CN102521403A - Refined meteorological information service system - Google Patents
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Abstract
The invention discloses a refined meteorological information service system, which comprises a background data processing layer, a service supplying layer and a user interaction layer. The background data processing layer enables meteorological elements observed by automatic meteorological stations distributed in an area to be analyzed and stored in a data base; after interpolation operation is carried out on the data in the data base, interpolation precision is assessed by a precision assessment module to obtain optimum meteorological interpolation data; an element value in a grid is extracted by using grid data built in the area; and the data are led in the meteorological data base and called by the service supplying layer. The service supplying layer is used for enabling meteorological data processed by the background data processing layer to be led in the meteorological data base. The user interaction layer is an access platform for providing users with the integrated uniform meteorological data and is used for inquiring and displaying information supplied by the service supplying layer. The refined meteorological information service system provides the public with rapid, convenient, accurate, timely and actual weather information, and meets requirements of the users for inquiry of the meteorological data and requirements of the public for meteorological information.
Description
Technical field
The present invention relates to a kind of meteorological information service system that becomes more meticulous, belong to computer software technical field.
Background technology
Along with the change of life style, people to the demand of weather information demonstrate at any time, everywhere, carry-on characteristics, the demand of the Weather information of the timing that becomes more meticulous, fixed point is constantly increased.But traditional Meteorological Services website is many to be main with the weather data service that city, county's one-level are provided; Traditional weather information publication channel is difficult to accurately, propagates the Weather information that becomes more meticulous all sidedly, can't offer the service of vast user for the weather information inquiry of anywhere, any time.The weather forecast that becomes more meticulous presses for accurate publication channel, for the user provides on-site short-term, in short-term the weather forecast early warning that becomes more meticulous.
Summary of the invention
The object of the invention is intended to the accurate transmission means of search gas image information service, erects the Weather information and based on the bridge between the meteorological demand of customer location of becoming more meticulous, and innovation Meteorological Services mode is expanded service clearance, the benefit that the performance Meteorological Modernization is built.Solve the problem that disaster early-warning information accurately sends simultaneously, improve specific aim, the promptness of early warning effectively, for masses provide quick, convenient, accurately, weather information information timely, satisfy the day by day urgent demand of the public for meteorological data information.
For achieving the above object, technical scheme of the present invention is:
A kind of meteorological information service system that becomes more meticulous is characterized in that, comprises
Back-end data processing layer: the meteorological element parsing of the interior meteorological website automatically observation that distributes in zone is deposited in the database; And after the data in the database are carried out interpolation arithmetic; By a precision evaluation module interpolation precision is assessed, obtained optimum meteorological interpolated data, and utilize the key element value in the lattice point data extract grid of setting up in the zone; And with these data importings in the middle of meteorogical phenomena database, the service that supplies provides layer to call;
Service provides layer: be used for the meteorological data data importing after the said back-end data processing layer processing to meteorogical phenomena database;
User interactions layer:, be used to the information of inquiring about, show that said service provides layer to provide for offering user's an integrated unified meteorological data data access platform.
The meteorological website automatically of extraction equally distributed 15% is as the checking website in the meteorological website automatically in the said zone, and remaining website is the interpolation website.
Said meteorological element comprises precipitation, temperature, air pressure, humidity and wind speed and direction.
Whenever from said database, to read once said temperature, humidity and wind speed and direction at a distance from ten minutes interval; Whenever from said database, to read precipitation one time at a distance from one hour interval; The data of these data that read and said interpolation website are carried out space correlation, will carry out interpolation with the meteorological element data input interpolating module of said interpolation site.
In the said interpolating module, utilize multiple linear regression model pair to carry out interpolation and obtain raw interpolated data with the meteorological element data of said interpolation site.
And by the multiple linear regression model revised said raw interpolated data is revised, obtain the meteorological interpolated data of said optimum.
The algorithm steps of the multiple linear regression model correction of revising comprises:
Utilize in anti-distance weighted method of interpolation, the gram golden method of interpolation, spline method, Cressman method of interpolation that the meteorological element data of interpolation site are carried out interpolation at first respectively, obtain interpolated data; And the absolute value that the Practical Meteorological Requirements factor data that observes that these interpolated datas and checking are contained in the website subtracts each other is as error; Utilize the average relative error method to calculate and judge a kind of interpolation method that obtains the error minimum, the interpolation method that this error is minimum is as the best interpolation method; And then utilize said best interpolation method that error is carried out interpolation; Obtain the error profile data; Through these error profile data said multiple linear regression model is revised; The raw interpolated data that said multiple linear regression model is obtained deducts said error profile data, obtains the output of final high precision best interpolation data as said interpolating module.
