Device for determining surface roughness classification using location-based automatic partitioning and atmospheric boundary layer model and method of the same

Abstract

The present invention relates to a device and method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model. The method according to the present invention, in a method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model performed by a device, may include: a step of obtaining latitude and longitude coordinates corresponding to location information for a target area that is a planned construction site using a geographic coordinate system; a step of extracting wind speed data and GIS data using transformed coordinates converted from the latitude and longitude coordinates into a plurality of different projected coordinate systems; a step of calculating an effective surface roughness length and a patch length using the GIS data; a step of calculating a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and the patch length; and a step of automatically determining the surface roughness classification of the target area based on the wind speed profile.

Description

Technology Field

[0001] The present invention relates to a device and method for determining surface roughness classification using a location-based automatic segmentation and atmospheric boundary layer model. Specifically, it relates to a device and method capable of automatically segmenting and quantifying surface roughness using wind speed data and topographic (GIS) data based on location information of a planned construction site, calculating a wind speed profile through an atmospheric boundary layer (ABL) model, and then automatically determining the surface roughness classification. Background Technology

[0002] In architectural design, the influence of wind is a factor that must be considered. Wind characteristics, such as wind speed, can be affected by the surrounding area of ​​a construction site, and if wind speeds are accelerated by the surrounding region, it can threaten the safety of the building. To account for the loads that wind exerts on a building, wind loads are calculated during the design process, and to calculate these loads, it is necessary to determine the design wind speed.

[0003] The design wind speed may vary depending on the surface roughness, which indicates the roughness of the ground surface.

[0004] However, during building design, ground surface illuminance is frequently determined by the designer's subjective judgment, which leads to problems such as reduced safety and economic efficiency of the building.

[0005] The matters described above as background technology are intended only to enhance understanding of the background of the present invention and should not be construed as an acknowledgment that they constitute prior art already known to those skilled in the art. Prior art literature

[0006] Republic of Korea Registered Patent No. 10-1396395 The problem to be solved

[0007] The problem that the present invention aims to solve is to provide a surface roughness classification determination device and method using a location-based automatic partitioning and atmospheric boundary layer model.

[0008] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0009] A method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention for solving the above-described problem may include, in a method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model performed by a device, a step of obtaining latitude and longitude coordinates corresponding to location information for a target area that is a planned construction site using a geographic coordinate system; a step of extracting wind speed data and GIS data using transformed coordinates converted from the latitude and longitude coordinates into a plurality of different projection coordinate systems; a step of calculating an effective surface roughness length and a patch length using the GIS data; a step of calculating a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and the patch length; and a step of automatically determining the surface roughness classification of the target area based on the wind speed profile.

[0010] In one embodiment of the present invention, the step of extracting wind speed data and GIS data comprises: a step of converting the latitude and longitude coordinates into first transformed coordinates using a first projection coordinate system, and then extracting the wind speed data using the first transformed coordinates; and a step of converting the latitude and longitude coordinates into second transformed coordinates using a second projection coordinate system, and then extracting the GIS data using the second transformed coordinates, wherein the first projection coordinate system is a coordinate system for mapping the wind speed data, and the second projection coordinate system is a coordinate system for mapping the GIS data.

[0011] In one embodiment of the present invention, the wind speed data and GIS data are extracted for a predetermined radius area centered on the target location, and the predetermined radius can be set to a maximum of 30 km.

[0012] In one embodiment of the present invention, the step of calculating the effective surface roughness length and the patch length may include: reclassifying the GIS data according to a terrain type reconstructed from a multi-stage classification system and assigning the surface roughness length corresponding to the reclassified terrain type; automatically dividing the target area by wind direction based on the surface roughness length; and calculating the effective surface roughness length and the patch length by reflecting the terrain history distributed along the windward direction defined for each wind direction.

[0013] In one embodiment of the present invention, the step of automatically dividing the target area by wind direction may include setting a plurality of zones according to the wind direction and creating detailed zones based on areas with similar surface roughness characteristics within each zone.

[0014] In one embodiment of the present invention, the step of automatically dividing the target area by wind direction may include defining a zone as an area including a predetermined angle range based on each wind direction, setting a zone having a range of ±15° for each wind direction, and dividing the entire 360° into predetermined intervals to form a plurality of wind direction zones.

[0015] In one embodiment of the present invention, the wind speed profile can be calculated by reflecting the change in frictional speed according to the patch length.

[0016] In one embodiment of the present invention, the atmospheric boundary layer model may be a model that calculates the wind speed distribution by considering the formation of an internal boundary layer due to changes in surface roughness.

