Fault detection method and device for photovoltaic module and computer program product

Abstract

The invention discloses a fault detection method and device of a photovoltaic module and a computer program product, and relates to the technical field of image processing, and the fault detection method of the photovoltaic module comprises the steps: obtaining a thermal infrared image of the photovoltaic module and a visible light image of the photovoltaic module; performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image; and inputting the fused feature image into a fault detection model to obtain a fault detection result output after the fault detection model performs fault detection on the photovoltaic module based on the image features of the fused feature image. By adopting the technical scheme, the problem of how to improve the fault detection efficiency of the photovoltaic module is solved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a photovoltaic module fault detection method, device and computer program product. Background Art

[0002] In the field of photovoltaic energy, fault detection is a key link in the maintenance of photovoltaic systems. It can promptly detect and repair problems in photovoltaic modules. In traditional methods, fault detection of photovoltaic modules mainly relies on manual inspections. With the development of technology, fault detection methods based on image processing have gradually become a research hotspot. However, extracting fault features from complex image data is a major challenge. Existing methods have poor accuracy in extracting fault features, which leads to low fault detection efficiency.

[0003] Therefore, in the related art, there is a problem of how to improve the fault detection efficiency of photovoltaic modules.

[0004] Regarding the problem of how to improve the fault detection efficiency of photovoltaic modules in related technologies, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a photovoltaic module fault detection method, device and computer program product to at least solve the problem of how to improve the photovoltaic module fault detection efficiency in the related art.

[0007] According to one aspect of an embodiment of the present application, a method for fault detection of a photovoltaic component is provided, comprising: acquiring a thermal infrared image of a photovoltaic component and a visible light image of the photovoltaic component; performing image feature fusion processing on temperature features of the thermal infrared image and texture features of the visible light image to obtain a fused feature image; inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0008] In an exemplary embodiment, obtaining a thermal infrared image of a photovoltaic component and a visible light image of the photovoltaic component includes: setting an acquisition parameter of a thermal infrared image acquisition device to a first acquisition parameter, wherein the first acquisition parameter is determined according to ambient temperature; performing image acquisition on the photovoltaic component by the thermal infrared image acquisition device to obtain the thermal infrared image; setting an acquisition parameter of a visible light image acquisition device to a second acquisition parameter, wherein the second acquisition parameter is determined according to ambient brightness; performing image acquisition on the photovoltaic component by the visible light image acquisition device to obtain the visible light image.

[0009] In an exemplary embodiment, before performing image feature fusion processing on the temperature features of the thermal infrared image and the texture features of the visible light image to obtain a fused feature image, the method further includes: identifying the thermal infrared image to obtain infrared radiation energy intensities of different regions in the thermal infrared image, and determining the temperature features based on the infrared radiation energy intensities of the different regions; identifying the visible light image to obtain color and brightness variation patterns between different regions in the visible light image, and determining the texture features based on the color and brightness variation patterns between the different regions.

[0010] In an exemplary embodiment, image feature fusion processing is performed on the temperature features of the thermal infrared image and the texture features of the visible light image to obtain a fused feature image, including: obtaining a first string segmentation map and a second string segmentation map, wherein the first string segmentation map is obtained by performing string segmentation on the thermal infrared image, and the second string segmentation map is obtained by performing string segmentation on the visible light image; obtaining a thermal infrared string vector corresponding to the first string segmentation map, and obtaining a visible light string vector corresponding to the second string segmentation map, wherein the thermal infrared string vector represents the temperature feature corresponding to the first string segmentation map, and the visible light string vector represents the texture feature corresponding to the second string feature map; performing string alignment on the thermal infrared string vector and the visible light string vector to obtain the fused feature image, and the string alignment is used to align the image positions of the thermal infrared string vector and the visible light string vector.

[0011] In an exemplary embodiment, before inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: determining a historical fault of the photovoltaic component and a historical image collected when the historical fault occurred, wherein the historical image includes a historical thermal infrared image and a historical visible light image; performing image fusion processing on the historical thermal infrared image and the historical visible light image to obtain a historical fused feature image; and training an initial machine learning model using the historical fused feature image as a training data set and the historical faults as a verification data set to obtain the fault detection model.

[0012] In an exemplary embodiment, after inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: obtaining the fault area of ​​the photovoltaic component and the fault information of the photovoltaic component from the fault detection result; and visually displaying the fault area and the fault information on a display device.

