KR102526581B1 - Method and apparatus for establishing an atmospheric pressure model - Google Patents

Abstract

A method according to an embodiment of the present invention is a method for establishing an air pressure model, comprising the steps of acquiring actual observation data of air pressure of a plurality of air pressure observation stations; generating a first predictive model for estimating an air pressure value over time based on the observation data; generating a second predictive model that obtains a standard elevation value for each of the plurality of barometric pressure stations and calculates an estimated value of a barometric model coefficient based on the standard elevation value; generating a pressure prediction model using an estimated value of the pressure model coefficient calculated in the second prediction model as a pressure model coefficient of the first prediction model; A method of establishing a barometric pressure model comprising a.

Description

Method and apparatus for establishing an atmospheric pressure model

The following embodiments relate to a method and apparatus for establishing an air pressure model considering tropospheric delay.

GNSS (Global Navigation Satellite System) is a system that provides information on the location and speed of objects on the ground using artificial satellites orbiting space. It can grasp precise location information with a resolution of 1m or less, and is widely applied not only for military purposes but also for civil fields such as location guidance for transportation means such as aircraft, ships, and automobiles, geodesy, emergency rescue, and communication. Existing GNSS systems include GPS (Global Positioning System) developed and operated by the US Department of Defense, Russia's GLONASS (GLObal Navigation Satellite System), EU's Galileo, and China's Beidou.北斗, Beidou).

GNSS satellite signals are refracted by dry gas and water vapor in the troposphere as they pass through the earth's atmosphere before reaching the receiving equipment, which increases the signal transmission distance. GNSS signal delay occurring in the troposphere can be divided into a dry delay (hydrostatic delay or dry delay) caused by dry gases such as carbon dioxide, nitrogen, and oxygen, and a wet delay caused by water vapor. In this specification, the combination of dry delay and wetting delay can be referred to as tropospheric signal delay in GNSS.

The foregoing background art is technical information that the inventor possessed for derivation of the present invention or acquired during the derivation process of the present invention, and cannot necessarily be said to be known art disclosed to the general public prior to filing the present invention.

An object of the present invention is to provide a method and apparatus for establishing an air pressure model considering tropospheric delay.

The problem to be solved by the present invention is not limited to the above-mentioned problems, and other problems and advantages of the present invention that are not mentioned can be understood by the following description and more clearly understood by the embodiments of the present invention. It will be. In addition, it will be appreciated that the problems and advantages to be solved by the present invention can be realized by the means and combinations indicated in the claims.

According to one embodiment of the present invention, there is provided a method for establishing an air pressure model, comprising: obtaining actual observation data of air pressure of a plurality of air pressure observation stations; generating a first predictive model for estimating an air pressure value over time based on the observation data; generating a second predictive model that obtains a standard elevation value for each of the plurality of barometric pressure stations and calculates an estimated value of a barometric model coefficient based on the standard elevation value; generating a pressure prediction model using an estimated value of the pressure model coefficient calculated in the second prediction model as a pressure model coefficient of the first prediction model; There is provided a method of establishing a pressure prediction model that includes.

In the present invention, the plurality of air pressure observation stations are air pressure observation stations existing in a preset target region, and the air pressure model may be an air pressure model optimized for an atmospheric state of the preset target region.

In the present invention, the root mean square deviation between the measured atmospheric pressure value of the observation data and the atmospheric pressure value estimated by the first predictive model is the average between the measured atmospheric pressure value of the observation data and the atmospheric pressure value estimated by the previously created global atmospheric pressure model. may be less than the square root deviation.

In the present invention, the first prediction model may have air pressure amplitude, period, and average air pressure according to seasons and time as air pressure model coefficients.

In the present invention, the first predictive model may be an air pressure estimation model that estimates air pressure values according to years in the form of real numbers by fitting the air pressure model coefficients into a sine function form.

In the present invention, based on the air pressure observation data of the plurality of air pressure observation stations, the air pressure amplitude, period, and average air pressure may be calculated for each of the plurality of air pressure observation stations.

In the present invention, the second predictive model may calculate estimated values of pressure model coefficients including pressure amplitude, period, and average pressure, respectively, based on the normal elevation value.

In the present invention, the second predictive model is a simple regression analysis equation using the fixed elevation as _a variable , and slope T 1 and After calculating the bias T ₀ value, estimated values of A, P, and C values based on the normal elevation may be calculated using the simple regression analysis equation in which the calculated T ₁ and T ₀ values are substituted.

