CN108802857A - A kind of Meteorology Forecast System based on meteorological data - Google Patents
A kind of Meteorology Forecast System based on meteorological data Download PDFInfo
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- CN108802857A CN108802857A CN201810359497.4A CN201810359497A CN108802857A CN 108802857 A CN108802857 A CN 108802857A CN 201810359497 A CN201810359497 A CN 201810359497A CN 108802857 A CN108802857 A CN 108802857A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The invention discloses a kind of Meteorology Forecast System based on meteorological data, including processing module, being connected with processing module is connected for the data analysis module and processing module of data analysis for the weather forecast module for carrying out weather forecast according to the combination of real time meteorological data and numerical weather pattern, the display module being connected for showing numerical weather prediction result with processing module for acquiring the meteorological data collection module of real time meteorological data, being connected with the processing module for storing the data memory module of real time meteorological data, being connected with processing module.The present invention is based on the Meteorology Forecast Systems of meteorological data to pass through the big data sunykatuib analysis to history meteorological data and real time meteorological data, and a period of time accurate weather forecast from now on can be provided by BP neural network algorithm, so that user can carry out corresponding precautionary measures according to weather forecast, precision is high, and effect is accurate.
Description
Technical field
The present invention relates to weather forecasting techniques field more particularly to a kind of Meteorology Forecast Systems based on meteorological data.
Background technology
Production activity, social activities, military activity and the daily life of Changes in weather and people suffer from very closely
Relationship.All the time, the people side of always wanting to tries to following Changes in weather of prediction, using advantageous weather, to carry simultaneously
The preceding meteorological disaster prevented adverse weather and brought.
In traditional technology, the principle of the method that people predict the weather using numerical prediction, numerical prediction is：Pass through weather map
Or satellite cloud picture obtains meteorological image, and under certain initial value and boundary value condition, make numerical computations using mainframe computer,
The hydrodynamics for describing weather modification process and thermodynamic (al) equation group are solved, predicts the air motion state of following certain period
With the method for weather phenomenon.Above-mentioned Numerical Predicting Method is to carry out pattern derivation based on mathematical model, and error is larger.
Forecaster is being given the correct time in advance, what kind of weather and its distribution shape can be caused by often considering this weather situation actually
Condition, and find similar weather situation as reference, but traditional meteorological data access is very inconvenient.First, meteorological data is every
It will be stored, and quantity is too many, and it is inconvenient to search.Secondly, even if meteorological data secondary when finding some, will also first use third
Square software carries out being shown as figure, then the subjective weather situation browse on the day of comparative analysis, last when could judge this
Whether secondary meteorological data has reference value.
Invention content
The technical problem to be solved in the present invention is, for the drawbacks described above of the prior art, provides a kind of based on meteorological number
According to Meteorology Forecast System.
The technical solution adopted by the present invention to solve the technical problems is：According to an aspect of the present invention, a kind of base is provided
In the Meteorology Forecast System of meteorological data, including processing module, it is connected for acquiring real time meteorological data with the processing module
Meteorological data collection module, be connected with the processing module for store the real time meteorological data data memory module,
It is connected with the processing module and is connected for according to the reality for the data analysis module of data analysis, with the processing module
When meteorological data and numerical weather pattern in conjunction with carry out weather forecast weather forecast module, being connected with the processing module is used for
Show the display module of numerical weather prediction result.
Preferably, the real time meteorological data includes wind speed, wind direction, wind scale, temperature, humidity, air pressure, rainfall, precipitation
At least one of in intensity, snowfall, light radiation intensity.
Preferably, the meteorological data collection module includes being connected for connecting multiple meteorological datas with the processing module
The Multi-serial port data acquisition interface of sensor.
Preferably, the data memory module includes the meteorological number that all real time meteorological datas are carried out with pooled classification arrangement
According to library.
Preferably, further include the data outputting module being connected for exporting meteorological result with the processing module.
Preferably, further include being connected with the processing module for according to the real time meteorological data, numerical weather pattern
The meteorological correction module of study is modified to the numerical weather prediction result in conjunction with neural network algorithm.
