CN112067118B - Illumination intensity detection method, device, equipment and medium based on satellite cloud picture - Google Patents
Illumination intensity detection method, device, equipment and medium based on satellite cloud picture Download PDFInfo
- Publication number
- CN112067118B CN112067118B CN202010945195.2A CN202010945195A CN112067118B CN 112067118 B CN112067118 B CN 112067118B CN 202010945195 A CN202010945195 A CN 202010945195A CN 112067118 B CN112067118 B CN 112067118B
- Authority
- CN
- China
- Prior art keywords
- satellite cloud
- grid
- pictures
- determining
- illumination intensity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005286 illumination Methods 0.000 title claims abstract description 95
- 238000001514 detection method Methods 0.000 title claims description 35
- 238000012545 processing Methods 0.000 claims abstract description 30
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 description 8
- 230000005855 radiation Effects 0.000 description 7
- 238000010248 power generation Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 241001085205 Prenanthella exigua Species 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J1/00—Photometry, e.g. photographic exposure meter
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Astronomy & Astrophysics (AREA)
- Remote Sensing (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Circuit Arrangement For Electric Light Sources In General (AREA)
Abstract
The invention discloses a method, a device, equipment and a medium for detecting illumination intensity based on a satellite cloud picture, wherein the method comprises the following steps: acquiring satellite cloud pictures corresponding to a plurality of time points; processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size; determining actual brightness change values of the grid pictures with the same size at adjacent time points; determining a reference brightness change value of each time point relative to adjacent time points according to the actual brightness change value; and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point. The invention determines the illumination intensity of different areas at different moments, does not need to deploy physical equipment and saves the cost.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a method, a device, equipment and a medium for detecting illumination intensity based on a satellite cloud picture.
Background
The photovoltaic power generation industry has a high requirement on the solar radiation intensity, and in order to enable a photovoltaic power generation system depending on solar energy to work more effectively, the solar radiation intensity of each region needs to be detected, and the regions with the solar radiation intensity meeting the working standard of the photovoltaic power generation system are screened out.
In the existing scheme, ground observation stations are established in each area, and an illumination radiation acquisition instrument is installed at each observation station to record the real-time illumination radiation intensity, however, the more measurement areas are, the more physical devices need to be deployed, and the higher cost for acquiring the illumination radiation intensity is caused.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a medium for detecting illumination intensity based on a satellite cloud picture, and aims to solve the problem of high cost for acquiring illumination radiation intensity.
In order to achieve the above object, the illumination intensity detection method based on the satellite cloud picture provided by the invention comprises the following steps:
acquiring satellite cloud pictures corresponding to a plurality of time points;
processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size;
determining actual brightness change values of the grid pictures with the same size at adjacent time points;
determining a reference brightness change value of each time point relative to adjacent time points according to the actual brightness change value;
and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point.
In an embodiment, the step of processing the satellite cloud image to obtain a plurality of grid pictures includes:
scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion mode;
and processing the scaled satellite cloud images to obtain a plurality of grid images.
In an embodiment, the step of processing the scaled satellite cloud image to obtain a plurality of grid pictures includes:
determining a central position point of the satellite cloud picture;
and cutting the satellite cloud picture according to the central position point and each preset grid to obtain a plurality of grid pictures.
In an embodiment, the step of determining the actual brightness variation values of the mesh pictures of the same size at adjacent time points includes:
determining HSV values of the satellite cloud pictures according to the RGB values of the satellite cloud pictures;
determining the actual brightness value of the grid picture with the same size at each time point according to the HSV value;
and determining the actual brightness change value of the grid pictures with the same size at the adjacent time points according to the actual brightness value of each time point.
In an embodiment, the step of determining the actual brightness variation values of the mesh pictures of the same size at adjacent time points includes:
determining brightness change values of the grid pictures at adjacent time points;
and determining the average value of the brightness change values of the grid pictures at the adjacent time points as the actual brightness change value of the grid pictures.
