KR102609126B1 - Method and system for predicting marine wind based on asymmetrical typhoon characteristics - Google Patents

Abstract

Provided is a method for predicting offshore wind based on asymmetric typhoon characteristics, which is performed by at least one processor. The method includes the following steps of: performing preprocessing on data on past typhoons; calculating parameters associated with offshore wind based on the preprocessed data; obtaining time series data on the typhoon; and predicting wind speed of offshore wind based on the parameters and time series data.

Description

Marine wind prediction method and system based on asymmetric typhoon characteristics {METHOD AND SYSTEM FOR PREDICTING MARINE WIND BASED ON ASYMMETRICAL TYPHOON CHARACTERISTICS}

The present disclosure relates to a method and system for predicting offshore wind structure based on asymmetric typhoon characteristics. Specifically, predicting wind speed of offshore wind by deriving typhoon parameter regression equations from data on past typhoons and synthesizing gridded time series data. It relates to methods and systems.

Generally, offshore wind can refer to wind that occurs at sea. Wind is caused by differences in air pressure, and offshore winds can be affected by various factors such as ocean surface temperature, ocean currents, and topography. These offshore winds can affect various activities, so it is necessary to predict offshore winds in advance.

Meanwhile, existing offshore wind prediction methods simply use the Holland method to predict the maximum wind speed of offshore winds such as typhoons. However, the Holland method assumes a symmetrical structure of offshore winds and predicts maximum wind speed through the central pressure of the typhoon, so it does not reflect the characteristics of typhoons with an asymmetric structure moving north from low latitudes to high latitudes. Accordingly, there is a problem that prediction errors such as waves and storm surges caused by actual offshore winds that are different from the forecast and insufficient disaster response may be linked.

The present disclosure provides a method and system (device) for predicting offshore winds based on asymmetric typhoon characteristics to solve the above problems.

The present disclosure may be implemented in various ways, including as a method, device (system), or computer program stored in a readable storage medium.

According to an embodiment of the present disclosure, a method of providing a big data-based marketing planning model includes performing preprocessing on data about past typhoons, and calculating parameters related to offshore winds based on the preprocessed data. It may include a step of acquiring time series data for a typhoon, and predicting the wind speed of an offshore wind based on the parameters and time series data.

According to an embodiment of the present disclosure, data on past typhoons may include typhoon observation data for each quadrant observed in the past over time.

According to an embodiment of the present disclosure, the typhoon observation data for each quadrant observed in the past includes maximum wind speed data for each quadrant, maximum wind speed radius data for each quadrant, and latitude data for each quadrant, and the step of performing preprocessing includes observations made in the past. It may include deriving a typhoon parameter regression equation based on the typhoon observation data for each quadrant.

According to an embodiment of the present disclosure, the step of calculating parameters may include calculating the typhoon radius for each quadrant using the derived typhoon parameter regression equation.

According to an embodiment of the present disclosure, calculating the parameter may further include calculating a strong wind radius parameter based on the preprocessed data and the typhoon radius for each quadrant.

According to an embodiment of the present disclosure, the step of predicting the wind speed of the offshore wind includes generating offshore wind data by inputting a strong wind radius parameter into an offshore wind generation model, and the offshore wind generation model includes the input strong wind radius. It may be a machine learning model that has been previously trained to model offshore winds corresponding to the parameters.

According to an embodiment of the present disclosure, the time series data includes typhoon observation data for each quadrant observed in real time over time, and the step of predicting the wind speed of the offshore wind includes gridding the time series data at a preset spatial resolution and It may include synthesizing gridded time series data and offshore wind data.

According to an embodiment of the present disclosure, the step of synthesizing gridded time series data and offshore wind data includes gridding the offshore wind data to a preset spatial resolution, and the larger of the time series data and offshore wind data within the same grid. It may include the step of determining data having a value as data of the same grid.

In order to execute the method according to an embodiment of the present disclosure on a computer, a computer program stored in a computer-readable recording medium may be provided.