Preferably, the amendment study is to be carried out to the numerical weather prediction result by using BP neural network algorithm
Feedback is corrected in study.
Preferably, the numerical weather prediction is in real time, timing, quantifies, subregion progress numerical weather prediction.
Preferably, the display module includes man-machine interface, and the man-machine interface is used for data form, graph or straight
Square diagram form shows real time meteorological data, numerical weather prediction result to user.
Implement, the present invention is based on the technical solution of the Meteorology Forecast System of meteorological data, to has the following advantages or beneficial to effect
Fruit：The present invention is calculated by the big data sunykatuib analysis to history meteorological data and real time meteorological data by BP neural network
Method can provide a period of time accurate weather forecast from now on, be arranged so that user can carry out corresponding prevention according to weather forecast
It applies, precision is high, and effect is accurate.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, attached needed in being described below to embodiment
Figure is briefly described, it is therefore apparent that drawings in the following description are only some embodiments of the invention, general for this field
For logical technical staff, without creative efforts, other drawings may also be obtained based on these drawings, attached
In figure：
Fig. 1 is the high-level schematic functional block diagram of the Meteorology Forecast System embodiment the present invention is based on meteorological data；
Fig. 2 is the second high-level schematic functional block diagram of the Meteorology Forecast System embodiment the present invention is based on meteorological data.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, the various embodiments that will be described below will
Corresponding attached drawing is referred to, these attached drawings constitute a part for embodiment, and which describe realize what the present invention may use
Various embodiments.It should be appreciated that other embodiments also can be used, or embodiment enumerated herein is carried out structurally and functionally
Modification, without departing from the scope of the present invention and essence.
As shown in Figure 1, the present invention provides a kind of one embodiment of the Meteorology Forecast System based on meteorological data, including place
The meteorological data collection module 20 and processing module managed module 10, be connected for acquiring real time meteorological data with processing module 10
10 are connected, and for storing, the data memory module 30 of real time meteorological data, be connected the data for data analysis with processing module 10
Analysis module 40, be connected with processing module 10 carries out weather forecast for being combined according to real time meteorological data and numerical weather pattern
Weather forecast module 50, the display module 60 that is connected for showing numerical weather prediction result with processing module 10.It is described aobvious
Show that module 60 includes man-machine interface, the man-machine interface with data form, graph or represented as histograms to user for showing
The real time meteorological data, numerical weather prediction result, numerical weather prediction result include prediction intermediate result and final result.
In the present embodiment, the real time meteorological data includes wind speed, wind direction, wind scale, temperature, humidity, air pressure, rainfall
At least one of in amount, precipitation intensity, snowfall, light radiation intensity, it can also include other meteorological datas, not limit herein
System.Correspondingly, meteorological data collection module 20 includes being connected with processing module 10 for connecting multiple meteorological data sensings
The Multi-serial port data acquisition interface 21 of device, specifically, the Multi-serial port data acquisition interface 21 can be used for connect acquire respectively it is multiple
Multiple meteorological data sensors 22 of real time meteorological data.Multiple meteorological data sensors 22 are air velocity transducer 221, temperature biography
Sensor 222, humidity sensor 223, baroceptor 224, rain sensor 225, snowfall sensor 226, light radiation sensor
227 at least one can also need to increase corresponding sensor according to actual acquisition meteorological data.
In the present embodiment, the data memory module 30 includes to all real time meteorological datas, numerical weather prediction knot
Fruit carries out the meteorogical phenomena database of pooled classification arrangement, and the real time meteorological data for being acquired to meteorological data collection module 20 carries out
Taxonomic revision, and then prolonged meteorology large database concept in forming region, to be consulted and to be predicted at any time.
In the present embodiment, further include being connected to export for exporting the data of numerical weather prediction result with processing module 10
Module 70, such as can be with output data report.Further include being connected with processing module 10 for according to real time meteorological data, day destiny
Value pattern combination neural network algorithm logarithm weather forecast result is modified the meteorological correction module 80 of study, it is preferred that
The neural network algorithm is BP neural network algorithm.Numerical weather forecast is in real time, timing, quantifies, subregion progress numerical value day
Gas is forecast.
