Estimation device, estimation system, and estimation method

Abstract

This river state estimation device (2) is equipped with: a water-surface point group extraction unit (21) for extracting a three-dimensional point group of the water surface from three-dimensional point group data in a region which includes the water surface; a water-surface state estimation unit (22) for estimating the state of a surface wave formed on the water surface by using the three-dimensional point group of the water surface; and a water-surface state output unit (23) for outputting water-surface-state information which expresses the state of the surface wave.

Description

Technical Field

[0001] The present disclosure relates to an estimation device, an estimation system, and an estimation method.

Background Art

[0002] River flow is the volume of water flowing through a cross-section at a certain position of a river per unit time, and is used for river management such as river maintenance planning, formulation of water resource plans, or flood prediction. Therefore, the observation of river flow is highly regarded. On the other hand, it is difficult to directly observe river flow. For this reason, river flow is estimated according to a relational expression based on physical laws or empirical laws using the observation results of other physical quantities observed by a water level gauge or a flow velocity gauge.

[0003] As a method for estimating river flow involving water level observation, for example, there is a method in which a relationship formula between water level and flow rate, the H-Q formula, is estimated in advance using the observation results of water level and flow rate, and the river flow is estimated using the observed value of the water level according to the estimated H-Q formula. Although this method is a simple method, it is difficult to explain the physical validity between the water level and the flow rate. Therefore, the validity when estimating the flow rate during a flood is not guaranteed, and the estimation error is also large.

[0004] Also, as a method for estimating river flow involving flow rate observation, for example, there is a method in which the river flow is estimated using the product of the flow velocity distribution and the cross-sectional area of a cross-section at a certain position of the river. While the physical validity between the water level and the flow rate is guaranteed in this method, when the riverbed height fluctuates during a flood or the like and the cross-sectional area of the cross-section changes, the estimation error of the river flow becomes large. For this reason, technologies have been proposed to estimate the fluctuation of the riverbed height during a flood and suppress the decrease in the estimation accuracy of the river flow.

[0005] For example, Non-Patent Document 1 describes a technique for estimating the water depth of a river based on the flow resistance generated during water discharge due to the riverbed waves, which are wave structures on the riverbed. This technique estimates the water depth of a river based on the observed value of the river flow velocity, the water surface gradient at the river flow velocity observation point, and the particle size of the riverbed material. The water surface gradient can be estimated by linearly fitting the observed values of a plurality of water level gauges using a linear function.

[0006] In the technique described in Non-Patent Document 1, when surface waves, which are wave structures on the water surface, are generated by riverbed waves, there is a problem that the estimation accuracy of the water surface gradient decreases in linear fitting using a linear function. Therefore, in order to maintain the estimation accuracy of the water surface gradient when surface waves occur, it is necessary to accurately estimate the surface waves and remove the influence of the surface waves on the linear fitting.

Prior Art Documents

Non-Patent Documents

[0007]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0008] The conventional technology has observed the water surface of a river using three-dimensional point cloud data of the water surface observed by a three-dimensional point cloud observation device such as a lidar, a camera, a radar, or a water level gauge. However, the three-dimensional point cloud data observed by the three-dimensional point cloud observation device generally includes three-dimensional point cloud data from the terrain of the river or structures existing around the river as errors other than the water surface, so there is a problem that the estimation accuracy of the state of the surface waves is low. In the technology described in Non-Patent Document 1, the intervals between water level gauges arranged at multiple locations in a river are large, and the state of surface waves with short wavelengths cannot be accurately estimated.

[0009] The present disclosure aims to solve the above problems and obtain an estimation device, an estimation system, and an estimation method capable of suppressing a decrease in the estimation accuracy of the state of surface waves.

Means for Solving the Problems

[0010] The estimation device according to the present disclosure includes a water surface point cloud extraction unit that acquires three-dimensional point cloud data of a region including the water surface and extracts a three-dimensional point cloud of the water surface from the acquired three-dimensional point cloud data, a water surface state estimation unit that estimates the state of surface waves generated on the water surface using the extracted three-dimensional point cloud of the water surface, and an output unit that outputs water surface state information indicating the estimated state of the surface waves.

Effects of the Invention

[0011] According to the present disclosure, a three-dimensional point cloud of the water surface is extracted from the three-dimensional point cloud data of the region including the water surface, and the state of surface waves generated on the water surface is estimated using the extracted three-dimensional point cloud of the water surface. Thereby, the river state estimation device according to the present disclosure can suppress a decrease in the estimation accuracy of the state of surface waves.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0013] Embodiment 1. FIG. 1 is a block diagram showing the configuration of the river state estimation system 1 according to Embodiment 1. In FIG. 1, the river state estimation system 1 is an estimation system for estimating the state of a river, and includes a river state estimation device 2 and a three-dimensional point cloud observation device 3. The river state estimation device 2 is an estimation device that estimates the state of the surface waves generated in the river among the states of the river, using the observation values of the three-dimensional point cloud A near the water surface of the river acquired from the three-dimensional point cloud observation device 3. The state of the surface waves is, for example, at least one of the wavelength, amplitude, direction, and phase of the surface waves. As shown in FIG. 1, the river state estimation device 2 includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22, and a water surface state output unit 23.

[0014] The three-dimensional point cloud observation device 3 is a device that is arranged in or around a river and observes a three-dimensional point cloud A, which is three-dimensional point cloud data of a region including the water surface. The three-dimensional point cloud observation device 3 is, for example, any one of a lidar, a camera, a radar, a water level gauge, or the like, or a device combining these, and it suffices as long as it can observe a plurality of water surfaces of a river as an observation region and observe the three-dimensional point cloud in these observation regions. The three-dimensional point cloud observation device 3 is connected to the river state estimation device 2 by wire or wirelessly, and can output the observed three-dimensional point cloud A to the river state estimation device 2.

[0015] (Water surface point cloud extraction unit) The water surface point cloud extraction unit 21 acquires the three-dimensional point cloud A of the region including the water surface from the three-dimensional point cloud observation device 3, and extracts the water surface three-dimensional point cloud B, which is the three-dimensional point cloud of the water surface, from the acquired three-dimensional point cloud A. For example, the water surface point cloud extraction unit 21 acquires the observation values of the three-dimensional point cloud A observed by the three-dimensional point cloud observation device 3, and extracts only the three-dimensional point cloud on the water surface from the acquired three-dimensional point cloud A.