A system according to an embodiment of the present disclosure includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and the at least one program is stored in the past. Perform preprocessing on data about typhoons, calculate parameters related to offshore winds based on the preprocessed data, obtain time series data about typhoons, and predict wind speed of offshore winds based on the parameters and time series data. It may contain commands for

According to various embodiments of the present disclosure, asymmetric information about the offshore wind can be derived from parameters associated with the offshore wind calculated through regression analysis. Accordingly, the spatial characteristics of past typhoons can be reproduced. In addition, by predicting the wind speed of the offshore wind considering the characteristics of asymmetric typhoons, information on the predicted offshore wind can be used as high-quality input data in future wind energy evaluation, wave prediction, storm surge prediction, etc.

According to various embodiments of the present disclosure, data reflecting key characteristics of a typhoon, such as an asymmetric structure, can be generated. These data can more accurately represent the maximum wind speed and spatial structure of past typhoons. Accordingly, by accurately predicting offshore winds such as typhoons, it can be used to respond to disasters and as analysis data for waves, storm surges, etc.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to a person skilled in the art (referred to as “a person skilled in the art”) in the technical field to which the present disclosure pertains from the description of the claims. It will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, in which like reference numerals indicate like elements, but are not limited thereto.
Figure 1 shows an example of an offshore wind prediction method provided according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a plurality of user terminals in order to predict offshore winds according to an embodiment of the present disclosure.
Figure 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
Figure 4 is a diagram illustrating an example of preprocessing data on past typhoons according to an embodiment of the present disclosure.
Figure 5 is a diagram illustrating an example of generating offshore wind data using an offshore wind generation model according to an embodiment of the present disclosure.
Figure 6 is a diagram showing an example of a method for predicting wind speed of offshore wind according to an embodiment of the present disclosure.
Figure 7 is a flowchart illustrating an example of a method according to an embodiment of the present disclosure.

Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, detailed descriptions of well-known functions or configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

In the accompanying drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, overlapping descriptions of identical or corresponding components may be omitted. However, even if descriptions of components are omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments and methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the present disclosure is complete and that the present disclosure does not convey the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification are general terms that are currently widely used as much as possible while considering the function in the present disclosure, but this may vary depending on the intention or precedent of a technician working in the related field, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Accordingly, the terms used in this disclosure should be defined based on the meaning of the term and the overall content of the present disclosure, rather than simply the name of the term.

In this specification, singular expressions include plural expressions, unless the context clearly specifies the singular. Additionally, plural expressions include singular expressions, unless the context clearly specifies plural expressions. When it is said that a certain part includes a certain element throughout the specification, this does not mean excluding other elements, but may further include other elements, unless specifically stated to the contrary.

Additionally, the term 'module' or 'unit' used in the specification refers to a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a 'module' or 'part' refers to components such as software components, object-oriented software components, class components and task components, processes, functions and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. Components and 'modules' or 'parts' may be combined into smaller components and 'modules' or 'parts' or further components and 'modules' or 'parts'. Could be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In some contexts, 'processor' may refer to an application-specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), etc. 'Processor' refers to a combination of processing devices, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such combination of configurations. You may. Additionally, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' refers to random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), May also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. A memory is said to be in electronic communication with a processor if the processor can read information from and/or write information to the memory. The memory integrated into the processor is in electronic communication with the processor.

In the present disclosure, 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated with a server device and a cloud device configured together.

In this disclosure, 'display' may refer to any display device associated with a computing device, e.g., any display device capable of displaying any information/data controlled by or provided by the computing device. can refer to.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A, or may refer to each of some components included in a plurality of A. .

In this disclosure, 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include multiple nodes. In this disclosure, each of the plurality of machine learning models is described as a separate machine learning model, but the present invention is not limited thereto, and some or all of the plurality of machine learning models may be implemented as one machine learning model. Additionally, one machine learning model may include multiple machine learning models. In this disclosure, the terms machine learning model and artificial neural network model may be used interchangeably to refer to the same or similar model.

In the present disclosure, 'sea wind' may refer to wind generated on the sea, such as a typhoon. Additionally, in the present disclosure, 'sea wind wind speed' may refer to the speed of the wind generated over the sea, but is not limited thereto, and may also include the direction of the wind generated over the sea (i.e., wind direction).

