Sea condition information estimation device

Abstract

To provide a sea condition information estimation device that enables estimation of flow velocity without using a flow velocity measurement sensor.SOLUTION: A sea condition information estimation device using sea buoy is moored to a fixed part, follows fluctuations of the sea surface, is provided with an antenna and a GNSS receiver and is configured to acquire location information using the GNSS receiver. The sea condition information estimation device calculates the azimuth from the location information of an installation point P0 of the sea buoy and the location information of a position Pt of the sea buoy, calculates the second-order difference of the time series of the azimuth, calculates the extreme value of the second difference value, calculates the product of the adjacent extreme values, and estimates the flow velocity from the approximate curve of the product of the extreme values and the actual measured flow velocity.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to sea condition information, particularly a sea condition information estimation device for estimating the flow velocity of ocean currents.

Background Art

[0002] As a conventional device for measuring the flow velocity of ocean currents, a configuration in which a dedicated sensor, for example, an ultrasonic flowmeter, is arranged outside a buoy is known. In this configuration, since a cable is drawn into the buoy, maintenance is troublesome and costly, such as water leakage, cable disconnection, sensor degradation due to attached organisms, and battery replacement.

[0003] The applicant of the present application has proposed a wave height measuring device that measures the wave height by mounting a receiver that receives radio waves from GPS (Global Positioning System) satellites on a buoy and measuring the three-dimensional position of the buoy (see Patent Document 1). This wave height measuring device includes a GPS antenna, a GPS receiver, a data recording device, a data processing device, and a transmitter, and can measure the wave height at the location where the buoy floats.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Patent Document 1 does not disclose measuring the flow velocity using GPS data. Information on the flow velocity is important information for fishermen such as stationary net fishermen, and is also necessary information for ensuring the safety of ships approaching the pier. Furthermore, it is also necessary information from the perspective of disaster prevention.

[0006] Accordingly, an object of the present invention is to provide a sea condition information estimation device that can measure the flow velocity of ocean currents using position information obtained by a GNSS (Global Navigation Satellite System) receiver. **Means for Solving the Problems**

[0007] The present invention is a sea condition information estimation device that uses a sea state buoy that is moored to a fixed part, fluctuates following the sea surface, has an antenna and a GNSS receiver, and is configured to obtain position information by the GNSS receiver, calculates the azimuth from the position information of the installation point P0 of the sea state buoy and the position information of the position Pt of the sea state buoy, calculates the second-order difference value of the time series of the azimuth, calculates the extreme value of the second-order difference value, calculates the product of adjacent extreme values, estimates the flow velocity from the product of the extreme values and the approximate curve of the measured flow velocity It is a sea condition information estimation device. **Advantages of the Invention**

[0008] According to the present invention, the flow velocity can be estimated only by using the position information received by the GNSS receiver. Note that the effects described here are not necessarily limited, and any of the effects described in this specification may be applicable. **Brief Description of the Drawings**

[0009]

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DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described. The embodiments described below are preferred specific examples of the present invention and are subject to various technically preferable limitations. However, the scope of the present invention is not limited to these embodiments unless otherwise specified in the following description.

[0011] FIG. 1 shows the configuration of the mooring type flow velocity and flow direction measuring device according to an embodiment of the present invention. Reference numeral 1 denotes a flow velocity and flow direction measuring buoy (hereinafter appropriately referred to as a sea elephant buoy). The sea elephant buoy 1 has a structure floating on the sea surface SF, and has a dome-shaped equipment storage part that is not easily affected by the wind, a ring-shaped floating ring provided around it, and a solar cell attached to the floating ring.

[0012] More specifically, the walrus buoy 1 has a shape that is small (about 80 cm) and disk-shaped, floating just below the sea surface, and has isotropy that makes it easy to follow the movement on the sea surface. As a result, the influence of wind and flow on the buoy body is extremely small compared to conventional cylindrical buoys such as spar buoys, and it exhibits behavior that is almost synchronized with the fluctuations of the sea surface.

[0013] By combining this unique buoy structure with machine learning, it becomes possible to estimate the sway characteristics of the buoy only from the sea surface fluctuations, in other words, the fluctuation pattern of the three-dimensional position information. That is, when generating an estimation model by machine learning, it is possible to learn the sea surface fluctuation characteristics peculiar to waves and estimate the wave height and flow direction only from the fluctuation pattern of the three-dimensional position information. Furthermore, one embodiment of the present invention estimates the flow velocity by processing the three-dimensional position information of the walrus buoy 1. In the following description, the explanation of the estimation of the flow direction and flow velocity will be mainly carried out, but the wave height can be measured by the method proposed by the applicant of the present application (for example, the method described in Patent Document 1 mentioned at the beginning).