In an embodiment, the step of obtaining the reference luminance change value of each time point relative to the adjacent time point according to the actual luminance change value includes:
determining weight information corresponding to the grid pictures of all sizes;
and carrying out weighted average on the weighting information and the actual brightness change value corresponding to each grid picture at the same time point to obtain a reference brightness change value corresponding to each time point.
In order to achieve the above object, the present invention further provides an illumination intensity detection apparatus based on a satellite cloud image, where the illumination prediction apparatus based on a satellite cloud image includes:
the acquisition module is used for acquiring satellite cloud pictures corresponding to a plurality of time points;
the processing module is used for processing the satellite cloud picture to obtain a plurality of grid pictures, and the grid pictures are different in size;
the first calculation module is used for determining the actual brightness change values of the grid pictures with the same size at adjacent time points;
the second calculation module is used for determining a reference brightness change value of each time point relative to an adjacent time point according to the actual brightness change value;
and the determining module is used for determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point.
In one embodiment, the processing module comprises:
the scaling unit is used for scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion mode;
and the processing unit is used for processing the zoomed satellite cloud picture to obtain a plurality of grid pictures.
In order to achieve the above object, the present invention further provides a satellite cloud picture-based illumination intensity detection apparatus, which includes a memory, a processor, and a satellite cloud picture-based illumination intensity detection program stored in the memory and executable on the processor, wherein the satellite cloud picture-based illumination intensity detection program, when executed by the processor, implements the steps of the satellite cloud picture-based illumination intensity detection method as described above.
In order to achieve the above object, the present invention further provides a medium storing a satellite cloud map-based illumination intensity detection program, wherein the satellite cloud map-based illumination intensity detection program, when executed by a processor, implements the steps of the satellite cloud map-based illumination intensity detection method as described above.
The invention provides a method, a device, equipment and a medium for detecting illumination intensity based on a satellite cloud picture, which are used for acquiring the satellite cloud pictures corresponding to a plurality of time points; processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size; determining actual brightness change values of the grid pictures with the same size at adjacent time points; determining a reference brightness change value of each time point relative to adjacent time points according to the actual brightness change value; and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point. The reference brightness change values of the satellite cloud pictures with multiple sizes are determined by calculating the actual brightness change values of the satellite cloud pictures with the same size, the illumination intensity of a preset time point is predicted or corrected according to the reference brightness values, the illumination intensity of different areas at different moments is determined, physical equipment does not need to be deployed, and the cost is saved.
Drawings
Fig. 1 is a schematic diagram of a hardware structure of an illumination intensity detection apparatus based on a satellite cloud chart according to an embodiment of the present invention;
FIG. 2 is a schematic flowchart of a first embodiment of a method for detecting illumination intensity based on a satellite cloud;
FIG. 3 is a graph of pixel value distributions at adjacent time points according to the present invention;
FIG. 4 is a detailed flowchart of step S20 of the illumination intensity detection method based on the satellite cloud chart according to the second embodiment of the present invention;
FIG. 5 is a detailed flowchart of step S22 of the illumination intensity detection method based on the satellite cloud chart according to the third embodiment of the present invention;
FIG. 6 is a detailed flowchart of step S30 of the illumination intensity detection method according to the fourth embodiment of the present invention;
FIG. 7 is a flowchart illustrating a detailed process of step S40 of the illumination intensity detection method according to the sixth embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an illumination intensity detection apparatus based on a satellite cloud;
fig. 9 is a schematic flow chart of a processing module of the illumination intensity detection apparatus based on a satellite cloud chart according to the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: acquiring satellite cloud pictures corresponding to a plurality of time points; processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size; determining actual brightness change values of the grid pictures with the same size at adjacent time points; determining a reference brightness change value of each time point relative to adjacent time points according to the actual brightness change value; and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point.
