CN110716968A - Atmospheric science calculation container pack system and method - Google Patents
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Abstract
The invention relates to the technical field of atmospheric science, in particular to an atmospheric science calculation container packet system and a method. In the atmosphere science calculation container pack system and the atmosphere science calculation container pack method, rainfall data, weather phenomenon data, relative humidity data and wind direction and wind power data are respectively input into the system, so that the atmosphere science database of the system is complete in type and is respectively input during input, the system can analyze the atmosphere science data conveniently, a sample training module is adopted to train sample data, deep analysis on the rainfall data, the weather phenomenon data, the relative humidity data and the wind direction and wind power data is facilitated, and the integrity of atmosphere science is improved.
Description
Technical Field
The invention relates to the technical field, in particular to an atmosphere science calculation container pack system and method.
Background
Atmospheric science is a discipline that studies the various phenomena of the atmosphere (including the effects of human activity on it), the evolutionary laws of these phenomena, and how to utilize these laws to serve humans. In atmospheric science, meteorological data are various in variety and complex in data acquisition, and meanwhile, data cannot be analyzed, so that the integrity of atmospheric scientific data is influenced.
Disclosure of Invention
It is an object of the present invention to provide an atmospheric science computing pod system and method that addresses one or more of the deficiencies set forth in the background above.
In order to achieve the above objects, in one aspect, the present invention provides an atmospheric science computing container pack system, which comprises an atmospheric data entry module, an atmospheric data processing module and a computing container pack, the atmospheric data recording module is used for recording atmospheric data, the atmospheric data processing module is used for processing the recorded atmospheric data, the computing container package is used for storing atmospheric data, the atmospheric data processing module comprises an original data transcription module, an atmospheric data analysis module, a data analysis and statistics module and a data transmission module, the original data transcription module is used for transcribing original atmospheric data, the atmospheric data analysis module is used for analyzing the atmospheric data, the data analysis and statistics module is used for carrying out statistics on the analyzed atmospheric data, and the data transmission module is used for transmitting the analyzed atmospheric data to the calculation container package.
Preferably, the original data transcription module comprises a data reading module, a sample training module, a sample analysis module and a sample generation and storage module, wherein the data reading module is used for reading atmospheric data and generating sample data, the sample training module is used for training the sample data, the sample analysis module is used for analyzing the sample data, and the sample generation and storage module is used for storing and generating a new statistical sample.
Preferably, the atmospheric data analysis module includes a rainfall analysis module, a temperature analysis module, a relative humidity analysis module and a wind direction and wind force analysis module, the rainfall analysis module is used for analyzing rainfall data, the temperature analysis module is used for analyzing temperature data, the relative humidity analysis module is used for analyzing relative humidity data, and the wind direction and wind force analysis module is used for analyzing wind direction and wind force data.
Preferably, the data analysis and statistics module comprises a statistical histogram module and a statistical pie chart module, and the statistical histogram module is used for reflecting the statistical data in the form of a histogram.
As preferred, the atmospheric data entry module includes temperature data entry module, rainfall data entry module, weather phenomenon data entry module, relative humidity data entry module and wind direction wind power data entry module, the temperature data entry module is used for entering temperature data, rainfall data entry module is used for entering rainfall data, weather phenomenon data entry module is used for entering weather phenomenon data, relative humidity data entry module is used for entering relative humidity data, wind direction wind power data entry module is used for entering wind direction wind power data.
Preferably, the computing container includes a data storage module, a run-on module, a centralized deployment module, a run-on module, a data processing module, and a node status module, where the data storage module is configured to store data, the run-on module is configured to take charge of a Vertical gatekeeper run in the Arda framework, the centralized deployment module is configured to take charge of deployment of a Vertical created by the Arda framework in a cluster, the run-on module is configured to take charge of services such as starting and closing of the Vertical in the operation of the Arda framework, the data processing module is configured to collect, copy, migrate, and output data, and the node status module is configured to take charge of a status of the Vertical run in the Arda framework and a status of each node.
