KR20140037631A - Cloud server providing rainfall information - Google Patents

Cloud server providing rainfall information Download PDF

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KR20140037631A
KR20140037631A KR1020120104011A KR20120104011A KR20140037631A KR 20140037631 A KR20140037631 A KR 20140037631A KR 1020120104011 A KR1020120104011 A KR 1020120104011A KR 20120104011 A KR20120104011 A KR 20120104011A KR 20140037631 A KR20140037631 A KR 20140037631A
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rainfall
data
image
raster image
raw data
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KR1020120104011A
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Korean (ko)
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장우정
김순연
원영진
제영호
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(주)헤르메시스
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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    • G01W1/00—Meteorology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

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Abstract

The present invention relates to a cloud server for providing rainfall information. The cloud server, which is established to have a plurality of virtual machines and storages based on cloud computing, comprises a data receiving unit for receiving UF radar raw data from an external system for providing weather information; an image generating unit for generating a rainfall raster image based on the UF radar raw data; and a data providing unit for providing the rainfall raster image in response to a request of a user terminal. As such, the cloud server provides the rainfall raster image based on the cloud computing, thereby flexibly operating a server according to service usage and economically using resources by performing the processing reflecting the service usage. [Reference numerals] (110) Data receiving unit; (120) Image generating unit; (130) Data providing unit; (140) Data allocating unit; (150) Usage predicting unit; (160) Virtual machine variation unit; (200) Communication network; (AA) Red-hot coke; (BB) Boudouard reaction & Water-gas shift reaction

Description

Cloud server providing rainfall information {CLOUD SERVER PROVIDING RAINFALL INFORMATION}

The present invention relates to a server for providing rainfall information, and more particularly, to a cloud server for processing rainfall data through cloud computing and providing rainfall information for providing a generated image.

In recent years, heavy rains and typhoons have caused heavy rains and flooded houses, resulting in many property and casualties. In order to minimize such damages, it is important to analyze the complex rainfall distribution using distributed rainfall runoff models and predict floods in real time so that measures for flooding can be performed quickly.

Rainfall estimation is generally based on radar observations. On the other hand, radar observation institutes provide UF (Universal Format) radar observation data having a radial shape to be used for rainfall estimation.

Radar observation is performed by changing the azimuth angle while the antenna elevation angle is fixed, and repeatedly performing observation while repeatedly changing the antenna elevation angle to observe three-dimensionally the high altitude. Based on the obtained volume observation data, the rainfall is calculated using the constant altitude plan position indicator (CAPPI) data, which is a horizontal cross section, by extracting a specific altitude value. In this case, the CAPPI data may be generated as a raster data structure in the form of a grid.

In response to this, the present applicant has proposed a web service system and a web service method for estimating rainfall by generating rainfall raster images and providing the same to a user, and performing research analysis using the same. At present, application number 2012-0060191 is still pending application.

Applicant can increase the efficiency in generating the rainfall raster image while maintaining the advantages of the web service system that provides the conventional rainfall raster image, and can operate the system reasonably by adaptively processing according to the user's service usage. A new cloud server based on cloud computing has been proposed.

The present invention has been proposed to achieve the above object, and provides a rainfall raster image by processing based on cloud computing, and rationalizes system construction and operation by performing adaptive processing according to a user's service usage. To provide a cloud server that can be performed with.

According to an aspect of the present invention, there is provided a cloud server including a plurality of virtual machines and storage based on cloud computing, comprising: a data receiving unit configured to receive UF radar raw data from an external system providing weather information; An image generator for generating a rainfall raster image based on the UF radar raw data; And a data provider configured to provide the rainfall raster image in response to a request of a user terminal.

In addition, the cloud server according to the present invention further comprises a data allocator for dividing or replicating the received UF radar raw data into a plurality of the image generation unit, the image generator is divided or duplicated the UF radar raw data Dispersion processing may generate a plurality of rainfall raster partial images, and increase the processing speed by integrating the rainfall raster partial images to generate the rainfall raster images.

And a usage estimator for predicting a service usage amount based on statistical information of the number of times of rainfall raster image requests of the user terminal, wherein the image generator is configured to estimate the service usage amount when the estimated service usage amount is less than or equal to a predetermined reference amount. Processing for generating the rainfall raster image in response to a request received from the terminal; and processing the generation of the rainfall raster image in response to receiving the UF radar raw data when the service usage exceeds the reference amount. Can be implemented economically.

The apparatus may further include a virtual machine increase / decrease unit configured to increase or decrease the number of virtual machines constituting at least one of the data receiver, the data allocator, the image generator, and the data provider in response to the estimated service usage amount. can do.