The reference brightness change values of the satellite cloud pictures with multiple sizes are determined by calculating the actual brightness change values of the satellite cloud pictures with the same size, the illumination intensity of a preset time point is predicted or corrected according to the reference brightness values, the illumination intensity of different areas at different moments is determined, physical equipment does not need to be deployed, and the cost is saved.
As an implementation, the illumination intensity detection device based on the satellite cloud map may be as shown in fig. 1.
The embodiment scheme of the invention relates to illumination intensity detection equipment based on a satellite cloud picture, which comprises the following components: a processor 101, e.g. a CPU, a memory 102, a communication bus 103. Wherein a communication bus 103 is used for enabling the connection communication between these components.
The memory 102 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). As shown in fig. 1, a storage 102 as a medium may include therein an illumination intensity detection program based on a satellite cloud map; and the processor 101 may be configured to invoke the satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
acquiring satellite cloud pictures corresponding to a plurality of time points;
processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size;
determining actual brightness change values of the grid pictures with the same size at adjacent time points;
determining a reference brightness change value of each time point relative to adjacent time points according to the actual brightness change value;
and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point.
In an embodiment, the processor 101 may be configured to invoke a satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion mode;
and processing the scaled satellite cloud images to obtain a plurality of grid images.
In an embodiment, the processor 101 may be configured to invoke a satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
determining a central position point of the satellite cloud picture;
and cutting the satellite cloud picture according to the central position point and each preset grid to obtain a plurality of grid pictures.
In an embodiment, the processor 101 may be configured to invoke a satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
determining HSV values of the satellite cloud pictures according to the RGB values of the satellite cloud pictures;
determining the actual brightness value of the grid picture with the same size at each time point according to the HSV value;
and determining the actual brightness change values of the grid pictures with the same size at the adjacent time points according to the actual brightness values of the time points.
In an embodiment, the processor 101 may be configured to invoke a satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
determining brightness change values of the grid pictures at adjacent time points;
and determining the average value of the brightness change values of the grid pictures at the adjacent time points as the actual brightness change value of the grid pictures.
In an embodiment, the processor 101 may be configured to invoke a satellite cloud based illumination intensity detection program stored in the memory 102 and perform the following operations:
determining weight information corresponding to the grid pictures of all sizes;
and carrying out weighted average on the weighting information and the actual brightness change value corresponding to each grid picture at the same time point to obtain a reference brightness change value corresponding to each time point.
Based on the hardware architecture of the illumination intensity detection device based on the satellite cloud picture, the embodiment of the illumination intensity detection method based on the satellite cloud picture is provided.
Referring to fig. 2, fig. 2 is a first embodiment of the illumination intensity detection method based on a satellite cloud image, and the illumination intensity detection method based on the satellite cloud image includes the following steps:
and step S10, acquiring satellite cloud maps corresponding to a plurality of time points.
Specifically, the satellite cloud map is the cloud layer distribution in the atmosphere captured by a meteorological satellite. The satellite cloud picture can be a visible light satellite cloud picture, the area and the thickness covered by cloud layers can be displayed, the reflecting capacity of the thicker cloud layers is strong, bright white can be displayed on the visible light satellite cloud picture, and dark gray can be displayed when the cloud layers are thinner. The device can also be combined with an infrared satellite cloud picture, the infrared satellite cloud picture measures the temperature of a cloud layer by using an infrared instrument of a meteorological satellite, and the height of the cloud layer is judged according to different temperatures of a cloud top. The cloud layer with low temperature is displayed in bright white, that is, the cloud layer is higher, and the dark gray part represents that the cloud layer is lower in height, because the cloud layer closer to the ground has higher temperature. The illumination intensity information of the ground area in the satellite cloud picture can be determined according to the cloud area, the thickness and the height of the cloud layer area in the satellite cloud picture, the illumination intensity information and the illumination intensity information of the non-cloud layer area.