Also comprise a quality control module in the said back-end data processing layer, be used for when each meteorological station automatically meteorological element data is gone into database, rejecting wrong observation data.
The data that said service provides layer to provide comprise map datum and the meteorological data data after said back-end data processing layer is handled; Wherein map datum is externally to issue with the rest service manner, and the meteorological data data after handling provide with the mode that WebService encrypts.
The beneficial effect that the present invention reached:
System of the present invention passes through the real-time processed to the weather data of various classifications, form and data source; It is stored in the middle of the database disaggregatedly; And with the service form these resources are published on the network, the public just can obtain its needed meteorological service information easily and efficiently like this.Native system utilizes the source book data of precipitation, temperature, air pressure, humidity and wind speed and direction of a large amount of website real-time release of meteorological department; In conjunction with multiple interpolation method; The weather data that these are real-time is carried out interpolation; And utilize the precision evaluation module that the data that interpolation obtains are estimated, draw optimum interpolation scheme.Can obtain high precision, real-time weather data like this.Can be controlled within the 4%-5% through its relative error of practical proof.For masses provide quick, convenient, accurately, weather information information timely; The short-term that the user is concerned about, the weather forecast early warning that becomes more meticulous in short-term are provided; Realized the demand of user for the weather data inquiry; Satisfy the day by day urgent demand of the public for meteorological data information, the Meteorological Services product is popular has brought into play bigger effect for improving.
Description of drawings
Fig. 1 weather information service procedure figure that becomes more meticulous;
Fig. 2 is that the best interpolation data are obtained process flow diagram.
Embodiment
Below in conjunction with accompanying drawing the present invention is further described.Following examples only are used for technical scheme of the present invention more clearly is described, and can not limit protection scope of the present invention with this.
As shown in Figure 1, the meteorological information service system that becomes more meticulous comprises following functional module:
1. back-end data processing layer: the data owner that back-end data is handled is wanted to be divided into original meteorological data database data, early warning text data, weather forecast text data, radar binary data.
The data of the parsing that wherein, needs in the original meteorological data storehouse can be divided into 39 kinds of weather datas such as conventional meteorological measuring, forecast data, communique data again.At first accomplished parsing warehouse-in work through this back-end data processing layer module to these original meteorological data database datas; Carry out the meteorological element of interpolation for needs; Comprise meteorological elements such as precipitation, temperature, air pressure, humidity and wind speed and direction; Utilize interpolating module to carry out interpolation, and interpolation precision is assessed by precision evaluation module MRE.Key element value in the zone of utilize setting up in the lattice point data extract grid of 10*10,15*15 and 40*40 kilometer, and the key element value imported in the middle of the meteorogical phenomena database, the service that supplies provides layer to call.
The vector data that resolves to for the scale-of-two radar data of source book is accomplished in the parsing of radar binary data, and these data can be browsed through playing up easily.The radar binary data is a radar echo intensity.
2. service provides layer: the data owner that native system provided will be divided into the meteorological data data after map datum and the processing; Wherein map datum is externally to issue with the rest service manner, and the meteorological data data after handling provide with the mode that WebService encrypts.
3. user interactions layer: this layer fundamental purpose is for the user an integrated unified meteorological data data access platform to be provided; Information in the meteorogical phenomena database that layer provides can the query display service be provided, and the trend that main function can be divided into mark, all kinds of meteorological elements in optional position of the inquiry of Weather information, 14 kinds of early warning information demonstrations in real time, temperature and wind speed and directions shows and the demonstration etc. of nowcasting in short-term.
As the interpolating module in the back-end data layer of the system core, the quality of the weather data that it provided directly concerns the correctness and the reliability of total system.Interpolating module and precision evaluation module MRE through native system can provide high precision, high-quality meteorological interpolated data, can be controlled within the 4%-5% through its relative error of practical proof.Like Fig. 1, shown in Figure 2, its process is following:
At first obtain the parsing warehouse-in work of conventional meteorological measuring, comprise the meteorological element data such as precipitation, temperature, air pressure, humidity and wind speed and direction that to carry out interpolation in the conventional meteorological measuring by meteorological website automatically observation all in a certain zone of setting.Automatic websites all in this zone extracted 15% website as the checking website; Contain the meteorological element data that actual observation arrives in the checking website; The distribution of checking website is wanted evenly, and remaining website need utilize quality control module to reject for the observation data of mistake as the interpolation website in the time of each website meteorological element data loading; Can control the quality of weather data well from data source header like this, for interpolating module provides accurate data.
