of people under the condition of ensuring that the information source is accurate.
In the weather positioning system, based on step S13, specifically, the found date is transmitted to the established database for weather positioning. After a city is positioned, meteorological elements on a corresponding date are called, all weather which generates precipitation or snow in the daytime is eliminated, in order to prevent the record of historical weather phenomena from being incorrect, a neural network model is utilized, and on the premise that the selected date meets the conditions that the visibility grade is better (defined by a meteorological bureau) and the air quality is better (defined by an environmental protection bureau), the meteorological elements which are obviously related to the occurrence of the blue day and the satisfying conditions are selected, such as a cloud cover threshold value, an absolute value, a visibility threshold value and an absolute value which are met by the extracted blue day are judged. Therefore, the site alternative definition is obtained, and the alternative sky blue index comprises various possible combinations of the absolute value of the meteorological element and the threshold value, namely the alternative sky blue index is multiple and comprises a limit range of the absolute value of the meteorological element and/or a limit range of the threshold value of the meteorological element. The meteorological elements are meteorological elements corresponding to sky blue.
In the verification system, based on step S14, specifically, a set of long-term blue records of the station is first established based on the above alternative definition indexes. And then downloading the urban air quality data by using a Python crawler technology to form an AQI (air quality index) record corresponding to the time length. The date when the AQI level was good (i.e., AQI < = 100), no rain or snow was marked by establishing a weather database. And (3) calculating the forecast rate of each alternative index to the marked date by utilizing a forecast quality inspection method, and ensuring the explanation variance of 'good air'. And finally, scoring each alternative index by using the weight of TS 0.6+ PO 0.2+ FAR 0.2. The highest score is the blue index of the place, and the corresponding blue record data set is the long-term blue record of the place. The forecast quality inspection method comprises the following calculation formula:
with respect to the prediction accuracy TS,
NA + ND is the correct forecasting times, NB is the empty reporting times, and NC is the missed reporting times;
for the rate of missing reports PO,
for the empty reporting rate FAR, the rate of the report,
referring to fig. 3, fig. 3 is a flow chart of a regional/national blue implementation system. For the regional/national blue implementation system, when the region (such as the whole province and the whole region) or the country needs to be analyzed in a unified standard, the method is also applicable, and the customized scheme can be implemented for different requirements, and the implementation of the chinese blue index is taken as an example below.
After the preprocessing system is finished, 32 province cities in the country are taken as province representatives, the 32 province cities are subjected to webpage searching by the aid of the keywords through a semantic engine technology, and semantic selection is finished by means of analysis and refinement. In meteorological positioning, the national satisfaction of the neural network model is recorded as the main meteorological elements corresponding to the clear blue day, the blue day numerical values are subjected to indiscriminate recombination by using a box diagram, the upper quartile, the lower quartile and the median are judged, and the meteorological elements which are most suitable for the national range and the satisfaction conditions are judged.
In the verification system, a representative site AQI is selected to verify the accuracy of the candidate index. And averaging the obtained 32 city quality test results, and then carrying out weighted scoring to obtain a result.
Referring to fig. 4, fig. 4 is a schematic flow chart of a blue dynamic detection system. For a blue dynamic detection system, after the blue index is obtained, the blue can be judged in real time by combining weather data forecast in real time on the same day and air quality data downloaded by a Python crawler technology, meanwhile, the national popularization forecast of the air quality data after 2013 is utilized, the misjudgment is timely changed, and the result is timely released to a website by utilizing a real-time forecasting system.
