Image generation method and device of photovoltaic system, photovoltaic system, electronic equipment, storage medium and computer program product

Abstract

The invention discloses an image generation method and device of a photovoltaic system, the photovoltaic system, electronic equipment, a storage medium and a computer program product, and belongs to the technical field of photovoltaic systems. The image generation method of the photovoltaic system comprises the following steps: acquiring a first image corresponding to the photovoltaic system; the first image comprises feature code images corresponding to the inverters, and pixel relative position information of the feature code images in the first image is matched with actual relative position information of the inverters; inputting the first image into a target fusion model, and performing multi-layer feature extraction and feature fusion processing on the first image by the target fusion model to obtain a plurality of fused feature images of different scales; and generating a target layout map based on at least part of the fused feature images in the plurality of fused feature images with different scales. According to the image generation method of the photovoltaic system, the target layout map is automatically generated, the generation efficiency of the target layout map is improved, the delivery time is shortened, and the user experience is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of photovoltaic systems, and in particular relates to a method and device for generating an image of a photovoltaic system, a photovoltaic system, an electronic device, a storage medium, and a computer program product. Background Art

[0002] The electronic layout diagram of the photovoltaic system can facilitate users to view the electrical information of each photovoltaic module in the photovoltaic system. In the related art, the electronic layout diagram is mainly obtained by the user manually drawing the electronic layout diagram on the power station management software. However, the above method requires manual operation, which is time-consuming, inefficient, and affects the user experience. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a method, device, photovoltaic system, electronic device, storage medium and computer program product for generating an image of a photovoltaic system, which automatically generates a target layout map without manual operation, reduces the time to obtain the target layout map, improves the efficiency of generating the target layout map, reduces the delivery time and improves the user experience.

[0004] In a first aspect, the present application provides an image generation method for a photovoltaic system, wherein the photovoltaic system includes a plurality of inverters, each of which is electrically connected to at least one photovoltaic component; the method includes: Acquire a first image corresponding to the photovoltaic system; the first image includes a feature code image corresponding to each of the inverters, and the pixel relative position information of each of the feature code images in the first image matches the actual relative position information of the multiple inverters; Inputting the first image into a target fusion model, and performing multi-layer feature extraction and feature fusion processing on the first image by the target fusion model to obtain a plurality of fused feature images of different scales; A target layout map is generated based on at least part of the fused feature images of the multiple fused feature images of different scales.

[0005] According to the image generation method of the photovoltaic system of the present application, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection between the inverter and the photovoltaic module through the feature code image through the target fusion model, so as to obtain multiple fused feature images of different scales, and obtain the rich features and details included in the first image, so as to automatically generate a target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0006] According to the image generation method of the photovoltaic system of the present application, the first image is input into the target fusion model, and the target fusion model performs multi-layer feature extraction and feature fusion processing on the first image to obtain multiple fused feature images of different scales, including: The first image is input into the target fusion model, and the target fusion model performs feature extraction and feature fusion processing of at least four modules on the first image to obtain at least four fused feature images of different scales.

[0007] According to the image generation method of the photovoltaic system of the present application, the target fusion model includes: a first feature processing module, a second feature processing module, a third feature processing module and a fourth feature processing module; The first feature processing module includes: a first submodule, a second submodule and a third submodule; the second feature processing module includes: a fourth submodule, a fifth submodule and a sixth submodule; the third feature processing module includes: a seventh submodule, an eighth submodule and a ninth submodule; the fourth feature processing module includes: a tenth submodule, an eleventh submodule and a twelfth submodule; The first input port corresponding to the first submodule is used to input the first image; The first sub-input port corresponding to the second sub-module is connected to the first sub-output port corresponding to the first sub-module; The third input port corresponding to the third submodule is respectively connected to the first suboutput port corresponding to the first submodule and the second output port corresponding to the second submodule; The fourth input port corresponding to the fourth submodule is connected to the second suboutput port corresponding to the first submodule; The first sub-input port corresponding to the fifth sub-module is connected to the first sub-output port corresponding to the fourth sub-module, and the second sub-output port corresponding to the fifth sub-module is connected to the second sub-input port corresponding to the second sub-module; The first sub-input port corresponding to the sixth sub-module is connected to the first sub-output port corresponding to the fifth sub-module and the first sub-output port corresponding to the fourth sub-module respectively; the second sub-input port corresponding to the sixth sub-module is connected to the second sub-output port corresponding to the third sub-module; The seventh input port corresponding to the seventh submodule is connected to the second suboutput port corresponding to the fourth submodule; The first sub-input port corresponding to the eighth sub-module is connected to the first sub-output port corresponding to the seventh sub-module, and the second sub-output port corresponding to the eighth sub-module is connected to the second sub-input port corresponding to the fifth sub-module; The first sub-input port corresponding to the ninth sub-module is respectively connected to the first sub-output port corresponding to the eighth sub-module and the first sub-output port corresponding to the seventh sub-module, and the second sub-input port corresponding to the ninth sub-module is connected to the second sub-output port corresponding to the sixth sub-module; The tenth input port corresponding to the tenth submodule is connected to the second suboutput port corresponding to the seventh submodule; The eleventh input port corresponding to the eleventh submodule is connected to the tenth output port corresponding to the tenth submodule, and the second sub-output port corresponding to the eleventh submodule is connected to the second sub-input port corresponding to the eighth submodule; The first sub-input port corresponding to the twelfth sub-module is respectively connected to the first sub-output port corresponding to the eleventh sub-module and the tenth output port corresponding to the tenth sub-module, and the second sub-input port corresponding to the twelfth sub-module is connected to the second sub-output port corresponding to the ninth sub-module; The first sub-output port corresponding to the third sub-module, the first sub-output port corresponding to the sixth sub-module, the first sub-output port corresponding to the ninth sub-module, and the twelfth output port corresponding to the twelfth sub-module are respectively used to output the fused feature images of different scales.

[0008] According to the photovoltaic system image generation method of the present application, the feature fusion is performed based on the following steps: The target feature image and the adjacent feature image input to the target feature processing module are processed by the model parameters and the first constant corresponding to the target feature processing module in the target fusion model to obtain a target fusion image; the target feature image is output by the target feature processing module, and the adjacent feature image is output by a module adjacent to the target feature processing module; the model parameters are obtained by training based on multiple sample feature code images.

[0009] According to the photovoltaic system image generation method of the present application, the target layout map is generated based on at least part of the fused feature images of the multiple fused feature images of different scales, including: Performing feature recognition on at least part of the fused feature images of the multiple fused feature images of different scales to obtain pixel position information of each feature code image in the at least part of the fused feature image and identification information corresponding to each feature code image; the identification information is obtained by identifying the feature code image and is used to characterize the identification information of the inverter and the identification information of the photovoltaic module electrically connected to the inverter; The target layout map is generated based on each of the pixel position information and each of the identification information.

[0010] According to the image generation method of the photovoltaic system of the present application, the feature recognition of at least part of the fused feature images in the multiple fused feature images of different scales to obtain pixel position information of each feature code image in the at least part of the fused feature image includes: Calculate the rotation angle corresponding to each of the feature code images based on at least part of the positioning points corresponding to each of the feature code images in the first fused feature image in the at least part of the fused feature image and an affine transformation matrix; Based on each of the rotation angles, the pixel position information of each of the feature code images in the first fused feature image is updated to obtain new pixel position information.

[0011] According to the photovoltaic system image generation method of the present application, after generating the target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales, the method further includes: Respectively obtaining electrical information collected by each of the inverters; According to the pixel position information corresponding to each of the feature code images in the target layout diagram, each of the electrical information is added to the target layout diagram.

[0012] According to the photovoltaic system image generation method of the present application, the generating of the target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales further includes: Inputting at least part of the fused feature images of the plurality of fused feature images of different scales into a target prediction model to obtain the target layout map output by the target prediction model; The target prediction model is trained based on at least one of a classification loss function, a confidence loss function and an intersection-over-union loss function.

