A photovoltaic panel problem identification method and device, a storage medium and a program product

Abstract

This invention relates to the field of photovoltaic power generation technology, and discloses a method, device, storage medium, and program product for identifying photovoltaic panel problems. This invention comprehensively considers multiple factors such as ambient temperature, irradiance, time, and image color information, and combines functions such as time correction function, irradiance function, ambient temperature influence function, and irradiance correction function to calculate the temperature variation coefficient value, which can accurately characterize the surface temperature uniformity of photovoltaic panels. Furthermore, the temperature variation coefficient value can effectively identify potential / manifest defects, overcoming the limitations of traditional methods in dealing with complex and variable operating conditions. It can more accurately identify the non-uniformity of temperature distribution on the surface of photovoltaic panels, thereby determining whether there are potential problems such as hot spots and microcracks, providing a more scientific and accurate decision-making basis for the operation and maintenance of photovoltaic power plants, and improving the reliability and power generation efficiency of photovoltaic power plants.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to a method, device, storage medium, and program product for identifying problems in photovoltaic panels. Background Technology

[0002] In photovoltaic (PV) power generation systems, PV panels are the core component, and their performance directly affects power generation efficiency and system stability. As the scale of PV power plants continues to expand, efficient and accurate monitoring of PV panels becomes particularly important.

[0003] Currently, commonly used photovoltaic (PV) panel monitoring methods mainly include manual inspection and monitoring based on electrical parameters. Manual inspection is not only inefficient and costly, but also greatly affected by human factors, making it difficult to achieve real-time and comprehensive monitoring. While monitoring based on electrical parameters can reflect some performance characteristics of PV panels, it cannot directly detect physical defects on the PV panel surface, such as hot spots and microcracks. These defects often lead to abnormally high local temperatures on the PV panel, thereby affecting power generation efficiency and even causing damage to the PV panel.

[0004] Using drones equipped with infrared cameras to monitor photovoltaic (PV) modules provides a new approach to PV panel condition monitoring. Infrared imaging technology can acquire the temperature distribution on the PV panel surface. However, existing PV panel monitoring technologies have many shortcomings in using temperature information obtained from infrared imaging to identify PV panel problems, and cannot meet the needs of efficient and accurate monitoring for large-scale PV power plants. Summary of the Invention

[0005] This invention provides a photovoltaic panel problem identification method, device, storage medium, and program product to solve the problem that existing photovoltaic panel monitoring technologies have many shortcomings in using temperature information obtained by infrared imaging to identify photovoltaic panel problems, and cannot meet the needs of efficient and accurate monitoring of large-scale photovoltaic power plants.

[0006] In a first aspect, the present invention provides a method for identifying problems in photovoltaic panels, the method comprising:

[0007] The process involves acquiring infrared images, ambient temperature datasets, irradiance datasets, and time datasets of the photovoltaic (PV) panel to be identified. The infrared images are analyzed to determine the HSV color contribution values ​​of pixels within the images to the temperature of the PV panel, as well as multiple region validity values ​​for multiple pixels. These region validity values ​​characterize whether a pixel is within the valid image region of the PV panel. Based on the time and irradiance datasets, multiple time contribution values ​​and multiple irradiance contribution values ​​are obtained through time correction and irradiance function processing, respectively. Similarly, based on the ambient temperature and irradiance datasets, multiple environmental influence values ​​and multiple irradiance influence values ​​are obtained through ambient temperature influence and irradiance correction functions, respectively. The temperature variation coefficient of the PV panel is calculated based on these multiple color contribution values, region validity values, time contribution values, irradiance contribution values, environmental influence values, and irradiance influence values. Finally, based on the temperature variation coefficient, the PV panel is used to identify the target problem, yielding the target problem identification result.

