Photovoltaic power station cloud layer detection method, device, equipment, medium and program product

Abstract

The embodiment of the invention provides a photovoltaic power station cloud layer detection method. The method comprises the following steps: acquiring a cloud layer monitoring graph of a photovoltaic power station; performing channel separation on the cloud layer monitoring image to obtain a red channel image, a green channel image and a blue channel image which are separated; according to the red channel image, the green channel image and the blue channel image, a dark channel image and a bright channel image of the cloud layer monitoring image are determined, and the dark channel image is obtained according to the minimum values of the red channel image, the green channel image and the blue channel image; the bright channel image is obtained according to the maximum values of the red channel image, the green channel image and the blue channel image; obtaining a channel information difference graph of the cloud layer monitoring graph according to the dark channel graph and the bright channel graph; and determining the cloud coverage rate of the photovoltaic power station according to the channel information difference graph. According to the technical scheme provided by the embodiment of the invention, the detection accuracy of the cloud coverage rate can be improved, the support of various devices or a large amount of computing power is not needed, and the implementation cost of the cloud detection of the photovoltaic power station can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of solar cells, and in particular to a method, device, computer equipment, storage medium and program product for detecting cloud layers in a photovoltaic power station. Background Art

[0002] The power generation of photovoltaic power stations is directly affected by solar radiation, and clouds, as the main factor blocking sunlight, have a significant impact on photovoltaic power generation. In order to better adjust the operation strategy of photovoltaic power stations, it is very necessary to detect cloud conditions.

[0003] Among the related technologies, there are mainly the following methods: 1. Using binocular cameras to monitor clouds; 2. Using multiple devices (such as anemometers, wind vanes, all-sky cameras, irradiators, etc.) to monitor clouds; 3. Using neural network models to identify cloud types. However, the binocular camera method has poor restoration effect on high-altitude clouds and low accuracy; the method using multiple devices will have the problem of high implementation costs; and the method using neural network models requires a lot of computing power support, which also has the problem of high implementation costs.

[0004] It should be noted that the above content is not necessarily prior art, nor is it intended to limit the scope of patent protection of this application. Summary of the invention

[0005] The embodiments of the present application provide a photovoltaic power station cloud detection method, device, computer equipment, storage medium and program product to solve or alleviate one or more technical problems raised above.

[0006] A first aspect of an embodiment of the present application provides a cloud layer detection method for a photovoltaic power station, comprising: Obtain cloud monitoring images of photovoltaic power plants; Performing channel separation on the cloud layer monitoring image to obtain a separated red channel image, a green channel image, and a blue channel image; Determine a dark channel image and a bright channel image of the cloud layer monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image; Obtaining a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map; The cloud coverage rate of the photovoltaic power station is determined according to the channel information difference map.

[0007] Optionally, determining the cloud coverage rate of the photovoltaic power station according to the channel information difference map includes: Constructing row and column coordinates of the channel information difference map; The cloud coverage of the photovoltaic power station is determined by using a coverage calculation formula according to the channel information difference map. The coverage calculation formula is:

[0008] Among them, S is the cloud coverage rate, I(x, y) is the grayscale value of the channel information difference map at the pixel position (x, y), (x, y) is the pixel row and column coordinates of the channel information difference map, and size is the image size of the channel information difference map.

[0009] Optionally, obtaining the channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map includes: Corroding the dark channel image and the bright channel image respectively to obtain a corroded dark channel image and a corroded bright channel image; The corroded bright channel image is subtracted from the corroded dark channel image to obtain a channel information difference image of the cloud layer monitoring image.

[0010] Optionally, the corroding the dark channel image and the bright channel image respectively comprises: When a target image is corroded, a target corrosion core is placed at each position of the target image. When the target corrosion core is placed at a certain position of the target image, a minimum value of each pixel currently covered by the target corrosion core is determined, and the minimum value is assigned to an element at a center position of the target corrosion core, wherein the target image is the dark channel image or the bright channel image.