[0013] In a second aspect, the present application provides an image generation device for a photovoltaic system, wherein the photovoltaic system includes a plurality of inverters, each of which is electrically connected to at least one photovoltaic component; the device includes: A first processing module is used to obtain a first image corresponding to the photovoltaic system; the first image includes a feature code image corresponding to each of the inverters, and the pixel relative position information of each of the feature code images in the first image matches the actual relative position information of the multiple inverters; a second processing module, configured to input the first image into a target fusion model, and perform multi-layer feature extraction and feature fusion processing on the first image by the target fusion model to obtain a plurality of fused feature images of different scales; The third processing module is used to generate a target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales.

[0014] According to the image generation device of the photovoltaic system of the present application, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection status of the inverter and the photovoltaic module through the feature code image through the target fusion model, so as to obtain multiple fused feature images of different scales, obtain the rich features and details included in the first image, and then automatically generate the target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0015] In a third aspect, the present application provides a photovoltaic system, comprising: Multiple photovoltaic panels; A plurality of inverters, each of the inverters being electrically connected to at least one of the photovoltaic modules; The photovoltaic system generates a layout diagram based on the photovoltaic system image generation method according to the first aspect.

[0016] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the image generation method for the photovoltaic system as described in the first aspect above is implemented.

[0017] In a fifth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for generating an image of a photovoltaic system as described in the first aspect above is implemented.

[0018] In a sixth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating an image of a photovoltaic system as described in the first aspect above.

[0019] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects: Through the target fusion model, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection status of the inverter and the photovoltaic module through the feature code image, so as to obtain multiple fused feature images of different scales, and obtain the rich features and details included in the first image, so as to automatically generate the target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0020] Furthermore, the target feature image and the adjacent feature image are processed through the model parameters and the first constant corresponding to the target feature processing module, and the target feature image and the adjacent feature image are effectively fused to obtain a target fused image, thereby improving the detection accuracy and positioning accuracy of the feature code image in the target fused image, reducing the information loss of the obtained target fused image, and enhancing the robustness and adaptability of the target fused image.

[0021] Furthermore, by performing feature recognition on at least part of the fused feature image, pixel position information and identification information corresponding to the feature code image are obtained, the photovoltaic components connected to the inverter are effectively determined based on the identification information, and the photovoltaic components and the inverter are automatically paired. There is no need for users to manually scan feature codes one by one for pairing, thereby reducing manual operations, improving the efficiency of generating a target layout diagram, and shortening the delivery time of the target layout diagram.

[0022] Furthermore, by respectively determining a plurality of positioning points in each feature code image in the first fused image, the rotation angle of the feature code image is accurately calculated based on the positioning points and the affine transformation matrix, thereby updating the pixel position information, making the new pixel position information more accurate, thereby improving the positioning accuracy of the feature code image, making the subsequently generated target layout diagram more in line with the actual setting situation, and improving the accuracy of the generated target layout diagram.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 It is one of the flow diagrams of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Figure 2 It is one of the principle schematic diagrams of the image generation method of the photovoltaic system provided in the embodiment of the present application; Figure 3 This is the second principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Figure 4 This is the third principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Figure 5 This is the fourth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Figure 6 This is the fifth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Figure 7This is the sixth principle schematic diagram of the image generation method of the photovoltaic system provided in the embodiment of the present application; Figure 8 This is the second flow chart of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig. 9 This is a third flow chart of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.10 This is the seventh principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.11 This is the eighth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.12 This is a fourth flow chart of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.13 This is the ninth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.14 This is the tenth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.15 This is the eleventh principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.16 This is the twelfth principle schematic diagram of the method for generating an image of a photovoltaic system provided in an embodiment of the present application; Fig.17 is a schematic diagram of the structure of an image generating device for a photovoltaic system provided in an embodiment of the present application; Fig.18 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] Reference numerals: The first input port 901 corresponding to the first submodule; the first sub-input port 9021 corresponding to the second submodule; The second sub-input port 9022 corresponding to the second sub-module; the third input port 903 corresponding to the third sub-module; The fourth input port 904 corresponding to the fourth submodule; the first sub-input port 9051 corresponding to the fifth submodule; The fifth submodule corresponds to the second sub-input port 9052; the sixth submodule corresponds to the first sub-input port 9061; The second sub-input port 9062 corresponding to the sixth sub-module; the seventh input port 907 corresponding to the seventh sub-module; The first sub-input port 9081 corresponding to the eighth sub-module; the second sub-input port 9082 corresponding to the eighth sub-module; The first sub-input port 9091 corresponding to the ninth sub-module; the second sub-input port 9092 corresponding to the ninth sub-module; The tenth input port 910 corresponding to the tenth submodule; the eleventh input port 911 corresponding to the eleventh submodule; The first sub-input port 9121 corresponding to the twelfth sub-module; The second sub-input port 9122 corresponding to the twelfth sub-module; The first sub-output port 9211 corresponding to the first sub-module; the second sub-output port 9212 corresponding to the first sub-module; The second submodule corresponds to the second output port 922; the third submodule corresponds to the first suboutput port 9231; The third submodule corresponds to the second sub-output port 9232; the fourth submodule corresponds to the first sub-output port 9241; The fourth submodule corresponds to the second sub-output port 9242; the fifth submodule corresponds to the first sub-output port 9251; The fifth submodule corresponds to the second sub-output port 9252; the sixth submodule corresponds to the first sub-output port 9261; The second sub-output port 9262 corresponding to the sixth sub-module; the first sub-output port 9271 corresponding to the seventh sub-module; The seventh submodule corresponds to the second sub-output port 9272; the eighth submodule corresponds to the first sub-output port 9281; The second sub-output port 9282 corresponding to the eighth sub-module; the first sub-output port 9291 corresponding to the ninth sub-module; The second sub-output port 9292 corresponding to the ninth sub-module; the tenth output port 930 corresponding to the tenth sub-module; The first sub-output port 9311 corresponding to the eleventh sub-module; The second sub-output port 9312 corresponding to the eleventh sub-module; A twelfth output port 932 corresponding to the twelfth submodule; First submodule 9001; second submodule 9002; third submodule 9003; fourth submodule 9004; The fifth submodule 9005; the sixth submodule 9006; the seventh submodule 9007; the eighth submodule 9008; Ninth submodule 9009; tenth submodule 9010; eleventh submodule 9011; twelfth submodule 9012. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] In the related art, there are two main ways to obtain an electronic layout diagram: First, when installing each inverter, the characteristic code label representing its serial number is attached to the corresponding position of the tabular paper layout diagram; then the user scans the code one by one to form an equipment list, uses the pre-made tabular template to generate an electronic layout sketch, and matches the PV modules and inverter serial numbers in the electronic layout diagram one by one, so that the data reported by the inverter to the background can be displayed in the correct relative position on the electronic layout diagram.

[0029] Second, for PV systems installed on complex rooftops, installers attach the feature code label of each inverter on a piece of white paper when installing it, and record as much information as possible about the relative position and orientation (horizontal or vertical) of the PV modules. They then take the paper back to the office, scan the codes one by one to form an equipment list, and manually draw a sketch of the electronic layout. Finally, they match the PV modules and inverter serial numbers in the electronic layout one by one, so that the data reported by the inverter to the background can be displayed in the correct relative position on the electronic layout.

[0030] However, it can be seen that both of the above methods require a lot of manual operations, especially in complex rooftop scenarios, which require going back and forth between the office and the installation site. The delivery and commissioning of the photovoltaic system may take several days to complete.

[0031] In conjunction with the accompanying drawings, the image generation method of the photovoltaic system, the image generation device of the photovoltaic system, the photovoltaic system, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail below through specific embodiments and their application scenarios.

[0032] The method for generating an image of a photovoltaic system may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0033] The terminal includes but is not limited to portable communication devices such as mobile phones or tablet computers. It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer.