[0016] For example, the water surface point cloud extraction unit 21 extracts the three-dimensional points corresponding to the positions in the plane including the water surface of the river among the three-dimensional point cloud A and within the range of the water surface of the river. Also, the water surface point cloud extraction unit 21 may extract the three-dimensional points whose vertical position is lower than the water surface height of the river among the three-dimensional point cloud A. Furthermore, the water surface point cloud extraction unit 21 may extract the three-dimensional points corresponding to the positions in the plane including the water surface of the river among the three-dimensional point cloud A, within the range of the water surface of the river, and whose vertical position is lower than the water surface height of the river.

[0017] (Water surface state estimation unit) The water surface state estimation unit 22 estimates the state C of the water surface wave generated on the water surface using the water surface three-dimensional point cloud B extracted by the water surface point cloud extraction unit 21 from the three-dimensional point cloud A. Figure 2 is an explanatory diagram showing an outline of the estimation of the water surface state in Embodiment 1. The left figure of Fig. 2 (hereinafter referred to as the left figure of Fig. 2) shows an overview of the water surface of a river as seen from above, where x and y are the axes of a rectangular coordinate system. The water surface of the river is included in the xy plane. In the left figure of Fig. 2, the dashed line indicates the trough part of the water surface wave, and the solid line indicates the crest part of the water surface wave. The point cloud observation range is the observation range of the river by the three-dimensional point cloud observation device 3. The right figure of Fig. 2 (hereinafter referred to as the right figure of Fig. 2) shows the relationship between the water level and the position of the river in a cross-section perpendicular to the equiphase surface of the water surface wave (a cross-section along a straight line drawn perpendicular to the dashed line and the solid line in the point cloud observation range of the left figure of Fig. 2). The circular spots in the right figure of Fig. 2 indicate the three-dimensional point cloud of the water surface wave (hereinafter referred to as the observed point cloud) observed by the three-dimensional point cloud observation device 3. The dashed line in the right figure of Fig. 2 is a virtual line indicating the actual water surface of the river, and the solid line in the right figure of Fig. 2 is an estimated line indicating the water surface wave.

[0018] The water surface state estimation unit 22 calculates, for example, a curved surface passing through each three-dimensional point of the water surface three-dimensional point cloud B. For example, the water surface state estimation unit 22 may assume the water surface wave as a plane wave in an arbitrary direction, and estimate the amplitude, wavelength, and phase of the plane wave by the least squares method with the observed point cloud. The water surface state estimation unit 22 determines, as the curved surface indicating the water surface wave, the plane wave with the shortest wavelength (the length indicated by the double arrow) among the results of estimating the state of the plane wave in an arbitrary direction within the point cloud observation range (for example, cross-section 1 and cross-section 2). For example, the water surface state estimation unit 22 may calculate a curve by taking the arithmetic mean of the observed point cloud. The water surface state estimation unit 22 determines, as the curve indicating the water surface wave, the curve with the shortest distance between the trough and the crest, like the curve indicated by the solid line in the right figure of Fig. 2, among the results of calculating the curve in an arbitrary direction within the point cloud observation range. When the water surface state estimation unit 22 calculates a curve corresponding to the water surface wave generated in the river, it estimates the wavelength and phase of the water surface wave based on the distance between the trough and the crest of the curve, estimates the amplitude of the water surface wave based on the magnitude of the trough and the crest, and estimates the direction of the water surface wave based on the straight line determining the cross-section in which the observed point cloud used for calculating the curve is included.

[0019] FIG. 3 is an explanatory diagram showing an overview of the conventional estimation of the water surface state, and shows the relationship between the water level and the position of the river in a cross section perpendicular to the equiphase surface of the water surface wave. The triangular spots in FIG. 3 indicate the observation point groups of the observed water surface waves of the river. The dashed line is a virtual line indicating the actual water surface of the river, and the solid line is an estimated line indicating the water surface wave by the conventional water surface state estimation. The three-dimensional point cloud used for the conventional estimation of the water surface state includes a three-dimensional point cloud caused by structures such as bridges around the river or terrain other than the water surface such as riverbeds. For this reason, in the conventional estimated line of the water surface wave, when strongly affected by the point cloud other than the water surface, for example, as shown by the solid line in FIG. 3, a water surface wave that greatly deviates from the virtual line indicating the actual water surface is estimated.

[0020] On the other hand, the river state estimation device 2 extracts the water surface three-dimensional point cloud B from the three-dimensional point cloud A, and uses the water surface three-dimensional point cloud B to estimate the state C of the water surface wave generated on the water surface. As a result, the influence of the point cloud other than the water surface of the river is removed, and the river state estimation device 2 can suppress a decrease in the estimation accuracy of the state C of the water surface wave in the river.

[0021] The water surface state estimation unit 22, for example, accumulates in advance the information of the water surface three-dimensional point cloud B for a plurality of times and the data of the amplitude, wavelength, phase, and direction of the water surface wave, performs learning by a machine learning model using these as inputs, inputs the water surface three-dimensional point cloud B into the learned machine learning model, and estimates the amplitude, wavelength, phase, and direction of the water surface wave, thereby estimating the state C of the water surface wave.

[0022] (Water surface state output unit) The water surface state output unit 23 is an output unit that outputs water surface state information indicating the state C of the water surface wave estimated by the water surface state estimation unit 22. The water surface state information is information indicating at least one of the wavelength, amplitude, direction, and phase of the water surface wave. For example, it is display control information for displaying the information indicating the state C of the water surface. The water surface state output unit 23 outputs the water surface state information to the display device. The display device displays the wavelength, amplitude, direction, and phase of the water surface wave based on the water surface state information.