In the present invention, the root mean square deviation between the measured atmospheric pressure value of the observation data and the atmospheric pressure value estimated by the atmospheric pressure prediction model is the root mean square deviation between the measured atmospheric pressure value of the observation data and the atmospheric pressure value estimated by the generated global atmospheric pressure model. may be less than the deviation.

In the present invention, the pressure model coefficient of the first prediction model is determined regardless of the normal elevation, and the air pressure model coefficient of the air pressure prediction model may be determined based on the normal elevation.

According to one embodiment of the present invention, an air pressure model establishment device, comprising: a communication unit for performing communication with a user terminal; a memory in which at least one program is stored; and a processor that performs an operation by executing the at least one program, wherein the processor obtains actual observation data of air pressures of a plurality of air pressure stations, and estimates air pressure values over time based on the observation data. generating a first prediction model that obtains a standard elevation value for each of the plurality of barometric pressure stations, and generates a second prediction model that calculates an estimated value of a barometric model coefficient based on the standard elevation value; An air pressure prediction establishment device is provided that generates an air pressure prediction model using estimated values of air pressure model coefficients calculated in a prediction model as air pressure model coefficients of the first prediction model.

Additionally, a computer-readable recording medium recording a program for executing the method of the present invention on a computer is provided.

According to the above-mentioned problem solving means of the present disclosure, it is possible to establish an atmospheric pressure prediction model optimized for the atmosphere of a specific target area.

In addition, according to an embodiment of the present invention, it is possible to establish an air pressure prediction model having a smaller error with an actual observed value than a pre-generated global air pressure model.

In addition, according to an embodiment of the present invention, an air pressure prediction model considering both time and space of a specific target area may be established.

In addition, according to an embodiment of the present invention, it is possible to simply calculate the air pressure at the user's location, and it is possible to easily perform the calculation of the air pressure at the user's location that requires calculation of the tropospheric delay amount.

1 illustrates a GNSS including a barometric pressure model establishment device according to an embodiment of the present invention.
2 is a time-sequential diagram illustrating a method of establishing a pressure model according to an exemplary embodiment.
3 is a graph showing observed air pressure for each air pressure observation station in a target area according to an exemplary embodiment.
4 is a graph showing estimated air pressure of a first prediction model for each air pressure observation station in a target area according to an exemplary embodiment.
5 is a graph showing barometric pressure model coefficients calculated from the first prediction model and barometric pressure model coefficients calculated from the second prediction model according to the present embodiment.
6 is a graph showing atmospheric pressure values calculated based on the atmospheric pressure prediction model of the present invention.
7 is a block diagram illustrating a method of establishing a pressure prediction model according to an embodiment of the present invention.
8 is a block diagram of an apparatus for establishing a pressure model according to an exemplary embodiment.

Advantages and features of the present invention, and methods for achieving them will become clear with reference to the detailed description of embodiments in conjunction with the accompanying drawings. However, it should be understood that the present invention is not limited to the embodiments presented below, but may be implemented in a variety of different forms, and includes all conversions, equivalents, and substitutes included in the spirit and scope of the present invention. .

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Some embodiments of the present disclosure may be represented as functional block structures and various processing steps. Some or all of these functional blocks may be implemented as a varying number of hardware and/or software components that perform specific functions. For example, functional blocks of the present disclosure may be implemented by one or more microprocessors or circuit configurations for a predetermined function. Also, for example, the functional blocks of this disclosure may be implemented in various programming or scripting languages. Functional blocks may be implemented as an algorithm running on one or more processors. In addition, the present disclosure may employ prior art for electronic environment setup, signal processing, and/or data processing, etc. Terms such as “mechanism,” “element,” “means,” and “composition” will be used broadly. It can be, and is not limited to mechanical and physical configurations.

In addition, connecting lines or connecting members between components shown in the drawings are only examples of functional connections and/or physical or circuit connections. In an actual device, connections between components may be represented by various functional connections, physical connections, or circuit connections that can be replaced or added.

In the embodiments of the present disclosure, terms such as “module,” “unit,” and “part” are terms used to refer to components that perform at least one function or operation, and these components are hardware or software. It may be implemented or implemented as a combination of hardware and software. In addition, a plurality of "modules", "units", "parts", etc. are integrated into at least one module or chip, except for cases where each of them needs to be implemented with separate specific hardware, so that at least one processor can be implemented as

Terms used in this document are only used to describe a specific embodiment, and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the technical field described in this document. Among the terms used in this document, terms defined in a general dictionary may be interpreted as having the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in this document, an ideal or excessively formal meaning. not be interpreted as In some cases, even terms defined in this document cannot be interpreted to exclude the embodiments of this document.