[0034] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0035] The image generation method of the photovoltaic system provided in the embodiment of the present application can be executed by the photovoltaic system, or by an image generation device of the photovoltaic system arranged on the photovoltaic system, or by a server electrically connected (wired or wirelessly connected) to the photovoltaic system, or by a user terminal communicatively connected to the photovoltaic system, including but not limited to a mobile terminal and a non-mobile terminal.

[0036] like Figure 1 As shown, the method for generating an image of a photovoltaic system includes: step 110 , step 120 , and step 130 .

[0037] A photovoltaic system includes multiple inverters.

[0038] Each inverter is electrically connected to at least one photovoltaic module.

[0039] For example, one inverter is connected to one PV panel.

[0040] For another example, an inverter is connected to two or more photovoltaic modules, that is, the inverter is a one-to-many inverter.

[0041] Step 110, acquiring a first image corresponding to the photovoltaic system; In this step, the first image includes the feature code images corresponding to each inverter, and the pixel relative position information of each feature code image in the first image matches the actual relative position information of the plurality of inverters.

[0042] The characteristic code image is an image obtained by encoding the relevant information of the inverter into specific image symbols.

[0043] The characteristic code image may include: barcode, QR code and other forms of identification images.

[0044] The information in the feature code image may include: the serial number of the inverter and the initial password of the inverter wireless hotspot and other information.

[0045] It should be noted that, when the number of photovoltaic modules connected to the inverter is greater than one (ie, a one-to-many inverter), the characteristic code image corresponding to the inverter can be designed accordingly.

[0046] For example, a one-to-many inverter has multiple removable labels with PV channel number features. The PV channel number is used to identify the PV modules connected to the inverter.

[0047] The pixel relative position information is the relative position information between the target feature code image in the first image and other feature code images among the multiple feature code images.

[0048] The target feature code image is any feature code image among the multiple feature code images.

[0049] The other feature codes are feature code images other than the target feature code image among the multiple feature code images.

[0050] The actual relative position is the actual relative position relationship of each inverter in the actual application scenario.

[0051] In the actual implementation process, based on the actual installation position relationship between the inverter and the photovoltaic assembly, the feature code image corresponding to the inverter can be posted on the drawing to obtain the first image.

[0052] The first image may include: a tabular image and a non-tabular image.

[0053] The characteristic code images corresponding to the inverters included in the first image in the table format may be posted in a corresponding table.

[0054] The characteristic code image corresponding to each inverter included in the non-tabular image can be posted on a blank paper or a design drawing of the photovoltaic system.

[0055] The design drawings of the photovoltaic system are drawings that record the layout of each photovoltaic component in the photovoltaic system.

[0056] The layout of the PV modules recorded in the design drawings of the PV system is the same as the actual installation of the PV system.

[0057] It should be noted that the design drawings may not necessarily reflect the exact position and relative arrangement angle of the photovoltaic modules from a top-down perspective, but they can reflect the relative position of the photovoltaic modules and at least accurately locate any photovoltaic module.

[0058] The symbols representing the positions of the PV modules in the design drawings can match the characteristic code size corresponding to the inverter and can reflect the information of whether the PV modules are placed horizontally or vertically.

[0059] Positioning point symbols can also be set in the design drawings to more accurately locate the symbol position of each photovoltaic component.

[0060] For a one-to-many photovoltaic system, the labels corresponding to the inverters can be attached to the corresponding photovoltaic component symbols on the design drawings.

[0061] The environments in which photovoltaic systems are set up include: complex environments and simple environments.

[0062] like Figure 2 As shown, the roof environment where the photovoltaic system is located is relatively simple.

[0063] like Figure 3 As shown, the roof environment where the photovoltaic system is located is relatively complex.

[0064] It should be noted that the photovoltaic systems installed in complex environments are not aligned horizontally or vertically like the photovoltaic systems installed in simple environments. In addition, there are also multiple photovoltaic modules.

[0065] For Figure 2 The first image corresponding to the photovoltaic system shown can be Figure 4 As shown, that is, the first image in a tabular format, based on the actual installation location of the photovoltaic system, the characteristic code image corresponding to the inverter is posted in the corresponding table, such as, posted in the first column A row, the second column A row, the third column A row and the fourth column A row, etc.

[0066] For Figure 3 For the photovoltaic system shown in FIG. 1 , when the characteristic code image is a barcode image and the first image is a design drawing, the first image corresponding to the photovoltaic system can be as follows: Figure 5 As shown, the first image is in non-tabular format.

[0067] In the case where the characteristic code image is a barcode image and the first image is a blank drawing, the first image corresponding to the photovoltaic system can be as follows: Figure 6 As shown, the first image is in non-tabular format.

[0068] In the case where the characteristic code image is a two-dimensional code image and the first image is a design drawing, the first image corresponding to the photovoltaic system can be as follows: Figure 7 As shown, the first image is in non-tabular format.

[0069] In the actual execution process, taking the first image as a table image as an example, a blank table image can be obtained first. Based on the actual installation situation of the photovoltaic system, the user posts the feature code image corresponding to the inverter in the corresponding grid of the table to characterize the connection relationship between the photovoltaic module and the inverter, and obtains a tabular image.

[0070] Taking the first image as a non-tabular image as an example, the design drawings of the photovoltaic system can be obtained in advance. Based on the actual installation situation of the photovoltaic system, the user can post the feature code image corresponding to the inverter at the corresponding position of the design drawing to obtain a non-tabular image, that is, Figure 5 The first image is shown.

[0071] In the actual implementation process, the characteristic code corresponding to the inverter can also be posted at the corresponding position on the white paper according to the actual installation situation of the photovoltaic system to indicate the relative position relationship between the photovoltaic components, such as Figure 6 The first image is shown.

[0072] Step 120: input the first image into the target fusion model, and the target fusion model performs multi-layer feature extraction and feature fusion processing on the first image to obtain a plurality of fused feature images of different scales; In this step, the target fusion model processes the first image to obtain a model with more comprehensive and richer features of the first image.

[0073] The target fusion model can be a neural network model or a deep learning model.

[0074] The target fusion model can also be obtained by fusing the feature extraction model and the feature fusion model.

[0075] Among them, the feature extraction model can be a convolutional neural network, a recurrent neural network, and a feature extraction network formed by combining CSPDarknet53 as the basic structure.

[0076] The feature fusion model can be an additive fusion model in ResNet and FPN, a compact bilinear pooling (CBP), and a weighted bidirectional feature pyramid model (BiFPN).

[0077] The specific selection of the target fusion model can be determined based on actual conditions and is not limited in this application.

[0078] Multi-layer feature extraction is an operation of extracting low-level features and high-level features from the first image respectively.

[0079] The low-level features may include features such as edges, corner points, and textures of each feature code image in the first image.

[0080] High-level features may include information such as shape, position, overall structure, and features of the extracted feature code image.

[0081] The features extracted from each layer are different.

[0082] It should be noted that the number of layers for multi-layer feature extraction can be determined based on actual conditions and is not limited in this application.

[0083] Feature fusion is to fuse the features extracted from each layer to obtain features with more accurate details.

[0084] Feature fusion can include: additive fusion, multiplicative fusion, splicing fusion and weighted fusion.

[0085] It should be noted that by efficiently fusing feature maps from different scales, the extracted low-level features and high-level features can be effectively combined, thereby better processing feature codes at different positions and sizes in the first image.

[0086] In the actual implementation process, a suitable feature fusion method can be selected based on the actual situation of the first image, and this application does not limit it.

[0087] The fused feature image is an image obtained by fusing the features extracted from each layer.

[0088] It should be noted that the scale of the fused feature image obtained will be different if the level of feature fusion is different.

[0089] Taking the target fusion model for three-layer feature extraction and feature fusion processing as an example, the scale of the fused feature image obtained in the first layer may be 320×320; the scale of the fused feature image obtained in the second layer may be 160×160, and the scale of the fused feature image obtained in the third layer may be 80×80.

[0090] The specific size of the feature scale obtained by feature extraction at each layer can be customized by the user or determined based on actual conditions, and this application does not limit it.