On the other hand, when the area of the rainfall raster image requested by the user terminal is part of the image generator, the portion of the UF radar raw data corresponding to the area requested by the user terminal to generate the rainfall raster image for efficient processing We can plan.

As described above, according to the present invention, by providing a rainfall raster image based on cloud computing, the server can be flexibly operated according to the service usage amount, and resource processing is performed by performing processing for image generation reflecting the service usage amount. Economically available.

In addition, according to the present invention, the processing speed is increased by distributing the UF radar raw data, and only a part of data is extracted in response to a user's request to generate a rain raster image, thereby achieving efficiency.

1 is a block diagram of a cloud computing-based rainfall information providing system constructed by including a cloud server according to an embodiment of the present invention;
2 is a block diagram of a cloud server according to an embodiment of the present invention;
3 and 4 are diagrams for explaining an example of a method in which a cloud server extracts a portion of UF radar raw data to generate a rainfall raster image according to an embodiment of the present invention;
5 is a flowchart illustrating a process of generating a rainfall raster image through a cloud server according to an embodiment of the present invention; And
6 is a flowchart illustrating a process of providing rainfall information to a user through a cloud server according to an exemplary embodiment of the present invention.

The cloud server described in the present specification includes a plurality of virtual machines and storage generated by virtualizing resources on a network, and means a server built on a cloud computing base that can flexibly expand computing resources as needed.

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings.

1 is a block diagram of a cloud computing-based rainfall information providing system constructed by including a cloud server 100 according to an embodiment of the present invention.

Referring to FIG. 1, the cloud server 100 receives UF (Universal Format) radar raw data from a related organization that provides weather information, and generates a rainfall raster image based on the raster image and provides the user with a service. do. UF radar raw data is a type of radar data structure with three-dimensional radial shape and consists of six headers. It includes radar information, azimuth and altitude, start time of volume observation, beam information, and pulse. Include radar observation information, including intervals.

Meanwhile, the rainfall raster image provided by the cloud server 100 is a grid-like image in which RGB values are inputted to pixels according to the rainfall values, and the visual raster images have file formats such as BMP, JPG, and PNG. In addition, as raster data in the field of spatial information, numerical lattice information files such as ESRI ASC and Surfer GRD, in which rainfall numerical value information is written in a grid cell, are also included.

In addition, the cloud server 100 predicts the service usage amount of the user using the rainfall information, adaptively reflects the estimated service usage amount, performs a distributed process when generating the rainfall raster image, and only a part of the data. Operational efficiency is increased by including the ability to perform rainfall raster image generation processing only on the areas that need to be extracted. This will be described later with reference to FIGS. 2 to 4.

The user terminal 200 requests rainfall information from the cloud server 100 and receives a rainfall raster image from the cloud server 100. The user terminal 200 includes not only a computer but also various electronic devices capable of wired and wireless communication, and includes, for example, a smartphone, a PDA, a tablet PC, a server computer, a desktop, a notebook, a PCS phone, and the like.

On the other hand, when the user terminal 200 requests the rainfall information to the cloud server 100, the size of the rainfall raster image, grid scale, file format (BMP, PNG, JPG, ASC, GRD, etc.), time zone of the required rainfall information, and Requests may include additional information such as the region of rainfall information required. In this regard, the cloud server 100 processes and provides the rainfall raster image to meet the request of the user terminal 200.

2 is a block diagram illustrating a configuration of a cloud server 100 according to an exemplary embodiment of the present invention.

Referring to FIG. 2, the cloud server 100 according to an exemplary embodiment of the present invention includes a data receiver 110, an image generator 120, a data provider 130, a data allocator 140, and a usage estimator. 150, and a virtual machine sensitizer 160.

The data receiver 110 receives the UF radar raw data from a server of a related organization that provides weather information.

The image generator 120 generates a rainfall raster image based on the UF radar raw data received through the data receiver 110. The rainfall raster image may be generated by arranging color values in pixels according to rainfall values estimated based on UF radar raw data. In this case, the unit of the rainfall raster may be mm / h. In addition, if the rainfall raster image is a numerical grid format other than the image formats such as BMP, JPG, and PNG as described above, the process of converting the rainfall value to the color value is omitted, and the rainfall raster image is generated by writing the rainfall value into the corresponding cell. can do. Meanwhile, estimating rainfall based on radar data may be performed by various known algorithms showing the relationship between radar reflectivity and rainfall, such as the Marshall and Palmer relations. Therefore, detailed description thereof will be omitted for simplicity.

For example, the rainfall raster image may be a constant altitude plan position indicator (CAPPI) rainfall raster image obtained by extracting a specific altitude value from the UF radar raw data and displaying a horizontal cross section. In this case, the extraction of the CAPPI data from the UF radar raw data may be performed by spatial calculation using Mohr's method.