Hereinafter, various embodiments of the present invention will be described in detail using the accompanying drawings.

1 illustrates a barometric pressure model establishment system including a barometric pressure model establishment apparatus according to an embodiment of the present invention.

Referring to FIG. 1 , the air pressure model establishment system of the present invention may include a plurality of air pressure observation stations 20 and an air pressure model establishment device 30 . Although the embodiment of FIG. 1 illustrates that the barometric pressure model establishment device 30 is connected to the barometric pressure stations through a network, according to another embodiment of the present invention, the barometric pressure model establishment unit 30 is directly connected to the barometric pressure station 20 Observation data of atmospheric pressure can be obtained from.

As described above, a global navigation satellite system (GNSS) satellite signal may cause signal delay in the troposphere. GNSS signal delay occurring in the troposphere can be divided into a dry delay (hydrostatic delay or dry delay) caused by dry gas and a wet delay caused by water vapor. In this specification, the combination of dry delay and wetting delay can be referred to as tropospheric signal delay in GNSS.

The signal delay in the troposphere, which is the sum of the drying delay and the wet delay, is about several meters in the zenith direction, and may be approximately 2-3 m. Of the signal delays in the troposphere, the drying delay accounts for 90%, accounting for most of the total delay in the troposphere. However, because the drying gas related to the drying delay is in a static dynamic equilibrium state, it is possible to accurately determine only ground observation. That is, it is possible to precisely measure the drying delay by measuring the amount of dry gas through ground observation. In contrast, the wetting delay, which accounts for about 10% of the total tropospheric delay, is difficult to accurately measure only by ground observation because the temporal and spatial variations of water vapor are large.

와 습윤지연량

의 합으로 나타낼 수 있고, 이는 지상의 수신기와 위성의 시선방향 신호경로 상에 존재하는 대류권 신호 지연량을 의미한다. 일반적으로

는 시선방향의 총 지연량(STD, Slant Total Delay),

는 시선방향 건조지연량(SHD, Slant Hydrostatic Delay),

는 시선방향 습윤지연량(SWD, Slant Wet Delay)을 의미한다.That is, the tropospheric signal delay is the drying delay amount as shown in [Equation 1] below

and wetting delay

It can be expressed as the sum of , which means the amount of tropospheric signal delay existing on the line-of-sight signal path between the receiver on the ground and the satellite. Generally

is the total delay amount in the gaze direction (STD, Slant Total Delay),

is the amount of drying delay in the visual direction (SHD, Slant Hydrostatic Delay),

Means the amount of wet delay in the visual direction (SWD, Slant Wet Delay).

[Equation 1]

라고 하면, 이는 천정방향으로의 건조지연(ZHD, Zenith Hydrostatic Delay)

과 천정방향 습윤지연(ZWD, Zenith Wet Delay)

의 합으로 표현할 수 있다.And the tropospheric delay in the zenith direction (ZTD, Zenith Total Delay)

, this is a drying delay in the direction of the ceiling (ZHD, Zenith Hydrostatic Delay)

and Zenith Wet Delay (ZWD)

can be expressed as the sum of

[Equation 2]

As described above, the ZHD is relatively large at several meters in size (more specifically, 2 to 3 m), but due to the hydrostatic equilibrium of the dry gas, using meteorological data measured at ground observation points, the accuracy of the level is 2-3 mm. its size can be determined. In addition, when using a barometer with an error of less than 0.3 hPa, ZHD can be calculated with an accuracy of about 1 mm. As a model for calculating ZHD, ZHD can be simply calculated as [Equation 3] below through observation of surface air pressure at an observation point.

[Equation 3]

In the above [Equation 3], P _s , φ, and h are the atmospheric pressure [hPa], latitude [rad], and elevation [km] of the observation point on the ground, respectively. And f(φ,h)=1-0.00266cos2φ-0.00028h located in the denominator is the amount of gravitational acceleration change correction, and the ZHD unit is mm.

That is, in order to accurately calculate the tropospheric signal delay amount in relation to the drying delay amount in the zenith direction, the meteorological observation value of the pressure according to the altitude above the station is required. However, it is difficult to obtain meteorological observations according to altitude only by general ground observation. Therefore, an empirical model can be used or an a priori method can be used. The empirical model is a model using a function based on actual meteorological observation data, and the a priori method is a method of estimating the tropospheric delay after assuming the drying delay and wet delay as a priori values.