[0091] In the actual implementation process, after obtaining the first image, the first image can be input into the target fusion model, and the target fusion model is used to perform multi-layer feature extraction and feature fusion on the first image to obtain a fused feature image with richer semantics and more details.

[0092] Step 130 : Generate a target layout map based on at least part of the fused feature images in the plurality of fused feature images of different scales.

[0093] In this step, at least part of the fused feature images is one or more fused feature images among a plurality of fused feature images of different scales.

[0094] The specific number of at least partially fused feature images can be determined based on actual conditions. For example, at least partially fused feature images can be 2 or 4, etc., which is not limited in this application.

[0095] The target layout diagram is an electronic layout diagram that records the installation conditions of each photovoltaic module in the photovoltaic system.

[0096] In the actual implementation process, after obtaining multiple fused feature images of different scales, at least part of the fused feature images can be screened from the multiple fused feature images of different scales based on the clarity of each fused feature image and the features included in each fused feature image, and feature recognition can be performed on at least part of the fused images, such as identifying feature code images, texts, feature code sizes and other information in at least part of the fused images, and based on the recognition results, a drawing tool is called to automatically generate a target layout diagram.

[0097] In the actual implementation process, after obtaining the target layout diagram, the position and angle of the photovoltaic component symbols in the target layout diagram can be manually fine-tuned to make the target layout diagram closer to the actual installation situation or more in line with the user's usage preferences.

[0098] During the research and development process, the inventors discovered that in the related art, the electronic layout diagram is mainly obtained by the user manually drawing the electronic layout diagram on the power plant management software; however, the above method requires manual operation, is time-consuming, inefficient, and affects the user experience.

[0099] The present application obtains the first image corresponding to the photovoltaic system to effectively obtain the relative position information of the installation of each photovoltaic component in the photovoltaic system, and determines the connection status of each photovoltaic component and the inverter through the feature code image corresponding to the inverter in the first image; thereby, the first image is subjected to multi-layer feature extraction and feature fusion processing through the target fusion model, and on the basis of capturing the detail features of the first image at different levels, the detail features at different levels are fused to generate a fused feature image with more comprehensive representation capability and resistance to noise interference; based on one or more fused feature images, a target layout diagram is automatically generated without manual operation, thereby reducing the time to obtain the target layout diagram, improving the generation efficiency of the target layout diagram, and being able to ensure the accuracy of the generated target layout diagram, thereby improving the user experience.

[0100] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection status of the inverter and the photovoltaic module through the feature code image through the target fusion model, so as to obtain multiple fused feature images of different scales, and obtain the rich features and details included in the first image, so as to automatically generate the target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0101] In some embodiments, step 120 may further include: The first image is input into the target fusion model, and the target fusion model performs feature extraction and feature fusion processing of at least four modules on the first image to obtain at least four fused feature images of different scales.

[0102] In this embodiment, during the actual execution, after the first image is obtained, the first image is input into the target fusion model, and feature extraction and feature fusion processing are performed on the first image through the modules included in the target fusion model.

[0103] It should be noted that each module includes: a feature extraction model and a feature fusion model.

[0104] The input end of the feature fusion model is connected to the output end of the feature extraction module.

[0105] like Figure 8 As shown, taking the target fusion model that combines CSPDarknet53 as the basic structure to form a feature extraction network as an example, the target fusion model can perform feature extraction of four modules to obtain large-scale features, medium-scale features, small-scale features, and extremely small-scale features.

[0106] Medium-scale features can be obtained by convolving large-scale features, small-scale features can be obtained by convolving medium-scale features, and extremely small-scale features can be obtained by convolving small-scale features. After each module obtains features of the corresponding scale, each module can fuse the features of the corresponding scale to obtain a fused image of different scales.

[0107] Smaller-scale features can also be upsampled to obtain larger-scale features, for example, extremely small-scale features can be upsampled to obtain small-scale features.

[0108] Of course, in the actual implementation process, you can also Figure 8 The target fusion model shown includes four modules, and more modules are set to extract features smaller than extremely small scale features to improve the accuracy of the acquired features.

[0109] The number of modules included in the target fusion model can be determined based on actual conditions. For example, the target fusion model can include 3 modules or 5 modules, etc.; this application does not limit this.

[0110] It should be noted that, the more modules the target fusion model includes, the more obvious and clearer the features of the fusion feature image output by the target fusion model are.

[0111] After obtaining features of different scales, the features of different scales can be fused respectively by weighted fusion, multiplication fusion, splicing fusion and the like to obtain at least four fused feature images.

[0112] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, the feature extraction and feature fusion of at least four modules of the first image are performed through the target fusion model, so as to effectively improve the image quality of the fused feature image output by the target fusion model, so that the target fusion model can extract the features of the first image at different levels and fuse the features of different scales, so as to improve the adaptability of the fused feature image.

[0113] In some embodiments, the target fusion model may include: a first feature processing module, a second feature processing module, a third feature processing module, and a fourth feature processing module.

[0114] In this embodiment, the first feature processing module includes: a first submodule 9001 , a second submodule 9002 , and a third submodule 9003 .

[0115] The second feature processing module includes: a fourth submodule 9004 , a fifth submodule 9005 and a sixth submodule 9006 .

[0116] The third feature processing module includes: a seventh submodule 9007 , an eighth submodule 9008 and a ninth submodule 9009 .

[0117] The fourth feature processing module includes: a tenth submodule 9010 , an eleventh submodule 9011 and a twelfth submodule 9012 .

[0118] The first input port 901 corresponding to the first submodule is used to input the first image.

[0119] The first sub-input port 9021 corresponding to the second sub-module is connected to the first sub-output port 9211 corresponding to the first sub-module.

[0120] The third input port 903 corresponding to the third submodule is connected to the first suboutput port 9211 corresponding to the first submodule and the second output port 922 corresponding to the second submodule respectively.

[0121] The fourth input port 904 corresponding to the fourth submodule is connected to the second suboutput port 9212 corresponding to the first submodule.

[0122] The first sub-input port 9051 corresponding to the fifth sub-module is connected to the first sub-output port 9241 corresponding to the fourth sub-module.

[0123] The second sub-output port 9252 corresponding to the fifth sub-module is connected to the second sub-input port 9022 corresponding to the second sub-module.

[0124] The first sub-input port 9061 corresponding to the sixth sub-module is respectively connected to the first sub-output port 9251 corresponding to the fifth sub-module and the first sub-output port 9241 corresponding to the fourth sub-module.

[0125] The second sub-input port 9062 corresponding to the sixth sub-module is connected to the second sub-output port 9232 corresponding to the third sub-module.

[0126] The seventh input port 907 corresponding to the seventh submodule is connected to the second suboutput port 9242 corresponding to the fourth submodule.

[0127] The first sub-input port 9081 corresponding to the eighth sub-module is connected to the first sub-output port 9271 corresponding to the seventh sub-module.

[0128] The second sub-output port 9282 corresponding to the eighth sub-module is connected to the second sub-input port 9052 corresponding to the fifth sub-module.

[0129] The first sub-input port 9091 corresponding to the ninth sub-module is respectively connected to the first sub-output port 9281 corresponding to the eighth sub-module and the first sub-output port 9271 corresponding to the seventh sub-module.

[0130] The second sub-input port 9092 corresponding to the ninth sub-module is connected to the second sub-output port 9262 corresponding to the sixth sub-module.

[0131] The tenth input port 910 corresponding to the tenth submodule is connected to the second suboutput port 9272 corresponding to the seventh submodule.

[0132] The eleventh input port 911 corresponding to the eleventh submodule is connected to the tenth output port 930 corresponding to the tenth submodule.

[0133] The second sub-output port 9312 corresponding to the eleventh sub-module is connected to the second sub-input port 9082 corresponding to the eighth sub-module.

[0134] The first sub-input port 9121 corresponding to the twelfth sub-module is respectively connected to the first sub-output port 9311 corresponding to the eleventh sub-module and the tenth output port 930 corresponding to the tenth sub-module.