[0011] Optionally, the target corrosion core is 3 3 matrix.

[0012] Optionally, the cloud layer monitoring image is collected by a fisheye camera according to a preset frequency.

[0013] A second aspect of an embodiment of the present application provides a photovoltaic power station cloud detection device, comprising: An acquisition module is used to obtain a cloud monitoring map of a photovoltaic power station; A separation module is used to perform channel separation on the cloud monitoring image to obtain a separated red channel image, a green channel image and a blue channel image; A first determination module is used to determine a dark channel image and a bright channel image of the cloud layer monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image; A difference module, used for obtaining a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map; The second determination module is used to determine the cloud coverage rate of the photovoltaic power station according to the channel information difference map.

[0014] A third aspect of an embodiment of the present application provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein: the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0015] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method described above is implemented.

[0016] A fifth aspect of an embodiment of the present application provides a computer program product, including a computer program, which implements the method described above when executed by a processor.

[0017] The above technical solution adopted in the embodiment of the present application may have the following advantages: By obtaining a cloud monitoring map of a photovoltaic power station, performing channel separation on the cloud monitoring map, determining a dark channel map and a bright channel map of the cloud monitoring map based on the separated red channel image, green channel image and blue channel image, obtaining a channel information differential map of the cloud monitoring map based on the dark channel map and the bright channel map, and determining the cloud coverage of the photovoltaic power station based on the channel information differential map, the accuracy of cloud coverage detection can be improved, and the support of multiple devices or a large amount of computing power is not required, which can reduce the implementation cost of cloud detection in photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0019] Figure 1It is a flow chart of a photovoltaic power station cloud detection method provided in an embodiment of the present application; Figure 2 yes Figure 1 Schematic diagram of the sub-step flow chart of step S106; Figure 3 It is a cloud monitoring image of different cloud cover conditions of photovoltaic power stations collected by fisheye cameras; Figure 4 is with Figure 3 The corresponding dark channel image; Figure 5 is with Figure 3 The corresponding bright channel image; Figure 6 is with Figure 3 The corresponding channel information difference map; Figure 7 A block diagram of a photovoltaic module shading identification device provided in an embodiment of the present application; and Figure 8 It is a schematic diagram of the hardware architecture of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings. In the accompanying drawings, for clarity, the sizes of layers, regions, elements and their relative sizes may be exaggerated. The same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present application.

[0021] It should be noted that the descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0022] In the description of the present application, it should be understood that the numerical labels before the steps do not indicate the order in which the steps are executed, but are only used to facilitate the description of the present application and to distinguish each step, and therefore should not be understood as a limitation on the present application.

[0023] In the present application, when it comes to a numerical interval (i.e., a numerical range), unless otherwise specified, the distribution of the optional numerical values ​​in the numerical interval is considered to be continuous, and includes the two numerical endpoints (i.e., the minimum and maximum values) of the numerical interval, and each numerical value between the two numerical endpoints. Unless otherwise specified, when the numerical interval only refers to an integer in the numerical interval, it includes the two endpoint integers of the numerical range, and each integer between the two endpoints, which is equivalent to directly listing each integer. When multiple numerical ranges are provided to describe features or characteristics, these numerical ranges can be merged. In other words, unless otherwise specified, the numerical range disclosed in the present application should be understood to include any and all sub-ranges included therein. The "numerical value" in the numerical interval can be any quantitative value, such as a number, a percentage, a ratio, etc. "Numerical interval" allows for broadly including quantitative intervals such as percentage intervals, ratio intervals, and ratio intervals.

[0024] The embodiment of the present application provides a cloud detection technology solution for photovoltaic power stations. Based on this, the accuracy of cloud detection can be improved, and it does not require multiple devices or a large amount of computing power support, which can reduce the implementation cost of cloud detection. See below for details.

[0025] Hereinafter, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. It should be noted that these exemplary embodiments can be implemented in many different forms and should not be construed as being limited to the embodiments described herein.