As an example of an existing empirical model, a specific empirical model uses meteorological equipment to obtain the amount of tropospheric delay using atmospheric pressure, temperature, partial water vapor pressure, etc. at an observation point as input variables. If there is no separate meteorological equipment, a meteorological default such as standard atmospheric pressure can be used, but there is a problem that continuously changing atmospheric conditions cannot be accurately reflected. Nevertheless, in general GNSS receivers or simulators, tropospheric errors can be generated based on the above-described empirical model.

An empirical model according to an embodiment of the present invention may use an air pressure model based on a spherical harmonic function in order to relatively accurately model atmospheric pressure that varies depending on regions and seasons. Through the empirical model of the present disclosure, it is possible to simply calculate the air pressure at the user's location, and it is possible to easily perform the calculation of the air pressure at the user's location, which requires calculation of tropospheric delay. In addition, the empirical model according to an embodiment may be an air pressure model optimized for the target region derived by acquiring a global air pressure model made based on data around the world and comparing the global air pressure model with the atmospheric state of a preset target region. there is.

That is, the empirical model according to an embodiment may be an air pressure model optimized for a predetermined target region, particularly atmospheric conditions in Korea. In addition, according to an embodiment, the global barometric pressure model made based on world data may be a Global Pressure and Temperature (GPT) model developed by Boehm et al., but is not necessarily limited thereto, and an empirical barometric pressure measurement model made based on world data. can be a global barometric model.

2 is a time-sequential diagram illustrating a method of establishing a pressure model according to an exemplary embodiment.

Referring to FIG. 2 , the air pressure model establishment device first acquires actual observation data of air pressures of a plurality of air pressure observation stations (S10).

Next, based on the observation data, a first prediction model for estimating the air pressure value over time is generated (S20).

Next, a standard elevation value for each of a plurality of barometric pressure stations is obtained, and a second predictive model for calculating an estimated value of a barometric model coefficient based on the standard elevation value is generated (S30).

Next, a pressure prediction model using the estimated value of the pressure model coefficient calculated in the second prediction model as a pressure model coefficient of the first prediction model is generated (S40).

Hereinafter, embodiments of the present invention will be described in more detail based on each step of FIG. 2 .

First, the air pressure model establishment method of the present disclosure acquires air pressure observation data of a plurality of air pressure observation stations. More specifically, the plurality of air pressure observation stations are air pressure observation stations existing in a predetermined target region, and may be specifically, GNSS regular observation stations built and operated in Korea. In the present specification, a GNSS regular observation station that operates within a preset target region, which is the cosmological application range of the air pressure model of the present invention, and can acquire air pressure observation data, will be referred to as a 'barometric pressure observation station'. As a specific example, barometric pressure observatories may be nine regular observatories in Seoul, Sokcho, Sobaeksan, Daejeon, Bohyeonsan, Miryang, Mokpo, Goheung, and Jeju operated by the Korea Astronomical Research Institute.

In other words, a meteorological sensor is installed in an air pressure observation station existing in a predetermined target area. Accordingly, the air pressure model establishment apparatus or processor of the present invention may obtain air pressure observation data observed by a meteorological sensor of an air pressure observatory. Accordingly, it is possible to compare the global atmospheric pressure model with the atmospheric pressure model of the present invention by comparing the atmospheric pressure data of the target region with the atmospheric pressure data predicted by the global atmospheric pressure model.

3 is a graph showing observed air pressure for each air pressure observation station according to an exemplary embodiment.

Referring to FIG. 3 , a graph plotting air pressure in a target region observed for a predetermined period of five years and air pressure calculated by a global air pressure model on a corresponding date for each observation station is shown.

At the top center of each graph, the name of the station is indicated. SKCH is the Sokcho Observatory, SKMA is the Seoul Observatory, SBAO is the Sobaeksan Observatory, DAEJ is the Daejeon Observatory, BHAO is the Bohyeonsan Observatory, MLYN is the Miryang Observatory, MKPO is the Mokpo Observatory, and KOHG is the Goheung Observatory. The observatory, JEJU, may be a Jeju observatory. In addition, two graphs are shown for each station. The first row shows the observed air pressure (red) and the estimated air pressure with the global air pressure model (blue), and the second row shows the air pressure and observed air pressure based on the global air pressure model. It shows the difference in air pressure (blue). In the graphs of SKMA, BHAO, etc., the section where some barometric pressure is not visible is the section where the barometric pressure data of the station is missing.

