IP Library Granted Patent US 12,499,650
Granted Patent B2
US 12,499,650 · App. 17/940,856 · Granted Dec 16, 2025

Information processing apparatus, information processing method, and non-transitory computer readable medium

Inventors: Mitsuru Kakimoto (Kawasaki Kanagawa, JP); Ryoma Fukuhara (Tokyo, JP); Hiromasa Shin (Yokohama Kanagawa, JP)
Assignees: Kabushiki Kaisha Toshiba; Toshiba Energy Systems & Solutions Corporation
G06V10/60G06V20/13
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,499,650
App. No.
17/940,856
Granted
Dec 16, 2025
Kind
B2
Abstract

An information processing apparatus according to an embodiment includes a first estimator configured to estimate height information of a cloud based on data obtained by sensing the cloud, a divider configured to divide the cloud into a plurality of portions based on the height information, and a second estimator configured to estimate states of the cloud at target time for each of the portions.

Claims (54)

1 . An information processing apparatus comprising:

receiving circuitry configured to receive data acquired by sensing a cloud by a meteorological satellite at predetermined intervals via radio from the meteorological satellite, the data including a temperature distribution or a captured image of the cloud,

processing circuitry configured to

divide the cloud according to a plurality of height ranges based on the data to acquire one or more portions for each of the height ranges,

collect one or more portions belonging to distances less than or equal to a threshold value to form one or more cloud masses, for each of the height ranges,

estimate a direction and speed of movement of each cloud mass based on the data,

estimate at least one of a position and a shape of each cloud mass at target time in future later than sensing time of the data based on the estimated direction and the estimated speed, and

estimate advection of the cloud based on the position and the shape of each cloud mass estimated at the predetermined intervals; and

a solar radiation estimation circuit configured to estimate a solar irradiance to a ground surface based on an estimated result of advection of the cloud

wherein the estimated result of advection of the cloud includes reflection intensities at each position of the cloud in a horizontal direction.

2 . The information processing apparatus according to claim 1 , wherein the temperature distribution is an infrared image of the cloud.

3 . The information processing apparatus according to claim 1 , wherein the processing circuitry acquires the one or more portions by dividing the cloud for each plurality of temperature ranges based on the temperature distribution, and the plurality of temperature ranges corresponds to the plurality of height ranges.

4 . The information processing apparatus according to claim 1 , wherein the processing circuitry generates a visible image of the cloud based on the estimated position and the estimated shape of each cloud mass.

5 . The information processing apparatus according to claim 1 , wherein

the processing circuitry divides the cloud into the one or more portions by segmenting the captured image based on feature values of pixels included in the captured image of the cloud.

6 . The information processing apparatus according to claim 1 , wherein the processing circuitry estimates, based on a range adjacent between a first portion and another portion in a boundary of the first portion among the one or more portions, whether another cloud is present in a lower layer under the first portion or a range in which another cloud is present in a lower layer under the first portion.

7 . The information processing apparatus according to claim 1 , wherein the processing circuitry estimates, based on a position of the cloud for each of the portions at second time estimated based on the sensing data at first time, whether another cloud is present or a range in which another cloud is present in a lower layer under the one or more portions of the cloud obtained based on the sensing data at the second time.

8 . The information processing apparatus according to claim 1 , wherein the processing circuitry estimates, based on a difference between a development state of the cloud mass based on the sensing data at first time and a development state of the cloud mass based on the sensing data at second time later than the first time, a development state of the cloud mass at the target time later than the second time.

9 . The information processing apparatus according to claim 1 , wherein the processing circuitry estimates a position of the cloud mass based on the estimated direction and the estimated speed of the movement and a wind direction and wind speed of numerical weather calculation.

10 . The information processing apparatus according to claim 9 , wherein the processing circuitry sets first weight in a first vector of the direction and the speed of the movement estimated for each cloud mass, sets second weight in a second vector of a wind direction and wind speed corresponding to a position of the cloud mass by the numerical weather calculation, and combines the first vector and the second vector based on the first weight and the second weight to thereby estimate the position of each cloud mass.