[0026] like Figure 1 As shown, the embodiment of the present application provides a cloud layer detection method for a photovoltaic power station, which may specifically include steps S100 to S108, wherein: Step S100, obtaining a cloud layer monitoring map of the photovoltaic power station.

[0027] The cloud monitoring map of the photovoltaic power station can be obtained specifically by using a full sky camera. Optionally, the cloud monitoring map is collected by a fisheye camera according to a preset frequency. Among them, the preset frequency can be, for example, once every few seconds, once every tens of seconds, or once every few minutes, etc., which can be determined according to factors such as monitoring needs, geographical location and / or season. Due to the wide-angle characteristics of the fisheye camera, a wider range of the sky can be captured, which helps to achieve comprehensive monitoring of the cloud layer in the photovoltaic power station with less equipment. In addition, since the fisheye camera has a simple structure and is easy to install, it can be applied to photovoltaic power stations in various environments. In addition, by reasonably setting the preset frequency, real-time monitoring of the cloud layer can be achieved, and changes in the cloud layer can be grasped in a timely manner, providing a timely basis for the operation and management of the photovoltaic power station.

[0028] Step S102, performing channel separation on the cloud monitoring image to obtain a separated red channel image, a green channel image and a blue channel image.

[0029] Specifically, after obtaining the cloud monitoring map of the photovoltaic power station, the cloud monitoring map can be separated into three channels of red (r), green (g), and blue (b). After the channel separation is completed, the separated red channel image, green channel image, and blue channel image of the cloud monitoring map are obtained.

[0030] Step S104, determining a dark channel image and a bright channel image of the cloud monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image.

[0031] The red channel image, green channel image and blue channel image obtained after channel separation of the cloud monitoring image are:

[0032] The dark channel is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, which can be recorded as:

[0033] Among them, I ij =min(r ij , g ij , b ij ), I ij represents the value of row i and column j, r ij represents the r value of the i-th row and j-th column in the red channel, g ij represents the g value of the i-th row and j-th column in the green channel, b ij Represents the b value of the i-th row and j-th column in the blue channel.

[0034] The bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image, which can be recorded as:

[0035] Among them, J ij =max(r ij , g ij , b ij ), J ij Represents the value of the i-th row and j-th column.

[0036] Step S106, obtaining a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map.

[0037] Specifically, the channel information difference map of the cloud layer monitoring map can be obtained by subtracting the dark channel map from the bright channel map. Optionally, the dark channel map and the bright channel map can be corroded respectively, and then the channel information difference map of the cloud layer monitoring map can be obtained according to the corroded dark channel map and the corroded bright channel map.

[0038] In an optional embodiment, if Figure 2 As shown, step S106 may further include steps S200 to S202: Step S200, corroding the dark channel image and the bright channel image respectively to obtain a corroded dark channel image and a corroded bright channel image.

[0039] When collecting cloud monitoring images, fisheye cameras may introduce some random noise due to interference from dust, water vapor in the atmosphere, which may affect the edge clarity of the clouds and interfere with the recognition of clouds. Therefore, the dark channel image and the bright channel image can be eroded separately to remove these noises, making the edges of the clouds clearer and facilitating the recognition of clouds.

[0040] In an exemplary embodiment, step S200 may further include: when corroding the target image, placing the target corrosion core at each position of the target image; when placing the target corrosion core at a certain position of the target image, determining the minimum value of each pixel currently covered by the target corrosion core, and assigning the minimum value to the element at the center position of the target corrosion core, wherein the target image is a dark channel image or a bright channel image.

[0041] The shape of the target erosion kernel can be rectangular, circular, elliptical, or cross-shaped. The size of the target erosion kernel can be selected according to actual needs. A larger target erosion kernel will increase the range of erosion and is suitable for removing large-scale interference, but will lose more image details; while a smaller target erosion kernel will have a smaller range of erosion and can retain better details in the image. Considering the cloud monitoring data collected by the fisheye camera, Figure 1 The general interference is small, so a smaller target corrosion core can be used. Optionally, the target corrosion core can be 3 A matrix of 3 can better preserve the details of the image and avoid excessive corrosion. Compared with a larger corrosion kernel, it has less computational complexity and can process the image faster.