[0017] In addition, a surface roughness classification determination device using a location-based automatic partitioning and atmospheric boundary layer model according to another embodiment of the present invention for solving the above-described problem comprises: a database unit storing at least one command for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model; and a processor performing an operation according to the command, wherein the processor obtains latitude and longitude coordinates corresponding to location information for a target area that is a planned construction site using a geographic coordinate system, extracts wind speed data and GIS data using transformed coordinates converted from the latitude and longitude coordinates into a plurality of different projection coordinate systems, calculates an effective surface roughness length and a patch length using the GIS data, calculates a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and the patch length, and can automatically determine the surface roughness classification of the target area based on the wind speed profile.

[0018] A program according to one embodiment of the present invention is combined with a computer, which is hardware, and is stored on a recording medium readable by a computer so as to be able to perform a method for determining surface roughness classification using the location-based automatic partitioning and atmospheric boundary layer model.

[0019] Other specific details of the present invention are included in the detailed description and drawings. Effects of the invention

[0020] According to the present invention, surface roughness can be automatically classified and quantified using wind speed data and topographic (GIS) data based on location information of a planned construction site, and surface roughness classification can be automatically determined after calculating a wind speed profile through an Atmospheric Boundary Layer (ABL) model. That is, by combining automated quantitative analysis using location-based data and GIS information with an atmospheric boundary layer model, the existing subjective and manual-dependent method of evaluating surface roughness classification can be improved, thereby enhancing the accuracy, objectivity, and efficiency of surface roughness classification determination.

[0021] According to the present invention, by quantifying and automating the determination of surface roughness classification, objectivity and reproducibility of the evaluation are secured, the precision of wind speed calculation is improved, and the analysis time is drastically shortened, while simultaneously ensuring reliability of data management and economic efficiency of the design.

[0022] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0023] FIG. 1 is a block diagram illustrating a surface roughness classification determination device using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention. FIG. 2 is a diagram illustrating a method for determining surface roughness using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention. Figure 3 is a diagram illustrating a method for extracting wind speed data illustrated in Figure 2. Figure 4 is a diagram illustrating a method for extracting GIS data illustrated in Figure 2. FIG. 5 is a diagram illustrating the step of calculating the effective surface roughness length and patch length shown in FIG. 2. FIG. 6 is a diagram illustrating a method for reclassifying GIS data according to terrain type according to an embodiment of the present invention. FIG. 7 is a drawing for explaining the surface roughness length assigned according to a reclassified terrain type according to an embodiment of the present invention. FIG. 8 is a diagram illustrating a method for automatically dividing a target area shown in FIG. 5 by wind direction. Figure 9 is a diagram illustrating a method for calculating the patch length shown in Figure 5. FIG. 10 is a diagram illustrating a method for automatically determining the surface roughness classification shown in FIG. 2. Specific details for implementing the invention

[0024] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the present invention, and the present invention is defined only by the scope of the claims.

[0025] The terms used in this specification are for describing the embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms "comprises" and / or "comprising" used in this specification do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and "and / or" includes each of the mentioned components and all combinations of one or more. Although terms such as "first," "second," etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Therefore, the first component mentioned below may be the second component within the technical scope of the invention.

[0026] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which the present invention pertains. Furthermore, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0028] FIG. 1 is a block diagram illustrating a surface roughness classification determination device using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention.

[0029] As illustrated in FIG. 1, the surface roughness classification determination device (10) using a location-based automatic partitioning and atmospheric boundary layer model according to one embodiment of the present invention may be a device capable of objectively and quantitatively determining the surface roughness of a target area by converting latitude and longitude coordinates into a plurality of projected coordinate systems based on location information to spatially align wind speed data and GIS data, reclassifying the terrain from the GIS data to assign a surface roughness length, calculating an effective surface roughness length and patch length reflecting the windward terrain distribution by wind direction, and applying an atmospheric boundary layer model with this as input to calculate a wind speed profile.

[0030] Specifically, the surface roughness classification determination device (10) may include a data communication unit (100), a database unit (120), and a processor (140).

[0031] In this embodiment, the surface roughness classification determination device (10) may include a server as a computing system that provides services to other computers or devices in a computer network or stores and manages data. The surface roughness classification determination device (10) may accept requests from other computers or devices called clients and provide a response or data to the request.

[0032] The data communication unit (100) can be configured regardless of the communication mode, such as wired and wireless, and can be configured with various communication networks such as a short-range communication network (PAN: Personal Area Network) and a short-range communication network (WAN: Wide Area Network).

[0033] Additionally, the data communication unit (100) can operate based on the known World Wide Web (WWW) and may also use wireless transmission technology used for short-range communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0034] For example, the data communication unit (100) may be responsible for transmitting and receiving data necessary to perform a technique according to one embodiment of the present disclosure.

[0035] The database section (120) may mean any type of storage medium.

[0036] For example, the database unit (120) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk.

[0037] The database section (120) can store at least one instruction that can be executed by the processor (140).