Figure 1 shows an example of an offshore wind prediction method provided according to an embodiment of the present disclosure. In one embodiment, data 110 regarding past storms may be received. Here, the data 110 for past typhoons may include typhoon observation data for each quadrant, maximum wind speed data for each quadrant, maximum wind speed radius data for each quadrant, and latitude data for each quadrant. Additionally, data 110 on past typhoons may be preprocessed, and variables representing typhoon characteristics such as maximum wind speed, maximum wind speed radius, and central pressure may be extracted. Using these variables, a typhoon parameter regression equation can be derived.

In one embodiment, based on data 110 for past typhoons, parameters associated with offshore winds may be calculated. Specifically, the typhoon radius for each quadrant can be calculated using the typhoon parameter regression equation. Additionally, the strong wind radius parameter can be calculated based on the preprocessed data and the typhoon radius for each quadrant. Here, the strong wind radius parameter may include a strong wind radius such as 34 knots, 50 knots, and 64 knots.

In one embodiment, offshore wind data may be generated by inputting a strong wind radius parameter into the offshore wind generation model 120. Here, the offshore wind generation model may be a machine learning model trained in advance to model the offshore wind corresponding to the input strong wind radius parameter. The method by which offshore wind data is generated will be described in detail later with reference to FIG. 5.

In one embodiment, data 140 about the wind speed of the offshore wind may be generated based on the time series data 130 and the offshore wind data. Specifically, time series data 130 and offshore wind data may be gridded with a preset spatial resolution. Additionally, by combining the time series data 130 and the offshore wind data, data 140 on the wind speed of the offshore wind can be generated. Accordingly, the wind speed of the offshore wind can be predicted. The method by which time series data 130 and offshore wind data are synthesized will be described in detail later with reference to FIG. 6 .

With this configuration, asymmetric information about the offshore wind can be derived from parameters related to the offshore wind calculated through regression analysis. Accordingly, the spatial characteristics of past typhoons can be reproduced. In addition, by predicting the wind speed of the offshore wind considering the characteristics of asymmetric typhoons, information on the predicted offshore wind can be used as high-quality input data in future wind energy evaluation, wave prediction, storm surge prediction, etc.

Figure 2 is a schematic diagram showing a configuration in which the information processing system 230 is connected to enable communication with a plurality of user terminals 210_1, 210_2, and 210_3 in order to predict offshore winds according to an embodiment of the present disclosure. As shown, a plurality of user terminals 210_1, 210_2, and 210_3 may be connected to an information processing system 230 capable of providing an offshore wind prediction service through the network 220. Here, the plurality of user terminals 210_1, 210_2, and 210_3 may include terminals of users provided with a maritime wind prediction service.

In one embodiment, information processing system 230 is one or more server devices and/or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data associated with providing offshore wind forecast services, etc. , or may include one or more distributed computing devices and/or distributed databases based on cloud computing services.

The offshore wind prediction service provided by the information processing system 230 may be provided to users through an offshore wind prediction service application web browser, web browser extension program, etc. installed on each of the plurality of user terminals 210_1, 210_2, and 210_3. You can. For example, the information processing system 230 may provide information corresponding to a maritime wind prediction request received from the user terminals 210_1, 210_2, and 210_3 through a maritime wind prediction service application or perform processing in response. .

A plurality of user terminals 210_1, 210_2, and 210_3 may communicate with the information processing system 230 through the network 220. The network 220 may be configured to enable communication between a plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of wireless networks such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include communication methods utilizing communication networks that the network 220 may include (e.g., mobile communication networks, wired Internet, wireless Internet, broadcasting networks, satellite networks, etc.) as well as user terminals (210_1, 210_2, 210_3). ) may also include short-range wireless communication between

In Figure 2, the mobile phone terminal (210_1), tablet terminal (210_2), and PC terminal (210_3) are shown as examples of user terminals, but they are not limited thereto, and the user terminals (210_1, 210_2, 210_3) use wired and/or wireless communication. This is possible and may be any computing device on which a maritime wind prediction service application or a web browser, etc. can be installed and executed. For example, user terminals include AI speakers, smartphones, mobile phones, navigation, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, It may include wearable devices, IoT (internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, set-top boxes, etc. In addition, in Figure 2, three user terminals (210_1, 210_2, 210_3) are shown as communicating with the information processing system 230 through the network 220, but this is not limited to this, and a different number of user terminals are connected to the network ( It may be configured to communicate with the information processing system 230 through 220).