Because only the bad weather phenomenon such as haze is reported at present, the people's demand can be directly reflected to blue report, satisfies the needs of people's trip, work. However, for blue, the weather bureau only focuses on the cloud amount, and the percentage of the cloud area occupying the sky is used as the criterion. On the other hand, the environmental protection agency only concerns the air quality, and uses the AQI as the only judgment standard for determining whether the air quality is good weather. However, the definition of the weather bureau does not consider the phenomenon of air pollution caused by the economic development of the country, and the definition of the environmental protection bureau does not consider the desire of people for the blue sky white cloud. The appearance of blue is a dual concept of weather and air quality, belongs to the category of interdiscipline, and the existing two definitions cannot accurately reflect the blue sky and even reflect the direct expectation of people. The method firstly proposes the cross concept of 'blue', establishes blue models aiming at different conditions and different requirements of each region and obtains real-time and long-term Chinese blue data sets. Referring to fig. 5, 6 and 7, fig. 5 is a distribution diagram of a chinese weather observation site, fig. 6 is a schematic diagram of a historical weather phenomenon and air quality data recording website, and fig. 7 is a schematic diagram of a weather data original format.
The method has important application prospect as follows:
(1) On one hand, a blue index is formulated, dynamic monitoring of blue is realized, a real-time broadcast platform and a historical data platform are established, real-time information is provided for people, the travel of specific people is facilitated, the needs of various industries are served in real time, and the happiness of people is improved;
(2) The statistics of national blue distribution and the tracking update of blue information are realized, the tracking update of blue information is used as a government monitoring index, the execution degree of blue sky China can be quantitatively tested, a reference is provided for policy making, and a selection service is provided for large-scale events and activities, for example, a designated city selects the most suitable event-holding time, or a designated time selects the most suitable event-holding city, and in addition, a reference is provided for national energy strategy in the aspect of solar clean energy application;
(3) The understanding of blue distribution can help people to better formulate an energy-saving and emission-reduction list, help to judge the proportion of nature and man-made pollution in the pollution generation, can serve the research of the pollution generation in the past, serve the man-made regulation and control service before the large activity event is held, and is more beneficial to the sustainable development of the country;
(4) The sky blue index also has commercial value, can be used as a tourism index after the definition is modified according to different requirements, can be used as a city tourism business card, can be quietly changed in a city suitable for tourism health when the haze pollution of a large city is serious, and is particularly important for tourists pursuing good weather and good air; meanwhile, the distribution and the occurrence frequency of blue in each urban area are different, so that the urban area is suitable for different tourism seasons, and the application of the blue index can provide favorable information for judging the film shooting time, designing tourism festivals, making urban tourism economy development decisions and the like;
(5) As the aging problem of China is serious, the endowment community is an important development prospect in the future, and the urban blue can provide key reference for community site selection construction, so that the urban blue can be used for urban selection service of the endowment hospital and can promote the development of small and medium-sized urban insurance industry and sunset industry;
(6) The establishment of the blue long-term record is beneficial to the evaluation of livable cities, and the blue index has important significance for promoting the development of small and medium-sized cities because the population of large cities is saturated and young people tend to flow to the small and medium-sized cities in the future;
(7) The established blue index model has certain prediction capability through modeling, is used as a potential prediction index, can more accurately predict the future development trend of China through establishing a relation with a meteorological model, and is beneficial to the formulation of future policies;
(8) At present, no unified blue sky judgment standard exists in each place, and difficulties are brought to the national development of blue sky detection work and policy execution force judgment and inspection work in each place; the definition is beneficial to unifying national definitions, and is convenient for comparing execution degrees and standard days of different cities, so that the comparison is more strict;
(9) With the development of global economic trade, the global immigration may be a high-speed development direction in the future. The expansion of Chinese blue into global blue will serve for immigration and international tourism industry, and provide reference for the selection of immigration countries and cities;
(10) By improving time and spatial resolution and expanding to hour and global scale, the long-term space-time distribution of blue is more finely described, and the national industrial and agricultural layout can be served according to historical data, so that the industrial structure adjustment is facilitated;
(11) The definition is simple in calculation and strong in transportability, and by introducing artificial intelligence, the approach and long-time rapid forecast can be performed. The expansibility is strong, and the requirements of various industries are met;
(12) The index can be converted into various derivative indexes according to the requirements of various industries, such as a tourism index, an agricultural development index, a immigration index and the like.