As can be seen from the graph of FIG. 3 , it can be seen that amplitude and phase differences occur between the predicted value of the global atmospheric pressure model and the observed value of the target area. This may be because the global air pressure model is designed based on data from all over the world, so the change in air pressure due to seasonal changes in the target area is not accurately reflected. In addition, referring to FIG. 3 , it can be confirmed that the measurement data of barometric pressure stations in the target area is higher than the estimated data of the global barometric pressure model on average. Accordingly, the present invention provides a method for establishing an air pressure model suitable for the air pressure characteristics of a target region, which has higher accuracy than the global air pressure model.

The atmospheric pressure model establishment method of the present invention includes generating a first predictive model for modeling according to time, generating a second predictive model for modeling according to space, and finally generating an atmospheric pressure predictive model considering space and time together. steps may be included.

First, in the step of generating the first predictive model, the air pressure amplitude ( A ), period ( P ), and average air pressure ( C ) according to the season and time are fitted into a sine function form, and a real number as shown in [Equation 4] below A pressure estimation model according to the year t of the shape can be created. Meanwhile, since the target region of the barometric pressure model according to an embodiment is Korea and the barometric pressure observation data is data measured at a barometric pressure monitoring station in Korea, the name of the first prediction model may be referred to as KP _MS (Korea Pressure Monitoring Station) . However, the name of the model is only an example, and the scope of the right of invention is not affected by the name of the model, and the target area is not limited to Korea, of course.

KP _MS = Acos(2πt+P)+C

[Equation 4]

Referring to [Equation 4], based on the barometric pressure observation data, the barometric pressure amplitude ( A ), period ( P ), and average barometric pressure ( C ) may be calculated for each of the plurality of barometric pressure stations. In a specific embodiment, when using air pressure measurement data of air pressure observation stations in the target region illustrated in FIG. 3 , air pressure model coefficients A, P, and C may be calculated as shown in [Table 1] below.

observatory A P C SKCH 8.187 6.295 1013.0 SKMA 9.935 6.231 1012.0 SBAO 2.298 7.442 864.4 DAEJ 9.411 6.241 1006.0 BHAO 2.971 6.839 889.7 MLYN 8.843 6.199 1014.0 MKPO 9.584 6.172 1012.0 KOHG 8.926 6.176 1015.0 JEJU 6.835 6.224 968.8

4 is a graph showing estimated air pressure of a first prediction model for each air pressure observation station in a target area according to an exemplary embodiment.

Fig. 4 is an embodiment that will continue from Fig. 3; Therefore, the target area and air pressure observation station of FIG. 4 are the same as those of FIG. 3, and overlapping descriptions will be omitted. 4, the estimated air pressure of the first prediction model (green) and the estimated air pressure of the global air pressure model (blue) estimated using the air pressure model coefficients A, P, and C for each air pressure station calculated in [Table 1] above. ), and the observed air pressure (red) is plotted as a time series. Referring to the graph of FIG. 4 , it can be confirmed that the estimated air pressure of the first prediction model of the present invention is closer to the actual observed air pressure than the estimated air pressure of the global air pressure model in all nine air pressure observation stations.

Additionally, [Table 2] below shows the root-mean-square deviation (RMSE) of the observed air pressure, the estimated air pressure of the global air pressure model and the estimated air pressure of the first prediction model of the present invention. More specifically, based on the observed air pressure, the left column is the RMSE of the global air pressure model, and the right column is the RMSE of the first prediction model. As can be seen from the table, it can be seen that the RMSE of the estimated atmospheric pressure of the first prediction model is lower.

weather
observatory RMSE (hPa) global barometric model 1st prediction model DAEJ 6.6 4.7 SKMA 6.5 4.9 SKCH 6.3 5.3 SBAO 7.2 4.5 BHAO 6.4 4.3 MKPO 6.6 4.6 MLYN 6.3 4.9 KOHG 6.4 4.7 JEJU 6.3 4.2

Next, generating a second predictive model is modeling an air pressure model for space in consideration of air pressure that varies according to altitude. The second predictive model is a barometric pressure model with a simple regression analysis formula using the standard elevation as a variable. After calculating the slope T ₁ and bias T ₀ values for each barometric model coefficient A, P, and C using the observed data, the calculation is performed again. Estimates of A, P, and C values based on the standard elevation can be calculated using a simple regression analysis equation in which the values of T ₁ and T ₀ are substituted.