[0008] The photovoltaic panel problem identification method provided by this invention solves the problem of single data dimension in traditional monitoring methods (manual inspection, single electrical parameter monitoring) by acquiring infrared images, ambient temperature datasets, irradiance datasets, and time datasets of the photovoltaic panel to be identified. Furthermore, by analyzing the infrared images and extracting multiple color contribution values, it can capture temperature changes in extremely small areas on the photovoltaic panel surface, achieving higher resolution. Simultaneously, invalid pixels are eliminated through region validity values, ensuring the accuracy of the data source for subsequent temperature analysis and improving temperature calculation accuracy. Furthermore, the influence of time on temperature is quantified using a time correction function, and the effect of local illumination on temperature is quantified using an irradiance function, eliminating time and local irradiance interference and making the temperature analysis more closely reflect the actual operating conditions of the photovoltaic panel. Furthermore, the nonlinear effects of ambient temperature and average irradiance on temperature and health status are quantified using ambient temperature influence functions and irradiance correction functions, respectively, eliminating assessment errors caused by environmental interference, making the temperature variation coefficient calculation more realistic, and avoiding misjudgments of faults due to environmental factors. Furthermore, by integrating multi-dimensional data and calculating the temperature variation coefficient, the surface temperature uniformity of photovoltaic panels can be accurately characterized, providing a core quantitative indicator for health assessment and avoiding subjective judgment errors. Moreover, the temperature variation coefficient can effectively identify potential / manifest defects, avoiding misjudgments and omissions, providing clear fault information for operation and maintenance, and reducing power generation efficiency losses due to faults. Therefore, by implementing this invention, and by comprehensively considering factors such as ambient temperature, irradiance, time, and image color information, the limitations of traditional methods in dealing with complex and variable operating conditions are overcome. It can more accurately identify the non-uniformity of temperature distribution on the surface of photovoltaic panels, thereby determining whether there are potential problems such as hot spots and microcracks, providing a more scientific and accurate decision-making basis for the operation and maintenance of photovoltaic power plants, and improving the reliability and power generation efficiency of photovoltaic power plants.

[0009] In one optional implementation, the infrared image is analyzed, and multiple color contribution values ​​of the HSV color of pixels in the infrared image to the temperature of the photovoltaic panel to be identified are determined, as well as multiple region validity values ​​of multiple pixels in the infrared image, including:

[0010] The process involves acquiring multiple hue values, saturation values, and brightness values ​​for multiple pixels in an infrared image; analyzing these values ​​to determine the HSV color contribution of each pixel to the temperature of the photovoltaic panel to be identified; and using a rectangular function to determine whether multiple pixels are within the effective area of ​​the photovoltaic panel image and obtaining multiple area validity values.

[0011] The photovoltaic panel problem identification method provided by this invention can determine the degree of influence of different color dimensions on temperature by analyzing each color component, providing a detailed basis for subsequent pixel-level temperature calculation and improving the accuracy of temperature-color mapping. Furthermore, the rectangular function effectively eliminates the influence of invalid pixels, ensuring that calculations are performed only within the effective area of ​​the photovoltaic panel image, thus improving the accuracy of the calculations.

[0012] In one optional implementation, the multiple color contribution values ​​include multiple hue contribution values, multiple saturation contribution values, and multiple brightness contribution values; the multiple hue values, multiple saturation values, and multiple brightness values ​​are analyzed and the multiple color contribution values ​​of the pixels in the infrared image to determine the temperature of the photovoltaic panel to be identified are determined, including:

[0013] Based on multiple hue values, the average hue value of the infrared image is determined; using a preset error function, the deviation between each hue value and the average hue value is normalized to obtain multiple hue contribution values; using a Gaussian function, multiple saturation values ​​are analyzed and processed to determine multiple saturation contribution values; using an exponential function, multiple brightness values ​​are analyzed and processed to determine multiple brightness contribution values.

[0014] The photovoltaic panel problem identification method provided by this invention, by normalizing tone deviation through a preset error function, can accurately quantify the impact of tone changes on temperature calculation, avoiding inaccurate temperature calculations caused by differences in tone values. Furthermore, by using a Gaussian function, the distribution characteristics of pixel saturation values ​​in the photovoltaic panel's infrared image can be accurately characterized, thus more precisely considering the contribution of saturation factors to temperature calculation and improving the accuracy of temperature calculation. Furthermore, by utilizing the nonlinear characteristics of an exponential function to process the brightness values ​​of pixels in the photovoltaic panel's infrared image, the complex relationship between brightness values ​​and temperature can be better simulated, making the temperature calculation more consistent with reality.