[0014] The walrus buoy 1 is connected to the mooring device via a rope. The mooring device has a configuration of a single-point mooring system. A small and lightweight buoy 13 as a floating body is connected to the anchor 11 on the seabed via ropes 12a, 12b, and 12c. The ropes are connected to each other via a swivel so as not to twist. The walrus buoy 1 is connected to the swivels for connecting the ropes 12b and 12c via the swivel and rope 14a, swivel 15, and rope 14b. Also, a chain W1 and a weight W2 as weights are connected to the buoy 13.

[0015] The total length of the ropes 12a to 12c is made longer than the water depth at the installation location. Furthermore, a disk-shaped walrus buoy 1 that is less affected by the sea breeze is connected to the mooring device via ropes 14a and 14b, and a weight W3 is connected to the rope 14a. Since the ropes are connected by a plurality of swivels, the behavior of the walrus buoy 1 becomes synchronized with the sea surface fluctuations, and the flow direction and flow velocity can be accurately measured.

[0016] The walrus buoy 1 has an antenna and a GNSS (Global Navigation Satellite System) receiver that receives radio waves from satellites by means of the antenna. Note that GNSS is a general term for satellite positioning systems such as GPS, GLONASS, Galileo, and Quasi-Zenith Satellite System (QZSS). For example, recently, with the start of the operation of the Quasi-Zenith Satellite System (Michibiki), it compensates for GPS and enables more accurate positioning.

[0017] The GNSS receiver also receives error correction information obtained by receiving radio waves from satellites at an electronic reference point whose position is known in advance by means of the antenna. The error correction information is received at a frequency of 5 Hz to 10 Hz. As a result, the positioning accuracy becomes high-precision. The three-dimensional position information of the walrus buoy 1 (antenna) is obtained by the GNSS receiver. The three-dimensional position information is represented by (X: longitude, Y: latitude, Z: altitude).

[0018] FIG. 2 is a schematic diagram for explaining the position on the XY plane of the three-dimensional position information measured by the GNSS receiver. The center point (reference point) P0 corresponds to the position of the buoy 13 of the mooring device, and its coordinates are known values (X0, Y0). For example, the position of the buoy 13 at the time of no current (slack tide) is taken as the center point. Pt corresponds to the position of the measured walrus buoy 1, and its coordinates are represented as (Xt, Yt). The azimuths of north, east, south, and west with respect to the center point P0 are 0°, 90°, 180°, and 270°, respectively. In the example of FIG. 2, for example, the angle θ with respect to the north is measured as the tidal direction at that time. Note that the method of representing the tidal direction is not limited to (0° to 360°), and (180° to 180°) may also be used.

[0019] Furthermore, the walrus buoy 1 has a wireless communication unit and a power supply unit for transmitting three-dimensional position information in addition to a GNSS receiver. The wireless communication unit has an antenna and performs wireless communication with a website on the Internet or a ground center. For example, communication is performed using a mobile phone network. Communication may also be performed using a satellite phone network. The power supply unit is composed of, for example, a secondary battery and a solar power generation device.

[0020] FIG. 3 shows the system configuration of the sea state information according to an embodiment of the present invention, and FIG. 4 shows the flow of the process for estimating the flow direction and flow velocity from the position change of the walrus buoy 1. As described above, the walrus buoy 1 is connected to the mooring device via the rope 14a, the return rope 15, and the rope 14b. In FIGS. 3 and 4, the illustration of the return rope and the weight of the mooring device is omitted.

[0021] The three-dimensional position information obtained by the walrus buoy 1 is transmitted to the position information receiving server 21 of the cloud server 2 via, for example, mobile communication or satellite communication. For example, the three-dimensional position information is transmitted at intervals of 0.1 second. The position information receiving server 21 decodes the satellite positioning data, and the decoded three-dimensional position information and time information are registered in the database server 22.

[0022] Information of (year / month / day, time, longitude, latitude, altitude) is registered in the database server 22. A preprocessing server 23 is connected to the database server 22, and an analysis server 24 is connected to the database server 22 and the preprocessing server 23.

[0023] The preprocessing server 23 performs a process of extracting continuous data for a predetermined time, for example, 5 to 20 minutes, from the database server 22, a process of cutting off satellite-specific noise, a process of smoothing the position information, and a process of calculating the average value (Xt, Yt, Zt) of the three-dimensional position information.

[0024] The data preprocessed by the preprocessing server 23 is provided to the analysis server 24. The analysis server 24 generates an approximation curve for estimating the flow velocity by performing the following processes, and estimates the flow velocity based on the approximation curve.