In more detail, the second predictive model can be designed with the following [Equation 5] calculated by simple regression analysis of the pressure model coefficients A, P, and C with the stationary height h of the pressure stations, and the second predictive model has a slope T ₁ , bias T ₀ . That is, according to the second barometric pressure prediction model, it can be calculated by simple regression analysis on the standard elevation for each barometric pressure station.

A(or P,C)=f(h)=T _{1, A (or P, C)} h + T _{0,A (or P,C)}

[Equation 5]

According to an embodiment, T ₁ and T ₀ calculated for each barometric model coefficient A, P, and C may be as shown in [Table 3] below. That is, according to an embodiment of the present invention, after calculating T ₁ and T ₀ using the above-described stationary heights and barometric pressure observation data of the barometric pressure stations, using the calculated T ₁ and T ₀ , the barometric pressure model coefficient A, It is possible to calculate estimates based on the normal elevations of P and C.

barometric model coefficients T _One T ₀ A -0.0054300 9.295 P 0.0007906 6.155 C -0.1136000 1016.000

5 is a graph showing barometric pressure model coefficients calculated from the first prediction model and barometric pressure model coefficients calculated from the second prediction model according to the present embodiment.

Referring to FIG. 5, first, from the top graph , A, P, and C (red) calculated by the first prediction model are plotted. In addition, the barometric model coefficients A, P, and C (green) calculated using T ₁ and T ₀ calculated based on the normal elevations (h) of barometric stations are plotted.

Finally, according to an embodiment of the present invention, since the estimated values of the barometric pressure model coefficients A, P, and C considering the normal elevation are calculated by the second predictive model, the estimated values are used as the final barometric pressure model coefficients of the first predictive model. It is possible to create a predictive barometric pressure model. That is, according to an embodiment, the air pressure prediction model extended in time and space may be finally generated by considering the first prediction model according to time and the second prediction model according to space together. The generated air pressure prediction model is as shown in [Equation 6] below. Like the first prediction model, since the target region of the barometric pressure model according to an embodiment is Korea, and the barometric pressure observation data is data measured at a barometric pressure station in Korea, the name of the barometric pressure prediction model can be referred to as KP (Korea Pressure). . However, the name of the model is only an example, and the scope of the right of invention is not affected by the name of the model, and the target area is not limited to Korea, of course.

KP=A(h)cos(2πt+P(h))+C(h)

[Equation 6]

6 is a graph showing atmospheric pressure values calculated based on the atmospheric pressure prediction model of the present invention.

In more detail, FIG. 6 shows the comparison result of the air pressure calculated based on the air pressure prediction model of the present invention (green), the air pressure calculated based on the global air pressure model (blue), and the actual air pressure observation data (red) of the air pressure station. These are plotted graphs. FIG. 6 is a continuation of FIG. 3, and the target area and barometric pressure station of FIG. 6 are the same as those in FIG. 3, and overlapping descriptions will be omitted.

Referring to FIG. 6 , it can be seen that the air pressure calculated based on the air pressure prediction model of the present invention is closer to the actual observed air pressure than the air pressure calculated based on the global air pressure model.

Additionally, [Table 4] below shows the estimated air pressure of the global air pressure model, the estimated air pressure of the first prediction model of the present invention, and the RMSE of the air pressure prediction model of the present invention against the actual observed air pressure. In more detail, in contrast to the observed air pressure, the first row is the RMSE of the global air pressure model, and the second row is the RMSE of the first prediction model, and the RMSE values of the first and second columns are the same as the values in Table 2 described above. Also, the third row is the RMSE value of the atmospheric pressure prediction model against the actual observed atmospheric pressure.

Site RMSE (hPa) global barometric model 1st prediction model barometric pressure prediction model DAEJ 6.6 4.7 4.8 SKMA 6.5 4.9 5.0 SKCH 6.3 5.3 5.5 SBAO 7.2 4.5 4.6 BHAO 6.4 4.3 4.4 MKPO 6.6 4.6 4.6 MLYN 6.3 4.9 4.9 KOHG 6.4 4.7 4.7 JEJU 6.3 4.2 4.6