[0038] Additionally, the database unit (120) can store information of any form generated or determined by the processor (140) and information of any form received by the surface roughness classification determination device (10).

[0039] In addition, the database section (120) can store various types of modules, instruction sets, or models.

[0040] The processor (140) can perform technical features according to embodiments of the present disclosure described below by executing at least one instruction stored in the database unit (120). In one embodiment, the processor (140) may be composed of at least one core and may include a processor for data analysis and / or processing, such as a central processing unit (CPU) of a computer device, a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU).

[0041] Specifically, the processor (140) can receive location information of the target area input by the user.

[0042] In this case, the target area refers to a planned construction site where a building or structure is scheduled to be installed, and may refer to an area subject to analysis for calculating design wind speed and evaluating wind load.

[0043] According to an embodiment, the processor (140) can receive location information of a target area from a client terminal (10).

[0044] Additionally, the processor (140) can obtain latitude and longitude coordinates expressed in a geographic coordinate system based on the received location information.

[0045] In this embodiment, latitude and longitude coordinates can be obtained using a WGS84-based geographic coordinate system (EPSG:4326). In this case, the WGS84-based geographic coordinate system (EPSG:4326) is a globally common coordinate standard that models the Earth as a reference ellipsoid and expresses location as angular values ​​of latitude and longitude, and is a coordinate system standardly used in GPS and the like.

[0046] Additionally, the processor (140) can convert the latitude and longitude coordinates into different multiple projected coordinate systems so that the wind speed data and geographic information system (GIS) data are aligned in the same spatial standard.

[0047] Here, a plurality of projected coordinate systems may include a first projected coordinate system (EPSG:5181) for mapping wind speed data and a second projected coordinate system (EPSG:5179) for mapping GIS data. The first projected coordinate system is EPSG:5181, which is a projected coordinate system based on the central region of South Korea and can be set to be suitable for spatial mapping of wind speed data, and the second projected coordinate system is EPSG:5179, which can be set as a projected coordinate system for processing nationwide Geographic Information System (GIS) data. That is, EPSG:5181 is a coordinate system for precise spatial analysis of wind speed data, and EPSG:5179 is a coordinate system for wide-area processing of GIS data, and they can be used complementarily with each other.

[0048] Additionally, the processor (140) can extract wind speed data and GIS data for a target area using the converted conversion coordinates, reclassify the GIS data according to terrain type, and then assign a surface roughness length corresponding to each terrain type.

[0049] That is, the processor (140) can extract GIS data based on wind speed maps and land cover within a predetermined radius centered on the target area using the converted conversion coordinates, reclassify the GIS data into terrain types reconstructed from a multi-stage classification system, and then assign a preset surface roughness length corresponding to each terrain type.

[0050] At this time, reclassification is performed by integrating detailed topographic information included in the multi-level classification system according to physical surface characteristics, and through this, it can be converted into physical parameters suitable for wind speed analysis.

[0051] Additionally, the processor (140) can automatically divide the target area by wind direction based on the surface roughness length and calculate the effective surface roughness length and patch length by reflecting the terrain information distributed on the windward side of each wind direction, and can calculate the wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model using the calculated effective surface roughness length and patch length as input values.

[0052] Specifically, the processor (140) can divide the target area according to a plurality of wind directions based on the surface roughness length, and create detailed divisions based on areas with similar surface roughness characteristics within the zones set for each wind direction.

[0053] Next, the processor (140) can calculate the effective surface roughness length and patch length by reflecting terrain information distributed along the windward side of each wind direction.

[0054] Specifically, the processor (140) can calculate an effective surface roughness length and a patch length by creating detailed sections based on areas with similar surface roughness characteristics within a plurality of zones set according to wind direction, and spatially combining a plurality of surface roughness lengths distributed in the upwind direction for each wind direction.

[0055] The processor (140) can calculate a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model using the calculated effective surface roughness length and patch length as input values, and the wind speed profile can be calculated in a form that reflects the wind speed distribution characteristics according to the change in surface roughness.

[0056] In this case, the Atmospheric Boundary Layer (ABL) model is a physical model for interpreting the wind speed distribution in the lower atmospheric region influenced by the roughness characteristics of the Earth's surface.

[0057] In this embodiment, the atmospheric boundary layer (ABL) model can be applied using the effective surface roughness length and patch length as input values.

[0058] Specifically, the atmospheric boundary layer (ABL) model can be configured to calculate the vertical distribution of wind speeds according to changes in surface roughness and to calculate wind speed profiles by wind direction by reflecting topographic hysteresis effects according to the windward direction.

[0059] The Atmospheric Boundary Layer (ABL) model reflects surface friction effects and boundary layer development characteristics based on effective surface roughness length and patch length, thereby providing a wind speed distribution that reflects upstream topographic distribution rather than local characteristics of a single point, and through this, can generate a physics-based wind speed profile suitable for determining surface roughness classification.