[0023] Next, the river state estimation method according to Embodiment 1 will be described. FIG. 4 is a flowchart showing the river state estimation method according to Embodiment 1, and shows the estimation process of the water surface state of the river by the river state estimation device 2. The water surface point cloud extraction unit 21 extracts the water surface 3D point cloud B from the 3D point cloud A of the region including the water surface (step ST1). Next, the water surface state estimation unit 22 estimates the state C of the water surface waves generated on the water surface using the extracted water surface 3D point cloud B (step ST2). After that, the water surface state output unit 23 outputs water surface state information indicating the estimated state C of the water surface waves (step ST3). Thereby, a decrease in the estimation accuracy of the state C of the water surface waves can be suppressed.

[0024] Next, the hardware configuration for realizing the functions of the river state estimation device 2 will be described. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23 provided in the river state estimation device 2 are realized by a processing circuit. That is, the river state estimation device 2 includes a processing circuit for executing the processes of steps ST1 to ST3 shown in FIG. 4. The above processing circuit may be dedicated hardware, or may be a CPU (Central Processing Unit) that executes a program stored in a memory. The processing circuit may be one or more processors.

[0025] FIG. 5A is a block diagram showing a hardware configuration for realizing the functions of the river state estimation device 2. FIG. 5B is a block diagram showing a hardware configuration for executing software for realizing the functions of the river state estimation device 2. In FIGS. 5A and 5B, the input interface 100 is an interface that relays the three-dimensional point cloud A acquired by the water surface point cloud extraction unit 21 from the three-dimensional point cloud observation device 3 via an input line connecting between the river state estimation device 2 and the three-dimensional point cloud observation device 3. The output interface 101 is an interface that relays the water surface state information output from the water surface state output unit 23 to the external device via an output line connecting between the river state estimation device 2 and an external device (for example, a display device) connected to the subsequent stage of the river state estimation device 2.

[0026] When the processing circuit is the dedicated hardware processing circuit 102 shown in FIG. 5A, the processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23 provided in the river state estimation device 2 may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0027] When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23 provided in the river state estimation device 2 are realized by software, firmware, or a combination of software and firmware. Note that the software or firmware is described as a program and stored in the memory 104.

[0028] By reading and executing the program stored in the memory 104, the processor 103 realizes the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23 provided in the river state estimation device 2. For example, the river state estimation device 2 includes a memory 104 for storing a program that, when executed by the processor 103, results in the execution of the processes from step ST1 to step ST3 shown in FIG. 4. These programs cause the computer to execute the procedures or methods of the processes performed by the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23. Also, the memory 104 may be a computer-readable storage medium storing a program for causing the computer to function as the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23.

[0029] The memory 104 includes, for example, non-volatile or volatile semiconductor memories such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically-EPROM) (registered trademark), magnetic disks, flexible disks, optical disks, compact disks, mini disks, DVDs, etc.

[0030] Part of the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, and the water surface state output unit 23 provided in the river state estimation device 2 may be realized by dedicated hardware, and the other part may be realized by software or firmware. For example, the functions of the water surface point cloud extraction unit 21 and the water surface state output unit 23 may be realized by a processing circuit 102 which is dedicated hardware, and the function of the water surface state estimation unit 22 may be realized by the processor 103 reading and executing the program stored in the memory 104. Thus, the processing circuit can realize the above functions by hardware, software, firmware, or a combination thereof.

[0031] Incidentally, the river state estimation device 2 is suitable for a river water depth estimation system. For example, in the river state estimation system 1, a river water depth estimation device for estimating the water depth of a river is provided. The river water depth estimation device can suppress a decrease in the accuracy of water depth estimation even when surface waves occur by performing water depth estimation using the information on the wavelength, amplitude, direction, and phase of the surface waves estimated by the river state estimation device 2. Further, the river state estimation device 2 may be an estimation device for estimating the surface state of the sea, a lake, a pond, or the like. Furthermore, the surface on which the estimation device estimates the state is not limited to the water surface, and may be the surface of a fluid that generates waves. Examples of the fluid include magma.

[0032] As described above, the river state estimation device 2 according to Embodiment 1 includes a water surface point group extraction unit 21 that extracts a water surface three-dimensional point group B from among the three-dimensional point group A of the region including the water surface, a water surface state estimation unit 22 that estimates the state C of the surface waves generated on the water surface using the water surface three-dimensional point group B, and a water surface state output unit 23 that outputs water surface state information indicating the state C of the surface waves. Since the state C of the surface waves is estimated using only the water surface three-dimensional point group B, even when there are many three-dimensional point groups caused by structures such as bridges around the river or terrain other than the water surface such as riverbeds, these influences are removed. Thereby, the river state estimation device 2 can suppress a decrease in the estimation accuracy of the state C of the surface waves.

[0033] In the river state estimation device 2 according to Embodiment 1, the water surface state estimation unit 22 estimates at least one of the wavelength, amplitude, direction, and phase of the surface waves. In this way, the river state estimation device 2 can estimate various information indicating the state C of the water surface.

[0034] Since the river state estimation system 1 according to Embodiment 1 includes the river state estimation device 2 and the three-dimensional point group observation device 3 that observes the three-dimensional point group A of the region including the water surface, a decrease in the estimation accuracy of the state C of the surface waves can be suppressed.

[0035] The river state estimation method according to Embodiment 1 includes a step in which a water surface point cloud extraction unit 21 extracts a water surface 3D point cloud B from a 3D point cloud A of a region including the water surface, a step in which a water surface state estimation unit 22 estimates a state C of water surface waves generated on the water surface using the water surface 3D point cloud B, and a step in which a water surface state output unit 23 outputs water surface state information indicating the state C of the water surface waves. By the river state estimation apparatus 2 executing this method, it is possible to suppress a decrease in the estimation accuracy of the state C of the water surface waves.

[0036] Embodiment 2. FIG. 6 is a block diagram showing the configuration of a river state estimation system 1A according to Embodiment 2. In FIG. 6, the river state estimation system 1A is an estimation system that estimates the state of a river, and includes a river state estimation apparatus 2A and a 3D point cloud observation apparatus 3. The river state estimation apparatus 2A is an estimation apparatus that estimates a state C of water surface waves generated in the river among the states of the river, using the observation value of the 3D point cloud A in the vicinity of the water surface of the river acquired from the 3D point cloud observation apparatus 3.