[0135] The second sub-input port 9122 corresponding to the twelfth sub-module is connected to the second sub-output port 9292 corresponding to the ninth sub-module.

[0136] The first sub-output port 9231 corresponding to the third sub-module, the first sub-output port 9261 corresponding to the sixth sub-module, the first sub-output port 9291 corresponding to the ninth sub-module and the twelfth output port 932 corresponding to the twelfth sub-module are respectively used to output fused feature images of different scales.

[0137] like Figure 8As shown, the fused feature images of different scales outputted by the first sub-output port 9231 corresponding to the third sub-module, the first sub-output port 9261 corresponding to the sixth sub-module, the first sub-output port 9291 corresponding to the ninth sub-module, and the twelfth output port 932 corresponding to the twelfth sub-module can be input into the prediction module.

[0138] The prediction module generates a target layout map based on multiple fused feature images of different scales.

[0139] In some embodiments, the first feature processing module, the second feature processing module, the third feature processing module and the fourth feature processing module may further include more sub-modules.

[0140] For example, the first feature processing module, the second feature processing module, the third feature processing module and the fourth feature processing module are each provided with four sub-modules or five sub-modules, and the connection method between the modules is the same as the connection method of providing three sub-modules.

[0141] That is, the sub-modules in each feature processing module are connected in sequence, and the subsequent sub-modules can receive the outputs of the previous sub-modules. The sub-modules in the target feature processing modules in the first feature processing module, the second feature processing module, the third feature processing module and the fourth feature processing module are respectively connected to the sub-modules in the upper-layer feature processing module and the lower-layer feature processing module. To avoid repetition, they are not described here.

[0142] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, by setting the target fusion model to a model including four modules, feature extraction and feature fusion are performed on the first image through multiple modules to obtain multiple fused feature images of different scales, thereby improving the accuracy of the obtained fused feature image, reducing the loss of the fused feature image, and enhancing the adaptability of the fused feature image.

[0143] In some embodiments, feature fusion may be performed based on the following steps: The target feature image and the adjacent feature images input to the target feature processing module are processed by the model parameters and the first constant corresponding to the target feature processing module in the target fusion model to obtain a target fusion image; In this embodiment, the target feature processing module is any one of the multiple modules included in the target fusion model.

[0144] The model parameters are obtained by training based on multiple sample feature code images.

[0145] The model parameters may be learnable parameters corresponding to the target feature processing module.

[0146] The first constant is a preset value.

[0147] The specific value of the first constant can be determined based on actual conditions and is not limited in this application.

[0148] The target feature image is output by the target feature processing module.

[0149] The adjacent feature image is output by the module adjacent to the target feature processing module.

[0150] The target feature image is a feature image extracted from the first image by a feature extraction model corresponding to the target feature processing module.

[0151] The adjacent feature image is a feature extraction model corresponding to a module adjacent to the target feature processing module, and is a feature image extracted from the first image.

[0152] The adjacent feature image can be a feature image extracted from the first image by a feature extraction model corresponding to the previous module of the target feature processing module; or it can be a feature image extracted from the first image by a feature extraction model corresponding to the next module of the target feature processing module.

[0153] like Fig. 9 As shown in the figure, taking the target fusion model as an example of feature fusion through the weighted bidirectional feature pyramid (BiFPN) structure, the medium-scale feature P2_1 can be obtained based on the following formula:

[0154] The medium-scale feature P2_2 can be obtained based on the following formula:

[0155] Among them, Pi_j is the target fusion image; is the target feature image; Pi+1_j and Pi-1_j are adjacent feature images; Wi is the model parameter corresponding to the target feature processing module, and ϵ is the first constant, which is used to ensure that the denominator is not a minimum value of zero.

[0156] It should be noted that the fused feature images of other modules can be obtained by the same calculation method as P2_1 and P2_2. To avoid repetition, they are not described here.

[0157] BiFPN uses a learnable weighted fusion mechanism to integrate features from different scales; this means that BiFPN can more accurately select which features need to be retained and which features should be suppressed, especially when the signature image is small in size or of low quality; thereby improving the positioning accuracy of small targets (such as the signature image in the first image).

[0158] In addition, BiFPN uses bidirectional feature propagation (i.e., top-down and bottom-up), which effectively reduces the loss of information included in the first image during the transmission process between feature layers (modules). For example, in the process of signature image detection, it is particularly important to reduce the loss of signature images, because signature images often contain small high-frequency information (such as thin edges), and BiFPN can better retain these details and reduce the key features lost due to the downsampling process.

[0159] When the collected signature image is affected by factors such as viewing angle, illumination changes and blur, BiFPN can enhance the model's adaptability to these changes by fusing multi-scale features; that is, no matter how the angle of the signature image changes or how the illumination of the image changes, BiFPN can extract enough useful information, improve the robustness of the extracted signature image features, and avoid feature extraction failure in complex environments.

[0160] BiFPN uses an efficient feature fusion method and an adaptive weighting mechanism to alleviate the redundant information problem that may be caused by simple additive fusion in traditional FPN. The weighting method of BiFPN allows the key information in the signature image to receive more attention, thereby improving the detection effect.

[0161] The feature fusion method of BiFPN is highly adaptable and can automatically adjust the feature fusion strategy according to the size, position and background complexity of the feature code image in the first image; thus ensuring that no matter how the size and clarity of the feature code image changes, the model can be flexibly adjusted to obtain the best target fusion image.

[0162] BiFPN can more accurately fuse features from different layers, so that the model can not only detect the existence of feature code images, but also improve positioning accuracy. Especially for the detection of feature code images, accurate positioning is very important, so that the position and direction of the feature code image can be accurately framed and decoded in the future to obtain the information included in the feature code image.

[0163] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, the target feature image and the adjacent feature image are processed by the model parameters and the first constant corresponding to the target feature processing module, and the target feature image and the adjacent feature image are effectively fused to obtain a target fused image, thereby improving the detection accuracy and positioning accuracy of the feature code image in the target fused image, reducing the information loss of the obtained target fused image, and enhancing the robustness and adaptability of the target fused image.

[0164] In some embodiments, step 130 may further include: Performing feature recognition on at least part of the fused feature images of a plurality of fused feature images of different scales to obtain pixel position information of each feature code image in at least part of the fused feature image and identification information corresponding to each feature code image; Based on the pixel position information and the identification information, a target layout map is generated.

[0165] In this embodiment, the pixel position information is the pixel coordinate information of the feature code image in the fused feature image.

[0166] The pixel position information may include coordinate information of each vertex of the feature code image, one vertex of the feature code image, and information such as the width and height of the feature code image.

[0167] Of course, the pixel position information may also be other information that can locate the feature code image from the fused feature image, and this application does not limit this.

[0168] The identification information is obtained by identifying the characteristic code image and is used to characterize the identification information of the inverter and the identification information of the photovoltaic module electrically connected to the inverter.

[0169] The identification information of the inverter may be information such as the serial number of the inverter and the serial number of the inverter.

[0170] In the actual implementation process, multiple identification information can be obtained by respectively decoding each feature code image in at least a part of the fused feature image.

[0171] In the actual implementation process, a fused feature image with higher definition may be selected from at least part of the fused feature images as an image for decoding the feature code image.

[0172] After the fused feature image for decoding is determined, the fused feature image may be cropped to separate the feature code images so as to obtain identification information more accurately.

[0173] The inverter is automatically paired with the PV panels it is connected to through the identified identification information.

[0174] After obtaining the position information and identification information of each pixel, the drawing tool is called to automatically generate the target layout diagram.

[0175] For Figure 2 The photovoltaic system shown can generate Fig.10 The target layout diagram is shown.

[0176] For Figure 3 The photovoltaic system shown can generate Fig.11 The target layout diagram is shown.

[0177] like Fig.12 As shown, in the actual execution process, after obtaining at least part of the fused feature image, at least part of the fused feature image is input into the prediction module, and based on the pixel position information corresponding to the feature code image, at least part of the fused feature image is cropped to obtain the original feature code area; the original feature code area is scaled to different degrees, such as reducing it by 0.5 times or enlarging it by 2 times; after obtaining the 0.5 times feature code area, the 0.25 times feature code area and the 2 times feature code area, the feature code area is decoded by the pyzbar library to obtain the identification information.