In Figure 2, a configuration in which the user's request is transmitted to the information processing system 230 through the user terminals 210_1, 210_2, and 210_3 is shown as an example, but the present invention is not limited thereto, and the user's request is transmitted to the information processing system 230 through the user terminals 210_1, 210_1, and 210_3. It may be provided to the information processing system 230 through an input device associated with the information processing system 230 without going through 210_2, 210_3), and the result of processing the user's request may be sent to an output device associated with the information processing system 230 ( For example, it may be provided to the user through a display, etc.).

Figure 3 is a block diagram showing the internal configuration of the user terminal 210 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing applications, web browsers, etc. and capable of wired/wireless communication, for example, the mobile phone terminal 210_1, tablet terminal 210_2 of FIG. 2, It may include a PC terminal (210_3), etc. As shown, the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input/output interface 318. Similarly, information processing system 230 may include memory 332, processor 334, communication module 336, and input/output interface 338. As shown in FIG. 3, the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be. Additionally, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or to output information and/or data generated from the user terminal 210.

Memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-permanent mass storage devices such as read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. It can be included. As another example, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be included in the user terminal 210 or the information processing system 230 as a separate persistent storage device that is distinct from memory. Additionally, an operating system and at least one program code may be stored in the memories 312 and 332.

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332. This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, disk, tape, DVD/CD- It may include computer-readable recording media such as ROM drives and memory cards. As another example, software components may be loaded into the memories 312 and 332 through the communication modules 316 and 336 rather than computer-readable recording media. For example, at least one program is loaded into memory 312, 332 based on a computer program installed by files provided over the network 220 by developers or a file distribution system that distributes installation files for applications. It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336. For example, the processors 314 and 334 may be configured to execute instructions received according to program codes stored in a recording device such as the memory 312 and 332.

The communication modules 316 and 336 may provide a configuration or function for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and may provide a configuration or function for the user terminal 210 and/or information processing. The system 230 may provide a configuration or function for communicating with other user terminals or other systems (for example, a separate cloud system, etc.). For example, a request or data (e.g., a request for offshore wind prediction, etc.) generated by the processor 314 of the user terminal 210 according to a program code stored in a recording device such as the memory 312 is sent to the communication module 316. It may be transmitted to the information processing system 230 through the network 220 under the control of . Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 316 of the user terminal 210 through the communication module 336 and the network 220. It may be received by the user terminal 210.

The input/output interface 318 may be a means for interfacing with the input/output device 320. As an example, input devices may include devices such as cameras, keyboards, microphones, mice, etc., including audio sensors and/or image sensors, and output devices may include devices such as displays, speakers, haptic feedback devices, etc. You can. As another example, the input/output interface 318 may be a means for interfacing with a device that has components or functions for performing input and output, such as a touch screen, integrated into one. For example, the processor 314 of the user terminal 210 uses information and/or data provided by the information processing system 230 or another user terminal when processing instructions of a computer program loaded in the memory 312. A service screen, etc. constructed by doing so may be displayed on the display through the input/output interface 318. In FIG. 3 , the input/output device 320 is shown not to be included in the user terminal 210, but the present invention is not limited to this and may be configured as a single device with the user terminal 210. Additionally, the input/output interface 338 of the information processing system 230 may be connected to the information processing system 230 or means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. It can be. In FIG. 3, the input/output interfaces 318 and 338 are shown as elements configured separately from the processors 314 and 334, but the present invention is not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. there is.