The satellite cloud images corresponding to the plurality of time points are obtained, and the plurality of time points can be determined according to the time interval of the meteorological satellite transmitting the satellite cloud images. For example, if the time interval transmitted by the meteorological satellite is ten minutes, the number of time points corresponding to one day is 143, each time point is separated by ten minutes, and each time point corresponds to one satellite cloud map.
Step S20, processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size.
Specifically, the satellite cloud images are processed according to the grid sizes, and one satellite cloud image can obtain grid images with multiple grid sizes, that is, the grid sizes of the multiple grid images corresponding to one satellite cloud image are different from each other. For example, the grid size set into which the satellite cloud image can be divided is { W1 × H1, W2 × H2, … …, Wn × Hn }, and the satellite cloud image is sequentially processed according to the grid sizes in the grid size set, so that grid images with n grid sizes can be obtained.
Step S30, determining actual brightness variation values of the grid pictures of the same size at adjacent time points.
Specifically, the grid pictures of the same size refer to the grid pictures of the same grid size corresponding to different time points, each grid in the grid pictures of the same size is determined, the brightness value of each grid is determined, and the actual brightness change value of the grid at the same position at adjacent time points can be determined.
Illustratively, as shown in fig. 3, a curve L (t-1, d) represents a pixel value distribution curve of a grid at a coordinate (i, j) position at a time point t-1 on day d, a curve L (t, d) represents a pixel value distribution curve of a grid at a time point t on day d of the grid at a coordinate (i, j), and the change amount of the solar illumination luminance at the (i, j) position can be calculated by L (t-1, d) and L (t, d):
light (i, j, t, d) represents the actual luminance variation of the solar illumination at the d-th day t at the coordinate (i, j), P is 256, x represents a pixel value, x is greater than or equal to 0 and less than or equal to 255, L (x, t-1, d) represents the number of pixels corresponding to the pixel value x on the distribution curve L (t-1, d), and L (x, t, d) represents the number of pixels corresponding to the pixel value x on the distribution curve L (t, d).
And determining the brightness change values of the grid pictures at the adjacent time points, and determining the average value of the brightness change values of the grid pictures at each adjacent time point as the actual brightness change value of the grid pictures. That is, after the actual luminance change values of the adjacent time points of each grid in the grid picture are calculated, the actual luminance change values of the same time point in different days are averaged, and the average value is used as the actual luminance change value corresponding to the grid picture in the grid size.
And step S40, determining a reference brightness change value of each time point relative to the adjacent time point according to the actual brightness change value.
Specifically, after the actual brightness change values of the grids at adjacent time points in the grid picture under each grid size are determined, the weight information corresponding to each grid size is determined, where the weight information may be a weight corresponding to a gaussian distribution. And calculating a weighted average value according to the weight information and the actual brightness change value corresponding to each grid size, so as to determine the reference brightness change value of each grid at the adjacent time point in the grid picture.
And step S50, determining the illumination intensity at the preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud image at the current time point.
Specifically, according to a reference brightness change value corresponding to each time point, the illumination intensity at a preset time point can be determined on the basis of the actual brightness value of the satellite cloud image at the current time point. And correcting the actual brightness value of the satellite cloud image at the current time point according to the reference brightness change value at the current time point and the actual brightness value at the last time point.
In the technical scheme of the embodiment, the reference brightness change values of the satellite cloud pictures with multiple sizes are determined by calculating the actual brightness change values of the satellite cloud pictures with the same size, the illumination intensity of the preset time point is predicted or corrected according to the reference brightness values, the illumination intensities of different areas at different moments are determined, physical equipment does not need to be deployed, and the cost is saved. In addition, the problem that the illumination brightness change of each area of the satellite cloud image is caused by different solar altitude angles at each moment, and the existing satellite cloud image prediction algorithm can only predict the change of data such as cloud layers and the like and cannot predict the illumination change of the satellite cloud image is solved.