[0015] In one optional implementation, based on the ambient temperature dataset and the irradiance dataset, multiple environmental impact values ​​and multiple irradiance impact values ​​are obtained by processing them through the ambient temperature influence function and the irradiance correction function, respectively, including:

[0016] Based on the ambient temperature dataset, the influence of ambient temperature on the temperature and health status assessment of the photovoltaic panel to be identified is quantified using the ambient temperature influence function, resulting in multiple environmental influence values; based on the irradiance dataset, the influence of irradiance on the temperature of the photovoltaic panel is quantified using the irradiance correction function, resulting in multiple irradiance influence values.

[0017] The photovoltaic panel problem identification method provided by this invention can simulate the complex relationship between ambient temperature and photovoltaic panel temperature through an ambient temperature influence function, quantifying the role of ambient temperature in assessing photovoltaic panel temperature and health status, thereby improving the accuracy of the assessment. Furthermore, by using an irradiance correction function, the influence of irradiance on health status assessment can be adjusted more flexibly and accurately according to the actual characteristics of the photovoltaic panel and the operating environment, thus improving adaptability to different photovoltaic panels and operating conditions.

[0018] In one optional implementation, the temperature variation coefficient value of the photovoltaic panel to be identified is calculated based on multiple color contribution values, multiple area validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental impact values, and multiple irradiance impact values, including:

[0019] Based on multiple color contribution values, multiple irradiance contribution values, multiple regional validity values, and multiple time contribution values, multiple initial temperature values ​​for multiple target regions in the infrared image are calculated. These multiple target regions are non-overlapping minimal regions defined by the infrared image. A temperature-weighted average is calculated based on the multiple initial temperature values. The multiple initial temperature values ​​are then normalized using a normalization function to obtain multiple target temperature values. Finally, the temperature variation coefficient of the photovoltaic panel to be identified is calculated based on the multiple initial temperature values, the temperature-weighted average, the multiple target temperature values, multiple environmental influence values, and multiple irradiance influence values.

[0020] The photovoltaic panel problem identification method provided by this invention achieves pixel-level to micro-region-level precise temperature extraction by dividing the panel into extremely small regions and combining multiple influence values ​​to calculate the initial temperature, thereby capturing temperature anomalies in minute areas. Furthermore, by normalizing multiple initial temperature values ​​using a normalization function, these temperature values ​​can be analyzed under a unified standard, eliminating the influence of differences in temperature magnitude and distribution, and improving the reliability of the temperature variation coefficient calculation. Finally, by integrating multi-dimensional data to calculate the temperature variation coefficient, it can accurately characterize the temperature uniformity of the photovoltaic panel itself after excluding environmental and irradiation interference, providing a scientific and accurate quantitative indicator for problem identification.

[0021] In an optional implementation, the method further includes: identifying problems with the photovoltaic panel to be identified based on multiple initial temperature values ​​to obtain initial problem identification results.

[0022] The photovoltaic panel problem identification method provided by this invention directly identifies obvious temperature anomaly areas through initial temperature values, realizing a two-layer identification of preliminary warning and subsequent accurate verification. This gives maintenance personnel more time to troubleshoot problems and prevents the problems in the abnormal areas from worsening.

[0023] In one optional implementation, the method further includes: acquiring multiple temperature variation coefficient values ​​of the photovoltaic panel to be identified; analyzing the multiple temperature variation coefficient values; and determining the trend of health status changes of the photovoltaic panel to be identified.

[0024] The photovoltaic panel problem identification method provided by this invention helps predict the long-term performance trend of photovoltaic panels by analyzing multiple temperature variation coefficient values. As a result, maintenance personnel can plan maintenance plans and prepare replacement parts in advance based on the prediction results, thus avoiding serious impacts of sudden failures on the power generation system.