In another aspect, the present invention further provides an atmospheric science computing container pack method, including any one of the above atmospheric science computing container systems, the operating steps of which are as follows:
s1, inputting meteorological data: the method comprises the steps of inputting temperature data through a temperature data input module, inputting rainfall data through a rainfall data input module, inputting weather phenomenon data through a weather phenomenon data input module, inputting relative humidity data through a relative humidity data input module, and inputting wind direction and wind power data through a wind direction and wind power data input module;
s2, raw data transcription: reading in atmospheric data through a data reading module, generating sample data, and training the sample data through a sample training module;
s3, meteorological data analysis: the rainfall data is analyzed through a rainfall analysis module, the temperature data is analyzed through a temperature analysis module, the relative humidity data is analyzed through a relative humidity analysis module, and the wind direction and wind power data is analyzed through a wind direction and wind power analysis module;
s4, operating a calculation container package: the data storage module is used for storing data, the operation of the stationer module is used for stationing the Vertical operated in the Arda framework, the centralized deployment module is used for deploying the Vertical created by the Arda framework in a cluster, the operation module is used for starting and closing the Vertical operated in the Arda framework, the data processing module is used for collecting, copying, transferring and outputting the data, and the node state module is used for controlling the state of the Vertical operated in the Arda framework and the state of each node.
Compared with the prior art, the invention has the beneficial effects that:
1. in the atmosphere science calculation container pack system and the atmosphere science calculation container pack method, rainfall data, weather phenomenon data, relative humidity data and wind direction and wind force data are respectively input into the system, so that the atmosphere science database of the system is complete in type and is respectively input during input, and the system can conveniently analyze the atmosphere science data.
2. In the atmosphere science calculation container bag system and the atmosphere science calculation container bag method, the sample data is trained by the sample training module, so that the rainfall data, the weather phenomenon data, the relative humidity data and the wind direction and wind power data can be deeply analyzed, and the integrity of atmosphere science is improved.
3. In the atmosphere science calculation container package system and the atmosphere science calculation container package method, containerized big data are adopted to store data, so that the deployment is convenient, and the clustering degree is high. .
Drawings
FIG. 1 is a block diagram of the overall structure of the present invention;
FIG. 2 is a block diagram of an atmospheric data processing module of the present invention;
FIG. 3 is a block diagram of the raw data transcription module of the present invention;
FIG. 4 is a block diagram of an atmospheric data analysis module of the present invention;
FIG. 5 is a diagram of a data analysis statistics module of the present invention;
FIG. 6 is a block diagram of the atmospheric data entry of the present invention;
FIG. 7 is a block diagram of a computing container package in accordance with the present invention.
Detailed Description
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.
Example 1
The invention provides an atmosphere science calculation container package system, which comprises an atmosphere data entry module, an atmosphere data processing module and a calculation container package, wherein the atmosphere data entry module is used for entering atmosphere data, the atmosphere data processing module is used for processing the entered atmosphere data, the calculation container package is used for storing the atmosphere data, the atmosphere data processing module comprises an original data transcription module, an atmosphere data analysis module, a data analysis and statistics module and a data transmission module, the original data transcription module is used for transcribing the original atmosphere data, the atmosphere data analysis module is used for analyzing the atmosphere data, the data analysis and statistics module is used for carrying out statistics on the analyzed atmosphere data, and the data transmission module is used for transmitting the analyzed atmosphere data to the calculation container package.
In this embodiment, the original data transcription module includes a data reading module, a sample training module, a sample analysis module, and a sample generation and storage module, where the data reading module is configured to read in atmospheric data and generate sample data, the sample training module is configured to train the sample data, the sample analysis module is configured to analyze the sample data, and the sample generation and storage module is configured to store and generate a new statistical sample.
The data reading module is used for reading the transcribed meteorological data into the data processing system so as to facilitate further analysis. Because the records of the meteorological environment data in the original file are stored according to the months, in order to analyze the change trend of the meteorological environment data under the long-time condition, the data reading module is divided into two modules of reading single-month meteorological data and reading multi-month meteorological data, so that the statistical analysis work of the meteorological environment data under various readable modules is completed.