[0037] As shown in FIG. 6, the river state estimation apparatus 2A includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22A, and a water surface state output unit 23. The water surface state estimation unit 22A estimates the state C of the water surface waves using the water surface 3D point cloud B extracted from the 3D point cloud A by the water surface point cloud extraction unit 21. As shown in FIG. 6, the water surface state estimation unit 22A includes a spatial direction DFT unit 221 and a filter unit 222.

[0038] (Spatial direction DFT unit) The spatial direction DFT unit 221 acquires the water surface 3D point cloud B extracted from the 3D point cloud A by the water surface point cloud extraction unit 21, and calculates the spatial spectrum of the water surface 3D point cloud B by performing a discrete Fourier transform in the spatial direction (hereinafter referred to as DFT) on the acquired water surface 3D point cloud B. Specifically, the spatial direction DFT unit 221 converts the water surface 3D point cloud B on the spatial axis into a spatial spectrum on the spatial frequency axis as a set of sine waves of a plurality of wavelengths.

[0039] In addition, the spatial-direction DFT unit 221 may perform a non-uniform two-dimensional DFT on the three-dimensional water surface point cloud B. Furthermore, the spatial-direction DFT unit 221 may perform a uniform two-dimensional DFT on the result of interpolating the three-dimensional water surface point cloud B inside and outside a uniform grid. Furthermore, the spatial-direction DFT unit 221 may extract the three-dimensional water surface point cloud B on a one-dimensional spatial axis and perform a one-dimensional DFT along the one-dimensional axis on the extracted three-dimensional water surface point cloud B.

[0040] (Filter unit) The filter unit 222 extracts the water surface wave component from the spatial spectrum of the three-dimensional water surface point cloud B calculated by the spatial-direction DFT unit 221. Specifically, the spatial spectrum calculated by the spatial-direction DFT unit 221 may contain spatial spectra caused by factors other than water surface waves, such as the observation error of the three-dimensional point cloud observation device 3, disturbances caused by the three-dimensional point clouds of ground objects such as trees near the water surface shaken by the wind, disturbances caused by the three-dimensional point clouds of moving objects such as animals appearing on the water surface, and the water surface gradient of the river. The filter unit 222 removes the spatial spectrum components other than the water surface wave from the spatial spectrum of the three-dimensional water surface point cloud B.

[0041] For example, the filter unit 222 extracts only the spatial spectrum components caused by the water surface wave from the spatial spectrum of the three-dimensional water surface point cloud B calculated by the spatial-direction DFT unit 221 using a window function. In addition, the filter unit 222 may use a window function to extract the vicinity of the peak where the amplitude of the spatial spectrum is the largest. Furthermore, the filter unit 222 may use a window function based on the characteristic information of the wavelength of the water surface wave to extract the spatial spectrum within a certain wavelength range. Furthermore, the filter unit 222 may use a window function to remove the long-wavelength components caused by the water surface gradient of the river from the spatial spectrum of the three-dimensional water surface point cloud B. Furthermore, the filter unit 222 may pre-estimate filter coefficients using the correct data of the spatial spectrum of the surface waves, and adaptively filter only the spatial spectrum components caused by the surface waves from the spatial spectrum of the three-dimensional surface point cloud B based on the estimated filter coefficients.

[0042] FIG. 7 is a flowchart showing the estimation of the surface state in the second embodiment, and shows the details of the process of step ST2 in FIG. 4. The spatial direction DFT unit 221 acquires the three-dimensional surface point cloud B extracted from the three-dimensional point cloud A by the surface point cloud extraction unit 21, and calculates the spatial spectrum of the three-dimensional surface point cloud B by performing DFT in the spatial direction on the acquired three-dimensional surface point cloud B (step ST2-1). The filter unit 222 extracts the surface wave components from the spatial spectrum of the three-dimensional surface point cloud B calculated by the spatial direction DFT unit 221 (step ST2-2). After that, the surface state estimation unit 22A estimates the state of the surface waves using the surface wave components extracted from the spatial spectrum of the three-dimensional surface point cloud B by the filter unit 222. For example, the surface state estimation unit 22A identifies the surface waves using the surface wave components in the spatial spectrum of the three-dimensional surface point cloud B, and estimates the wavelength, phase, and direction of the surface waves based on the amplitude, spatial frequency, and declination angle of the spatial frequency at the peak position of the spatial spectrum.

[0043] Also, the functions of the surface point cloud extraction unit 21, the surface state estimation unit 22A, and the surface state output unit 23 provided in the river state estimation device 2A may be realized by the dedicated hardware processing circuit 102 shown in FIG. 5A. The processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the water surface point group extraction unit 21, the water surface state estimation unit 22A, and the water surface state output unit 23 included in the river state estimation device 2A are realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 104. The functions of the water surface point group extraction unit 21, the water surface state estimation unit 22A, and the water surface state output unit 23 included in the river state estimation device 2A may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0044] Further, the river state estimation device 2A may be an estimation device that estimates the water surface state of the sea, a lake, a pond, or the like. Furthermore, the water surface whose state is estimated by the estimation device is not limited to the water surface, and may be the surface of a fluid that generates waves. Examples of the fluid include magma.

[0045] As described above, in the river state estimation device 2A according to the second embodiment, the water surface state estimation unit 22A includes a spatial direction DFT unit 221 that calculates the spatial spectrum of the water surface three-dimensional point group B by performing a DFT in the spatial direction on the water surface three-dimensional point group B, and a filter unit 222 that extracts the water surface wave component from the spatial spectrum of the water surface three-dimensional point group B, and estimates the state C of the water surface wave using the water surface wave component. Since the state of the water surface wave generated on the water surface is estimated by removing the spatial spectrum other than the water surface wave, even when there are many three-dimensional point groups caused by structures such as bridges around the river or terrain other than the water surface such as riverbeds, these influences are removed. Thereby, the river state estimation device 2A can suppress a decrease in the estimation accuracy of the state of the water surface wave in the river.