[0060] That is, in this embodiment, the atmospheric boundary layer (ABL) model can be used as a means to interpret the wind speed distribution by wind direction using parameters calculated based on terrain.

[0061] Additionally, the processor (140) can be configured to determine the surface roughness of the target area based on the wind speed profile, and to provide the determination result to a user terminal or store it in a database (120) to enable history management.

[0062] Specifically, the processor (140) can determine the classification of surface roughness of the target area based on the wind speed profile calculated for each wind direction, and the determination can be made into one of a plurality of roughness grades using criteria corresponding to the wind speed altitude distribution characteristics and the wind speed altitude distribution coefficient.

[0063] Additionally, the processor (140) can store the wind speed profile, input parameters used for the judgment (effective surface roughness length and patch length, etc.), and location information of the target area in the database (120) along with the judgment result, and can be configured to enable history tracking and comparative analysis by managing multiple judgment results for the same target area in chronological order or on a project basis.

[0064] According to an embodiment, the surface roughness classification determination device (10) receives location information regarding a planned construction site from a client terminal and can transmit a surface roughness classification determination result corresponding to the received location information to the client terminal.

[0065] According to an embodiment, the surface roughness classification determination device (10) can receive location information regarding a planned construction site from a client terminal (not shown) and transmit it to a server, and receive a surface roughness classification determination result corresponding to the received location information from the server and transmit it to the client terminal.

[0066] Such a surface illuminance distinction determination device (10) can be implemented by a hardware circuit (e.g., a CMOS-based logic circuit), firmware, software, or a combination thereof. For example, it can be implemented by utilizing transistors, logic gates, and electronic circuits in the form of various electrical structures.

[0068] The operation of a surface roughness classification determination device using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention having such a structure is as follows. FIG. 2 is a diagram illustrating a method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model according to an embodiment of the present invention; FIG. 3 is a diagram illustrating a method for extracting wind speed data illustrated in FIG. 2; FIG. 4 is a diagram illustrating a method for extracting GIS data illustrated in FIG. 2; FIG. 5 is a diagram illustrating a step for calculating the effective surface roughness length and patch length illustrated in FIG. 2; FIG. 6 is a diagram illustrating a method for reclassifying GIS data according to terrain type according to an embodiment of the present invention; FIG. 7 is a diagram illustrating the surface roughness length assigned according to the reclassified terrain type according to an embodiment of the present invention; FIG. 8 is a diagram illustrating a method for automatically partitioning a target area illustrated in FIG. 5 by wind direction; FIG. 9 is a diagram illustrating a method for calculating the patch length illustrated in FIG. 5; and FIG. 10 is a diagram illustrating a method for automatically determining surface roughness classification illustrated in FIG. 2. Here, FIG. 4(a) is a diagram for explaining a method of extracting a satellite map using transformed coordinates, FIG. 4(b) is a diagram for explaining a method of extracting a land cover map, FIG. 6(a) is a diagram showing the composition of major categories included in the land cover map, FIG. 6(b) is a diagram showing the composition of minor categories included in the land cover map, FIG. 8(a) is a diagram showing a structure in which a target area is divided according to multiple wind directions, and FIG. 8(b) is a diagram showing a structure in which space is subdivided according to surface roughness characteristics within the wind direction zone.

[0069] First, as illustrated in FIG. 2, the processor (140) can obtain latitude and longitude coordinates corresponding to location information for a target area that is a planned construction site using a geographic coordinate system (S100).

[0070] Specifically, the processor (140) can identify the location of a target area based on an address, lot number, road name address, or location designation information entered from a user or client terminal. In this case, the target area is a location where a building or structure is scheduled to be installed, and may refer to an area for analysis for calculating design wind speed and evaluating wind load.

[0071] The processor (140) can obtain latitude and longitude coordinates of a WGS84-based geographic coordinate system (EPSG:4326) corresponding to location information by using a location information providing API.

[0072] Here, the WGS84-based geographic coordinate system (EPSG:4326) is a global standard coordinate system that expresses location as angular values ​​of latitude and longitude, and can subsequently be used as a reference coordinate for spatial alignment with wind speed data and GIS data.

[0073] Next, the processor (140) can extract wind speed data and GIS data using transformed coordinates that have been converted from latitude and longitude coordinates into multiple different projected coordinate systems (S110).

[0074] Specifically, the processor (140) can convert the latitude and longitude coordinates into first transformed coordinates using a first projected coordinate system, extract the wind speed data using the first transformed coordinates, convert the latitude and longitude coordinates into second transformed coordinates using a second projected coordinate system, and extract GIS data using the second transformed coordinates.