The sample training module trains an initial sample by adopting an RBF neural network, and the RBF neural network is set as follows:
1) relative humidity and temperature are respectively used as input layers of the self-organizing competition network, and the competition layer is set to be 6 neurons (representing the types of input vectors); the mutual distance between two-dimensional neurons is calculated by using a mantist Euclidean distance weight function, and the operation principle is D ═ sprt [ sum ((x-y)2)]Wherein x and y are respectively input column vectors, D is a distance matrix, and the learning rate is fixed to be 0.1; the training times are set to be 100; training error of 10-5(ii) a Performing network training by adopting default values for other parameters;
2) after the network training is completed, the network is tested so as to observe the effect and precision of the network training, new untrained meteorological data can be input for inspection, and the trained meteorological data can be used for testing to judge the test result.
The analysis flow of the RBF neural network is as follows:
1) and data normalization processing: and (3) carrying out normalization processing on the relative humidity and temperature lines, wherein the formula is as follows:
in the formula, Y is normalized data, X is original sample data, and max [ X (i) ] is the maximum value in the original sample data.
2) Calculating a weight value and filtering: network training is carried out, the obtained network weight, and normalized relative humidity and temperature after training are carried out;
3) and normalizing, namely recovering the original value, performing inverse normalization on the sample data processed after network training, and finishing filtering.
The sample analysis module adopts a point averaging method, the detection and replacement method is to analyze pseudo data obtained by detection, and a seven-point averaging method is adopted for replacement, and the process is as follows:
1) firstly, analyzing extracted original data, and determining singular data a and a position i of the singular data a; the interpretation criteria for the singular data are: if a (i-1) > 1000 × a (i) or a (i-1) < 10-3If x a (i) is true, the a (i) is considered as singular data;
2) extracting a (i) first three data a (i-3), a (i-2) and a (i-1) and last three data a (i +1), a (i +2) and a (i + 3);
3) the average of six data was calculated, and the formula is as follows:
4) the calculated a (j) replaces the original singular data a (i).
Furthermore, the atmospheric data analysis module comprises a rainfall analysis module, a temperature analysis module, a relative humidity analysis module and a wind direction and wind power analysis module, wherein the rainfall analysis module is used for analyzing rainfall data, the temperature analysis module is used for analyzing temperature data, the relative humidity analysis module is used for analyzing relative humidity data, and the wind direction and wind power analysis module is used for analyzing wind direction and wind power data.
The rainfall analysis module comprises the following processes:
1) importing preprocessed meteorological data;
2) entering a rainfall statistic interface;
3) carrying out related calculation of rainfall;
4) and selecting to generate a report and storing or quitting the rainfall analysis process.
The temperature analysis module comprises a statistical calculation module for calculating the highest temperature, the lowest temperature, the average temperature and the like, and the specific flow is as follows:
1) importing preprocessed meteorological data;
2) entering a temperature analysis interface;
3) carrying out relevant calculation of temperature;
4) graphically displaying the calculation result of the temperature analysis;
5) and selecting to generate a report and storing or quitting the temperature analysis process.
Wherein, the relative humidity analysis module includes total information of relative humidity, total time length of relative humidity, duration of relative humidity interval, and concrete flow:
1) importing preprocessed meteorological data;
2) entering a relative humidity analysis interface;
3) carrying out relative calculation of relative humidity;
4) graphically displaying the calculation result of the relative humidity analysis;
5) and selecting to generate a report and storing or quitting the relative humidity analysis process.
The wind direction and wind power analysis module comprises total duration of eight different wind directions including north wind, northbound wind, east wind, eastern wind, south wind, southeast wind, west wind and westward wind, specific occurrence time information of the eight wind directions and total duration of 1-10 grades of wind, and the specific flow is as follows:
1) importing preprocessed meteorological data;
2) entering a wind direction or a wind power statistical analysis interface;
3) carrying out related calculation of wind direction and wind power;
4) and selecting to generate a report and storing or quitting the temperature analysis process.
Specifically, the data analysis and statistics module comprises a statistical histogram module and a statistical pie chart module, and the statistical histogram module is used for reflecting statistical data in a histogram mode.
The histogram module is used for drawing a series of connected rectangular charts with group distance as a bottom edge and frequency as a height according to the distribution condition of quality data collected from the production process.
The statistical pie chart module is characterized in that the data points are single values drawn in a chart, the values are represented by a bar, a column, a broken line, a sector of a pie chart or a ring chart, dots and other graphs called data marks, and the data marks with the same color form a data series.