[0042] In practical applications, the cloud monitoring image before channel separation can be corroded first, and then the channel separation can be performed based on the corroded image, and then the dark channel image and the bright channel image can be obtained based on the channel separation image, and the channel information difference image can be obtained by subtracting the dark channel image from the bright channel image, that is, the corrosion operation is placed before the channel separation. However, placing the corrosion operation before the channel separation will cause a large amount of calculation because the entire image is corroded; while corroding the dark channel image and the bright channel image will have a relatively smaller amount of calculation.

[0043] Step S202, subtracting the corroded dark channel image from the corroded bright channel image to obtain a channel information difference image of the cloud monitoring image.

[0044] In this optional embodiment, by corroding the dark channel image and the bright channel image respectively to obtain a corroded dark channel image and a corroded bright channel image, the corroded dark channel image is subtracted from the corroded bright channel image to obtain a channel information difference image of the cloud monitoring image. The corrosion can effectively remove noise and alleviate the influence of foreground objects, thereby improving the accuracy of subsequent calculations.

[0045] Please refer to Figures 3 to 6 ,in Figure 3 Cloud monitoring images of different cloud cover conditions of photovoltaic power stations collected by fisheye cameras. Figure 4 For Figure 3 The corresponding dark channel image, Figure 5 For Figure 3 The corresponding bright channel image, Figure 6 For Figure 3 Corresponding channel information difference map. From the dark channel map and the bright channel map, it can be seen that the texture features of the cloud-covered area and the clear area in the sky image are obviously different; and the channel information difference map can further amplify the difference between them.

[0046] Step S108, determining the cloud coverage rate of the photovoltaic power station according to the channel information difference map.

[0047] Specifically, a threshold can be determined according to the channel information difference map, and the image can be segmented into cloud area and non-cloud area according to the threshold. Then, the number of pixels in the cloud area is calculated and compared with the total number of pixels in the image to obtain the cloud coverage rate.

[0048] In an optional embodiment, step S108 may further include: constructing row and column coordinates of a channel information difference map, and determining the cloud coverage of the photovoltaic power station using a coverage calculation formula according to the channel information difference map, the coverage calculation formula being:

[0049] Among them, S is the cloud coverage rate, I(x,y) is the grayscale value of the channel information difference map at the pixel position (x,y), (x,y) is the pixel row and column coordinates of the channel information difference map, and size is the image size of the channel information difference map.

[0050] It should be understood that since the channel information difference map can effectively highlight the cloud area and make the contrast between the cloud and the background more obvious, the cloud coverage of the photovoltaic power station can be calculated directly according to the grayscale value of the channel information difference map by using the above coverage calculation formula. Compared with complex machine learning models, it is simpler, more efficient and real-time, and has a higher calculation accuracy. At the same time, since the channel information difference map can reduce the impact of illumination changes on cloud detection to a certain extent, the algorithm has strong robustness.

[0051] The photovoltaic power station cloud detection method provided in the embodiment of the present application obtains a cloud monitoring map of the photovoltaic power station, performs channel separation on the cloud monitoring map, determines a dark channel map and a bright channel map of the cloud monitoring map according to the separated red channel image, green channel image and blue channel image, obtains a channel information difference map of the cloud monitoring map according to the dark channel map and the bright channel map, and determines the cloud coverage of the photovoltaic power station according to the channel information difference map. The accuracy of cloud coverage detection can be improved, and the support of multiple devices or a large amount of computing power is not required, so the implementation cost of cloud detection in photovoltaic power stations can be reduced.