[0013] According to another aspect of an embodiment of the present application, a fault detection device for a photovoltaic component is also provided, including: an image acquisition module, used to acquire a thermal infrared image of a photovoltaic component and a visible light image of the photovoltaic component; an image processing module, used to perform image feature fusion processing on the temperature features of the thermal infrared image and the texture features of the visible light image to obtain a fused feature image; and a fault detection module, used to input the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned photovoltaic component fault detection method when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the photovoltaic component fault detection method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0017] Through this application, after obtaining the thermal infrared image and visible light image of the photovoltaic module, the temperature features of the thermal infrared image and the texture features of the visible light image can be fused, and then the obtained fused feature image can be input into the fault detection model, so that the fault detection model can perform fault detection on the photovoltaic module based on the image features of the fused feature image, and then obtain the fault detection result output by the model. This solves the problem of how to improve the fault detection efficiency of the photovoltaic module in the related art, and achieves the effect of improving the fault detection efficiency of the photovoltaic module. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of a photovoltaic module fault detection method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a photovoltaic module fault detection method according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a photovoltaic module fault detection method according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a photovoltaic module fault detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 11 is a hardware structure block diagram of a computer terminal of a photovoltaic module fault detection method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the fault detection method of the photovoltaic module in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a photovoltaic module fault detection method is provided. Figure 2 is a flow chart of a photovoltaic module fault detection method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0030] Step S202, acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module;

[0031] Step S204, performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image;

[0032] Step S206 , inputting the fused feature image into a fault detection model, and obtaining a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0033] Through the above steps, after obtaining the thermal infrared image and visible light image of the photovoltaic module, the temperature feature of the thermal infrared image and the texture feature of the visible light image can be fused, and then the obtained fused feature image is input into the fault detection model, so that the fault detection model performs fault detection on the photovoltaic module based on the image feature of the fused feature image, and then the fault detection result output by the model is obtained. This solves the problem of how to improve the fault detection efficiency of the photovoltaic module in the related art, and achieves the effect of improving the fault detection efficiency of the photovoltaic module.

[0034] In an exemplary embodiment, obtaining a thermal infrared image of a photovoltaic component and a visible light image of the photovoltaic component includes: setting an acquisition parameter of a thermal infrared image acquisition device to a first acquisition parameter, wherein the first acquisition parameter is determined according to ambient temperature; performing image acquisition on the photovoltaic component by the thermal infrared image acquisition device to obtain the thermal infrared image; setting an acquisition parameter of a visible light image acquisition device to a second acquisition parameter, wherein the second acquisition parameter is determined according to ambient brightness; performing image acquisition on the photovoltaic component by the visible light image acquisition device to obtain the visible light image.

[0035] Optionally, in the above embodiment, the quality of images captured by the image acquisition device is easily affected by environmental factors. For example, there are differences in ambient temperature and ambient brightness on cloudy days and sunny days. Therefore, different acquisition parameters can be configured for the image acquisition device in different environments to improve the quality of the captured images, which in turn helps to improve the subsequent image processing efficiency.

[0036] In an exemplary embodiment, before performing image feature fusion processing on the temperature features of the thermal infrared image and the texture features of the visible light image to obtain a fused feature image, the method further includes: identifying the thermal infrared image to obtain infrared radiation energy intensities of different regions in the thermal infrared image, and determining the temperature features based on the infrared radiation energy intensities of the different regions; identifying the visible light image to obtain color and brightness variation patterns between different regions in the visible light image, and determining the texture features based on the color and brightness variation patterns between the different regions.

[0037] Optionally, in the above embodiment, the thermal infrared image may be analyzed to identify the temperature abnormality area by applying a thermal infrared temperature feature recognition algorithm.

[0038] Optionally, in the above embodiment, a visible light camera can be used to take high-resolution images of photovoltaic modules to capture the surface texture and morphology of the photovoltaic modules, and then texture features are extracted from the image through a visible light texture extraction algorithm. The above texture features include but are not limited to cracks, stains, occlusions, etc.

[0039] In an exemplary embodiment, image feature fusion processing is performed on the temperature features of the thermal infrared image and the texture features of the visible light image to obtain a fused feature image, including: obtaining a first string segmentation map and a second string segmentation map, wherein the first string segmentation map is obtained by performing string segmentation on the thermal infrared image, and the second string segmentation map is obtained by performing string segmentation on the visible light image; obtaining a thermal infrared string vector corresponding to the first string segmentation map, and obtaining a visible light string vector corresponding to the second string segmentation map, wherein the thermal infrared string vector represents the temperature feature corresponding to the first string segmentation map, and the visible light string vector represents the texture feature corresponding to the second string feature map; performing string alignment on the thermal infrared string vector and the visible light string vector to obtain the fused feature image, and the string alignment is used to align the image positions of the thermal infrared string vector and the visible light string vector.

[0040] Optionally, in the above embodiment, the specific process of obtaining the fused feature image is as follows:

[0041] 1. Perform string segmentation on thermal infrared images and visible light images, and divide the images into independent photovoltaic string units for subsequent analysis and processing. Extract string vectors from the segmented images to form thermal infrared string vectors and visible light string vectors.