On the other hand, when the area of rainfall information requested by the user terminal 200 is limited to a part, the image generator 120 extracts a part corresponding to a required part of the UF radar raw data, and rainfall raster only for the extracted part. You can create an image to increase efficiency. Hereinafter, a description will be given with reference to FIGS. 3 and 4.

3 and 4 are diagrams for explaining an example of a method in which the cloud server 100 according to an embodiment of the present invention extracts a part of UF radar raw data to generate a CAPPI rainfall raster image. For convenience, the earth curvature is not considered.

First, referring to FIG. 3, when it is assumed that a part of the area requested by the user terminal 200 is a rectangular area consisting of p1, p2, p3, and p4, the UF radar raw data has a three-dimensional radial coordinate system value. The coordinates of some regions are converted into polar coordinates p1, p2, p3, and p4 so as to correspond to each other, and based on this, an azimuth angle Θ and a distance d that need to be extracted from the UF radar raw data are calculated. If the above process is expressed as an equation, it is as follows.

First, the coordinates of the required area are converted into polar coordinates using the following equation.

Figure pat00001

Subsequently, the minimum and maximum azimuth angles and distance values are calculated by comparing the converted polar coordinates p1, p2, p3, and p4, and a range of azimuth angles and distances to be extracted from the UF radar raw data is derived.

However, as can be seen from FIG. 4, the range extracted through the above process is a result of projecting the data having a radial curved shape to the ground, so that the calculated distance d from the ground surface is expressed by the following equation. The distance d Φ in the plane position indicator (PPI) plane must be calculated by correcting through.

Figure pat00002

As a result, the image generator 120 extracts azimuth angles corresponding to Θ 4 to Θ 2 and distances from d Φ 1 to d Φ3 of the UF radar raw data, and extracts the CAPPI rainfall raster image based thereon. Can be generated.

As such, when the user terminal 200 requests only rainfall information regarding a certain region, the cloud server 100 according to the present invention may extract the data corresponding thereto and generate a rainfall raster image for efficiency. Meanwhile, in addition to the above-described method, various known methods for generating a rain raster image by extracting some of the UF radar raw data may be applied.

Referring back to FIG. 2, the data provider 130 provides a rainfall raster image generated by the image generator 120 in response to a request for rainfall information of the user terminal 200. In this case, the data provider 130 may process and transmit the rainfall raster image so as to correspond to additional information requested by the user terminal 200 among the generated rainfall raster images. For example, if the user terminal 200 requests the rainfall raster image by specifying the time zone of the rainfall information required, only the image corresponding to the requested time zone among the generated and stored rainfall raster images may be selected and provided to the user terminal 200.

The data allocator 140 divides or duplicates the received UF radar raw data into a plurality of images and allocates the generated UF radar raw data to the image generator 120. Accordingly, the plurality of virtual machines constituting the image generating unit 120 generates a rainfall raster partial image based on the allocated UF radar raw data, and integrates the generated plurality of rainfall raster partial images, and finally the user terminal 200. We will create a rainfall raster image for. In this way, by distributing the UF radar raw data, processing time can be reduced to quickly generate rainfall raster images.

The usage estimator 150 estimates the service usage based on statistical information of the number of times of rainfall information request of the user terminal 200. The statistical information is data representing the number of times that the user terminal 200 requests rainfall information. For example, the statistical information may be information regarding the number of requests corresponding to the same month in the previous year or the number of requests in the same time zone. It doesn't happen.

The image generator 120 adaptively performs processing based on the service usage amount predicted by the usage estimator 150. That is, if the estimated service usage amount is less than the predetermined reference amount, since there are few service users, rainfall raster image generation is performed only when the user terminal 200 requests rainfall information, thereby reducing unnecessary processing, and concentrating on rainy season and typhoon. When heavy rain is expected and the estimated service usage exceeds the reference amount, the rainfall raster image may be generated in response to receiving the UF radar raw data from the data receiver 110 so as to quickly respond to many requests. In this case, a variety of rainfall raster images can be generated and stored in advance with the CAPPI altitude, grid scale, and file format requested by the user, and can be provided immediately upon request by the user.

The virtual machine increase / decrease unit 160 corresponds to the service usage amount predicted by the usage estimator 150, the data receiver 110, the image generator 120, the data provider 130, and the data allocator 140. Increase or decrease the number of virtual machines constituting. That is, when the service usage is large, the number of virtual machines is increased to quickly respond to the request of the user terminal 200, and when the service usage is low, the number of virtual machines is reduced to prevent unnecessary resource waste. .

As such, the cloud server 100 according to the present invention may provide a raster image of rainfall based on cloud computing to flexibly operate the server according to the service usage amount, and perform the processing by reflecting the expected service usage amount. Resource can be used economically.