Secondly to different meteorological element data; Temperature, humidity and wind speed and direction are whenever to read from database once at a distance from ten minutes interval; From database, read once with every that (precipitation was defined as whenever at a distance from one hour quantity of precipitation totally in 1 hour for 1 hour precipitation, 24 hours precipitation at a distance from one hour interval; Precipitation was defined as the quantity of precipitation that plays current time accumulative total from the zero point on the same day in 24 hours); Carry out space correlation for these data that read and interpolation station data, for interpolation is got ready.
To with the meteorological element data of interpolation site; Utilize the interpolating module in system's back-end data layer to carry out interpolation, the interpolation method that comprises in the interpolating module has the multiple linear regression model of golden method of interpolation, spline method, Cressman method of interpolation, multiple linear regression model and correction in anti-distance weighted method of interpolation, the gram.
Multiple linear regression model mainly considers to influence longitude, latitude and the sea level elevation of precipitation in the native system; Multiple linear regression model through setting up these three kinds of factors of influence carries out interpolation to the meteorological element data of interpolation site; Obtain raw interpolated data; But the error of in the middle of the operation of reality, finding the method is still bigger, does not embody higher interpolation longitude.
Therefore; The present invention is on the basis of multiple linear regression model; Multiple linear regression model is revised, set up the multiple linear regression model of revising, utilize the multiple linear regression model of revising that raw interpolated data is revised; Can promote the interpolation longitude of data, obtain high-precision meteorological interpolated data.
The correction algorithm step of the multiple linear regression model of revising is following:
At first utilize in anti-distance weighted method of interpolation, the gram golden method of interpolation, spline method, Cressman method of interpolation that the meteorological element data of interpolation site are carried out interpolation, obtain interpolated data; And the absolute value that the Practical Meteorological Requirements factor data that observes that these interpolated datas and checking are contained in the website subtracts each other is as error; Utilize average relative error MRE method to calculate and judge a kind of interpolation method that obtains the error minimum, the interpolation method that this error is minimum is as the best interpolation method; And then utilize the best interpolation method that error is carried out interpolation; Obtain the error profile data; Through these error profile data multiple linear regression model is revised; The raw interpolated data that multiple linear regression model is obtained deducts the error profile data, obtains final high precision best interpolation data, through the multiple linear regression model output of revising.
For the best interpolation data that obtain; Utilize the corresponding key element value of lattice point data extract of 10*10,15*15 and 40*40 kilometer; Simultaneously these key element values are imported in the middle of the meteorogical phenomena database; Based on these data in the middle of the meteorogical phenomena database all kinds of weather information services are provided, mainly contain 1 hour precipitation trend and change service, 24 hours precipitation trends and change that service, temperature trend change service, humidity trend changes service etc.
The above only is a preferred implementation of the present invention; Should be pointed out that for those skilled in the art, under the prerequisite that does not break away from know-why of the present invention; Can also make some improvement and distortion, these improvement and distortion also should be regarded as protection scope of the present invention.
Claims (9)
1. the meteorological information service system that becomes more meticulous is characterized in that, comprises
Back-end data processing layer: the meteorological element parsing of the interior meteorological website automatically observation that distributes in zone is deposited in the database; And after the data in the database are carried out interpolation arithmetic; By a precision evaluation module interpolation precision is assessed, obtained optimum meteorological interpolated data, and utilize the key element value in the lattice point data extract grid of setting up in the zone; And with these data importings in the middle of meteorogical phenomena database, the service that supplies provides layer to call;
Service provides layer: be used for the meteorological data data importing after the said back-end data processing layer processing to meteorogical phenomena database;
User interactions layer:, be used to the information of inquiring about, show that said service provides layer to provide for offering user's an integrated unified meteorological data data access platform.
2. the meteorological information service system that becomes more meticulous according to claim 1 is characterized in that, the meteorological website automatically of extraction equally distributed 15% is as the checking website in the meteorological website automatically in the said zone, and remaining website is the interpolation website.