[0042] 2. Align the thermal infrared string vector with the visible light string vector to ensure that the two image data remain aligned in space. String alignment can ensure that the features of the thermal infrared image and the features of the visible light image can accurately correspond in subsequent fault analysis.

[0043] 3. Based on the extracted string vector, the thermal infrared image and the visible light image are fused to generate a fused feature image that combines temperature features and texture features. The fused feature image provides richer contextual information for fault analysis, which helps to more accurately identify and locate PV module faults.

[0044] In an exemplary embodiment, before inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: determining a historical fault of the photovoltaic component and a historical image collected when the historical fault occurred, wherein the historical image includes a historical thermal infrared image and a historical visible light image; performing image fusion processing on the historical thermal infrared image and the historical visible light image to obtain a historical fused feature image; and training an initial machine learning model using the historical fused feature image as a training data set and the historical faults as a verification data set to obtain the fault detection model.

[0045] Optionally, in the above embodiment, the above fault detection model can combine the analysis results of thermal infrared and visible light images through a thermal infrared-visible light defect fusion analysis algorithm to perform a fusion analysis of photovoltaic module defects. The temperature anomaly and texture characteristics of the photovoltaic module are comprehensively considered to identify the fault type and severity of the photovoltaic module.

[0046] In an exemplary embodiment, after inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: obtaining the fault area of ​​the photovoltaic component and the fault information of the photovoltaic component from the fault detection result; and visually displaying the fault area and the fault information on a display device.

[0047] Optionally, in the above embodiment, the fault detection result is displayed on the display device in a visual manner, which can facilitate the operator to respond quickly. The output fault area and fault information can be used to guide the maintenance work of the photovoltaic power station and improve the efficiency of fault handling.

[0048] Obviously, the above-described embodiments are only a part of the embodiments of the present application, not all of the embodiments. In order to better understand the above-mentioned method for determining the power generation area, the above-mentioned process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0049] In an optional embodiment, in combination Figure 3 The photovoltaic module fault detection method of the present application is further described. Figure 3 As shown, the thermal infrared image and the visible light image are respectively segmented into strings, and the thermal infrared temperature features and visible light texture features in the images after string segmentation are extracted. At the same time, the thermal infrared string vector and the visible light string vector are extracted from the images after string segmentation, and the thermal infrared string vector and the visible light string vector are aligned. After the string alignment, the thermal infrared temperature features and the visible light texture features are combined to perform image fusion to obtain a feature fusion image. Finally, the defects of the photovoltaic modules are identified based on the feature fusion image to obtain the fault detection result of the photovoltaic modules.

[0050] Through the above embodiments, by fusing thermal infrared images and visible light images, more comprehensive photovoltaic module status information is provided, and through string segmentation and registration technology, accurate alignment between images is achieved, providing technical support for accurate fault location. On this basis, the thermal infrared temperature features and visible light texture features in the image are used for comprehensive defect analysis, which improves the accuracy and reliability of fault detection.

[0051] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment 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 prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0052] In this embodiment, a photovoltaic module fault detection device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0053] Figure 4: is a structural block diagram of a photovoltaic module fault detection device according to an embodiment of the present application, the device comprising:

[0054] An image acquisition module 42, used to acquire a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module;

[0055] An image processing module 44 is used to perform image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image;

[0056] The fault detection module 46 is used to input the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0057] Through the above device, after obtaining the thermal infrared image and visible light image of the photovoltaic module, the temperature feature of the thermal infrared image and the texture feature of the visible light image can be fused, and then the obtained fused feature image is input into the fault detection model, so that the fault detection model performs fault detection on the photovoltaic module based on the image feature of the fused feature image, and then the fault detection result output by the model is obtained. This solves the problem of how to improve the fault detection efficiency of the photovoltaic module in the related art, and achieves the effect of improving the fault detection efficiency of the photovoltaic module.

[0058] In an exemplary embodiment, the image acquisition module 42 is also used to: set the acquisition parameter of the thermal infrared image acquisition device to a first acquisition parameter, wherein the first acquisition parameter is determined according to the ambient temperature; perform image acquisition on the photovoltaic component by the thermal infrared image acquisition device to obtain the thermal infrared image; set the acquisition parameter of the visible light image acquisition device to a second acquisition parameter, wherein the second acquisition parameter is determined according to the ambient brightness; perform image acquisition on the photovoltaic component by the visible light image acquisition device to obtain the visible light image.

[0059] In an exemplary embodiment, the image processing module 44 is also used to: identify the thermal infrared image to obtain the infrared radiation energy intensity of different areas in the thermal infrared image, and determine the temperature characteristics based on the infrared radiation energy intensity of the different areas; identify the visible light image to obtain the color and brightness change pattern between different areas in the visible light image, and determine the texture characteristics based on the color and brightness change pattern between the different areas.