Referring to [Table 4], the RMSE difference between the barometric pressure prediction model and the first prediction model at 8 stations except BHAO is similar or less than 0.4 hpa, and, like the first prediction model, the barometric pressure prediction model is larger than the global barometric pressure model. Accuracy is excellent. That is, both the first prediction models KP and KP _MS have more accurate predicted atmospheric pressure values than the global atmospheric pressure model. According to one embodiment, when limiting the target area to the entire Korean Peninsula, it is preferable to use an air pressure prediction model capable of estimating air pressure using altitude, rather than a first prediction model capable of estimating only air pressure at the location of an air pressure observation station. do. However, since the second prediction model can predict air pressure only for a predetermined air pressure observation station location, the first prediction model can be used when air pressure is predicted for the entire target region (eg, the Korean Peninsula). In the above-described example, only the observation data of 9 air pressure observation stations was used, but when the method of establishing the air pressure model of the present invention is applied by securing the air pressure observation data at more locations, the air pressure estimation accuracy of the air pressure prediction model of the present invention is can be improved

7 is a block diagram illustrating a method of establishing a pressure prediction model according to an embodiment of the present invention.

Referring to FIG. 7 , the air pressure model establishment device performs air pressure prediction modeling with respect to time (710). More specifically, the air pressure model establishment device acquires the observed air pressure of air pressure stations (711), designs a first air pressure prediction model for time (712), and models the air pressure of the first air pressure prediction model based on the air pressure observation data (712). estimate the coefficients. In this case, according to an embodiment, a preset target region of the first atmospheric pressure prediction model may be the entire Korean Peninsula, and the first atmospheric pressure prediction model may be KP _ms .

In addition, the air pressure model establishment device performs air pressure prediction modeling for space (710). In more detail, the barometric pressure model establishment device acquires the standard elevations of the barometric pressure stations (721), and generates a second atmospheric pressure prediction model that calculates estimated values of the atmospheric pressure model coefficients based on the standard elevations (722). At this time, the barometric pressure prediction model may be generated by replacing the barometric pressure model coefficients in the first barometric pressure prediction model with the barometric pressure model coefficients estimated in the second barometric pressure prediction model.

That is, the air pressure model establishment device may generate a pressure prediction model for space and time, and according to an embodiment, the air pressure prediction model may be KP .

8 is a block diagram of an apparatus for establishing a pressure model according to an exemplary embodiment.

The atmospheric pressure model establishment apparatus 800 of FIG. 8 may be the atmospheric pressure model establishment apparatus 30 of FIG. 1 .

Referring to FIG. 8 , an air pressure model establishment apparatus 800 may include a communication unit 810, a processor 820, and a DB 830. In the barometric pressure model establishment device 800 of FIG. 8 , only components related to the embodiment are shown. Accordingly, those skilled in the art can understand that other general-purpose components may be further included in addition to the components shown in FIG. 8 .

The communication unit 810 may include one or more components that enable wired/wireless communication with other nodes. For example, the communication unit 810 may include at least one of a short-range communication unit (not shown), a mobile communication unit (not shown), and a broadcast reception unit (not shown).

The DB 830 is hardware for storing various data processed in the air pressure model establishment device 800, and may store programs for processing and controlling the processor 820. The DB 830 may store payment information, user information, and the like.

The DB 830 includes random access memory (RAM) such as dynamic random access memory (DRAM) and static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and CD-ROM. ROM, Blu-ray or other optical disk storage, hard disk drive (HDD), solid state drive (SSD), or flash memory.

The processor 820 controls the overall operation of the barometric pressure model establishment device 800 . For example, the processor 820 may generally control an input unit (not shown), a display (not shown), a communication unit 810, and the DB 830 by executing programs stored in the DB 830. The processor 820 may control the operation of the atmospheric pressure model establishment device 800 by executing programs stored in the DB 830 . The processor 820 may control at least some of the operations of the content pressure model establishment device 2000 or the mediation pressure model establishment device 3000 described above with reference to FIGS. 1 to 8 .

The processor 820 may include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. It may be implemented using at least one of micro-controllers, microprocessors, and electrical units for performing other functions.

Embodiments according to the present invention may be implemented in the form of a computer program that can be executed on a computer through various components, and such a computer program may be recorded on a computer-readable medium. At this time, the medium is a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, an optical recording medium such as a CD-ROM and a DVD, a magneto-optical medium such as a floptical disk, and a ROM hardware devices specially configured to store and execute program instructions, such as RAM, flash memory, and the like.

Meanwhile, the computer program may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. An example of a computer program may include not only machine language code generated by a compiler but also high-level language code that can be executed by a computer using an interpreter or the like.