Referring to fig. 4, fig. 4 is a second embodiment of the illumination intensity detection method based on a satellite cloud map, where based on the first embodiment, the step S20 includes:
step S21, scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion;
and step S22, processing the zoomed satellite cloud picture to obtain a plurality of grid pictures.
Specifically, the satellite cloud images are scaled to a preset size of the satellite cloud images, where the preset size may be a plurality of image sizes. For example, the size of the satellite cloud image is M × N, the set of preset sizes for scaling the satellite cloud image may be { M1 × N1, M2 × N2, … …, Mn × Nn }, and the satellite cloud image is sequentially scaled according to the preset sizes to obtain satellite cloud images with different image sizes. And then processing the zoomed satellite cloud picture according to the size of the grid picture to obtain the grid picture.
In the technical scheme of this embodiment, the satellite cloud image is scaled to the satellite cloud image with the preset size, the actual brightness value of the pixel point position is inaccurate due to the fact that the image size of the satellite cloud image is too large or too small, and the satellite cloud image is scaled to the preset size, so that statistics of the actual brightness value of the satellite cloud image is facilitated.
Referring to fig. 5, fig. 5 is a third embodiment of the illumination intensity detection method based on a satellite cloud map according to the present invention, and based on the second embodiment, the step S22 includes:
step S221, determining a central position point of the satellite cloud picture;
step S222, clipping the satellite cloud image according to the central position point and each preset grid to obtain a plurality of grid pictures.
Specifically, a plurality of central position points in the satellite cloud image can be determined according to preset intervals, a plurality of grids centered around the central position points can be determined according to the central position points and preset grids, grid pictures can be generated according to the grids, and the preset grids are different in size and different in grid picture.
In the technical scheme of the embodiment, a plurality of grid pictures are generated according to the central position point of the satellite cloud picture and the preset grid, so that the subsequent determination of the actual illumination brightness value is facilitated.
Referring to fig. 6, fig. 6 is a fourth embodiment of the illumination intensity detection method based on a satellite cloud map according to the present invention, where based on any one of the first to third embodiments, the step S30 includes:
step S31, determining HSV values of the satellite cloud images according to the RGB values of the satellite cloud images;
step S32, determining the actual brightness value of the grid picture with the same size at each time point according to the HSV value;
step S33, determining the actual luminance change values of the mesh pictures of the same size at adjacent time points according to the actual luminance values at each time point.
Specifically, the RGB values represent the colors of red, green and blue channels in the satellite cloud picture, and HSV values of the satellite cloud picture are determined according to the RGB values of the satellite cloud picture, wherein the H value represents hue, the S value represents saturation, and the V value represents brightness. The actual brightness value of the grid picture with the same size at each time point can be determined according to the HSV value, and the actual brightness change value of the grid picture at the adjacent time point can be determined by determining the brightness change value of each time point and the adjacent time point.
In the technical scheme of this embodiment, the actual brightness value of each pixel point can be determined by converting the RGB value of the satellite cloud image into the HSV value, and the actual brightness change value of the grid image at the adjacent time point can be determined according to the actual brightness value.
Referring to fig. 7, fig. 7 is a sixth embodiment of the illumination intensity detection method based on a satellite cloud map according to the present invention, where based on any one of the first to fifth embodiments, the step S40 includes:
step S41, determining weight information corresponding to the grid pictures of each size;
step S42, performing weighted average on the weighting information and the actual luminance change value corresponding to each grid picture at the same time point to obtain a reference luminance change value corresponding to each time point.
Specifically, the actual luminance change value corresponding to each grid picture at the same time point may be the actual luminance change value corresponding to the grid picture at the same time point every day, for example, three points in the afternoon every day. Determining weight information corresponding to the mesh pictures of each size, and performing weighted average operation according to the weight information and an actual brightness change value, for example, the mesh size set is { W1 × H1, W2 × H2, … …, Wn × Hn }, the actual brightness change value of the mesh picture with the mesh size W1 × H1 is a1, the weight information is b1, the actual brightness change value of the mesh picture with the mesh size W2 × H2 is a2, the weight information is b2, the actual brightness change value of the mesh picture with the mesh size Wn × Hn is an, the weight information is bn, and the reference brightness change value is:
wherein the weight information ai may follow a gaussian distribution.