After 2011, there were website data that recorded weather phenomena in cities of china. After 2013, the method allows for the incorporation of air quality data. Then, in the absence of meteorological data of a demand site, that is, in the absence of air quality data, how to define a long-term blue index in the absence of air quality data should be implemented, alternatively, the method uses distance weight interpolation to replace data of a nearby site, and implements extraction of the blue index by the following scheme, and the main scheme is as follows:
firstly, for determining the blue index, the processing method mainly comprises a preprocessing system, a semantic selection system, a meteorological positioning system, a verification system and a real-time forecasting system.
Secondly, in the preprocessing system, firstly, the data of the relevant meteorological departments are docked, and national ground meteorological observation station data including common data such as visibility, cloud cover, temperature, precipitation, sunshine hours, relative humidity and the like are collected. After the data is subjected to quality control by using the R language, the visibility is converted into dry visibility so as to eliminate the influence of humidity on visibility observation data. Preprocessing the data by utilizing processing software such as matlab and the like, and establishing a meteorological element database based on a single site. Other stations (XX) within 20KM (here the specific number is determined by city size) near a station (AA) lacking meteorological data and meteorological data are found by radius search, interpolated by distance weights as a reference for the meteorological data for that station.
In the semantic selection system, a semantic engine technology is utilized to perform webpage search by keywords such as 'XX/AA (city name) blue sky', 'XX/AA blue', 'XX/AA good weather', and the like, related news reports, microblog information and the like are found out, an artificial intelligence system is used for analyzing and refining data, and a date recorded as blue is determined. And meanwhile, downloading AA historical weather phenomenon data of the site by utilizing a Python crawler technology, if the site does not exist, replacing the AA historical weather phenomenon data by using an XX through a distance weighting method, positioning the date which is recorded as 'clear' and has no rain or snow, comparing the date with the date when blue appears, and determining that the date when blue appears has reliability.
And in the positioning meteorological system, transmitting the found date to an established database for meteorological positioning. The method comprises the steps of calling meteorological elements on corresponding dates after a city is positioned, firstly removing all weather which produces precipitation or snow in the daytime, selecting the meteorological elements which are obviously related to the occurrence of the blue days and meeting conditions by utilizing a neural network model on the premise that the selected dates are determined to meet the conditions that the visibility grade is higher than good (defined by a meteorological office) and the air quality is higher than good (defined by an environmental protection office), and if the cloud cover threshold value, the absolute value, the visibility threshold value and the absolute value which are met by the blue days are judged and extracted, thus obtaining the station alternative definition. The alternate sky blue index includes various possible combinations of absolute values and thresholds for meteorological elements.
And finally, in a verification system, establishing a set of long-term blue records of the site based on the alternative definition indexes. And then downloading the urban air quality data by using a Python crawler technology, and if the station does not exist, replacing the urban air quality data by using an XX (distance weighting) method to form an AQI (air quality index) record corresponding to the time length. The date when the AQI level was good (i.e., AQI < = 100), no rain or snow was marked by establishing a weather database. And (3) calculating the forecast rate of each alternative index to the marked date by utilizing a forecast quality inspection method, and ensuring the explanation variance of 'good air'. And finally, scoring each alternative index by using the weight of TS 0.6+ PO 0.2+ FAR 0.2. The highest score is the blue index of the place, and the corresponding blue record data set is the long-term blue record of the place.
However, when the weather data cannot be acquired, a high-precision blue distribution is required, or a wide-range real-time blue detection is required. The method comprises the steps of downloading high-score satellite data (such as MODIS satellites and HIMAWARI-8 satellites) by using a Python crawler technology, fusing the data by using an inversion algorithm, overcoming the defect of excessive missing and measuring of the satellite data, carrying out inversion on AOD after preprocessing, and positioning air quality data and weather phenomenon records of all stations in a range by combining the Python crawler technology. The data are transmitted to a blue judging system, and the real-time blue distribution condition is judged through the combination of AOD data and air quality index (AQI < = 100) and weather phenomenon (no precipitation and snowfall). And the data is dynamically broadcasted in a real-time forecasting system in the form of images and single-site forecasting.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a sky blue index determining system based on meteorological elements according to an embodiment of the present invention, the sky blue index determining system is configured to implement the above method, and the method includes:
the preprocessing system 101 is used for collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
the semantic selection system 102 is used for setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by using a web crawler technology, and removing rain and snow dates from the blue-sky dates by using the historical weather phenomenon data;
the weather positioning system 103 is used for transmitting the blue-sky date to the weather element database for weather positioning, extracting weather elements corresponding to the blue-sky date, removing the snow and rain date from the blue-sky date again by using the weather elements, and selecting the weather elements associated with the blue sky and the satisfied conditions of the weather elements by using a neural network model to obtain an alternative sky blue index;
and the verification system 104 is used for acquiring urban air quality data, and selecting an optimal sky blue index from the alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data as an urban standard sky blue index.