Specifically, BP（Back Propagation）Network is 1986 by the section headed by Rumelhart and McCelland
Scholar group proposes, is a kind of Multi-layered Feedforward Networks trained by Back Propagation Algorithm, is current most widely used nerve
One of network model.BP networks can learn and store a large amount of input-output mode map relationship, without disclosing description in advance
The math equation of this mapping relations.Its learning rules are to use steepest descent method, and constantly net is adjusted by backpropagation
The weights and threshold value of network keep the error sum of squares of network minimum.BP neural network model topology structure includes input layer
（input）, hidden layer (hide layer) and output layer (output layer).
The present invention passes through BP nerve nets by the big data sunykatuib analysis to history meteorological data and real time meteorological data
Network algorithm can provide a period of time accurate weather forecast from now on, so that user can carry out corresponding prevention according to weather forecast
Measure, precision is high, and effect is accurate.
After reading content which will be described, it should be apparent to a person skilled in the art that described herein each
Kind feature can be realized by method, data processing system or computer program product.Therefore, these features can portion use hardware
Mode, showed in conjunction with by the way of all by the way of software or using hardware and software.In addition, features described above also may be used
It is showed in the form of the computer program product being stored on one or more computer readable storage mediums, the computer
Include computer readable program code section or instruction in readable storage medium storing program for executing, is stored in a storage medium.It can use and appoint
The computer readable storage medium what is used, including hard disk, CD-ROM, light storage device, magnetic storage apparatus and/or above equipment
Combination.
The foregoing is merely illustrative of the preferred embodiments of the present invention, and those skilled in the art know, is not departing from the present invention
Spirit and scope in the case of, various changes or equivalent replacement can be carried out to these features and embodiment.In addition, in this hair
Under bright introduction, it can modify to these features and embodiment to adapt to particular situation and material without departing from this hair
Bright spirit and scope.Therefore, the present invention is not limited to the particular embodiment disclosed, the power of fallen with the application
Embodiment in sharp claimed range belongs to protection scope of the present invention.
Claims (9)
1. a kind of Meteorology Forecast System based on meteorological data, which is characterized in that including processing module（10）And the processing mould
Block（10）The meteorological data collection module being connected for acquiring real time meteorological data（20）And the processing module（10）Mutually it is used in conjunction
In the data memory module for storing the real time meteorological data（30）And the processing module（10）It is connected for data analysis
Data analysis module（40）And the processing module（10）It is connected for according to the real time meteorological data and numerical weather pattern
In conjunction with the weather forecast module for carrying out weather forecast（50）And the processing module（10）It is connected for showing that numerical weather is predicted
As a result display module（60）.
2. the Meteorology Forecast System according to claim 1 based on meteorological data, which is characterized in that the real-time weather number
According to including in wind speed, wind direction, wind scale, temperature, humidity, air pressure, rainfall, precipitation intensity, snowfall, light radiation intensity at least
One.
3. the Meteorology Forecast System according to claim 2 based on meteorological data, which is characterized in that the meteorological data is adopted
Collect module（20）Including with the processing module（10）It is connected and is adopted for connecting the Multi-serial port data of multiple meteorological data sensors
Collect interface（21）.
4. the Meteorology Forecast System according to claim 2 based on meteorological data, which is characterized in that the data store mould
Block（30）It include the meteorological data that pooled classification arrangement is carried out to all real time meteorological datas, numerical weather prediction result
Library.
5. the Meteorology Forecast System according to claim 1 based on meteorological data, which is characterized in that further include and the place
Manage module（10）The data outputting module being connected for exporting meteorological result（70）.
6. the Meteorology Forecast System according to claim 5 based on meteorological data, which is characterized in that further include and the place
Manage module（10）Be connected for according to the real time meteorological data, numerical weather pattern combination neural network algorithm to the numerical value
Weather forecasting result carries out learning modified meteorological correction module（80）.
7. the Meteorology Forecast System according to claim 6 based on meteorological data, which is characterized in that the neural network is calculated
Method is BP neural network algorithm.