11 . The information processing apparatus according to claim 10 , wherein the processing circuitry calculates the first weight and the second weight based on a difference between a direction and speed of the movement estimated based on the sensing data at third time and a direction and speed of the movement estimated based on the sensing data at fourth time later than the third time, and a difference between the direction and the speed of the movement estimated based on the sensing data at the fourth time and a wind direction and wind speed of the numerical weather calculation for the fourth time.

12 . The information processing apparatus according to claim 1 , wherein

the sensing data includes a captured image of a first region including the cloud, and a pixel of the captured image represent reflection intensity of a position corresponding to the pixel,

the processing circuitry

estimates reflection intensities in positions of the first region at the target time, and

combines the estimated reflection intensities in the positions and reflection intensities in positions of the first region of numerical weather calculation.

13 . The information processing apparatus according to claim 12 , wherein the processing circuitry sets third weight in the estimated reflection intensities in the positions, sets fourth weight in the reflection intensities in the positions of the first region of the numerical weather calculation, and combines the reflection intensities based on the third weight and the fourth weight.

14 . The information processing apparatus according to claim 13 , wherein the processing circuitry calculates the third weight and the fourth weight based on a difference between reflection intensity for each of the positions estimated for fourth time based on a captured image at third time and reflection intensity for each of the pixels based on a captured image at the fourth time, and a difference between the reflection intensity for each of the pixels based on the captured image at the fourth time and reflection intensities in the positions of the numerical weather calculation for the fourth time.

15 . The information processing apparatus according to claim 1 , wherein

the sensing data includes a capture image of a first region including the cloud, and a pixel of the captured image represents reflection intensity in a position corresponding to the pixel,

the processing circuitry

estimates reflection intensities in positions of the first region at the target time, and

calculates a solar irradiance on a ground surface based on the reflection intensities in the estimated positions.

16 . The information processing apparatus according to claim 1 , wherein the target time is time later than time when the cloud is sensed.

17 . The information processing apparatus according to claim 1 , wherein the processing circuitry further estimates position information of the cloud based on the sensing data.

18 . The information processing apparatus according to claim 1 , wherein the height information includes height for each position in a horizontal direction of the cloud.

19 . An information processing method comprising:

receiving data acquired by sensing a cloud by a meteorological satellite at predetermined intervals via radio from the meteorological satellite, the data including a temperature distribution or a captured image of the cloud;

dividing the cloud according to a plurality of height ranges based on the data to acquire one or more portions for each of the height ranges;

collecting one or more portions belonging to distances less than or equal to a threshold value to form one or more cloud masses, for each of the height ranges;

estimating a direction and speed of movement of each cloud mass based on the data;

estimating at least one of a position and a shape of each cloud mass at target time in future later than sensing time of the data based on the estimated direction and the estimated speed;

estimating advection of the cloud based on the position and the shape of each cloud mass estimated at the predetermined intervals; and

estimating a solar irradiance to a ground surface based on an estimated result of advection of the cloud;

wherein the estimated result of advection of the cloud includes reflection intensities at each position of the cloud in a horizontal direction.

20 . A non-transitory computer readable medium having a computer program stored therein which when executed by a computer, causes the computer to perform processes comprising:

receiving data acquired by sensing a cloud by a meteorological satellite at predetermined intervals via radio from the meteorological satellite, the data including a temperature distribution or a captured image of the cloud;

dividing the cloud according to a plurality of height ranges based on the data to acquire one or more portions for each of the height ranges;

collecting one or more portions belonging to distances less than or equal to a threshold value to form one or more cloud masses, for each of the height ranges;

estimating a direction and speed of movement of each cloud mass based on the data;

estimating at least one of a position and a shape of each cloud mass at target time in future later than sensing time of the data based on the estimated direction and the estimated speed;

estimating advection of the cloud based on the position and the shape of each cloud mass estimated at the predetermined intervals; and

estimating a solar irradiance to a ground surface based on an estimated result of advection of the cloud,