The user terminal 210 and information processing system 230 may include more components than those in FIG. 3 . However, there is no need to clearly show most prior art components. In one embodiment, the user terminal 210 may be implemented to include at least some of the input/output devices 320 described above. Additionally, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include components generally included in a smartphone, such as an acceleration sensor, a gyro sensor, a microphone module, a camera module, and various physical devices. Various components such as buttons, buttons using a touch panel, input/output ports, and vibrators for vibration may be implemented to be further included in the user terminal 210.

While a program or application for a maritime wind prediction service, etc. is operated, the processor 314 uses input devices such as a touch screen, a keyboard, a camera including an audio sensor and/or an image sensor, and a microphone connected to the input/output interface 318. Input or selected text, image, video, voice and/or motion can be received, and the received text, image, video, voice and/or motion can be stored in the memory 312 or stored in the communication module 316 and network. It can be provided to the information processing system 230 through (220).

The processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input/output device 320, other user terminals, the information processing system 230, and/or a plurality of external systems. It can be configured to do so. Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220. The processor 314 of the user terminal 210 may transmit information and/or data to the input/output device 320 through the input/output interface 318 and output the information. For example, the processor 314 may output or display the received information and/or data on a screen associated with the user terminal 210.

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220.

Figure 4 is a diagram illustrating an example of preprocessing data on past typhoons according to an embodiment of the present disclosure. In one embodiment, data about past storms may be received. Here, data on past typhoons may include typhoon observation data 410 for each quadrant observed in the past over time. In addition, the typhoon observation data for each quadrant observed in the past (410) may include the maximum wind speed data for each quadrant of the typhoon, maximum wind speed radius data for each quadrant, latitude data for each quadrant, central pressure data, etc., which are major variables representing the characteristics of the typhoon. You can.

In one embodiment, a typhoon parameter regression equation 420 may be derived based on typhoon observation data 410 for each quadrant observed in the past. The typhoon parameter regression equation 420 can be expressed as a first-order regression equation such as Equation 1 below.

Here, a _const is a constant, a ₁ to a ₃ are the first to third regression coefficients, lat represents the latitude, Vmax represents the maximum wind speed, and Rmax represents the maximum wind speed radius. Additionally, if the maximum wind speed radius does not exist in the typhoon observation data 410 for each quadrant, the maximum wind speed radius value may be replaced using the Willoughby-Rahn relational equation. Additionally, a typhoon parameter regression equation 420 can be derived by randomly selecting 80% of the typhoon observation data 410 for each quadrant as learning data and 20% as test data and performing regression analysis.

In one embodiment, typhoon radius data 430 for each quadrant may be generated using the typhoon parameter regression equation 420. Here, the typhoon radius data 430 for each quadrant may mean the reproduction results for each quadrant of past typhoons. In addition, the quadrant-specific reproduction results of the typhoon calculated using the typhoon parameter regression equation (420) may have higher reproducibility than the quadrant-specific reproduction results of the typhoon calculated using the Holland technique.

FIG. 5 is a diagram illustrating an example of generating offshore wind data 550 using the offshore wind generation model 540 according to an embodiment of the present disclosure. In one embodiment, based on the preprocessed data 510 including central pressure data, maximum wind speed radius data, etc., and the typhoon radius 520 for each quadrant calculated using a typhoon parameter regression equation, the strong wind radius parameter 530 can be calculated. Specifically, the wind speed and direction of the typhoon can be estimated using the preprocessed data 510 and the typhoon radius 520 for each quadrant. In addition, using the estimated wind speed and direction of the typhoon, the range of strong winds from the center of the typhoon can be estimated, and the size and influence of the typhoon can be quantified.

In one embodiment, based on the gale radius parameter 530, offshore wind data 550 may be generated. Specifically, the strong wind radius parameter 530 may be input to the offshore wind generation model 540. Here, the offshore wind generation model 540 may be a machine learning model trained in advance to model the offshore wind corresponding to the input strong wind radius parameter. Accordingly, offshore wind data 550 related to the structure of the typhoon may be generated. In this case, the characteristics of the typhoon according to the asymmetric structure (for example, the asymmetric structure of the wind field, etc.) may be reflected in the generated offshore wind data 550. Additionally, offshore wind data 550 can be used for reanalysis of past typhoons, reproduction data, etc.