[0046] Embodiment 3. FIG. 8 is a block diagram showing the configuration of the river state estimation system 1B according to Embodiment 3. In FIG. 8, the river state estimation system 1B is an estimation system for estimating the state of a river, and includes a river state estimation device 2B and a three-dimensional point cloud observation device 3. The river state estimation device 2B is an estimation device that estimates the water surface gradient D of the river among the states of the river, using the observation value of the three-dimensional point cloud A near the water surface of the river acquired from the three-dimensional point cloud observation device 3. As shown in FIG. 8, the river state estimation device 2B includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22, a water surface gradient estimation unit 24, and a water surface gradient output unit 25.

[0047] (Water surface gradient estimation unit) The water surface gradient estimation unit 24 estimates the water surface gradient D using the state C of the water surface wave estimated by the water surface state estimation unit 22. The water surface gradient D is the gradient in the longitudinal direction of the water surface. For example, the water surface gradient estimation unit 24 extracts information on the water level caused by the water surface wave using the wavelength, amplitude, direction, and phase of the water surface wave, and estimates the water surface gradient D using the three-dimensional point cloud of the water surface obtained by subtracting the extracted water level. The water surface gradient estimation unit 24 estimates the water surface gradient, for example, by performing linear fitting of the three-dimensional point cloud of the water surface obtained by subtracting the extracted water level to a linear function. By removing the influence of the water surface wave in this way, the water surface gradient estimation unit 24 can suppress a decrease in the estimation accuracy of the water surface gradient D even when the water surface wave occurs.

[0048] (Water surface gradient output unit) The water surface gradient output unit 25 is an output unit that outputs water surface state information indicating the water surface gradient D estimated by the water surface gradient estimation unit 24. The water surface state information is, for example, display control information for displaying information indicating the water surface gradient D. The water surface gradient output unit 25 outputs the water surface state information to a display device. The display device displays the water surface gradient D based on the water surface state information.

[0049] In addition, the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, the water surface gradient estimation unit 24, and the water surface gradient output unit 25 included in the river state estimation device 2B may be realized by the dedicated hardware processing circuit 102 shown in FIG. 5A. The processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, the water surface gradient estimation unit 24, and the water surface gradient output unit 25 included in the river state estimation device 2B are realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 104. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, the water surface gradient estimation unit 24, and the water surface gradient output unit 25 included in the river state estimation device 2B may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0050] Further, the river state estimation device 2B may be an estimation device that estimates the water surface state of the sea, a lake, a pond, or the like. Furthermore, the water surface whose state is estimated by the estimation device is not limited to the water surface, and may be the surface of a fluid that generates waves. Examples of the fluid include magma.

[0051] As described above, the river state estimation device 2B according to Embodiment 3 includes a water surface gradient estimation unit 24 that estimates the water surface gradient D of the river using the state C of the water surface wave. The water surface gradient output unit 25 outputs water surface state information indicating the water surface gradient D. The river state estimation device 2B can suppress a decrease in the estimation accuracy of the water surface gradient D even when a water surface wave occurs by removing the influence of the water surface wave.

[0052] Embodiment 4. FIG. 9 is a block diagram showing the configuration of the river state estimation system 1C according to Embodiment 4. In FIG. 9, the river state estimation system 1C is an estimation system for estimating the state of a river, and includes a river state estimation device 2C, a three-dimensional point cloud observation device 3, and a surface flow velocity meter 4. The river state estimation device 2C is an estimation device that estimates the flow velocity correction coefficient F among the states of the river, using the observed value of the three-dimensional point cloud A near the water surface of the river acquired from the three-dimensional point cloud observation device 3 and the surface flow velocity acquired from the surface flow velocity meter 4. The surface flow velocity meter 4 is a measuring instrument provided around the river for detecting the flow velocity on the surface of the river. The surface flow velocity meter 4 includes an ultrasonic type, a microwave type, an optical type, etc. As shown in FIG. 9, the river state estimation device 2C includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22, a flow velocity correction coefficient estimation unit 26, and a flow velocity correction coefficient output unit 27.

[0053] (Flow velocity correction coefficient estimation unit) The flow velocity correction coefficient estimation unit 26 estimates the phase of the surface water wave at the observation position E using the state C of the surface water wave estimated by the water surface state estimation unit 22 and the observation position E of the surface flow velocity, and estimates the flow velocity correction coefficient F using the estimated phase of the surface water wave. The average flow velocity, which is the average value of the vertical flow velocity of the river, cannot be directly observed, and is obtained by multiplying the observed value of the surface flow velocity of the river by the flow velocity correction coefficient F. The flow velocity correction coefficient F varies depending on whether the observation position E where the surface flow velocity meter 4 observes the surface flow velocity of the river is the crest or the trough of the surface water wave generated in the river. Therefore, the flow velocity correction coefficient estimation unit 26 estimates the phase of the surface water wave at the observation position of the surface flow velocity meter 4 based on the phase information of the surface water wave estimated by the water surface state estimation unit 22, and estimates the flow velocity correction coefficient F based on the estimated phase of the surface water wave. For example, when the phase θ at the observation position of the surface flow velocity meter 4 is 3 / 2π, the flow velocity correction coefficient F is α, and when it is π / 2, the flow velocity correction coefficient F is set to β. Here, 0 < α < β ≤ 1. The flow velocity correction coefficient F is represented by the following formula (1) using the phase θ at the observation position of the surface flow velocity meter 4. By estimating the phase θ of the surface wave at the observation position E of the surface velocity, the flow velocity correction coefficient estimation unit 26 can correctly estimate the flow velocity correction coefficient F, which has different values depending on the phase of the surface wave. [{(β - α)(θ + π / 2)} / π] + α (0 ≦ θ < π / 2) β - [{(β - α)(θ - π / 2)} / π] (π / 2 ≦ θ < 3π / 2) [{(β - α)(θ - 3π / 2)} / π] + α (3π / 2 ≦ θ < 2π) (1)

[0054] (Flow velocity correction coefficient output unit) The flow velocity correction coefficient output unit 27 is an output unit that outputs the surface state information indicating the flow velocity correction coefficient F estimated by the flow velocity correction coefficient estimation unit 26. For example, the surface state information is information including the flow velocity correction coefficient F. The flow velocity correction coefficient output unit 27 outputs the surface state information to an average flow velocity estimation device (not shown in FIG. 9). The average flow velocity estimation device calculates the average flow velocity of the river by multiplying the flow velocity correction coefficient F included in the surface state information by the observed value of the surface velocity of the river.