wherein the estimated result of advection of the cloud includes reflection intensities at each position of the cloud in a horizontal direction.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 8, 2022
From: KAKIMOTO, MITSURU; FUKUHARA, RYOMA; SHIN, HIROMASA
To: KABUSHIKI KAISHA TOSHIBA; TOSHIBA ENERGY SYSTEMS & SOLUTIONS CORPORATION
Reel/Frame 061030/0932 →
Priority Claims (1)
JP 2021-200975 · Dec 10, 2021 · national
Continuity (1)
Related Publication 20230186594A1 · Jun 15, 2023
References Cited (29)
US 5255190A · Sznaider · 1993 [cited by examiner]
US 10989839B1 · Matthews · 2021 [cited by applicant]
US 11294098B2 · Kakimoto et al. · 2022 [cited by applicant]
US 20100309330A1 · Beck · 2010 [cited by examiner]
US 20170299686A1 · Bertin · 2017 [cited by examiner]
US 20190004211A1 · Kakimoto · 2019 [cited by examiner]
US 20200142095A1 · Yan · 2020 [cited by examiner]
JP 2001264456A · 2001 [cited by examiner]
JP 200390887A · 2003 [cited by applicant]
JP 2009252940A · 2009 [cited by examiner]
JP 201032383A · 2010 [cited by applicant]
JP 2010151597A · 2010 [cited by applicant]
JP 201915517A · 2019 [cited by applicant]
JP 20219075A · 2021 [cited by applicant]
WO WO2016030951A1 · 2016 [cited by examiner]
Minnis, Patrick, et al. “Advances in neural network detection and retrieval of multilayer clouds for CERES using multispectral satellite data.” Remote Sensing of Clouds and the Atmosphere XXIV. vol. 11152. SPIE, Oct. 20… [cited by examiner]
Hayatbini, Negin, et al. “Effective cloud detection and segmentation using a gradient-based algorithm for satellite imagery: Application to improve PERSIANN-CCS.” Journal of Hydrometeorology 20.5 (2019): 901-913. (Year:… [cited by examiner]
Berthomier, Léa et al. “Cloud cover nowcasting with deep learning.” IEEE, published 2020 (Year: 2020). [cited by examiner]
Minnis, Patrick, et al. “Advances in neural network detection and retrieval of multilayer clouds for CERES using multispectral satellite data.” Remote Sensing of Clouds and the Atmosphere, published 2019 (Year: 2019). [cited by examiner]
Japan Patent Office, Office Action in JP App. No. 2021-200975, 2 pages, with machine translation, 4 pages (Dec. 3, 2024). [cited by applicant]
Kazuyori Ozeki et al., “Introduction of Himawari—8/9,” Meteorological Satellite Center Technical Note, pp. 3-16 (2016). [cited by applicant]
Atsushi Hashimoto et al., “Development of a Satellite-based Estimation and Forecasting Model of Solar Irradiance for Photovoltaic Power Prediction,” Central Res. Inst. of Elec. Power Ind., Tech. Report N13003, 30 pages … [cited by applicant]
Maarten Reyniers, “Quantitative Precipitation Forecasts based on radar observations: principles, algorithms and operational systems,” Royal Meteorological Institute of Belgium, 62 pages (2008). [cited by applicant]
Trevor Hastie et al., “The Elements of Statistical Learning, Data Mining, Inference, and Prediction,” 2d Ed., Springer, New York, 764 pages (2008). [cited by applicant]
Mryka Hall-Beyer, “GLCM Texture: A Tutorial,” v.3.0, University of Calgary Digital Library, 76 pages (2017). [cited by applicant]
M. Lalitha et al., “A Survey on Image Segmentation through Clustering Algorithm,” Int'l J. of Sci. and Res., vol. 2, No. 2, pp. 348-358 (2013). [cited by applicant]
Benoit Cushman-Roisin et al., “Introduction to Geophysical fluid Dynamics: Physical and Numerical Aspects,” 2d Ed., Academic Press, Waltham, Mass., pp. v-xi, and 183-186 (2011). [cited by applicant]
Gerhard Winkler, “Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction,” 2d Ed., Springer, New York, pp. I, XV, XVI, and 12-14 (2003). [cited by applicant]
G. Dedieu et al., “Satellite Estimation of Solar Irradiance at the Surface of the Earth and of Surface Albedo Using a Physical Model Applied to Metcosat Data,” J. of Applied Meteorology and Climatology, vol. 26, No. 1, … [cited by applicant]