[0025] · Calculate the azimuth and moving distance from the coordinates (X0, Y0) of the installation point P0 of the mooring device's sea elephant buoy 1, the coordinates (Xt, Yt, Zt) of the position Pt of the sea elephant buoy 1, and the time information T. · Cut noise by smoothing the time-series azimuth (flow direction). · Calculate the time difference value of the smoothed azimuth. · Calculate the second-order difference value and extract the extreme values of the second-order difference value. · Estimate the flow velocity from the product of adjacent extreme values and the approximation curve of the separately created measured flow velocity.

[0026] The second-order difference value is output (calculated) as a time series of discrete numerical values at regular intervals (for example, every 10 minutes). When this discrete value is graphed, it becomes the graph in Figure 6. The peaks and valleys of this graph are called extreme values, but they are only peak values calculated from the numerical relationship before and after. In that sense, they may also be referred to as nodes.

[0027] Obtain the coefficients A, B, C, and D of the polynomial approximation by the least squares method Y = A x (X cubed) + B x (X squared) + C x X + D

[0028] The flow velocity estimated value obtained by the analysis server 24 is made to have high accuracy by the AI (Artificial Intelligence) correction server 25. That is, the estimation accuracy of the flow velocity is made higher by the flow velocity estimation learning model (flow velocity estimation and correction utilizing a neural network) with the tidal level change in the vertical direction (Z) added.

[0029] The estimated flow velocity corrected by the AI correction server 25 is supplied to the analysis database 26. The analysis database 26 stores the sea condition data of (year / month / day · time · flow direction · flow velocity).

[0030] The content server 27 is connected to the analysis database 26, and the web server 28 is connected to the content server 27, enabling access to the data in the analysis database 26 from the outside. From the outside, access to the data in the analysis database 26 is enabled by a smartphone 31, a personal computer 32, a cable TV 33, etc.

[0031] The inventor of the present application confirmed as follows that the flow velocity can be estimated by the processing of the above-described analysis server 24. The estimation of the flow velocity will be described with reference to FIGS. 5, 6, 7, and 8. In order to generate an approximate curve for estimating the flow velocity, an ultrasonic flowmeter is attached to any one of, for example, ropes 12a to 12c of the mooring device, and the flow velocity is measured. After the approximate curve is generated, the ultrasonic flowmeter may be removed. That is, the measured value (teacher data) of the ultrasonic flowmeter serving as a model is used when creating the approximate formula. Therefore, in practice, the ultrasonic flowmeter is not attached to the buoy except for verification.

[0032] FIG. 5 is a graph 40 showing the flow velocity [cm / sec] measured by the ultrasonic flowmeter and a graph 41 showing the flow direction [0° to 360°] measured from the moving direction of the sea elephant buoy 1. The horizontal axis represents the date and time, and values at, for example, 10-minute intervals are plotted. The vertical axis represents the flow velocity and the flow direction.

[0033] FIG. 6 shows a graph 42 of the second-order difference value of the flow direction and a graph 43 of the average flow velocity. However, the horizontal axes of FIGS. 5 and 6 do not correspond. In FIG. 6, the white dots represent the extreme values extracted from the second-order difference values.

[0034] FIG. 7 shows a scatter diagram of the product of adjacent extreme values (horizontal axis) and the average flow velocity (vertical axis). An approximate curve 44 can be generated from this scatter diagram. The generated approximate curve 44 is stored in a storage device, for example, the storage device of the analysis server 24. By holding the approximate curve 44, the flow velocity can be estimated from the product of the extreme values. The flow velocity at other times that do not correspond to the extreme values can be obtained by extrapolation from a plurality of extreme values obtained up to the immediately preceding time.

[0035] FIG. 8 is a graph showing the superposition of the flow velocity estimated from the flow direction using the approximate curve 44 and the flow velocity actually measured using an ultrasonic flowmeter or the like. The thick-line graph 45 is the flow velocity estimated using the approximate curve 44, and the thin-line graph 46 is the actually measured flow velocity. As can be seen from FIG. 8, the difference between the estimated flow velocity and the actually measured flow velocity is small, and it can be seen that the estimated flow velocity is close to the actual measured value. Note that the above-described example of the data is an example that has not been corrected by the AI server 25, and the accuracy can be further improved by performing the correction.

[0036] According to the above-described embodiment, by generating an approximate curve, the flow direction and the flow velocity can be estimated from the position information from the sea elephant buoy 1. Therefore, maintenance (such as battery replacement and cleaning) is not required as in the case of measuring the flow velocity by a sensor such as an ultrasonic flowmeter. Also, although an ultrasonic flowmeter is required to create the approximate curve, after the approximate curve is created, the ultrasonic flowmeter is not required, and the cost can be reduced.

[0037] As described for the above-described embodiment, in the present invention, the following flow velocity estimation is performed. 1. Obtain high-precision (cm accuracy) positioning data (latitude, longitude, altitude) of the quasi-zenith satellite "Michibiki".