3. the meteorological information service system that becomes more meticulous according to claim 2 is characterized in that said meteorological element comprises precipitation, temperature, air pressure, humidity and wind speed and direction.
4. the meteorological information service system that becomes more meticulous according to claim 3; It is characterized in that; Whenever from said database, to read once said temperature, humidity and wind speed and direction at a distance from ten minutes interval; Whenever from said database, to read precipitation one time at a distance from one hour interval, the data of these data that read and said interpolation website are carried out space correlation, will carry out interpolation with the meteorological element data input interpolating module of said interpolation site.
5. the meteorological information service system that becomes more meticulous according to claim 4 is characterized in that, in the said interpolating module, utilizes multiple linear regression model pair to carry out interpolation with the meteorological element data of said interpolation site and obtains raw interpolated data.
6. the meteorological information service system that becomes more meticulous according to claim 5 is characterized in that, and by the multiple linear regression model of a correction said raw interpolated data is revised, and obtains the meteorological interpolated data of said optimum.
7. the meteorological information service system that becomes more meticulous according to claim 6 is characterized in that, the algorithm steps of the multiple linear regression model correction of correction comprises:
Utilize in anti-distance weighted method of interpolation, the gram golden method of interpolation, spline method, Cressman method of interpolation that the meteorological element data of interpolation site are carried out interpolation at first respectively, obtain interpolated data; And the absolute value that the Practical Meteorological Requirements factor data that observes that these interpolated datas and checking are contained in the website subtracts each other is as error; Utilize the average relative error method to calculate and judge a kind of interpolation method that obtains the error minimum, the interpolation method that this error is minimum is as the best interpolation method; And then utilize said best interpolation method that error is carried out interpolation; Obtain the error profile data; Through these error profile data said multiple linear regression model is revised; The raw interpolated data that said multiple linear regression model is obtained deducts said error profile data, obtains the output of final high precision best interpolation data as said interpolating module.
8. the meteorological information service system that becomes more meticulous according to claim 1 is characterized in that, also comprises a quality control module in the said back-end data processing layer, is used for when each meteorological station automatically meteorological element data is gone into database, rejecting wrong observation data.
9. the meteorological information service system that becomes more meticulous according to claim 1; It is characterized in that; The data that said service provides layer to provide comprise map datum and the meteorological data data after said back-end data processing layer is handled; Wherein map datum is externally to issue with the rest service manner, and the meteorological data data after handling provide with the mode that WebService encrypts.
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Cited By (15)
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| CN105447770A (en) * | 2015-12-10 | 2016-03-30 | 国网四川省电力公司电力科学研究院 | Assessment method for applying power grid monitoring data to refined weather forecast |
| CN105868443A (en) * | 2016-03-22 | 2016-08-17 | 国网安徽省电力公司电力科学研究院 | Construction method for microtopographical micrometeorological element field |
| CN106557580A (en) * | 2016-11-29 | 2017-04-05 | 福建四创软件有限公司 | The meteorological service system that becomes more meticulous based on gridding |
| CN107341134A (en) * | 2017-06-27 | 2017-11-10 | 洛阳市气象局 | A kind of method of logarithm value forecast lattice point temperature forecast data process of refinement |
| CN107679167A (en) * | 2017-09-29 | 2018-02-09 | 智慧天气风险管理（深圳）有限公司 | Weather Risk appraisal procedure and analysis and early warning platform based on lattice point meteorological data |
| CN108073644A (en) * | 2016-11-15 | 2018-05-25 | 南京南瑞继保电气有限公司 | A kind of power grid meteorology mass data processing method |