[0055] Also, the functions of the surface point group extraction unit 21, the surface state estimation unit 22, the flow velocity correction coefficient estimation unit 26, and the flow velocity correction coefficient output unit 27 provided in the river state estimation device 2C may be realized by the dedicated hardware processing circuit 102 shown in FIG. 5A. The processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the surface point group extraction unit 21, the surface state estimation unit 22, the flow velocity correction coefficient estimation unit 26, and the flow velocity correction coefficient output unit 27 provided in the river state estimation device 2C are realized by software, firmware, or a combination of software and firmware. Note that the software or firmware is described as a program and stored in the memory 104. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, the flow velocity correction coefficient estimation unit 26, and the flow velocity correction coefficient output unit 27 included in the river state estimation device 2C may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0056] As described above, the river state estimation device 2C according to the fourth embodiment includes a flow velocity correction coefficient estimation unit 26 that estimates the phase of the surface wave at the observation position E using the state C of the surface wave and the observation position E of the surface flow velocity of the river, and estimates the flow velocity correction coefficient F using the estimated phase of the surface wave. The flow velocity correction coefficient output unit 27 outputs water surface state information indicating the estimated flow velocity correction coefficient F. By including these components, the river state estimation device 2C estimates the phase of the surface wave at the observation position E of the surface flow velocity measured by the surface flow velocity meter 4. Thereby, the river state estimation device 2C can accurately estimate the flow velocity correction coefficient F that varies depending on the phase of the surface wave.

[0057] Embodiment 5. FIG. 10 is a block diagram showing the configuration of a river state estimation system 1D according to the fifth embodiment. In FIG. 10, the river state estimation system 1D is an estimation system that estimates the state of a river, and includes a river state estimation device 2D and a three-dimensional point cloud observation device 3. The river state estimation device 2D is an estimation device that estimates the flow direction of the river among the states of the river using the observation value of the three-dimensional point cloud A near the water surface of the river acquired from the three-dimensional point cloud observation device 3. As shown in FIG. 10, the river state estimation device 2D includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22, a flow direction estimation unit 28, and a flow direction output unit 29.

[0058] (Flow direction estimation unit) The downstream direction estimation unit 28 estimates the downstream direction G of the river using the state C of the surface waves estimated by the water surface state estimation unit 22. For example, the downstream direction estimation unit 28 estimates the downstream direction G of the river determined by the normal direction of the equiphase surface of the surface waves using the direction of the surface waves estimated by the water surface state estimation unit 22. The downstream direction estimation unit 28 outputs, for example, as the downstream direction G, a direction that forms a 90-degree angle with the direction of the surface waves estimated by the water surface state estimation unit 22. By using the downstream direction G estimated by the downstream direction estimation unit 28, even if the observation direction of the river flow velocity meter deviates from the downstream direction, it is possible to correctly correct the flow velocity observation vector.

[0059] (Downstream direction output unit) The downstream direction output unit 29 is an output unit that outputs water surface state information indicating the downstream direction G estimated by the downstream direction estimation unit 28. The water surface state information is, for example, display control information for displaying information indicating the downstream direction G. The downstream direction output unit 29 outputs the water surface state information to the display device. The display device displays the downstream direction G based on the water surface state information.

[0060] Moreover, the functions of the water surface point group extraction unit 21, the water surface state estimation unit 22, the downstream direction estimation unit 28, and the downstream direction output unit 29 provided in the river state estimation device 2D may be realized by the dedicated hardware processing circuit 102 shown in FIG. 5A. The processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel-programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the water surface point group extraction unit 21, the water surface state estimation unit 22, the downstream direction estimation unit 28, and the downstream direction output unit 29 provided in the river state estimation device 2D are realized by software, firmware, or a combination of software and firmware. Note that the software or firmware is described as a program and stored in the memory 104. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22, the flow direction estimation unit 28, and the flow direction output unit 29 included in the river state estimation device 2D may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0061] Further, the river state estimation device 2D may be an estimation device that estimates the water surface state of the sea, a lake, a pond, or the like. Furthermore, the water surface whose state is estimated by the estimation device is not limited to the water surface, and may be the surface of a fluid that generates waves. Examples of the fluid include magma.

[0062] As described above, the river state estimation device 2D according to Embodiment 5 includes a flow direction estimation unit 28 that estimates the flow direction G of the river using the state C of the surface wave. The flow direction output unit 29 outputs water surface state information indicating the estimated flow direction G. By using the flow direction G estimated by the flow direction estimation unit 28, the river state estimation device 2D can correctly correct the flow velocity observation vector even if the observation direction of the flow velocity meter of the river deviates from the flow direction.

[0063] Embodiment 6. FIG. 11 is a block diagram showing the configuration of a river state estimation system 1E according to Embodiment 6. In FIG. 11, the river state estimation system 1E is an estimation system that estimates the state of a river, and includes a river state estimation device 2E and a three-dimensional point cloud observation device 3. The river state estimation device 2E is an estimation device that estimates the state H of the surface wave generated in the river using the observation value of the three-dimensional point cloud A near the water surface of the river acquired from the three-dimensional point cloud observation device 3. As shown in FIG. 11, the river state estimation device 2E includes a water surface point cloud extraction unit 21, a water surface state estimation unit 22B, and a water surface state output unit 23A. The water surface state estimation unit 22B estimates the state H of the surface wave using the water surface three-dimensional point cloud B extracted from the three-dimensional point cloud A by the water surface point cloud extraction unit 21, and includes a spatial direction DFT unit 221, a time direction DFT unit 223, a data holding unit 224, and a filter unit 225.