In the technical scheme of the embodiment, the reference brightness change value is obtained by performing weighted average according to the actual brightness change value, and grid pictures of multiple sizes are considered, so that the generated reference brightness change value is more accurate.
Referring to fig. 8, fig. 8 is a first embodiment of an illumination intensity detection apparatus based on a satellite cloud image according to the present invention, wherein the illumination prediction apparatus based on a satellite cloud image includes:
an obtaining module 100, configured to obtain satellite clouds corresponding to multiple time points;
a processing module 200, configured to process the satellite cloud image to obtain a plurality of grid pictures, where each grid picture has a different size;
a first calculating module 300, configured to determine actual luminance change values of the grid pictures of the same size at adjacent time points;
a second calculating module 400, configured to determine, according to the actual luminance change value, a reference luminance change value of each time point relative to an adjacent time point;
the determining module 500 is configured to determine the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud map at the current time point.
Referring to fig. 9, fig. 9 is a first embodiment of an illumination intensity detection apparatus based on a satellite cloud image according to a second embodiment of the illumination intensity detection apparatus based on a satellite cloud image of the present invention, where the processing module includes:
a scaling unit 210, configured to scale the satellite cloud image into a satellite cloud image with a preset size;
a processing unit 220, configured to process the scaled satellite cloud image to obtain a plurality of grid pictures.
The invention also provides illumination intensity detection equipment based on the satellite cloud picture, which comprises a memory, a processor and an illumination intensity detection program based on the satellite cloud picture, wherein the illumination intensity detection program is stored in the memory and can be executed on the processor, and when being executed by the processor, the illumination intensity detection program based on the satellite cloud picture realizes the steps of the illumination intensity detection method based on the satellite cloud picture.
The present invention further provides a medium storing a satellite cloud map-based illumination intensity detection program, where the satellite cloud map-based illumination intensity detection program, when executed by a processor, implements the steps of the satellite cloud map-based illumination intensity detection method according to the above embodiments.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An illumination intensity detection method based on a satellite cloud picture is characterized in that the illumination prediction method based on the satellite cloud picture comprises the following steps:
acquiring satellite cloud pictures corresponding to a plurality of time points;
processing the satellite cloud picture to obtain a plurality of grid pictures, wherein the grid pictures are different in size;
determining actual brightness change values of the grid pictures with the same size at adjacent time points;
determining a reference brightness change value of each time point relative to adjacent time points according to a weighted average value of actual brightness change values of grid pictures with different sizes corresponding to the same position;
and determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud picture at the current time point.
2. The method according to claim 1, wherein the step of processing the satellite cloud to obtain a plurality of grid pictures comprises:
scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion mode;
and processing the scaled satellite cloud images to obtain a plurality of grid images.
3. The method of claim 2, wherein the step of processing the scaled satellite cloud to obtain the plurality of grid pictures comprises:
determining a central position point of the satellite cloud picture;
and cutting the satellite cloud picture according to the central position point and each preset grid to obtain a plurality of grid pictures.
4. The method of claim 1, wherein the step of determining the actual brightness variation values of the grid pictures of the same size at adjacent time points comprises:
determining HSV values of the satellite cloud pictures according to the RGB values of the satellite cloud pictures;
determining the actual brightness value of the grid picture with the same size at each time point according to the HSV value;
and determining the actual brightness change values of the grid pictures with the same size at the adjacent time points according to the actual brightness values of the time points.