Hereinafter, referring to the flowcharts of FIGS. 5 and 6, a series of processes in which rainfall information is generated through the cloud server 100 according to an exemplary embodiment of the present invention and provided to the user terminal 200 will be described.

First, referring to FIG. 5, the data receiver 110 of the cloud server 100 receives UF radar raw data from a related organization (S10). The usage estimator 150 estimates service usage by using statistical data such as the number of rainfall information requests (S11), and then adaptively performs rainfall raster image generation processing based on the estimated service usage. do.

That is, if the predicted service usage amount is larger than the predetermined reference amount, it is important to promptly respond to the request of the user terminal 200, so that the rainfall raster image is generated according to the reception of the UF radar raw data (S13 and S15). At this time, the rainfall raster image can be stored in various ways by reflecting the CAPPI altitude, grid scale, file format, etc., which are requested by users, and can immediately respond to the service request. In addition, as described above, when the rainfall raster image is generated, the UF radar raw data may be divided or copied into a plurality, and the image generator 120 may shorten the processing time by distributing the divided or copied data.

On the other hand, if the estimated service usage is less than the standard it is inefficient to generate all of the received UF radar raw data as the rain raster image as described above for the rainfall raster image generation when the user terminal 200 requests the rainfall information The processing may be performed to prevent unnecessary processing (S13).

Referring to FIG. 6, a process of providing a generated rainfall raster image to a user will be described. When receiving a request for the rainfall raster image from the user terminal 200 (S20), it is determined whether the requested rainfall raster image is a parasitic image stored by the image generator 120 (S21). In this case, whether the image is parasitic or not is determined by a result of comparing the estimated service usage amount with the reference amount according to step S13 of FIG. 5.

That is, when the rainfall raster image requested by the user is the information at the point of time at which the service usage is predicted to be high, the rainfall raster image stored in response to the request of the user terminal 200 is already generated by the image generator 120. To provide. On the other hand, if the requested rainfall raster image is information at the point of time when the estimated service usage amount is less than the reference amount, the image generation process is not performed. (S23, S25). In this case, the data provider 130 may process and provide the generated rainfall raster image to meet the request of the user terminal 200.

Each step described above may be added, changed, and omitted as necessary. For example, when the region of rainfall information requested by the user terminal 200 is limited to a part, the image generator 120 generates a rainfall raster image by extracting some data corresponding to the request region from the UF radar raw data. It may be further included to increase the efficiency of the processing.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Therefore, the scope of protection of the present invention should be determined by the appended claims and their equivalents.

100: cloud server 110: data receiving unit
120: image generating unit 130: data providing unit
140: data allocation unit 150: usage prediction unit
160: virtual machine increments 200: user terminal

Claims (5)

In the cloud server built with a plurality of virtual machines and storage based on cloud computing,
A data receiver for receiving UF radar raw data from an external system providing weather information;
An image generator for generating a rainfall raster image based on the UF radar raw data; And
And a data provider for providing the rainfall raster image in response to a request of a user terminal.
The method of claim 1,
Further comprising a data allocator for dividing or replicating the received UF radar raw data into a plurality of images and assigning them to the image generator,
The image generating unit generates a plurality of rainfall raster partial images by distributing the divided or duplicated UF radar raw data, and generates the rainfall raster images by integrating the rainfall raster partial images.
The method of claim 1,
And a usage estimating unit for predicting a service usage amount based on statistical information of the number of times of the rainfall raster image request of the user terminal.
The image generating unit performs processing for generating the rainfall raster image in response to the request reception of the user terminal when the estimated service usage amount is less than or equal to a predetermined reference amount, and when the service usage amount exceeds the reference amount, And processing for generating the rainfall raster image in response to receiving UF radar raw data.
The method of claim 3,
And a virtual machine increase / decrease unit configured to increase or decrease the number of virtual machines constituting at least one of the data receiver, the data allocator, the image generator, and the data provider in response to the estimated service usage amount. Cloud server characterized by.
The method of claim 1,
The image generating unit generates a rainfall raster image by extracting a portion corresponding to the region requested by the user terminal from the UF radar raw data when the region of the rainfall raster image requested by the user terminal is a part of the cloud; server.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104898122A (en) * 2015-06-18 2015-09-09 周卫平 System of obtaining three dimensional atmospheric wind field information products based on cloud computing platform
CN113253260A (en) * 2021-04-28 2021-08-13 广州铭子通科技有限公司 Ground penetrating radar parameter setting method and system based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104898122A (en) * 2015-06-18 2015-09-09 周卫平 System of obtaining three dimensional atmospheric wind field information products based on cloud computing platform
CN113253260A (en) * 2021-04-28 2021-08-13 广州铭子通科技有限公司 Ground penetrating radar parameter setting method and system based on big data

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