[0064] (Spatial Direction DFT Unit) The spatial direction DFT unit 221 acquires the water surface three-dimensional point group B extracted from the three-dimensional point group A by the water surface point group extraction unit 21, and calculates the spatial spectrum of the water surface three-dimensional point group B by performing DFT in the spatial direction on the acquired water surface three-dimensional point group B. Specifically, the spatial direction DFT unit 221 converts the water surface three-dimensional point group B on the spatial axis into a set of sine waves of a plurality of wavelengths and converts it into a spatial spectrum on the spatial frequency axis.

[0065] Also, the spatial direction DFT unit 221 may perform non-uniform two-dimensional DFT on the water surface three-dimensional point group B. Furthermore, the spatial direction DFT unit 221 may perform uniform two-dimensional DFT on the water surface three-dimensional point group B that has been interpolated inside and outside a uniform grid. Furthermore, the spatial direction DFT unit 221 may extract the water surface three-dimensional point group B on a one-dimensional spatial axis and perform one-dimensional DFT along the one-dimensional axis on the extracted water surface three-dimensional point group B.

[0066] (Temporal Direction DFT Unit) The temporal direction DFT unit 223 calculates the moving speed of the water surface wave by performing DFT in the temporal direction on the spatial spectrum of the water surface three-dimensional point group B held in the data holding unit 224. FIG. 12 is an explanatory diagram showing an overview of the estimation of the water surface state in Embodiment 6, and shows a process of performing DFT for a plurality of time instants in the temporal direction indicated by an arrow on the spatial spectrum of the water surface three-dimensional point group B in the space defined by the spatial frequency axis in the x direction and the spatial frequency axis in the y direction.

[0067] The spatial direction DFT unit 221 calculates the spatial spectra of the water surface three-dimensional point group B for a plurality of time instants as shown in FIG. 12 by performing DFT in the spatial direction on the water surface three-dimensional point group B at a plurality of time instants. These spatial spectra are held in the data holding unit 224. The temporal direction DFT unit 223 estimates the moving speed of the water surface wave, for example, by reading out the spatial spectra held in the data holding unit 224 in time order and performing DFT on the read-out spatial spectra. The spatial-direction DFT unit 221 calculates the temporal frequency for each spatial frequency component by performing a DFT in the temporal direction on each frequency component of the spatial spectrum for the plurality of times, as shown in FIG. 12, for example. The moving speed of each spatial frequency component is the value obtained by dividing the temporal frequency by the spatial frequency (temporal frequency / spatial frequency). The moving speed of the surface wave is calculated by extracting only the moving speed of the frequency component corresponding to the spatial spectrum of the surface wave in the filter unit 225.

[0068] (Data holding unit) The data holding unit 224 is a storage unit that sequentially holds the spatial spectrum of the surface three-dimensional point group B calculated by the spatial-direction DFT unit 221. For example, the data holding unit 224 stores the spatial spectra of the surface three-dimensional point group B for a plurality of consecutive times.

[0069] (Filter unit) The filter unit 225 extracts the surface wave component from the spatial spectrum of the surface three-dimensional point group B calculated by the spatial-direction DFT unit 221. For example, the filter unit 225 acquires the spatial spectrum of the surface three-dimensional point group B calculated by the spatial-direction DFT unit 221 via the temporal-direction DFT unit 223, and extracts the surface wave component from the acquired spatial spectrum of the surface three-dimensional point group B. The spatial spectrum of the surface three-dimensional point group B may include spatial spectra other than surface waves, such as the observation error of the three-dimensional point group observation device 3, disturbances caused by the three-dimensional point groups of ground objects such as trees near the surface shaken by the wind, disturbances caused by the three-dimensional point groups of moving objects such as animals appearing on the surface, and the gradient of the river. The filter unit 225 removes the spatial spectrum components other than the surface wave from the spatial spectrum of the surface three-dimensional point group B.

[0070] For example, the filter unit 225 extracts only the spatial spectrum components caused by the surface wave using a window function from the spatial spectrum of the surface three-dimensional point group B calculated by the spatial-direction DFT unit 221. Further, the filter unit 225 may extract the vicinity of the peak where the amplitude of the spatial spectrum is the largest using a window function. Furthermore, the filter unit 225 may extract the spatial spectrum in a certain wavelength range using a window function based on the characteristic information of the wavelength of the surface wave. Furthermore, the filter unit 225 may remove the long-wavelength components caused by the water surface gradient of the river from the spatial spectrum of the water surface three-dimensional point cloud B using a window function. Furthermore, the filter unit 225 may estimate the filter coefficients in advance using the correct data of the spatial spectrum of the surface wave, and adaptively filter only the spatial spectrum components caused by the surface wave from the spatial spectrum of the water surface three-dimensional point cloud B based on the estimated filter coefficients.

[0071] The water surface state estimation unit 22B estimates the state of the surface wave using the components of the surface wave extracted from the spatial spectrum of the water surface three-dimensional point cloud B by the filter unit 225. For example, the water surface state estimation unit 22B identifies the surface wave using the components of the surface wave in the spatial spectrum of the water surface three-dimensional point cloud B, estimates the wavelength, phase, and direction of the surface wave based on the interval between the valleys and peaks of the surface wave, and estimates the amplitude of the surface wave based on the magnitudes of the valleys and peaks.

[0072] (Water surface state output unit) The water surface state output unit 23A is an output unit that outputs water surface state information H including the state of the surface wave estimated by the water surface state estimation unit 22B and the moving speed of the surface wave estimated by the time-direction DFT unit 223. For example, the water surface state information H is information including at least one of the wavelength, amplitude, direction, and phase of the surface wave and the moving speed of the surface wave, and is, for example, display control information for displaying information indicating at least one of the wavelength, amplitude, direction, and phase of the surface wave and the moving speed of the surface wave. The water surface state output unit 23 outputs the water surface state information H to the display device. The display device displays the wavelength, amplitude, direction, phase, and moving speed of the surface wave based on the water surface state information H.

[0073] Further, the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22B, and the water surface state output unit 23A included in the river state estimation device 2E may be realized by the dedicated hardware processing circuit 102 shown in FIG. 5A. The processing circuit 102 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel-programmed processor, an ASIC, an FPGA, or a combination thereof. When the processing circuit is the processor 103 shown in FIG. 5B, the functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22B, and the water surface state output unit 23A included in the river state estimation device 2E are realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 104. The functions of the water surface point cloud extraction unit 21, the water surface state estimation unit 22B, and the water surface state output unit 23A included in the river state estimation device 2E may be realized by separate processing circuits, or these functions may be realized together by one processing circuit.