[0075] For example, the processor (140) can convert latitude and longitude coordinates expressed in a WGS84-based geographic coordinate system (EPSG:4326) into x and y (m) by converting them into a first projected coordinate system and a second projected coordinate system, respectively. In this case, EPSG:5181 may be used as the first projected coordinate system for mapping wind speed data, and EPSG:5179 may be used as the second projected coordinate system for mapping GIS data.

[0076] In other words, by transforming coordinates using different first and second projection coordinate systems, wind speed data and GIS data constructed according to different standards can be aligned according to the same spatial standard, and the accuracy of subsequent topographic analysis and wind speed analysis can be improved.

[0077] For example, referring to FIG. 3, the processor (140) can extract wind speed data from the KDS 2022 wind speed map centered on the target area using the converted x, y converted coordinates.

[0078] Additionally, referring to FIG. 4, the processor (140) can extract land cover-based GIS data by using the converted x, y converted coordinates to extract a land cover map of an area with a predetermined radius of up to 30 km centered on the target area (see FIG. 4(a)) (see FIG. 4(b)).

[0079] In other words, wind speed data can be extracted as a value corresponding to the corresponding coordinates on a wind speed map, and GIS data can be obtained by extracting data corresponding to the corresponding area from a land cover map or topographic information data.

[0080] Next, the processor (140) can calculate the effective surface roughness length and patch length using GIS data (S120).

[0081] That is, the processor (140) can calculate the effective surface roughness length and patch length by reflecting terrain information distributed along the windward side of each wind direction.

[0082] Specifically, as illustrated in FIG. 5, the processor (140) can reclassify GIS data according to terrain types reconstructed from a multi-level classification system and assign a surface roughness length corresponding to the reclassified terrain types (S200).

[0083] Specifically, the processor (140) can reclassify GIS data according to terrain types reconstructed from a multi-level classification system and assign a surface roughness length (z0) corresponding to the reclassified terrain types.

[0084] For example, referring to FIG. 6(a), the processor (140) can identify terrain types using major, medium, and detailed information included in land cover-based GIS data.

[0085] In this case, the land cover map may be composed of multiple major classification categories based on the surface use status, such as urbanized and dry areas, agricultural areas, forest areas, grasslands, wetlands, bare land, and water bodies. Each major classification category reflects the overall physical characteristics of the corresponding topography and can be used as a basic classification unit for calculating surface roughness length.

[0086] Afterwards, referring to FIG. 6(b), the processor (140) can integrate and reclassify topographic information of sub-classification units according to physical surface characteristics to be suitable for wind speed analysis.

[0087] In this case, the land cover map includes more detailed subcategories in addition to major and medium classifications, and may include various detailed topographic information such as airports, roads, ports, and residential areas.

[0088] In other words, since the subcategories are too subdivided to be used directly for wind speed analysis, the processor (140) can integrate and reclassify the subcategories according to physical surface characteristics.

[0089] For example, even if an item is included in the existing classification as an urbanized arid area, such as an airport, it can be reclassified as bare land if the actual topographical characteristics are flat.

[0090] That is, the processor (140) can reconfigure terrain groups with similar aerodynamic roughness characteristics, such as urban areas, forest areas, bodies of water, and farmlands.

[0091] Afterward, the processor (140) may assign a preset surface roughness length (z0) for each reclassified terrain type, and this value may be used as a physical parameter that reflects the aerodynamic characteristics of the terrain.

[0092] Specifically, for each reclassified terrain type, a surface roughness length reflecting aerodynamic characteristics can be set in correspondence.

[0093] For example, referring to Fig. 7, urbanized areas have a relatively large surface roughness length, while flat terrain such as bodies of water or bare land may have a small surface roughness length.

[0094] In this way, by assigning a surface roughness length corresponding to each terrain type, terrain information can be converted into physical parameters applicable to wind speed analysis.

[0095] Through this process, GIS data can be converted into physical-based input data that can be used for wind speed analysis, rather than simple topographic information.

[0096] Next, the processor (140) can automatically divide the target area by wind direction based on the surface roughness length (S210).

[0097] Specifically, the processor (140) can set zones based on multiple wind directions centered on the target area.

[0098] At this time, the wind direction angle can be set with the true north direction set to 0° and based on the clockwise direction. For each wind direction, an area including a predetermined angle range can be set as a single analysis zone.

[0099] For example, referring to FIG. 8(a), the processor (140) can define a total of 12 wind direction zones by setting zones with a range of ±15° for each wind direction and dividing the entire 360° into 30° intervals.

[0100] Additionally, the processor (140) can create detailed sections based on areas with similar surface roughness characteristics within each wind direction zone.

[0101] For example, referring to FIG. 8(b), the processor (140) can create detailed sections based on areas with similar surface roughness characteristics within a specific wind direction zone (e.g., a 90° zone).

[0102] At this time, areas with different topographical characteristics may be mixed within a single wind direction zone, and the processor (140) may divide the interior of the zone into multiple detailed sections based on the surface roughness length of each area.