Therefore, in the system, according to the existing meteorological observation stations and the air quality data and the historical weather phenomenon data published by the environmental protection bureau, the blue index is defined based on the comprehensive assessment of various stations by utilizing multiple factors, the sky blue index which is most suitable for the local is defined according to the long-term meteorological monitoring condition and the air pollution evolution condition of various stations, the urban sky blue model and the long-term blue data set are obtained, and the accuracy of the sky blue index is improved.
Based on sky blue index determination system, further, the preprocessing system includes:
the collection module is used for collecting meteorological observation data of a meteorological observation station;
the conversion module is used for performing quality control on meteorological observation data by utilizing an R language and converting visibility into dry visibility;
and the first establishing module is used for preprocessing the meteorological observation data after the quality control by utilizing matlab software and establishing a meteorological element database.
Further, the semantic selection system comprises:
the setting module is used for setting the blue sky keywords, performing webpage search on the blue sky keywords by adopting a semantic engine technology, and acquiring the blue sky date from a webpage search result;
the downloading module is used for downloading historical weather phenomenon data of the weather observation site by utilizing a Python crawler technology, and finding out a date recorded as sunny and no rain or snow and using the date as a reference date;
and the comparison module is used for comparing the blue-sky date acquired from the webpage search result with the reference date and taking the date in which the blue-sky date is the same as the reference date as the standard blue-sky date.
Further, the authentication system includes:
the download module is used for downloading urban air quality data of the meteorological observation site by utilizing a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
and the calculation module is used for calculating the forecast accuracy, the empty report rate and the missing report rate of each alternative sky blue index on the marked date by using a forecast quality inspection method, selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode, and taking the optimal alternative sky blue index as a standard sky blue index.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The sky blue index determining method and system based on meteorological elements provided by the invention are described in detail above. The principles and embodiments of the present invention have been described herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (4)
1. A meteorologic element-based sky blue index determination method is characterized by using a meteorologic element-based sky blue index determination system and a real-time forecasting system, and comprising the following steps:
s11: collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
s12: setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by a web crawler technology, and removing rain and snow dates from the blue-sky dates by utilizing the historical weather phenomenon data;
s13: transmitting the blue-sky date to a meteorological element database for meteorological positioning, extracting meteorological elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the meteorological elements, and selecting the meteorological elements associated with the blue sky and the meeting conditions of the meteorological elements by using a neural network model to obtain an alternative sky blue index;
s14: acquiring urban air quality data, and selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data as an urban standard sky blue index;
in step S11, the quality control of the meteorological observation data to obtain a meteorological element database includes:
s21: performing quality control on meteorological observation data by using an R language, and converting visibility into dry visibility;
s22: preprocessing the meteorological observation data after quality control by utilizing matlab software, and establishing a meteorological element database;
in step S12, the historical weather phenomenon data of the weather observation site is downloaded through a web crawler technology, and the rainy and snowy dates are removed from the blue-sky dates by using the historical weather phenomenon data, and the method comprises the following steps:
downloading historical weather phenomenon data of a weather observation site by using a Python crawler technology, finding out a date which is recorded as a sunny day and has no rain or snow and is used as a reference date, comparing a blue day date which is obtained from a webpage search result with the reference date, and using the same date as the reference date as a standard blue day date;
the process of step S14 specifically includes the following steps:
s31: downloading urban air quality data of a meteorological observation site by using a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