[0178] In the case of decoding failure, the original feature code region and the scaled feature code region may be binarized, sharpened, and subjected to Gaussian noise processing, and the processed feature code region may be decoded.

[0179] It should be noted that binarization is to process the feature code area into an image of black and white pixels, which is conducive to decoding. Sharpening and Gaussian noise processing are used to blur the feature code area and also to facilitate decoding.

[0180] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, by performing feature recognition on at least a portion of the fused feature image, pixel position information and identification information corresponding to the feature code image are obtained, the photovoltaic components connected to the inverter are effectively determined based on the identification information, and the photovoltaic components and the inverter are automatically paired. There is no need for the user to manually scan the feature codes one by one for pairing, thereby reducing manual operations, improving the efficiency of generating the target layout diagram, and shortening the delivery time of the target layout diagram.

[0181] In some embodiments, performing feature recognition on at least part of the fused feature images of a plurality of fused feature images of different scales to obtain pixel position information of each feature code image in at least part of the fused feature image may further include: Calculate the rotation angle corresponding to each feature code image based on at least part of the positioning points corresponding to each feature code image in the first fused feature image in at least part of the fused feature image and the affine transformation matrix; Based on each rotation angle, the pixel position information of each feature code image in the first fused feature image is updated to obtain new pixel position information.

[0182] In this embodiment, the first fused feature image is an image with higher image quality among at least some of the fused feature images.

[0183] In the actual implementation process, a clarity threshold may be set, and the clarity of each fused feature image may be compared with the clarity threshold to screen out the first fused feature image that meets the clarity threshold.

[0184] The clarity of multiple fused feature images of different scales may also be sorted, and the fused feature image with the greatest clarity may be selected as the first fused feature image.

[0185] Of course, in the actual implementation process, the first fused feature image may be determined from a plurality of fused feature images of different scales in any other feasible manner.

[0186] At least some of the positioning points are multiple vertices of the feature code image in the first fused image.

[0187] The affine transformation matrix is ​​a matrix that combines rotation, scaling, and translation to achieve coordinate transformation.

[0188] The rotation angle is the rotation angle of the feature code image in the horizontal direction or the vertical direction.

[0189] The new pixel position information includes rotation angle information and is used to locate the pixel position information of the feature code image in the first fused feature image.

[0190] It should be noted that, unlike tabular images, feature code images in non-tabular images may have various other angles in addition to horizontal and vertical, and the rotation angle of the feature code images needs to be calculated.

[0191] For Fig.13 The characteristic code image shown can be selected as follows Fig.14 At least some of the positioning points A, B and C shown are used to calculate the rotation angle corresponding to the feature code image through the affine transformation matrix.

[0192] Continue with Fig.14 Taking at least some of the positioning points shown as an example, the angles of the triangle determined by the positioning points A, B and C can be obtained through vectors. Considering the gradient distortion caused by the shooting angle of the actual scene, the triangle composed of at least some of the positioning points is not a regular right triangle, but the angle size still satisfies that the angle corresponding to the maximum angle is a right angle of the feature code image, and the maximum angle is the angle A corresponding to the positioning point A.

[0193] For the two positioning points other than the maximum angle, namely, positioning point B and positioning point C, according to the center point of the three positioning points, it can be deduced that the first positioning point in the counterclockwise direction is C and the second positioning point in the counterclockwise direction is B.

[0194] The center point is the angle with the anchor point A being 0.

[0195] In the actual implementation process, the angles of the triangle can be calculated by the following formulas:

[0196]

[0197]

[0198] in, is a directed line segment from anchor point A to anchor point B; is a directed line segment from the anchor point A to the anchor point C; is a directed line segment from anchor point B to anchor point A; is a directed line segment from anchor point B to anchor point C; is a directed line segment from the anchor point C to the anchor point B; is a directed line segment from the anchor point C to the anchor point A; is angle A; is angle B; It is angle C.

[0199] After the three vertices are determined, the rotation angle of the feature code image is calculated through the affine transformation matrix.

[0200] Select any one of at least some of the positioning points as the coordinate origin, for example, select the positioning point C, and then use the numerical direction as the y-axis and the horizontal direction as the x-axis.

[0201] In actual implementation, the affine transformation matrix can be determined based on the following formula:

[0202] in, is the multiple of scaling the feature code image; It is the multiple of the counterclockwise rotation of the scaled feature code image around the origin; is the horizontal translation vector; is the vertical translation vector; is the horizontal direction; y is the vertical direction.

[0203] The above formula can be transformed into:

[0204] Among them, a, b, c, d, e and f are unknown quantities.

[0205] Since at least some of the positioning points of the feature code image are not collinear, and the above affine transformation matrix has 6 degrees of freedom, a set of linear equations can be obtained from the three points, and a unique solution can be found.

[0206] The linear equation can be determined based on the following formula:

[0207]

[0208]

[0209]

[0210]

[0211]

[0212] Solve to get a, b, c, d, e and f; to get Fig.14 The triangle BAC shown is rotated around the origin (C) to Fig.15 The angle required for the triangle B'A'C' shown is the rotation angle.

[0213] The rotation angle can be calculated based on the following formula:

[0214] in, is the rotation angle.

[0215] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, by respectively determining multiple positioning points in each feature code image in the first fused image, the rotation angle of the feature code image is accurately calculated based on the positioning points and the affine transformation matrix, thereby updating the pixel position information, making the new pixel position information more accurate, improving the positioning accuracy of the feature code image, making the subsequently generated target layout diagram more consistent with the actual setting situation, and improving the accuracy of the generated target layout diagram.

[0216] In some embodiments, after step 130, the method may further include: Obtain electrical information collected by each inverter respectively; According to the pixel position information corresponding to each feature code image in the target layout diagram, each electrical information is added to the target layout diagram.

[0217] In this embodiment, the electrical information is data collected by the inverter related to the power generation of the photovoltaic components.

[0218] The electrical information may include electrical data for both the DC side and the AC side.

[0219] The electrical data on the DC side may include voltage, current, power and other data.

[0220] The electrical data measured by AC can include: grid voltage and grid connection point power, etc.

[0221] The specific content of the electrical information can be determined based on actual conditions and is not limited in this application.

[0222] The target layout diagram generated is as follows Fig.11 For example, based on the connection relationship between the inverter and the photovoltaic module, the electrical information collected by the inverter is added to the corresponding position in the target layout diagram.

[0223] For example, the PV modules connected to the inverter with the serial number (i.e., identification information) of 012345678932 are the data of the PV modules in the first vertical row. At this time, the electrical information collected by the inverter will be displayed at the position corresponding to the PV modules in the first vertical row.

[0224] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, by obtaining the electrical information collected by the inverter, the information collected by the inverter is displayed correspondingly at the pixel position information corresponding to the photovoltaic components connected to the inverter in the target layout diagram, so that the user can intuitively understand the power generation status of each photovoltaic component, thereby improving the user experience; it is also convenient to quickly locate the faulty photovoltaic component or inverter in the event of a photovoltaic system failure, thereby reducing the losses caused by the photovoltaic system failure.

[0225] In some embodiments, step 130 may further include: Inputting at least part of the fused feature images of the plurality of fused feature images of different scales into a target prediction model to obtain a target layout map output by the target prediction model; The target prediction model is trained based on at least one of a classification loss function, a confidence loss function, and an intersection-over-union loss function.

[0226] In this embodiment, the target prediction model is a model that predicts the fused feature image and generates a target layout map.

[0227] In such Figure 8 In the prediction module shown, the target prediction model can be encapsulated.

[0228] The classification loss function is a function that measures the classification accuracy of the recognition result of the target prediction model for the fused feature image.

[0229] The classification loss function can be a cross entropy loss function, such as focal loss function and label smoothing loss function.