Figure 6 is a diagram showing an example of a method for predicting wind speed of offshore wind according to an embodiment of the present disclosure. In one embodiment, time series data for typhoons may be obtained. Here, the time series data for typhoons may be data for current typhoons, but is not limited thereto, and may include time series data for reanalysis or reproduction of past typhoons. In addition, time series data on typhoons is high-resolution weather analysis data and may include weather variables, ocean variables, etc. that occur in three-dimensional spatial areas throughout the Earth. For example, time series data for major winds may include ERA5 data provided by the European Center for Medium-Range Weather Forecasts. Additionally, time series data for typhoons may be gridded to a preset spatial resolution (e.g., 5 km, etc.).

In one embodiment, data 630 about the wind speed of the offshore wind may be generated by combining the gridded time series data 610 and the offshore wind data 620. Specifically, the offshore wind data 620 may be gridded with the same spatial resolution as the gridded time series data 610. Additionally, among a plurality of grids, the data with a larger value among the offshore wind data 620 and the time series data within the same grid may be determined as the data within the same grid. Through this process, data 630 on the wind speed of the offshore wind can be generated by determining the data in all the plurality of grids.

With this configuration, data reflecting key characteristics of typhoons, such as asymmetric structure, can be generated. These data can more accurately represent the maximum wind speed and spatial structure of past typhoons. Accordingly, by accurately predicting offshore winds such as typhoons, it can be used to respond to disasters and as analysis data for waves, storm surges, etc.

Figure 7 is a flow chart illustrating an example method 700 according to one embodiment of the present disclosure. In one embodiment, method 700 may be performed by at least one processor (at least one processor of a user terminal or an information processing system). The method 700 may begin with the processor performing preprocessing on data about past typhoons (S710). Here, data on past typhoons may include typhoon observation data for each quadrant observed in the past over time. Additionally, typhoon observation data for each quadrant observed in the past may include maximum wind speed data for each quadrant, maximum wind speed radius data for each quadrant, and latitude data for each quadrant. In this case, the processor may derive a typhoon parameter regression equation based on typhoon observation data for each quadrant observed in the past.

Thereafter, the processor may calculate parameters related to offshore wind based on the preprocessed data (S720). Specifically, the processor can calculate the typhoon radius for each quadrant using the derived typhoon parameter regression equation. Additionally, the processor may calculate a strong wind radius parameter based on the preprocessed data and the typhoon radius for each quadrant.

Afterwards, the processor can acquire time series data about the typhoon (S730). Here, the time series data may include typhoon observation data for each quadrant observed in real time over time.

Afterwards, the processor can predict the wind speed of the offshore wind based on the parameters and time series data (S740). Specifically, the processor may generate offshore wind data by inputting the strong wind radius parameter into the offshore wind generation model. Here, the offshore wind generation model may be a machine learning model trained in advance to model the offshore wind corresponding to the input strong wind radius parameter.

In one embodiment, the processor can grid the time series data to a preset spatial resolution. Additionally, the processor may synthesize gridded time series data and offshore wind data. Specifically, the processor can grid offshore wind data to a preset spatial resolution. Additionally, the processor may determine the data with a larger value among the time series data and the offshore wind data within the same grid as the data of the same grid.

The above-described method may be provided as a computer program stored in a computer-readable recording medium for execution on a computer. Media may be used to continuously store executable programs on a computer, or may be temporarily stored for execution or download. In addition, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and There may be something configured to store program instructions, including ROM, RAM, flash memory, etc. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

The methods, operations, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or performed as any combination of those designed to perform the functions described in. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

For firmware and/or software implementations, techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), and PROM ( on computer-readable media such as programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

When implemented in software, the techniques may be stored on or transmitted through a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. By way of non-limiting example, such computer readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or the desired program code in the form of instructions or data structures. It can be used to transfer or store data and can include any other media that can be accessed by a computer. Any connection is also properly termed a computer-readable medium.