[0052] Figure 7 The block diagram of the photovoltaic power station cloud layer detection device 300 provided in the embodiment of the present application is schematically shown. The device 300 can be divided into one or more program modules, one or more program modules are stored in a storage medium, and are executed by one or more processors to complete the embodiment of the present application. The program module referred to in the embodiment of the present application refers to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. Figure 7 As shown, the apparatus 300 may include an acquisition module 310, a separation module 320, a first determination module 330, a difference module 340, and a second determination module 350, wherein: An acquisition module 310 is used to acquire a cloud layer monitoring map of a photovoltaic power station; A separation module 320 is used to perform channel separation on the cloud layer monitoring image to obtain a separated red channel image, a green channel image and a blue channel image; A first determining module 330 is used to determine a dark channel image and a bright channel image of the cloud layer monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image; A difference module 340 is used to obtain a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map; The second determination module 350 is configured to determine the cloud coverage rate of the photovoltaic power station according to the channel information difference map.

[0053] In an optional embodiment, the second determining module 350 is further configured to: Constructing row and column coordinates of the channel information difference map; The cloud coverage of the photovoltaic power station is determined by using a coverage calculation formula according to the channel information difference map. The coverage calculation formula is:

[0054] Among them, S is the cloud coverage rate, I(x, y) is the grayscale value of the channel information difference map at the pixel position (x, y), (x, y) is the pixel row and column coordinates of the channel information difference map, and size is the image size of the channel information difference map.

[0055] In an optional embodiment, the difference module 340 is further used for: Corroding the dark channel image and the bright channel image respectively to obtain a corroded dark channel image and a corroded bright channel image; The corroded bright channel image is subtracted from the corroded dark channel image to obtain a channel information difference image of the cloud layer monitoring image.

[0056] In an optional embodiment, the difference module 340 is further used for: When a target image is corroded, a target corrosion core is placed at each position of the target image. When the target corrosion core is placed at a certain position of the target image, a minimum value of each pixel currently covered by the target corrosion core is determined, and the minimum value is assigned to an element at a center position of the target corrosion core, wherein the target image is the dark channel image or the bright channel image.

[0057] In an optional embodiment, the target corrosion core is 3 3 matrix.

[0058] In an optional embodiment, the cloud layer monitoring image is collected by a fisheye camera according to a preset frequency.

[0059] Figure 8 The hardware architecture diagram of the computer device 10000 suitable for implementing the cloud layer detection method for photovoltaic power stations provided in the embodiment of the present application is schematically shown. In some embodiments, the computer device 10000 may be a terminal device such as a smart phone, a wearable device, a tablet computer, a personal computer, a vehicle terminal, a game console, a virtual device, a workbench, a digital assistant, a set-top box, a robot, etc. In other embodiments, the computer device 10000 may be a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or a server cluster composed of multiple servers), etc. Figure 8 As shown, the computer device 10000 includes but is not limited to: a memory 10010, a processor 10020, and a network interface 10030 that can communicate with each other through a system bus. Among them: The memory 10010 includes at least one type of computer-readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10010 can be an internal storage module of the computer device 10000, such as a hard disk or memory of the computer device 10000. In other embodiments, the memory 10010 can also be an external storage device of the computer device 10000, such as a plug-in hard disk equipped on the computer device 10000, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 10010 can also include both the internal storage module of the computer device 10000 and its external storage device. In this embodiment, the memory 10010 is generally used to store the operating system and various application software installed in the computer device 10000, such as the program code of the photovoltaic power station cloud layer detection method, etc. In addition, the memory 10010 can also be used to temporarily store various data that have been output or will be output.

[0060] In some embodiments, the processor 10020 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other chips. The processor 10020 is generally used to control the overall operation of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is used to run the program code stored in the memory 10010 or process data.

[0061] The network interface 10030 may include a wireless network interface or a wired network interface, and the network interface 10030 is generally used to establish a communication link between the computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal through a network, and to establish a data transmission channel and a communication link between the computer device 10000 and the external terminal. The network may be a wireless or wired network such as an intranet, the Internet, the Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, etc.