[0230] The classification loss function can be obtained by fusing different cross entropy loss functions, for example, fusing the focus loss function and the label smoothing loss function to obtain the classification loss function.

[0231] In the actual implementation process, the classification loss function can be determined based on the following formula:

[0232] in, is the classification loss value; is the number of pixels of the fused feature image in the target prediction model, and C is the number of categories of the target prediction model; It is 1 if it is category c, otherwise it is 0; is the predicted probability.

[0233] The categories of the target prediction model may include: the presence of a feature code image and the absence of a feature code image.

[0234] The confidence loss function is a function that measures the difference between the confidence of the prediction result of the target prediction model output for the fused feature image and the actual label.

[0235] The confidence loss functions include: confidence loss function in the form of mean square error (MSE) and confidence loss function in the form of cross entropy.

[0236] The actual selection of the confidence loss function can be determined based on actual conditions and is not limited in this application.

[0237] In the actual implementation process, the confidence loss function can be determined based on the following formula:

[0238] in, is the confidence loss value; If the pixel area of ​​the fused feature map corresponding to the first image actually contains an object, it is 1, otherwise it is 0; is the probability of predicting the existence of a target.

[0239] The intersection-over-union loss function is a function that measures the degree of overlap between the feature code images in the fused feature image.

[0240] like Fig.16 The figure shows the definition of the loss function of the predicted position, where c is the diagonal of the bounding box between the true value box and the predicted box, and d is the distance between the center points of the true value box and the predicted box.

[0241] In the actual implementation process, the intersection-over-union loss function can be determined based on the following formula:

[0242] in, is the intersection-over-union loss value; N is the predicted N boxes; is the intersection and comparison score; is the Euclidean distance of the bright spot; b is the coordinate of the center point of the prediction box, is the coordinate of the center point of the real frame; is the width of the outer frame, The height of the bounding box.

[0243] After determining the classification loss function, the confidence loss function and the intersection-over-union loss function, one or more of the classification loss function, the confidence loss function and the intersection-over-union loss function can be fused to obtain the target loss function, and the target prediction model can be optimized through the target loss function.

[0244] The target loss function is a function obtained by integrating one or more of the classification loss function, the confidence loss function, and the intersection-over-union loss function.

[0245] For example, the target loss function may be any one of a classification loss function, a confidence loss function, and an intersection-over-union loss function.

[0246] For another example, the target loss function can also be obtained by integrating the classification loss function and the confidence loss function.

[0247] In the actual implementation process, the target loss function can also be determined based on the following formula:

[0248] in, , as well as is the weight coefficient.

[0249] In the actual implementation process, multiple samples of different scales can be collected in advance to fuse feature images, and the machine learning model or neural network model can be trained to obtain the weight coefficient with the output weight coefficient as the goal.

[0250] The specific value of the weight coefficient can also be customized by the user or determined based on actual conditions, and this application does not limit this.

[0251] According to the image generation method of the photovoltaic system provided in the embodiment of the present application, the prediction accuracy of the target prediction model is optimized and the accuracy of the target layout map output by the target prediction model is improved by training the target prediction model through one or more of the classification loss function, the confidence loss function and the intersection-over-union loss function.

[0252] The photovoltaic system image generation method provided in the embodiment of the present application can be executed by an image generation device of the photovoltaic system. In the embodiment of the present application, the photovoltaic system image generation method is executed by an image generation device of the photovoltaic system as an example to illustrate the image generation device of the photovoltaic system provided in the embodiment of the present application.

[0253] An embodiment of the present application also provides an image generating device for a photovoltaic system.

[0254] A photovoltaic system includes multiple inverters.

[0255] Each inverter is electrically connected to at least one photovoltaic module.

[0256] like Fig.17 The image generating device of the photovoltaic system includes: a first processing module 1710 , a second processing module 1720 and a third processing module 1730 .

[0257] A first processing module 1710 is used to obtain a first image corresponding to the photovoltaic system; the first image includes a feature code image corresponding to each inverter, and the pixel relative position information of each feature code image in the first image matches the actual relative position information of the plurality of inverters; The second processing module 1720 is used to input the first image into the target fusion model, and the target fusion model performs multi-layer feature extraction and feature fusion processing on the first image to obtain a plurality of fused feature images of different scales; The third processing module 1730 is used to generate a target layout image based on at least part of the fused feature images of the multiple fused feature images of different scales.

[0258] According to the image generation device of the photovoltaic system provided in the embodiment of the present application, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection status of the inverter and the photovoltaic module through the feature code image through the target fusion model, so as to obtain multiple fused feature images of different scales, and obtain the rich features and details included in the first image, so as to automatically generate the target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0259] In some embodiments, the second processing module 1720 may also be used to: The first image is input into the target fusion model, and the target fusion model performs feature extraction and feature fusion processing of at least four modules on the first image to obtain at least four fused feature images of different scales.

[0260] In some embodiments, the second processing module 1720 may also be used to: The target feature image and the adjacent feature image input to the target feature processing module are processed by the model parameters and the first constant corresponding to the target feature processing module in the target fusion model to obtain a target fusion image; the target feature image is output by the target feature processing module, and the adjacent feature image is output by the module adjacent to the target feature processing module; the model parameters are obtained by training based on multiple sample feature code images.

[0261] In some embodiments, the third processing module 1730 may also be used to: Performing feature recognition on at least part of the fused feature images of multiple different scales to obtain pixel position information of each feature code image in at least part of the fused feature image and identification information corresponding to each feature code image; the identification information is obtained by identifying the feature code image and is used to characterize the identification information of the inverter and the identification information of the photovoltaic module electrically connected to the inverter; Based on the pixel position information and the identification information, a target layout map is generated.

[0262] In some embodiments, the third processing module 1730 may also be used to: Calculate the rotation angle corresponding to each feature code image based on at least part of the positioning points corresponding to each feature code image in the first fused feature image in at least part of the fused feature image and the affine transformation matrix; Based on each rotation angle, the pixel position information of each feature code image in the first fused feature image is updated to obtain new pixel position information.

[0263] In some embodiments, the apparatus may further include a fourth processing module, configured to: Obtain electrical information collected by each inverter respectively; According to the pixel position information corresponding to each feature code image in the target layout diagram, each electrical information is added to the target layout diagram.

[0264] In some embodiments, the third processing module 1730 may also be used to: Inputting at least part of the fused feature images of the plurality of fused feature images of different scales into a target prediction model to obtain a target layout map output by the target prediction model; The target prediction model is trained based on at least one of a classification loss function, a confidence loss function, and an intersection-over-union loss function.

[0265] The image generation device of the photovoltaic system in the embodiment of the present application can be a photovoltaic system, or can be an electronic device connected to the photovoltaic system for communication, or can be a component in the photovoltaic system or the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or can be other devices other than the terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a car electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc. It can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0266] The image generation device of the photovoltaic system in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0267] The image generation device of the photovoltaic system provided in the embodiment of the present application can achieve Figures 1 to 16 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0268] The embodiment of the present application also provides a photovoltaic system.

[0269] In some embodiments, the photovoltaic system may include: a plurality of photovoltaic modules and a plurality of inverters.

[0270] In this embodiment, each inverter is electrically connected to at least one photovoltaic module.

[0271] For example, one inverter is connected to one PV panel.

[0272] For another example, an inverter is connected to two or more photovoltaic modules, that is, the inverter is a one-to-many inverter.

[0273] The photovoltaic system generates a layout diagram based on the photovoltaic system image generation method described in any one of the above embodiments.

[0274] According to the photovoltaic system provided in the embodiment of the present application, multi-layer feature extraction and feature fusion are performed on the first image that intuitively reflects the actual setting of the photovoltaic system and characterizes the connection status of the inverter and the photovoltaic module through the feature code image through the target fusion model, so as to obtain multiple fused feature images of different scales, and obtain the rich features and details included in the first image, so as to automatically generate a target layout diagram based on the fused feature image without manual operation, thereby reducing the time to obtain the target layout diagram, improving the efficiency of generating the target layout diagram, reducing the delivery time, and improving the user experience.