[0025] In a second aspect, the present invention provides a photovoltaic panel problem identification device, the device comprising:

[0026] The system comprises the following modules: an acquisition module for acquiring infrared images, ambient temperature datasets, irradiance datasets, and time datasets of the photovoltaic panel to be identified; an analysis and determination module for analyzing the infrared images and determining the HSV color contribution values ​​of pixels in the infrared images to the temperature of the photovoltaic panel to be identified, as well as multiple region validity values ​​for multiple pixels in the infrared images, where multiple region validity values ​​characterize whether a pixel is within the effective image region of the photovoltaic panel to be identified; a first processing module for processing the time dataset and irradiance dataset using time correction functions and irradiance functions respectively, to obtain multiple time contribution values ​​and multiple irradiance contribution values; a processing module for processing the ambient temperature dataset and irradiance dataset using ambient temperature influence functions and irradiance correction functions respectively, to obtain multiple environmental influence values ​​and multiple irradiance influence values; a calculation module for calculating the temperature variation coefficient value of the photovoltaic panel to be identified based on the multiple color contribution values, multiple region validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental influence values, and multiple irradiance influence values; and an identification module for identifying problems in the photovoltaic panel to be identified based on the temperature variation coefficient value, to obtain the target problem identification result of the photovoltaic panel to be identified.

[0027] Thirdly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic panel problem identification method of the first aspect or any corresponding embodiment thereof.

[0028] Fourthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the photovoltaic panel problem identification method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of the first process of a photovoltaic panel problem identification method according to an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the second process of the photovoltaic panel problem identification method according to an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram of the third process of the photovoltaic panel problem identification method according to an embodiment of the present invention;

[0034] Figure 5 This is a structural block diagram of a photovoltaic panel problem identification device according to an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0038] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0039] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the photovoltaic panel problem identification method depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0040] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0041] Infrared imaging technology can be used to obtain the temperature distribution on the surface of photovoltaic panels. However, a mature algorithm has not yet been developed to fully utilize this temperature distribution information. On the one hand, temperature distribution causes different colors in images captured by drones, and accurately establishing the correspondence between temperature and image color is a challenge. On the other hand, even if the correspondence between temperature and color is obtained, how to effectively segment the temperature image to obtain temperature values ​​in extremely small areas, and further calculate parameters that accurately reflect the health status of the photovoltaic panel, are also problems that current technology urgently needs to solve. In addition, existing technologies also have shortcomings in how to use data obtained from multiple images of the same photovoltaic panel and compare data between multiple photovoltaic panels to achieve accurate photovoltaic module identification.

[0042] This invention provides a method for identifying problems in photovoltaic panels, and makes the following improvements to address the shortcomings of existing technologies:

[0043] 1. Establishing a Temperature-to-Image Color Correspondence Algorithm: This study delves into the principles of infrared imaging and the mapping relationship between temperature and color in images. By analyzing numerous infrared images of photovoltaic panels at different temperatures, and combining color space conversion models (such as RGB to HSV color space conversion) with actual temperature data obtained from temperature sensors, a precise algorithm for corresponding different temperatures to image colors is established. This algorithm can accurately convert the color information of each pixel in infrared images captured by drones into the corresponding temperature value, providing a foundation for subsequent temperature analysis.

[0044] 2. Temperature Image Segmentation and Temperature Value Acquisition: Advanced image segmentation algorithms, such as a combination of threshold-based segmentation, region growing, and edge detection, are used to segment the temperature images generated by these algorithms. By optimizing algorithm parameters, the segmented regions can correspond as accurately as possible to the smallest areas on the photovoltaic panel, thereby obtaining accurate temperature values ​​within each tiny area. For example, different cell areas and border areas on the photovoltaic panel can be precisely segmented to obtain their respective temperature values.