[0103] Each sub-section may consist of spatially adjacent areas with similar surface roughness characteristics, and multiple sub-sections may be defined within the same wind direction zone based on differences in surface roughness characteristics.

[0104] Next, the processor (140) can calculate the effective surface roughness length and patch length by reflecting the terrain history distributed along the windward direction defined for each wind direction (S220).

[0105] Specifically, the processor (140) can analyze terrain information distributed along the windward side of the corresponding wind direction for each wind direction zone.

[0106] For example, referring to FIG. 9, the processor (140) can obtain surface roughness length (z0) classified by detailed section and area information of the corresponding area.

[0107] The processor (140) can calculate the effective surface roughness length (z0,eff) and patch length (x) based on the surface roughness length and area for each detailed section.

[0108] That is, the processor (140) can spatially combine multiple surface roughness lengths to calculate a single representative value, which is an effective surface roughness length (z0,eff).

[0109] At this time, the area ratio or spatial distribution characteristics corresponding to each surface roughness length may be reflected, and through this, a representative roughness value considering the topographic history on the windward path can be derived.

[0110] Additionally, the processor (140) can calculate a patch length (x) based on the distance over which a region having the same surface roughness characteristics is continuously formed in the windward direction. The patch length refers to the distance traveled by the wind while maintaining constant roughness conditions and can be used as an input variable to reflect the characteristics of the atmospheric boundary layer development according to changes in surface roughness.

[0111] Next, the processor (140) can calculate wind speed profiles by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and patch length (S130).

[0112] Specifically, the processor (140) can calculate the vertical distribution of wind speed by setting the effective surface roughness length (z0,eff) and patch length (x) calculated for each wind direction as input values ​​and applying an atmospheric boundary layer (ABL) model based on Deaves & Harris.

[0113] At this time, the processor (140) can derive different wind speed profiles for each wind direction by reflecting the terrain history distributed along the windward direction and the boundary layer development characteristics according to changes in surface roughness.

[0114] In addition, by considering the change in friction velocity according to patch length and the growth characteristics of the internal boundary layer, the wind speed distribution under heterogeneous terrain conditions can be analyzed more precisely.

[0115] In addition, interpolation processing can be performed to continuously connect wind speed distributions in the transition region between boundary layers, thereby generating a wind speed profile that smoothly reflects wind speed changes between the upstream and downstream.

[0116] Next, the processor (140) can apply a numerical analysis procedure of an atmospheric boundary layer model using the effective surface roughness length and patch length set for each wind direction as input values. That is, the processor (140) can apply a numerical analysis model to analyze flow characteristics within the atmospheric boundary layer.

[0117] At this time, the processor (140) can reflect terrain conditions distributed along the windward direction and roughness hysteresis effects to interpret changes in wind speed distribution that occur due to changes in surface roughness.

[0118] Specifically, the processor (140) can analyze the influence of wind flowing in from the upwind side terrain with respect to the target wind direction on the change in surface roughness, and calculate the wind speed distribution so that the change in wind speed by altitude is continuously calculated. In this process, the numerical analysis model can be used to calculate the vertical distribution of wind speed in non-homogeneous terrain by reflecting the representative roughness condition according to the effective surface roughness length and the upwind side influence distance according to the patch length together.

[0119] Next, the processor (140) can reflect the effect of changes in surface roughness on the wind speed distribution by using the patch length (x) calculated for each wind direction. That is, the processor (140) can calculate the local friction velocity that changes according to the patch length (x).

[0120] Here, the patch length may refer to the distance traveled continuously in the windward direction over terrain having the same or similar surface roughness characteristics.

[0121] Specifically, the processor (140) can determine that for each wind direction, the longer the patch length, the more sufficiently the influence of the surface roughness characteristics on the wind speed distribution is developed, and the shorter the patch length, the more limitedly the influence of the roughness change is reflected.

[0122] Accordingly, the processor (140) can calculate a wind speed profile by reflecting the change in the surface friction effect according to the patch length, that is, the change in the local friction speed.

[0123] Next, the processor (140) can reflect the growth characteristics of the internal boundary layer formed according to the change in surface roughness by using the effective surface roughness length and patch length calculated for each wind direction. That is, the processor (140) can calculate the growth characteristics of the internal boundary layer formed according to the change in surface roughness.

[0124] Specifically, if surface roughness characteristics change as wind moves from the windward side toward the target area, an air layer influenced by the new surface conditions may form near the surface. This newly developed region resulting from changes in surface roughness can be viewed as an internal boundary layer.

[0125] The processor (140) can process that for each wind direction, the longer the patch length, the more the influence of the corresponding surface roughness condition develops to a higher altitude, and the shorter the patch length, the more the influence is reflected only within a limited altitude range. Through this, the degree of boundary layer development according to the change in surface roughness can be reflected in the wind speed profile for each wind direction.