[0062] It should be pointed out that Figure 8 Only a computer device having components 10010 - 10030 is shown, but it should be understood that implementation of all of the components shown is not a requirement, and more or fewer components may alternatively be implemented.

[0063] In this embodiment, the photovoltaic power station cloud layer detection method stored in the memory 10010 can also be divided into one or more program modules and executed by one or more processors (such as processor 10020) to complete the embodiment of the present application.

[0064] The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the photovoltaic power station cloud layer detection method in the embodiment are implemented.

[0065] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Of course, the computer-readable storage medium may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store an operating system and various application software installed on the computer device, such as the program code of the photovoltaic power station cloud layer detection method in the embodiment. In addition, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are to be output.

[0066] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method in the above embodiment when executed by a processor.

[0067] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present application can be implemented by general-purpose computer devices, they can be concentrated on a single computer device, or distributed on a network composed of multiple computer devices, optionally, they can be implemented by executable program codes of computer devices, so that they can be stored in a storage device and executed by the computer device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0068] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0069] It should also be noted that the above are only preferred embodiments of the present application, and the patent protection scope of the present application is not limited thereto. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A cloud layer detection method for a photovoltaic power station, characterized in that: include: Obtain cloud monitoring images of photovoltaic power plants; Performing channel separation on the cloud layer monitoring image to obtain a separated red channel image, a green channel image, and a blue channel image; Determine a dark channel image and a bright channel image of the cloud layer monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image; Obtaining a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map; The cloud coverage rate of the photovoltaic power station is determined according to the channel information difference map.

2. The method according to claim 1, characterized in that The determining the cloud coverage rate of the photovoltaic power station according to the channel information difference map includes: Constructing row and column coordinates of the channel information difference map; The cloud coverage of the photovoltaic power station is determined by using a coverage calculation formula according to the channel information difference map. The coverage calculation formula is: Among them, S is the cloud coverage rate, I(x, y) is the grayscale value of the channel information difference map at the pixel position (x, y), (x, y) is the pixel row and column coordinates of the channel information difference map, and size is the image size of the channel information difference map.

3. The method according to claim 2, characterized in that The channel information difference map of the cloud layer monitoring map is obtained according to the dark channel map and the bright channel map, comprising: Corroding the dark channel image and the bright channel image respectively to obtain a corroded dark channel image and a corroded bright channel image; The corroded bright channel image is subtracted from the corroded dark channel image to obtain a channel information difference image of the cloud layer monitoring image.

4. The method according to claim 3, characterized in that The corroding the dark channel image and the bright channel image respectively comprises: When a target image is corroded, a target corrosion core is placed at each position of the target image. When the target corrosion core is placed at a certain position of the target image, a minimum value of each pixel currently covered by the target corrosion core is determined, and the minimum value is assigned to an element at a center position of the target corrosion core, wherein the target image is the dark channel image or the bright channel image.

5. The method according to claim 4, characterized in that The target corrosion core is 3 3 matrix.

6. The method according to any one of claims 1 to 5, characterized in that: The cloud layer monitoring image is collected by a fisheye camera according to a preset frequency.

7. A photovoltaic power station cloud detection device, characterized in that: include: An acquisition module is used to obtain a cloud monitoring map of a photovoltaic power station; A separation module is used to perform channel separation on the cloud monitoring image to obtain a separated red channel image, a green channel image and a blue channel image; A first determination module is used to determine a dark channel image and a bright channel image of the cloud layer monitoring image according to the red channel image, the green channel image and the blue channel image, wherein the dark channel image is obtained according to the minimum value of the red channel image, the green channel image and the blue channel image, and the bright channel image is obtained according to the maximum value of the red channel image, the green channel image and the blue channel image; A difference module, used for obtaining a channel information difference map of the cloud layer monitoring map according to the dark channel map and the bright channel map; The second determination module is used to determine the cloud coverage rate of the photovoltaic power station according to the channel information difference map.

8. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images