Example 2
Referring to fig. 6, the atmospheric data entry module includes a temperature data entry module, a rainfall data entry module, a weather phenomenon data entry module, a relative humidity data entry module, and a wind direction and wind power data entry module, the temperature data entry module is used for entering temperature data, the rainfall data entry module is used for entering rainfall data, the weather phenomenon data entry module is used for entering weather phenomenon data, the relative humidity data entry module is used for entering relative humidity data, and the wind direction and wind power data entry module is used for entering wind direction and wind power data.
In this embodiment, the temperature data entry module enters the maximum temperature, the minimum temperature, and the average temperature.
Further, the relative humidity data entry module records the duration of the relative humidity in different intervals, and the duration is counted according to ten intervals of 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 8-90 and 90-100.
Specifically, the weather phenomenon data entry module enters the duration of weather phenomena such as sunny days, smoke, haze, dust, sand, fog, rain, snow, sand storms and the like.
In addition, the wind direction and the duration of the wind power are recorded by a wind direction and wind power data recording module, wherein the duration of the wind direction and the wind power is counted according to the category of northern wind, northbound wind, eastern wind, southern wind, southerly wind, western wind and westerly wind; "wind duration statistics" are the durations of the various levels of wind, including the duration of each wind from level l to level 10.
Example 3
As shown in fig. 7, the computation container includes a data storage module, an operation stationing module, a centralized deployment module, an operation module, a data processing module, and a node state module, where the data storage module is configured to store data, the operation stationing module is configured to be responsible for Vertical stationing in operation in an Arda framework, the centralized deployment module is configured to be responsible for deployment of Vertical created by the Arda framework in a cluster, the operation module is configured to be responsible for services such as starting and closing of Vertical in operation of the Arda framework, the data processing module is configured to collect, copy, migrate, and output data, and the node state module is configured to be responsible for a state of Vertical in operation in the Arda framework and a state of each node.
In the embodiment, the computing container comprises an infrastructure automation management layer applied to a big data cluster based on container technology, the aim is to simply manage DevOps automation deployment, Arda allows users to build a big data system, and a Vetical is created by the Arda as a management object, so that the automation operation and management system for the life cycle management of the big data cluster is realized.
In Arda, for the management of a large data cluster or a virtualization cluster, the unified management is mainly realized by using Vertical as a management object. Creating a big data cluster is to create a Vertical, where a Vertical may contain multiple services, and each service may contain multiple micro-services.
Further, the data storage module is mainly responsible for storing Arda data, information including all association relations of Arda and states of all components is stored in the group route, and at present, data storage is mainly realized through Etcd, but may also be realized through other database components.
Specifically, the operation stationing module is mainly responsible for Vertical stationing operated in the Arda framework and ensures that Vertical can be operated in the storage system of Arda persistently.
In another aspect, the present invention further provides an atmospheric science computing container pack method, including any one of the above atmospheric science computing container systems, the operating steps of which are as follows:
s1, inputting meteorological data: the method comprises the steps of inputting temperature data through a temperature data input module, inputting rainfall data through a rainfall data input module, inputting weather phenomenon data through a weather phenomenon data input module, inputting relative humidity data through a relative humidity data input module, and inputting wind direction and wind power data through a wind direction and wind power data input module;
s2, raw data transcription: reading in atmospheric data through a data reading module, generating sample data, and training the sample data through a sample training module;
s3, meteorological data analysis: the rainfall data is analyzed through a rainfall analysis module, the temperature data is analyzed through a temperature analysis module, the relative humidity data is analyzed through a relative humidity analysis module, and the wind direction and wind power data is analyzed through a wind direction and wind power analysis module;
s4, operating a calculation container package: the data storage module is used for storing data, the operation of the stationer module is used for stationing the Vertical operated in the Arda framework, the centralized deployment module is used for deploying the Vertical created by the Arda framework in a cluster, the operation module is used for starting and closing the Vertical operated in the Arda framework, the data processing module is used for collecting, copying, transferring and outputting the data, and the node state module is used for controlling the state of the Vertical operated in the Arda framework and the state of each node.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The utility model provides an atmosphere science calculation container package system, includes atmospheric data entry module, atmospheric data processing module and calculation container package, its characterized in that: the atmospheric data entry module is used for entering atmospheric data, the atmospheric data processing module is used for processing the entered atmospheric data, the calculation container package is used for storing the atmospheric data, the atmospheric data processing module comprises an original data transcription module, an atmospheric data analysis module, a data analysis and statistics module and a data transmission module, the original data transcription module is used for transcribing the original atmospheric data, the atmospheric data analysis module is used for analyzing the atmospheric data, the data analysis and statistics module is used for counting the analyzed atmospheric data, and the data transmission module is used for transmitting the analyzed atmospheric data to the calculation container package.