[0074] Further, the river state estimation device 2E may be an estimation device that estimates the water surface state of the sea, a lake, a pond, or the like. Furthermore, the water surface whose state is estimated by the estimation device is not limited to the water surface, and may be the surface of a fluid that generates waves. Examples of the fluid include magma.

[0075] As described above, in the river state estimation device 2E according to the sixth embodiment, the water surface state estimation unit 22B includes a spatial direction DFT unit 221 that calculates the spatial spectrum of the water surface three-dimensional point cloud B by performing a DFT in the spatial direction on the extracted water surface three-dimensional point cloud B, a temporal direction DFT unit 223 that calculates the moving speed of the water surface wave by performing a DFT in the temporal direction on the spatial spectrum of the water surface three-dimensional point cloud B, and a filter unit 225 that acquires the water surface wave component from the spatial spectrum of the water surface three-dimensional point cloud B, and estimates the state of the water surface wave using the water surface wave component. The river state estimation device 2E can estimate the state of the water surface wave outside the observation time or outside the observation area of the three-dimensional point cloud observation device 3 by using the estimated moving speed of the water surface wave.

[0076] In addition, combinations of each embodiment, or variations of any constituent elements of each embodiment, or omissions of any constituent elements in each embodiment are possible.

Industrial Applicability

[0077] The river state estimation device according to the present disclosure can be used, for example, in formulating a river maintenance plan or a water resources plan, or in predicting floods in a river.

Description of Reference Numerals

[0078] 1, 1A, 1B, 1C, 1D, 1E River state estimation system, 2, 2A, 2B, 2C, 2D, 2E River state estimation device, 3 3D point cloud observation device, 4 Surface flow velocity meter, 21 Water surface point cloud extraction unit, 22, 22A, 22B Water surface state estimation unit, 23, 23A Water surface state output unit, 24 Water surface gradient estimation unit, 25 Water surface gradient output unit, 26 Flow velocity correction coefficient estimation unit, 27 Flow velocity correction coefficient output unit, 28 Flow-down direction estimation unit, 29 Flow-down direction output unit, 100 Input interface, 101 Output interface, 102 Processing circuit, 103 Processor, 104 Memory, 221 Spatial direction DFT unit, 222, 225 Filter unit, 223 Temporal direction DFT unit, 224 Data holding unit.

Claims

1. A water surface point cloud extraction unit that acquires three-dimensional point cloud data of a region including a water surface and extracts a three-dimensional point cloud of the water surface from the acquired three-dimensional point cloud data; A water surface state estimation unit that estimates the state of water surface waves generated on the water surface using the extracted three-dimensional point cloud of the water surface; An output unit that outputs water surface state information indicating the estimated state of the water surface waves, comprising: The water surface state estimation unit: Acquires the extracted three-dimensional point cloud of the water surface, and calculates the spatial spectrum of the three-dimensional point cloud of the water surface by performing a discrete Fourier transform in the spatial direction on the acquired three-dimensional point cloud of the water surface, a spatial direction DFT unit; Calculates the moving speed of the water surface waves by performing a discrete Fourier transform in the time direction on the calculated spatial spectrum of the three-dimensional point cloud of the water surface, a time direction DFT unit; A filter unit that extracts components of the water surface waves from the calculated spatial spectrum of the three-dimensional point cloud of the water surface, comprising: An estimation device characterized in that the state of the water surface waves is estimated using the extracted components of the water surface waves.

2. The water surface state estimation unit estimates at least one of the wavelength, amplitude, direction, and phase of the water surface waves. The estimation device according to claim 1, characterized in that.

3. Comprises a water surface gradient estimation unit that estimates the water surface gradient using the estimated state of the water surface waves. The output unit outputs the water surface state information indicating the estimated water surface gradient. The estimation device according to claim 1, characterized in that.

4. Comprises a flow velocity correction coefficient estimation unit that estimates the phase of the water surface waves at the observation position using the estimated state of the water surface waves and the observation position of the surface flow velocity, and estimates a flow velocity correction coefficient using the estimated phase of the water surface waves. The output unit outputs the water surface state information indicating the estimated flow velocity correction coefficient. The estimation device according to claim 1, characterized in that.

5. Comprises a flow-down direction estimation unit that estimates the flow-down direction using the estimated state of the water surface waves. The output unit outputs the water surface state information indicating the estimated flow-down direction. The estimation device according to claim 1, characterized in that.

6. An estimation system characterized by comprising the estimation device according to any one of claims 1 to 5 and a three-dimensional point cloud observation device that observes three-dimensional point cloud data of a region including a water surface.

7. An estimation method of an estimation device, comprising: A step in which a water surface point cloud extraction unit acquires three-dimensional point cloud data of a region including a water surface and extracts a three-dimensional point cloud of the water surface from the acquired three-dimensional point cloud data. A step in which a water surface state estimation unit estimates a state of water waves generated on the water surface using the extracted three-dimensional point cloud of the water surface; A step in which an output unit outputs water surface state information indicating the estimated state of the water waves, and The step of estimating the state of the water waves by the water surface state estimation unit includes: A step in which a spatial direction DFT unit included in the water surface state estimation unit acquires the extracted three-dimensional point cloud of the water surface and calculates a spatial spectrum of the three-dimensional point cloud of the water surface by performing a discrete Fourier transform in the spatial direction on the acquired three-dimensional point cloud of the water surface; A step in which a time direction DFT unit included in the water surface state estimation unit calculates a moving speed of the water waves by performing a discrete Fourier transform in the time direction on the calculated spatial spectrum of the three-dimensional point cloud of the water surface; A step in which a filter unit included in the water surface state estimation unit extracts a component of the water waves from the calculated spatial spectrum of the three-dimensional point cloud of the water surface, and The state of the water waves is estimated using the extracted component of the water waves. The estimation method is characterized by the above.

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