8. the Meteorology Forecast System according to claim 1 based on meteorological data, which is characterized in that the weather forecast is
In real time, timing, quantitative, subregion carry out weather forecast.
9. the Meteorology Forecast System according to claim 8 based on meteorological data, which is characterized in that the display module
（60）Including man-machine interface, the man-machine interface is used to show to user with data form, graph or histogram described real-time
Meteorological data, numerical weather prediction result.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633786A (en) * | 2019-01-07 | 2019-04-16 | 靳霞 | Region big data dynamic correction system |
| CN110888948A (en) * | 2019-01-07 | 2020-03-17 | 靳霞 | Regional big data dynamic correction method |
| CN110934061A (en) * | 2019-12-26 | 2020-03-31 | 裕华生态环境股份有限公司 | Garden irrigation water-saving system |
| CN111210483A (en) * | 2019-12-23 | 2020-05-29 | 中国人民解放军空军研究院战场环境研究所 | Simulated satellite cloud picture generation method based on generation of countermeasure network and numerical mode product |
| CN112580844A (en) * | 2019-09-30 | 2021-03-30 | 北京金风慧能技术有限公司 | Meteorological data processing method, device, equipment and computer readable storage medium |
| TWI775521B (en) * | 2021-07-09 | 2022-08-21 | 國立勤益科技大學 | Simulation testing method of meteorological monitoring system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102830446A (en) * | 2012-08-13 | 2012-12-19 | 国电南京自动化股份有限公司 | Intelligent meteorological station system capable of forecasting meteorological data |
| CN103616734A (en) * | 2013-12-11 | 2014-03-05 | 山东大学 | System and method for large-range synchronous real-time meteorological data measurement and wind speed and direction prediction |
| CN203982389U (en) * | 2014-08-21 | 2014-12-03 | 大连理工大学 | A kind of Runoff Forecast system in mid-term based on weather information |
| CN104615848A (en) * | 2014-12-26 | 2015-05-13 | 中国南方电网有限责任公司 | Comprehensive application method for integrating forest fire danger forecasting and ground fire point monitoring |
-
2018
- 2018-04-20 CN CN201810359497.4A patent/CN108802857A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102830446A (en) * | 2012-08-13 | 2012-12-19 | 国电南京自动化股份有限公司 | Intelligent meteorological station system capable of forecasting meteorological data |
| CN103616734A (en) * | 2013-12-11 | 2014-03-05 | 山东大学 | System and method for large-range synchronous real-time meteorological data measurement and wind speed and direction prediction |
| CN203982389U (en) * | 2014-08-21 | 2014-12-03 | 大连理工大学 | A kind of Runoff Forecast system in mid-term based on weather information |
| CN104615848A (en) * | 2014-12-26 | 2015-05-13 | 中国南方电网有限责任公司 | Comprehensive application method for integrating forest fire danger forecasting and ground fire point monitoring |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109633786A (en) * | 2019-01-07 | 2019-04-16 | 靳霞 | Region big data dynamic correction system |
| CN110888948A (en) * | 2019-01-07 | 2020-03-17 | 靳霞 | Regional big data dynamic correction method |
| CN109633786B (en) * | 2019-01-07 | 2020-05-01 | 山东齐创石化工程有限公司 | Regional big data dynamic correction system |
| CN110888948B (en) * | 2019-01-07 | 2020-09-01 | 好活（昆山）网络科技有限公司 | Regional big data dynamic correction method |
| CN112580844A (en) * | 2019-09-30 | 2021-03-30 | 北京金风慧能技术有限公司 | Meteorological data processing method, device, equipment and computer readable storage medium |
| CN111210483A (en) * | 2019-12-23 | 2020-05-29 | 中国人民解放军空军研究院战场环境研究所 | Simulated satellite cloud picture generation method based on generation of countermeasure network and numerical mode product |
| CN110934061A (en) * | 2019-12-26 | 2020-03-31 | 裕华生态环境股份有限公司 | Garden irrigation water-saving system |
| TWI775521B (en) * | 2021-07-09 | 2022-08-21 | 國立勤益科技大學 | Simulation testing method of meteorological monitoring system |
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