5. The method of claim 1, wherein the step of determining the actual brightness variation values of the grid pictures of the same size at adjacent time points comprises:
determining brightness change values of the grid pictures at adjacent time points;
and determining the average value of the brightness change values of the grid pictures at the adjacent time points as the actual brightness change value of the grid pictures.
6. The method as claimed in claim 1, wherein the step of determining the reference luminance variation value of each time point relative to the adjacent time points according to the weighted average of the actual luminance variation values of the grid pictures with different sizes corresponding to the same position comprises:
determining weight information corresponding to the grid pictures of all sizes;
and carrying out weighted average on the weighting information and the actual brightness change value corresponding to each grid picture at the same time point to obtain a reference brightness change value corresponding to each time point.
7. An illumination intensity detection device based on a satellite cloud picture, characterized in that the illumination prediction device based on the satellite cloud picture comprises:
the acquisition module is used for acquiring satellite cloud pictures corresponding to a plurality of time points;
the processing module is used for processing the satellite cloud picture to obtain a plurality of grid pictures, and the grid pictures are different in size;
the first calculation module is used for determining the actual brightness change values of the grid pictures with the same size at adjacent time points;
the second calculation module is used for determining a reference brightness change value of each time point relative to adjacent time points according to the weighted average value of the actual brightness change values of the grid pictures with different sizes corresponding to the same position;
and the determining module is used for determining the illumination intensity at a preset time point according to the reference brightness change value corresponding to each time point and the actual brightness value of the satellite cloud image at the current time point.
8. The satellite cloud based illumination intensity detection apparatus of claim 7, wherein said processing module comprises:
the scaling unit is used for scaling the satellite cloud picture into a satellite cloud picture with a preset size in an equal proportion mode;
and the processing unit is used for processing the zoomed satellite cloud picture to obtain a plurality of grid pictures.
9. A satellite cloud based illumination intensity detection device, comprising a memory, a processor, and a satellite cloud based illumination intensity detection program stored in the memory and executable on the processor, wherein the satellite cloud based illumination intensity detection program, when executed by the processor, implements the steps of the satellite cloud based illumination intensity detection method according to any one of claims 1 to 6.
10. A medium storing a satellite cloud based illumination intensity detection program, wherein the satellite cloud based illumination intensity detection program, when executed by a processor, implements the steps of the satellite cloud based illumination intensity detection method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010945195.2A CN112067118B (en) | 2020-09-09 | 2020-09-09 | Illumination intensity detection method, device, equipment and medium based on satellite cloud picture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010945195.2A CN112067118B (en) | 2020-09-09 | 2020-09-09 | Illumination intensity detection method, device, equipment and medium based on satellite cloud picture |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112067118A CN112067118A (en) | 2020-12-11 |
| CN112067118B true CN112067118B (en) | 2022-07-12 |
Family
ID=73663337
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010945195.2A Active CN112067118B (en) | 2020-09-09 | 2020-09-09 | Illumination intensity detection method, device, equipment and medium based on satellite cloud picture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112067118B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113029332A (en) * | 2021-03-05 | 2021-06-25 | 阳光电源股份有限公司 | Satellite cloud picture prediction method, device and processor |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013096241A2 (en) * | 2011-12-19 | 2013-06-27 | General Electric Company | Apparatus and method for predicting solar irradiance variation |