[0275] In some embodiments, Fig.18 As shown, an embodiment of the present application further provides an electronic device 1800, including a processor 1801, a memory 1802, and a computer program stored in the memory 1802 and executable on the processor 1801. When the program is executed by the processor 1801, each process of the above-mentioned photovoltaic system image generation method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0276] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0277] The embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned photovoltaic system image generation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0278] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0279] An embodiment of the present application further provides a computer program product, including a computer program, which implements the above-mentioned method for generating an image of a photovoltaic system when executed by a processor.

[0280] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0281] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned photovoltaic system image generation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0282] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0283] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0284] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0285] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0286] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0287] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for generating an image of a photovoltaic system, characterized in that: The photovoltaic system comprises a plurality of inverters, each of which is electrically connected to at least one photovoltaic component; the method comprises: Acquire a first image corresponding to the photovoltaic system; the first image includes a feature code image corresponding to each of the inverters, and the pixel relative position information of each of the feature code images in the first image matches the actual relative position information of the multiple inverters; Inputting the first image into a target fusion model, and performing multi-layer feature extraction and feature fusion processing on the first image by the target fusion model to obtain a plurality of fused feature images of different scales; A target layout map is generated based on at least part of the fused feature images of the multiple fused feature images of different scales.

2. The method for generating an image of a photovoltaic system according to claim 1, characterized in that: The first image is input into a target fusion model, and the target fusion model performs multi-layer feature extraction and feature fusion processing on the first image to obtain a plurality of fused feature images of different scales, including: The first image is input into the target fusion model, and the target fusion model performs feature extraction and feature fusion processing of at least four modules on the first image to obtain at least four fused feature images of different scales.

3. The method for generating an image of a photovoltaic system according to claim 2, characterized in that: The target fusion model includes: a first feature processing module, a second feature processing module, a third feature processing module and a fourth feature processing module; The first feature processing module includes: a first submodule, a second submodule and a third submodule; the second feature processing module includes: a fourth submodule, a fifth submodule and a sixth submodule; the third feature processing module includes: a seventh submodule, an eighth submodule and a ninth submodule; the fourth feature processing module includes: a tenth submodule, an eleventh submodule and a twelfth submodule; The first input port corresponding to the first submodule is used to input the first image; The first sub-input port corresponding to the second sub-module is connected to the first sub-output port corresponding to the first sub-module; The third input port corresponding to the third submodule is respectively connected to the first suboutput port corresponding to the first submodule and the second output port corresponding to the second submodule; The fourth input port corresponding to the fourth submodule is connected to the second suboutput port corresponding to the first submodule; The first sub-input port corresponding to the fifth sub-module is connected to the first sub-output port corresponding to the fourth sub-module, and the second sub-output port corresponding to the fifth sub-module is connected to the second sub-input port corresponding to the second sub-module; The first sub-input port corresponding to the sixth sub-module is connected to the first sub-output port corresponding to the fifth sub-module and the first sub-output port corresponding to the fourth sub-module respectively; the second sub-input port corresponding to the sixth sub-module is connected to the second sub-output port corresponding to the third sub-module; The seventh input port corresponding to the seventh submodule is connected to the second suboutput port corresponding to the fourth submodule; The first sub-input port corresponding to the eighth sub-module is connected to the first sub-output port corresponding to the seventh sub-module, and the second sub-output port corresponding to the eighth sub-module is connected to the second sub-input port corresponding to the fifth sub-module; The first sub-input port corresponding to the ninth sub-module is respectively connected to the first sub-output port corresponding to the eighth sub-module and the first sub-output port corresponding to the seventh sub-module, and the second sub-input port corresponding to the ninth sub-module is connected to the second sub-output port corresponding to the sixth sub-module; The tenth input port corresponding to the tenth submodule is connected to the second suboutput port corresponding to the seventh submodule; The eleventh input port corresponding to the eleventh submodule is connected to the tenth output port corresponding to the tenth submodule, and the second sub-output port corresponding to the eleventh submodule is connected to the second sub-input port corresponding to the eighth submodule; The first sub-input port corresponding to the twelfth sub-module is respectively connected to the first sub-output port corresponding to the eleventh sub-module and the tenth output port corresponding to the tenth sub-module, and the second sub-input port corresponding to the twelfth sub-module is connected to the second sub-output port corresponding to the ninth sub-module; The first sub-output port corresponding to the third sub-module, the first sub-output port corresponding to the sixth sub-module, the first sub-output port corresponding to the ninth sub-module, and the twelfth output port corresponding to the twelfth sub-module are respectively used to output the fused feature images of different scales.

4. The method for generating an image of a photovoltaic system according to claim 3, characterized in that: The feature fusion is performed based on the following steps: The target feature image and the adjacent feature image input to the target feature processing module are processed by the model parameters and the first constant corresponding to the target feature processing module in the target fusion model to obtain a target fusion image; the target feature image is output by the target feature processing module, and the adjacent feature image is output by a module adjacent to the target feature processing module; the model parameters are obtained by training based on multiple sample feature code images.

5. The method for generating an image of a photovoltaic system according to any one of claims 1 to 4, characterized in that: The generating a target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales comprises: Performing feature recognition on at least part of the fused feature images of the multiple fused feature images of different scales to obtain pixel position information of each feature code image in the at least part of the fused feature image and identification information corresponding to each feature code image; the identification information is obtained by identifying the feature code image and is used to characterize the identification information of the inverter and the identification information of the photovoltaic module electrically connected to the inverter; The target layout map is generated based on each of the pixel position information and each of the identification information.

6. The method for generating an image of a photovoltaic system according to claim 5, characterized in that: The performing feature recognition on at least part of the fused feature images in the plurality of fused feature images of different scales to obtain pixel position information of each feature code image in the at least part of the fused feature image includes: Calculate the rotation angle corresponding to each of the feature code images based on at least part of the positioning points corresponding to each of the feature code images in the first fused feature image in the at least part of the fused feature image and an affine transformation matrix; Based on each of the rotation angles, the pixel position information of each of the feature code images in the first fused feature image is updated to obtain new pixel position information.

7. The method for generating an image of a photovoltaic system according to any one of claims 1 to 4, characterized in that: After generating the target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales, the method further includes: Respectively obtaining electrical information collected by each of the inverters; According to the pixel position information corresponding to each of the feature code images in the target layout diagram, each of the electrical information is added to the target layout diagram.

8. The method for generating an image of a photovoltaic system according to any one of claims 1 to 4, characterized in that: The generating of the target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales further includes: Inputting at least part of the fused feature images of the plurality of fused feature images of different scales into a target prediction model to obtain the target layout map output by the target prediction model; The target prediction model is trained based on at least one of a classification loss function, a confidence loss function and an intersection-over-union loss function.

9. An image generation device for a photovoltaic system, characterized in that: The photovoltaic system includes a plurality of inverters, each of which is electrically connected to at least one photovoltaic component; the device includes: A first processing module is used to obtain a first image corresponding to the photovoltaic system; the first image includes a feature code image corresponding to each of the inverters, and the pixel relative position information of each of the feature code images in the first image matches the actual relative position information of the multiple inverters; a second processing module, configured to input the first image into a target fusion model, and perform multi-layer feature extraction and feature fusion processing on the first image by the target fusion model to obtain a plurality of fused feature images of different scales; The third processing module is used to generate a target layout map based on at least part of the fused feature images of the multiple fused feature images of different scales.

10. A photovoltaic system, characterized in that: include: Multiple photovoltaic panels; A plurality of inverters, each of the inverters being electrically connected to at least one of the photovoltaic modules; The photovoltaic system generates a layout diagram based on the photovoltaic system image generation method according to any one of claims 1-8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the image generation method of the photovoltaic system according to any one of claims 1 to 8 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating an image of a photovoltaic system according to any one of claims 1 to 8 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for generating an image of a photovoltaic system according to any one of claims 1 to 8 is implemented.

Images