According to one embodiment, the method according to various embodiments of the present disclosure may be included and provided in a computer program product. Computer program products may be traded between sellers and buyers as commodities. A computer program product is distributed in the form of a device-readable storage medium (eg compact disc read only memory (CD-ROM)), or through an application store (eg Play Store™) or between two user devices. It can be distributed (e.g., downloaded or uploaded) directly or online. In the case of online distribution, at least part of the computer program product may be temporarily stored or temporarily created in a device-readable storage medium such as a manufacturer's server, an application store server, or a relay server's memory.

Claims (12)

As a method for establishing a pressure model,
obtaining actual observation data of air pressure of a plurality of air pressure observation stations;
generating a first predictive model for estimating an air pressure value over time based on the observation data;
generating a second predictive model that obtains a standard elevation value for each of the plurality of barometric pressure stations and calculates an estimated value of a barometric model coefficient based on the standard elevation value; and
Generating a barometric pressure prediction model using an estimated value of the barometric pressure model coefficient calculated in the second prediction model as a barometric pressure model coefficient of the first prediction model;
The second predictive model,
As a simple regression analysis equation with the fixed elevation as a variable, the slope T1 and bias T0 values are calculated for each barometric pressure model coefficient, such as the barometric amplitude A, the barometric pressure period P, and the average barometric pressure C, using the observed data, and then the calculated T1, An air pressure model establishment method of calculating estimated values of A, P, and C values based on the standard elevation using the simple regression analysis equation in which the T0 value is substituted. According to claim 1,
The plurality of air pressure observation stations are air pressure observation stations existing in a preset target region, and the air pressure model is an air pressure model for calculating air pressure characteristics of the preset target region. According to claim 2,
Establishing a barometric pressure model in which the root mean square deviation between the measured atmospheric pressure value of the observation data and the atmospheric pressure value estimated by the first predictive model is less than the root mean square deviation between the measured atmospheric pressure value of the observed data and the atmospheric pressure value estimated by the global atmospheric pressure model method. According to claim 1,
Wherein the first prediction model has, as air pressure model coefficients, air pressure amplitude, period, and average air pressure according to seasons and time, air pressure model establishment method. According to claim 4,
Wherein the first prediction model is a barometric pressure estimation model for estimating the barometric pressure value according to the year in the form of a real number by fitting the barometric pressure model coefficients in the form of a sinusoidal function, the barometric model establishment method. According to claim 5,
Based on the atmospheric pressure observation data of the plurality of atmospheric pressure observation stations, the atmospheric pressure amplitude, period and average atmospheric pressure are calculated for each of the plurality of atmospheric pressure observation stations. According to claim 1,
Wherein the second predictive model calculates estimated values of pressure model coefficients including pressure amplitude, period, and average air pressure, respectively, based on the normal elevation value. delete According to claim 1,
A method of establishing a barometric pressure model in which the root mean square deviation between the measured barometric pressure value of the observation data and the barometric pressure value estimated from the barometric pressure prediction model is smaller than the root mean square deviation between the measured barometric pressure value of the observed data and the barometric pressure value estimated from the global barometric model . According to claim 1,
The atmospheric pressure model coefficient of the first prediction model is determined regardless of the normal elevation, and the atmospheric pressure model coefficient of the atmospheric pressure prediction model is determined based on the normal elevation. As a barometric pressure model establishment device,
a communication unit that communicates with a user terminal;
a memory in which at least one program is stored; and
a processor that performs calculations by executing the at least one program;
the processor,
Acquiring actual observation data of air pressure of a plurality of air pressure stations,
Based on the observation data, a first predictive model for estimating the air pressure value over time is generated;
Obtaining a standard elevation value for each of the plurality of barometric pressure stations, and generating a second prediction model for calculating an estimated value of a barometric model coefficient based on the standard elevation value;
generating an air pressure prediction model using an estimated value of the air pressure model coefficient calculated in the second prediction model as a pressure model coefficient of the first prediction model;
The second predictive model,
As a simple regression analysis equation with the fixed elevation as a variable, the slope T1 and bias T0 values are calculated for each barometric pressure model coefficient, such as the barometric amplitude A, the barometric pressure period P, and the average barometric pressure C, using the observed data, and then the calculated T1, An air pressure model establishment device that calculates estimated values of A, P, and C values based on the standard elevation using the simple regression analysis equation in which the T0 value is substituted. A computer-readable recording medium recording a program for executing the method of claim 1 on a computer.

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