2. The atmospheric science computing pod system of claim 1, wherein: the original data transcription module comprises a data reading module, a sample training module, a sample analysis module and a sample generation and storage module, wherein the data reading module is used for reading in atmospheric data and generating sample data, the sample training module is used for training the sample data, the sample analysis module is used for analyzing the sample data, and the sample generation and storage module is used for storing and generating a new statistical sample.
3. The atmospheric science computing pod system of claim 1, wherein: the atmospheric data analysis module comprises a rainfall analysis module, a temperature analysis module, a relative humidity analysis module and a wind direction and wind power analysis module, wherein the rainfall analysis module is used for analyzing rainfall data, the temperature analysis module is used for analyzing temperature data, the relative humidity analysis module is used for analyzing relative humidity data, and the wind direction and wind power analysis module is used for analyzing wind direction and wind power data.
4. The atmospheric science computing pod system of claim 1, wherein: the data analysis and statistics module comprises a statistical histogram module and a statistical pie chart module, and the statistical histogram module is used for reflecting statistical data in a histogram mode.
5. The atmospheric science computing pod system of claim 1, wherein: the atmospheric data entry module comprises a temperature data entry module, a rainfall data entry module, a weather phenomenon data entry module, a relative humidity data entry module and a wind direction wind power data entry module, wherein the temperature data entry module is used for entering temperature data, the rainfall data entry module is used for entering rainfall data, the weather phenomenon data entry module is used for entering weather phenomenon data, the relative humidity data entry module is used for entering relative humidity data, and the wind direction wind power data entry module is used for entering wind direction wind power data.
6. The atmospheric science computing pod system of claim 1, wherein: the computing container comprises a data storage module, a running and stationing module, a centralized deployment module, an operation module, a data processing module and a node state module, wherein the data storage module is used for storing data, the running and stationing module is used for being responsible for Vertical stationing running in an Arda framework, the centralized deployment module is used for being responsible for deploying Vertical created by the Arda framework in a cluster, the operation module is used for being responsible for starting and closing services of Vertical in the operation of the Arda framework, the data processing module is used for collecting, copying, migrating and outputting data, and the node state module is used for being responsible for the state of the Vertical running in the Arda framework and the state of each node.
7. An atmospheric science computation container pack method comprising the atmospheric science computation container system of any one of claims 1 to 6, the operational steps of which are as follows:
s1, inputting meteorological data: the method comprises the steps of inputting temperature data through a temperature data input module, inputting rainfall data through a rainfall data input module, inputting weather phenomenon data through a weather phenomenon data input module, inputting relative humidity data through a relative humidity data input module, and inputting wind direction and wind power data through a wind direction and wind power data input module;
s2, raw data transcription: reading in atmospheric data through a data reading module, generating sample data, and training the sample data through a sample training module;
s3, meteorological data analysis: the rainfall data is analyzed through a rainfall analysis module, the temperature data is analyzed through a temperature analysis module, the relative humidity data is analyzed through a relative humidity analysis module, and the wind direction and wind power data is analyzed through a wind direction and wind power analysis module;
s4, operating a calculation container package: the data storage module is used for storing data, the operation of the stationer module is used for stationing the Vertical operated in the Arda framework, the centralized deployment module is used for deploying the Vertical created by the Arda framework in a cluster, the operation module is used for starting and closing the Vertical operated in the Arda framework, the data processing module is used for collecting, copying, transferring and outputting the data, and the node state module is used for controlling the state of the Vertical operated in the Arda framework and the state of each node.
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| 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 |
| WO2017181786A1 (en) * | 2016-04-19 | 2017-10-26 | 平安科技（深圳）有限公司 | Data analysis processing method, apparatus, computer device, and storage medium |
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