[0060] In an exemplary embodiment, the image processing module 44 is also used to: obtain a first string segmentation map and a second string segmentation map, wherein the first string segmentation map is obtained by performing string segmentation on the thermal infrared image, and the second string segmentation map is obtained by performing string segmentation on the visible light image; obtain a thermal infrared string vector corresponding to the first string segmentation map, and obtain a visible light string vector corresponding to the second string segmentation map, wherein the thermal infrared string vector represents the temperature feature corresponding to the first string segmentation map, and the visible light string vector represents the texture feature corresponding to the second string feature map; perform string alignment on the thermal infrared string vector and the visible light string vector to obtain the fused feature image, and the string alignment is used to align the image positions of the thermal infrared string vector and the visible light string vector.

[0061] In an exemplary embodiment, the fault detection module 46 is further used to: determine the historical faults of the photovoltaic component and the historical images collected when the historical faults occurred, wherein the historical images include historical thermal infrared images and historical visible light images; perform image fusion processing on the historical thermal infrared images and the historical visible light images to obtain historical fusion feature images; use the historical fusion feature images as a training data set and the historical faults as a verification data set to train an initial machine learning model to obtain the fault detection model.

[0062] In an exemplary embodiment, the fault detection module 46 is further used to: obtain the fault area of ​​the photovoltaic component and the fault information of the photovoltaic component from the fault detection result; and visually display the fault area and the fault information on a display device.

[0063] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0064] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0065] S1, acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module;

[0066] S2, performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image;

[0067] S3, inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0068] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0069] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0070] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0071] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0072] S1, acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module;

[0073] S2, performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image;

[0074] S3, inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0075] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0076] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0077] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0078] S1, acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module;

[0079] S2, performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image;

[0080] S3, inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

[0081] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0082] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0083] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A photovoltaic module fault detection method, characterized in that: include: Acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module; Performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image; The fused feature image is input into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

2. The method according to claim 1, characterized in that Acquiring a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module, including: Setting an acquisition parameter of a thermal infrared image acquisition device to a first acquisition parameter, wherein the first acquisition parameter is determined according to an ambient temperature; Capturing the image of the photovoltaic assembly by the thermal infrared image acquisition device to obtain the thermal infrared image; Setting an acquisition parameter of a visible light image acquisition device to a second acquisition parameter, wherein the second acquisition parameter is determined according to ambient brightness; The visible light image is obtained by performing image acquisition on the photovoltaic assembly through the visible light image acquisition device.

3. The method according to claim 1, characterized in that Before performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image, the method further includes: Identifying the thermal infrared image to obtain infrared radiation energy intensities of different regions in the thermal infrared image, and determining the temperature characteristics based on the infrared radiation energy intensities of the different regions; The visible light image is recognized to obtain the variation pattern of color and brightness between different regions in the visible light image, and the texture feature is determined based on the variation pattern of color and brightness between different regions.

4. The method according to claim 3, characterized in that: Performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image, including: Acquire a first group string segmentation map and a second group string segmentation map, wherein the first group string segmentation map is obtained by performing group string segmentation on the thermal infrared image, and the second group string segmentation map is obtained by performing group string segmentation on the visible light image; Obtain a thermal infrared string vector corresponding to the first string segmentation map, and obtain a visible light string vector corresponding to the second string segmentation map, wherein the thermal infrared string vector represents a temperature feature corresponding to the first string segmentation map, and the visible light string vector represents a texture feature corresponding to the second string feature map; The thermal infrared string vector and the visible light string vector are subjected to string registration to obtain the fused feature image, wherein the string registration is used to align the image positions of the thermal infrared string vector and the visible light string vector.

5. The method according to claim 1, characterized in that: Before inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: Determine a historical fault of the photovoltaic component and a historical image collected when the historical fault occurred, wherein the historical image includes a historical thermal infrared image and a historical visible light image; Performing image fusion processing on the historical thermal infrared image and the historical visible light image to obtain a historical fusion feature image; The initial machine learning model is trained using the historical fusion feature images as a training data set and the historical faults as a verification data set to obtain the fault detection model.

6. The method according to claim 1, characterized in that After inputting the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image, the method further includes: Acquire the fault area of ​​the photovoltaic module and the fault information of the photovoltaic module from the fault detection result; The fault area and the fault information are visually displayed on a display device.

7. A photovoltaic module fault detection device, characterized in that: include: An image acquisition module, used to acquire a thermal infrared image of a photovoltaic module and a visible light image of the photovoltaic module; An image processing module, used for performing image feature fusion processing on the temperature feature of the thermal infrared image and the texture feature of the visible light image to obtain a fused feature image; The fault detection module is used to input the fused feature image into a fault detection model to obtain a fault detection result output by the fault detection model after performing fault detection on the photovoltaic component based on the image features of the fused feature image.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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

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