| CN108415101A (en) * | 2018-02-14 | 2018-08-17 | 国家气象信息中心 | The rarefaction method of second grade Sounding Data |
| CN108830444A (en) * | 2018-04-20 | 2018-11-16 | 中国气象局气象探测中心 | A kind of assessment of raob data and modification method and device |
| CN109426491A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of intelligence meteorological service system |
| CN109523066A (en) * | 2018-10-29 | 2019-03-26 | 东华理工大学 | A kind of newly-increased mobile site site selecting method of the PM2.5 based on Kriging regression |
| CN110532319A (en) * | 2019-07-22 | 2019-12-03 | 广东华风海洋信息系统服务有限公司 | A kind of distribution meteorological data timing processing system |
| CN111008355A (en) * | 2019-09-16 | 2020-04-14 | 华风气象传媒集团有限责任公司 | Meteorological ground element interpolation method based on trust propagation |
| CN113138431A (en) * | 2021-04-13 | 2021-07-20 | 深圳市万向信息科技有限公司 | Smart city meteorological observation method and system |
| CN113254417A (en) * | 2021-06-29 | 2021-08-13 | 南京满星数据科技有限公司 | Meteorological grid data service method and system based on big data technology |
| CN114819501A (en) * | 2022-03-25 | 2022-07-29 | 云南省交通规划设计研究院有限公司 | Road traffic meteorological Internet of things multi-source heterogeneous data processing method and system |
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| CN105447770A (en) * | 2015-12-10 | 2016-03-30 | 国网四川省电力公司电力科学研究院 | Assessment method for applying power grid monitoring data to refined weather forecast |
| CN105868443B (en) * | 2016-03-22 | 2020-04-28 | 国网安徽省电力公司电力科学研究院 | Construction method of micro-terrain microclimate element field |
| CN105868443A (en) * | 2016-03-22 | 2016-08-17 | 国网安徽省电力公司电力科学研究院 | Construction method for microtopographical micrometeorological element field |
| CN108073644A (en) * | 2016-11-15 | 2018-05-25 | 南京南瑞继保电气有限公司 | A kind of power grid meteorology mass data processing method |
| CN106557580A (en) * | 2016-11-29 | 2017-04-05 | 福建四创软件有限公司 | The meteorological service system that becomes more meticulous based on gridding |
| CN107341134A (en) * | 2017-06-27 | 2017-11-10 | 洛阳市气象局 | A kind of method of logarithm value forecast lattice point temperature forecast data process of refinement |
| CN107341134B (en) * | 2017-06-27 | 2020-05-19 | 洛阳市气象局 | Method for refining numerical value forecast lattice temperature forecast data |
| CN109426491B (en) * | 2017-08-29 | 2022-05-27 | 北京思湃德信息技术有限公司 | Intelligent weather service system |
| CN109426491A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of intelligence meteorological service system |
| CN107679167A (en) * | 2017-09-29 | 2018-02-09 | 智慧天气风险管理（深圳）有限公司 | Weather Risk appraisal procedure and analysis and early warning platform based on lattice point meteorological data |
| CN108415101A (en) * | 2018-02-14 | 2018-08-17 | 国家气象信息中心 | The rarefaction method of second grade Sounding Data |
| CN108415101B (en) * | 2018-02-14 | 2020-05-19 | 国家气象信息中心 | Second-level sounding data thinning method |
| CN108830444B (en) * | 2018-04-20 | 2020-10-09 | 中国气象局气象探测中心 | Method and device for evaluating and correcting sounding observation data |
| CN108830444A (en) * | 2018-04-20 | 2018-11-16 | 中国气象局气象探测中心 | A kind of assessment of raob data and modification method and device |
| CN109523066A (en) * | 2018-10-29 | 2019-03-26 | 东华理工大学 | A kind of newly-increased mobile site site selecting method of the PM2.5 based on Kriging regression |
| CN110532319A (en) * | 2019-07-22 | 2019-12-03 | 广东华风海洋信息系统服务有限公司 | A kind of distribution meteorological data timing processing system |
| CN111008355A (en) * | 2019-09-16 | 2020-04-14 | 华风气象传媒集团有限责任公司 | Meteorological ground element interpolation method based on trust propagation |
| CN111008355B (en) * | 2019-09-16 | 2023-11-14 | 华风气象传媒集团有限责任公司 | Meteorological ground element interpolation method based on trust propagation |
| CN113138431A (en) * | 2021-04-13 | 2021-07-20 | 深圳市万向信息科技有限公司 | Smart city meteorological observation method and system |
| CN113254417A (en) * | 2021-06-29 | 2021-08-13 | 南京满星数据科技有限公司 | Meteorological grid data service method and system based on big data technology |
| CN114819501A (en) * | 2022-03-25 | 2022-07-29 | 云南省交通规划设计研究院有限公司 | Road traffic meteorological Internet of things multi-source heterogeneous data processing method and system |
| CN114819501B (en) * | 2022-03-25 | 2023-09-15 | 云南省交通规划设计研究院有限公司 | Multi-source heterogeneous data processing method and system for highway traffic meteorological Internet of things |
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Application publication date: 20120627 |