[0126] Next, the processor (140) can perform interpolation processing on the transition region between the inner boundary layer and the outer region so that the wind speed distribution calculated according to the change in surface roughness is not abruptly cut off in the elevation direction.

[0127] That is, the processor (140) can perform interpolation processing to smoothly connect the wind speed distribution in the transition section between the inner boundary layer and the outer region.

[0128] Specifically, when the windward terrain conditions and the surface conditions of the target area are different, the influence of the new roughness conditions is reflected near the surface, and the influence of the existing windward conditions may remain above a certain altitude. The processor (140) can set a transition area so that the wind speed distribution between these two areas is continuously connected, and can gradually correct the wind speed value in the transition area.

[0129] Through this, the processor (140) can produce a natural wind speed profile that simultaneously reflects the upwind terrain history and the surface roughness conditions of the target area, and can prevent the calculation results of the wind speed profile by wind direction from appearing physically discontinuous.

[0130] Next, the processor (140) can calculate a wind speed profile for each wind direction using the effective surface roughness length, patch length, surface friction effect, internal boundary layer growth characteristics, and transition region interpolation results reflected in the preceding steps.

[0131] That is, the processor (140) can calculate a wind speed profile representing the wind speed distribution according to altitude by wind direction using the results calculated through the above steps.

[0132] Specifically, the processor (140) can calculate the wind speed change value according to altitude for each wind direction and thereby generate a wind speed profile representing the vertical distribution of wind speed in the target area. In this case, the wind speed profile may not only reflect the terrain conditions of a single point, but may also be the result of reflecting the terrain history distributed in the windward direction of the corresponding wind direction and the influence of changes in surface roughness.

[0133] Next, the processor (140) can automatically determine the classification of surface roughness of the target area based on the wind speed profile for each wind direction (S140).

[0134] Specifically, the processor (140) can derive a wind speed altitude distribution coefficient by analyzing the characteristics of wind speed change according to altitude from the wind speed profile calculated for each wind direction. At this time, the wind speed altitude distribution coefficient is an indicator showing the tendency of wind speed to increase with altitude and can be used as a value that reflects the characteristics of surface roughness.

[0135] For example, referring to FIG. 10, the processor (140) can determine the classification of surface illuminance of the target area by comparing the calculated wind speed altitude distribution coefficient with a plurality of illuminance grade reference values ​​or reference curves defined in the design standards. At this time, the illuminance classification can be determined as one of a plurality of illuminance grades defined according to the building design standards (KDS 2022).

[0136] Additionally, the processor (140) can determine the final surface roughness classification by combining the judgment results calculated for each of the multiple wind directions, and can derive the roughness grade based on the extreme value in a specific wind direction or using a representative value for all wind directions.

[0137] Since the determination of surface roughness classification performed in this manner is based on quantitative data known as wind speed profiles, it can provide objective and consistent judgment results through comparison with reference curves and more accurately reflect the actual wind environment compared to existing methods that rely on simple topographic classification.

[0138] Finally, the processor (140) can update and store the determination result determined based on the wind speed profile (S150).

[0139] Specifically, the processor (140) can generate a judgment result including a wind speed altitude distribution coefficient derived using a wind speed profile calculated for each wind direction and a corresponding illuminance grade, and store the result in a database (120).

[0140] At this time, the data stored may include location information of the target area, wind speed profiles by wind direction, input parameters such as effective surface roughness length and patch length, and the final roughness classification result.

[0141] Additionally, the processor (140) can update the previously stored judgment results to maintain them in an up-to-date state when new wind speed data or analysis conditions are reflected for the same target area, and can be configured to enable history tracking and comparative analysis by managing multiple judgment results in chronological order or on a project basis.

[0142] The judgment results stored and managed in this manner can be utilized for future design review, result reanalysis, or report generation.

[0143] In other words, the processor (140) can generate and update the illuminance determination results based on the wind speed profile and store them in the database (120) to enable history management and utilization.

[0145] The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0146] Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. Explanation of the symbols

[0147] 10: Surface roughness classification and determination device 100 : Data Communications Department 120 : Database section 140 : Processor