For example, if the Software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, , fiber optic cable, twisted pair, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, disk and disk include CD, laser disk, optical disk, digital versatile disc (DVD), floppy disk, and Blu-ray disk, where disks are usually magnetic. It reproduces data optically, while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known. An exemplary storage medium may be coupled to the processor such that the processor may read information from or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and storage medium may reside within an ASIC. ASIC may exist within the user terminal. Alternatively, the processor and storage medium may exist as separate components in the user terminal.

Although the above-described embodiments have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the disclosure is not limited thereto and may also be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Furthermore, aspects of the subject matter of this disclosure may be implemented in multiple processing chips or devices, and storage may be similarly effected across the multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure as can be understood by a person skilled in the art to which the invention pertains. Additionally, such modifications and changes should be considered to fall within the scope of the claims appended hereto.

110: Data on past typhoons
120: Offshore wind generation model
130: Time series data
140: Data on wind speed of offshore wind

Claims (10)

In a method for predicting offshore winds based on asymmetric typhoon characteristics, performed by at least one processor,
Performing preprocessing on data about past typhoons;
Calculating parameters related to offshore wind based on the preprocessed data;
Obtaining time series data for typhoons; and
Predicting wind speed of offshore wind based on the parameters and the time series data
Including,
The data on the past typhoons include typhoon observation data for each quadrant observed in the past over time,
The typhoon observation data for each quadrant observed in the past includes maximum wind speed data for each quadrant, maximum wind speed radius data for each quadrant, and latitude data for each quadrant,
The step of performing the preprocessing is,
Deriving a typhoon parameter regression equation based on the typhoon observation data for each quadrant observed in the past
Including,
The step of calculating the parameter is,
Using the derived typhoon parameter regression equation, calculating the typhoon radius for each quadrant; and
Calculating a strong wind radius parameter based on the preprocessed data and the typhoon radius for each quadrant.
Including,
The step of predicting the wind speed of the offshore wind is,
Inputting the strong wind radius parameter into an offshore wind generation model to generate offshore wind data
Including,
The offshore wind generation model is a machine learning model trained in advance to model the offshore wind corresponding to the input strong wind radius parameter.
delete delete delete delete delete According to paragraph 1,
The time series data includes typhoon observation data for each quadrant observed in real time over time,
The step of predicting the wind speed of the offshore wind is,
gridding the time series data to a preset spatial resolution; and
Synthesizing the gridded time series data and the offshore wind data
Including, offshore wind prediction method.
In clause 7,
The step of synthesizing the gridded time series data and the offshore wind data,
gridding the offshore wind data to the preset spatial resolution; and
Determining the data with a larger value among the time series data and the offshore wind data within the same grid as the data of the same grid.
Including, offshore wind prediction method.
A computer program stored in a computer-readable recording medium for executing the offshore wind prediction method according to any one of claims 1, 7 to 8 on a computer.
As a system,
communication module;
Memory; and
At least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory,
The at least one program is,
Perform preprocessing on data about past typhoons,
Based on the preprocessed data, calculate parameters related to offshore wind,
Obtain time series data on typhoons,
Based on the parameters and the time series data, it includes instructions for predicting the wind speed of the offshore wind,
The data on the past typhoons include typhoon observation data for each quadrant observed in the past over time,
The typhoon observation data for each quadrant observed in the past includes maximum wind speed data for each quadrant, maximum wind speed radius data for each quadrant, and latitude data for each quadrant,
Performing the preprocessing is,
Deriving a typhoon parameter regression equation based on the typhoon observation data for each quadrant observed in the past.
Including,
Calculating the above parameters is,
Using the typhoon parameter regression equation derived above, calculate the typhoon radius for each quadrant,
Calculating a strong wind radius parameter based on the preprocessed data and the typhoon radius for each quadrant.
Including,
To predict the wind speed of the offshore wind,
Inputting the strong wind radius parameter into the offshore wind generation model to generate offshore wind data
Including,
The offshore wind generation model is a pre-trained machine learning model to model the offshore wind corresponding to the input strong wind radius parameter.

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