| JP2013253851A (en) * | 2012-06-06 | 2013-12-19 | Sharp Corp | Sunshine amount prediction device, sunshine amount prediction method, television receiver, program and recording medium |
| CN105701556A (en) * | 2015-11-05 | 2016-06-22 | 衢州职业技术学院 | Photovoltaic power station super-short-time power prediction correcting method |
| CN106779154A (en) * | 2016-11-22 | 2017-05-31 | 浙江工业大学 | Area distribution formula photovoltaic power generation output forecasting method based on satellite cloud picture |
| CN109416413A (en) * | 2016-05-11 | 2019-03-01 | 联邦科学和工业研究组织 | Solar energy forecast |
| CN110514298A (en) * | 2019-08-30 | 2019-11-29 | 河海大学常州校区 | A kind of solar irradiation strength calculation method based on ground cloud atlas |
-
2020
- 2020-09-09 CN CN202010945195.2A patent/CN112067118B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013096241A2 (en) * | 2011-12-19 | 2013-06-27 | General Electric Company | Apparatus and method for predicting solar irradiance variation |
| JP2013253851A (en) * | 2012-06-06 | 2013-12-19 | Sharp Corp | Sunshine amount prediction device, sunshine amount prediction method, television receiver, program and recording medium |
| CN105701556A (en) * | 2015-11-05 | 2016-06-22 | 衢州职业技术学院 | Photovoltaic power station super-short-time power prediction correcting method |
| CN109416413A (en) * | 2016-05-11 | 2019-03-01 | 联邦科学和工业研究组织 | Solar energy forecast |
| CN106779154A (en) * | 2016-11-22 | 2017-05-31 | 浙江工业大学 | Area distribution formula photovoltaic power generation output forecasting method based on satellite cloud picture |
| CN110514298A (en) * | 2019-08-30 | 2019-11-29 | 河海大学常州校区 | A kind of solar irradiation strength calculation method based on ground cloud atlas |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112067118A (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109416413B (en) | Solar energy forecast | |
| CN107590456B (en) | Method for detecting small and micro targets in high-altitude video monitoring | |
| KR101879332B1 (en) | Method for calculating amount of cloud from whole sky image and apparatus thereof | |
| CN103035001B (en) | Foundation automatic cloud detection method based on superpixel division | |
| CN106954051B (en) | A kind of image processing method and mobile terminal | |
| US20140267826A1 (en) | Apparatus and techniques for image processing | |
| CN110488391B (en) | Weather prediction system, method and storage medium for small area | |
| CN104517126A (en) | Air quality assessment method based on image analysis | |
| CN112067118B (en) | Illumination intensity detection method, device, equipment and medium based on satellite cloud picture | |
| CN112419231A (en) | Visibility determination method and device, computer equipment and storage medium | |
| CN111652126A (en) | Inversion radiation method based on satellite cloud picture | |
| CN116645413B (en) | Photovoltaic cell panel position identification method and system and photovoltaic robot | |
| US8019153B2 (en) | Wide luminance range colorimetrically accurate profile generation method | |
| CN117496426A (en) | Precast beam procedure identification method and device based on mutual learning | |
| CN105184296A (en) | Image processing method and device | |
| CN115620119A (en) | Weather type image detection and foggy day image processing method and device | |
| CN111928944B (en) | Laser ray detection method, device and system | |
| CN114359630A (en) | Green, blue and gray infrastructure classification method, apparatus, system and medium | |
| CN114339028A (en) | Photographing method, electronic device and computer-readable storage medium | |
| CN113870180A (en) | Image processing method, image processing device, computer equipment and storage medium | |
| CN116089546A (en) | Typhoon cloud system identification method, typhoon cloud system identification system, typhoon cloud system identification terminal and storage medium | |
| CN106846292A (en) | A kind of invalid pixel In-flight measurement method | |
| CN113435118A (en) | Irradiance determination method, irradiance determination device, irradiance determination equipment and storage medium | |
| CN110991567A (en) | Point-to-surface remote sensing instantaneous surface temperature data detection method | |
| EP4425222A1 (en) | Aerosol concentration inference method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221226 Address after: Room 423, Sunshine Power R&D Center Building, No. 1699, Xiyou Road, High-tech Zone, Hefei City, Anhui Province, 230088 Patentee after: Sunshine Hui Carbon Technology Co.,Ltd. Address before: 230088 No. 1699 Xiyou Road, Hefei High-tech Zone, Anhui Province Patentee before: SUNGROW POWER SUPPLY Co.,Ltd. |
|
| TR01 | Transfer of patent right |