Claims

Claim 1 A method for determining surface roughness classification using a location-based automatic segmentation and atmospheric boundary layer model performed on a device comprises: a step of obtaining latitude and longitude coordinates corresponding to location information of a target area, which is a planned construction site, using a geographic coordinate system; a step of extracting wind speed data and GIS data using transformed coordinates converted from the latitude and longitude coordinates into a plurality of different projection coordinate systems; a step of calculating an effective surface roughness length and a patch length using the GIS data; a step of calculating a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and the patch length; and a step of automatically determining the surface roughness classification of the target area based on the wind speed profile, wherein the step of extracting wind speed data and GIS data comprises: a step of converting the latitude and longitude coordinates into first transformed coordinates using a first projection coordinate system for wind speed data mapping, and extracting the wind speed data using the first transformed coordinates. The method includes the step of converting the latitude and longitude coordinates into second transformed coordinates using a second projected coordinate system for GIS data mapping, and extracting the GIS data using the second transformed coordinates; the step of calculating the effective surface roughness length and patch length comprises: the step of reclassifying the GIS data according to a terrain type reconstructed from a multi-stage classification system, and assigning a surface roughness length corresponding to the reclassified terrain type; the step of setting wind direction zones based on the windward direction corresponding to each of a plurality of wind directions, and creating detailed sections based on areas that are spatially adjacent and have similar surface roughness characteristics within each wind direction zone;A method for determining surface roughness classification using a location-based automatic sectioning and atmospheric boundary layer model, comprising the step of calculating the effective surface roughness length using the surface roughness length and area information for each detailed section distributed along the windward direction for each wind direction, and calculating the patch length as the distance over which areas having the same surface roughness characteristics are continuously formed in the windward direction; and the step of calculating the wind speed profile for each wind direction comprises the step of calculating the wind speed profile for each wind direction by setting the effective surface roughness length and the patch length calculated for each wind direction as input values ​​for the atmospheric boundary layer model. Claim 2 A method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model, wherein, in claim 1, the geographic coordinate system is a WGS84-based EPSG:4326 coordinate system, the first projection coordinate system is an EPSG:5181 coordinate system for spatial mapping of wind speed data, and the second projection coordinate system is an EPSG:5179 coordinate system for spatial mapping of GIS data. Claim 3 A method for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model, wherein, in paragraph 2, the wind speed data and GIS data are extracted for a predetermined radius area centered on the target location, and the predetermined radius is set to a maximum of 30 km. Claim 4 delete Claim 5 delete Claim 6 A method for determining surface roughness using a location-based automatic partitioning and atmospheric boundary layer model, wherein the step of setting the wind direction zone comprises defining the zone as an area including a predetermined angle range based on each wind direction, setting a zone having a range of ±15° for each wind direction, and dividing the entire 360° into predetermined intervals to form a plurality of wind direction zones. Claim 7 In claim 6, the above wind speed profile is calculated by reflecting the change in frictional velocity according to the patch length, a method for determining surface roughness using a location-based automatic partitioning and atmospheric boundary layer model. Claim 8 A method for determining surface roughness using a location-based automatic partitioning and atmospheric boundary layer model, wherein, in claim 1, the atmospheric boundary layer model is a model that calculates wind speed distribution by considering the formation of an internal boundary layer according to changes in surface roughness. Claim 9 A surface roughness classification determination device using a location-based automatic partitioning and atmospheric boundary layer model, comprising: a database unit storing at least one command for determining surface roughness classification using a location-based automatic partitioning and atmospheric boundary layer model;and includes a processor that performs operations according to the above command, wherein the processor obtains latitude and longitude coordinates corresponding to location information for a target area that is a planned construction site using a geographic coordinate system, extracts wind speed data and GIS data using transformed coordinates converted from the latitude and longitude coordinates into a plurality of different projection coordinate systems, calculates an effective surface roughness length and a patch length using the GIS data, calculates a wind speed profile by wind direction by applying an atmospheric boundary layer (ABL) model based on the effective surface roughness length and the patch length, and automatically determines the surface roughness classification of the target area based on the wind speed profile, wherein the processor converts the latitude and longitude coordinates into a first transformed coordinate using a first projection coordinate system for wind speed data mapping, extracts the wind speed data using the first transformed coordinate, converts the latitude and longitude coordinates into a second transformed coordinate using a second projection coordinate system for GIS data mapping, and extracts the GIS data using the second transformed coordinate, and A surface roughness classification and determination device using a location-based automatic segmentation and atmospheric boundary layer model, wherein the processor reclassifies the GIS data according to a terrain type reconstructed from a multi-stage classification system, assigns a surface roughness length corresponding to the reclassified terrain type, establishes wind direction zones based on the windward direction corresponding to each of a plurality of wind directions, creates detailed sections based on areas that are spatially adjacent and have similar surface roughness characteristics within each wind direction zone, calculates the effective surface roughness length using the surface roughness length and area information for each detailed section distributed along the windward direction for each wind direction, calculates the patch length as the distance over which areas having the same surface roughness characteristics are continuously formed in the windward direction, and the processor calculates the wind speed profile for each wind direction by setting the effective surface roughness length and the patch length calculated for each wind direction as input values ​​for the atmospheric boundary layer model. Claim 10 A computer program stored on a computer-readable recording medium that is combined with a computer, which is hardware, to enable the method of claim 1 to be performed.

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