s32: calculating the forecasting accuracy rate, the empty reporting rate and the missing reporting rate of each alternative sky blue index on the marked date by using a forecasting quality inspection method, and selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode to be used as a standard sky blue index;
after step S14, the method further includes the following steps:
s15: acquiring real-time forecast meteorological data, analyzing the real-time forecast meteorological data according to a standard sky blue index to obtain a judgment result of sky blue, and issuing the judgment result;
after step S15, the following steps are also included:
s16: for each city, establishing a long-term blue-day record database according to the sky blue index, updating the sky blue judgment result of each city in real time according to daily meteorological elements, and uploading the sky blue judgment result to a webpage;
thus, the webpage of the world blue space-time distribution is updated in real time through the step S16;
the sky blue index determining system based on meteorological elements comprises the following modules:
the pretreatment system is used for collecting meteorological observation data of a meteorological observation station, and performing quality control on the meteorological observation data to obtain a meteorological element database;
the semantic selection system is used for setting blue-sky keywords, performing webpage search on the blue-sky keywords by adopting a semantic engine technology, acquiring blue-sky dates from webpage search results, downloading historical weather phenomenon data of a weather observation site by using a web crawler technology, and removing rain and snow dates from the blue-sky dates by using the historical weather phenomenon data;
the weather positioning system is used for transmitting the blue-sky date to the weather element database for weather positioning, extracting weather elements corresponding to the blue-sky date, removing the rain and snow date from the blue-sky date again by using the weather elements, and selecting the weather elements associated with the blue sky and the meeting conditions of the weather elements by using the neural network model to obtain a candidate sky blue index;
the verification system is used for acquiring urban air quality data, and selecting an optimal sky blue index from alternative sky blue indexes by adopting a forecast quality inspection method and combining the urban air quality data as an urban standard sky blue index;
in the verification system, the calculation formula of the forecast quality inspection method is as follows:
with respect to the prediction accuracy TS,
NA + ND is the correct forecasting times, NB is the empty reporting times, and NC is the missed reporting times;
for the rate of missing reports PO,
for the empty reporting rate FAR, the rate of the report,
the executor in step S11 is a preprocessing system, the executor in step S12 is a semantic selection system, the executor in step S13 is a weather positioning system, the executor in step S14 is a verification system, and the executor in step S15 is a real-time forecasting system.
2. The sky blue index determination method of claim 1, wherein the preprocessing system comprises:
the collection module is used for collecting meteorological observation data of the meteorological observation station;
the conversion module is used for performing quality control on meteorological observation data by utilizing an R language and converting visibility into dry visibility;
and the establishing module is used for preprocessing the meteorological observation data after the quality control by utilizing matlab software and establishing a meteorological element database.
3. The sky blue index determination method of claim 1, wherein the semantic selection system comprises:
the setting module is used for setting blue sky keywords, performing webpage search on the blue sky keywords by adopting a semantic engine technology, and acquiring blue sky dates from webpage search results;
the downloading module is used for downloading historical weather phenomenon data of the weather observation site by utilizing a Python crawler technology, and finding out a date recorded as sunny and no rain or snow and using the date as a reference date;
and the comparison module is used for comparing the blue-sky date acquired from the webpage search result with the reference date and taking the date in which the blue-sky date is the same as the reference date as the standard blue-sky date.
4. The sky blue index determination method of claim 1, wherein said verification system comprises:
the download module is used for downloading urban air quality data of the meteorological observation site by utilizing a Python crawler technology, forming an Air Quality Index (AQI) record corresponding to the date length, and finding out a date with the AQI less than or equal to 100 as a marking date;
and the calculation module is used for calculating the forecast accuracy, the empty report rate and the missing report rate of each alternative sky blue index on the marked date by using a forecast quality inspection method, selecting the optimal alternative sky blue index from all the alternative sky blue indexes in a weight scoring mode, and using the optimal alternative sky blue index as a standard sky blue index.
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