[0038] 2. Monitor the sea surface displacement (mainly tidal level fluctuations) with cm accuracy, and accurately measure in real time the times and height differences of high and low tides that vary greatly depending on the sea area.

[0039] 3. Extract two parameters related to flow velocity estimation from the behavior (XYZ displacement) of the moored sea elephant buoy 1 that changes in time series due to tides. That is, normalize and parameterize the second-order differences in the time series of the moving distance and azimuth centered on the installation point (reference point) of the sea elephant buoy 1 for each time between high and low tides.

[0040] By normalizing the time between high and low tides with different time intervals (the tidal times of the rising and falling tides) and also normalizing the moving distance from the installation point of the sea elephant buoy 1 at this normalized tidal time, it becomes possible to estimate the measured flow velocity from tidal level fluctuations with common parameters regardless of spring tides or neap tides.

[0041] 4. Generate an approximate curve between the flow velocity and the parameter from the correlation between the measured flow velocity and the parameter obtained from the sea surface displacement. Use this normalized approximate curve to estimate the flow velocity of the tidal current in any sea area.

[0042] The results of the verification experiment on the present invention described above will be explained below. In the verification experiment, the sea elephant buoy 1 is equipped with a receiver of CLAS (Centimeter Level Augmentation Service). Therefore, position information can be obtained with an accuracy of several centimeters of error. Tidal current observations were carried out for about one month, and the flow velocity estimation parameters were recalculated from the measured flow velocity and the behavior of the buoy.

[0043] Figure 9 shows the trajectory of the sea elephant buoy 1 during the observation period. The center of the ellipse indicated by X corresponds to the launch point (reference point) of the sea elephant buoy 1. The recalculated flow velocity estimation parameters based on the launch point of this sea elephant buoy 1 for the moving distance and azimuth are shown in Figure 10 (a graph showing the relationship between the measured flow velocity and the moving distance of the sea elephant buoy 1) and Figure 11 (a graph showing the relationship between the second-order difference value of the azimuth of the sea elephant buoy 1 and the measured flow velocity).

[0044] In addition, time-series data of the water level calculated from the elevation values of the CLAS positioning system is required. This data highly coincides with the tide gauge (Hakodate Variation Tide Gauge) near the sea area where the demonstration experiment was conducted. From this, the exact high and low tide times at the site can be obtained from the elevation data of the oceanographic buoy 1.

[0045] Generally, the time intervals between uneven high and low tides are normalized, and the results of recalculating the flow velocity estimation parameters at arbitrary tide times (for example, spring ebb, neap ebb, small tide ebb, spring flood) are shown in Fig. 12. From Fig. 12, a clear correlation is particularly seen between the normalized moving distance and the measured flow velocity, and as shown in Fig. 12, an approximate curve can be obtained. Fig. 13 shows the comparison between the flow velocity estimated from this approximate curve and the measured flow velocity. The mean square error is about 3 cm / s, and it is confirmed that it is a flow velocity estimation formula that sufficiently meets practical use.

[0046] As described above, the embodiments of the present invention have been specifically described. However, the present invention is not limited to the above-described embodiments, and various modifications based on the technical idea of the present invention are possible. For example, the location where the oceanographic buoy 1 is moored is not limited to the seabed and may be a fixed part such as a pier in a port. Also, some functions of the cloud server, for example, the function of the preprocessing server, may be provided in the oceanographic buoy 1. The configurations, methods, processes, shapes, materials, and numerical values given in the above embodiments are merely examples, and different configurations, methods, processes, shapes, materials, and numerical values, etc. may be used as necessary.

Explanation of Reference Numerals

[0047] 1... Oceanographic buoy, 2... Cloud server, 11... Anchor, 12a, 12b, 12c, 14a, 14b... Ropes, 13... Buoy, 21... Position information receiving server, 22... Database server, 24... Analysis server, 25... AI correction server, 26... Analysis database

Claims

**Claim 1** A sea condition information estimation device that uses a sea state buoy moored to a fixed part, fluctuating following the sea surface, having an antenna and a GNSS receiver, and configured to obtain position information by the GNSS receiver, calculating an azimuth from the position information of the installation point P0 of the sea state buoy and the position information of the position Pt of the sea state buoy, calculating a second-order difference value of the time series of the azimuth, calculating an extreme value of the second-order difference value, calculating a product of adjacent extreme values, estimating a flow velocity from an approximation curve of the product of the extreme values and the measured flow velocity Sea condition information estimation device. **Claim 2** The sea condition information estimation device according to claim 1, wherein the measured flow velocity is separately measured at the installation point of the sea state buoy. **Claim 3** The sea condition measurement device according to claim 1 or 2, wherein the coordinates of the position of the sea state buoy are subjected to noise removal processing. **Claim 4** The sea condition measurement device according to claim 1 or 2, which corrects the estimated flow velocity.

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