[0045] 3. Calculating the Temperature Variation Coefficient as a Health Assessment Parameter: Based on the acquired temperature values ​​of a minimal area, a specialized algorithm is designed to calculate the temperature variation of the entire image, i.e., the temperature variation coefficient. The calculation of the temperature variation coefficient comprehensively considers statistical parameters such as the average and standard deviation of the temperature values ​​in the minimal area, and is derived using the formula [specific formula]. This temperature variation coefficient effectively reflects the degree of temperature unevenness on the photovoltaic panel surface, serving as an important parameter for evaluating the health status of the photovoltaic panel at that time. For example, the temperature variation coefficient of a normally functioning photovoltaic panel is within a relatively stable range, while when problems such as hot spots or microcracks appear on the photovoltaic panel, the temperature variation coefficient will change significantly.

[0046] 4. Photovoltaic Module Identification: By repeatedly photographing the same photovoltaic panel, the temperature variation coefficient obtained from each photograph is recorded, forming a time series data of the photovoltaic panel's temperature variation coefficient. Data analysis methods, such as moving average and exponential smoothing, are used to analyze the time series data to discover the changing trend of the temperature variation coefficient. Simultaneously, under uniform irradiance conditions, temperature variation coefficient data of multiple photovoltaic panels in the same area are collected and compared using statistical methods (such as hypothesis testing and cluster analysis). If the temperature variation coefficient of a particular photovoltaic panel is found to be significantly lower or higher than that of other photovoltaic modules, that photovoltaic module is considered to need replacement, thus achieving accurate identification of photovoltaic modules.

[0047] According to an embodiment of the present invention, a method for identifying problems in photovoltaic panels is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0048] This embodiment provides a method for identifying photovoltaic panel problems, which can be used in the aforementioned mobile terminals, such as mobile phones and tablets. Figure 2 This is a flowchart of a photovoltaic panel problem identification method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0049] Step S201: Obtain the infrared image of the photovoltaic panel to be identified, the ambient temperature dataset, the irradiance dataset, and the time dataset.

[0050] Among them, photovoltaic panels refer to semiconductor materials that can convert solar energy into electrical energy, also known as solar panels or photovoltaic cells; infrared images refer to remote sensing images based on the thermal radiation characteristics of ground objects, which can reflect the temperature distribution on the surface of photovoltaic panels.

[0051] Furthermore, the ambient temperature dataset represents a collection of ambient temperature data collected in real time by temperature sensors installed near the photovoltaic panel to be identified. Ambient temperature directly affects the actual operating temperature of the photovoltaic panel, which may interfere with the identification results. The irradiance dataset represents the irradiance values ​​at different locations on the surface of the photovoltaic panel. Furthermore, the dataset represents the time interval at which each infrared image of the photovoltaic panel was captured. Irradiance is a key factor affecting the temperature of the photovoltaic panel; different light intensities lead to differences in surface temperature. The collection takes into account that the performance of photovoltaic panels may change over time, and incorporates the time factor into temperature calculation and health status assessment.

[0052] in, For pixels in the horizontal direction ( The coordinates of the axes (axis and y) are used to determine the horizontal position of pixels in an image; For pixels in the vertical direction ( The coordinates of the axes, and Together, they determine the position of each pixel in the image, which is then used for traversal calculations across the entire image. For the infrared image of the photovoltaic panel in the horizontal direction ( The total number of pixels along the axis determines the size range of the image in that direction and is a boundary parameter in integral calculations; For the infrared image of the photovoltaic panel in the vertical direction ( The total number of pixels along the axis determines the size range of the image in that direction, and... Together they define the integration region.

[0053] Specifically, a drone equipped with a high-precision infrared camera can be used to photograph the photovoltaic panel to be identified, obtaining an infrared image of the surface temperature distribution of the photovoltaic panel.

[0054] For example, firstly, a drone equipped with a high-precision infrared camera is selected to ensure that the camera has the ability to clearly capture the temperature distribution differences on the surface of the photovoltaic panel, meeting the accuracy requirements for subsequent pixel-level HSV color information extraction. Secondly, to reduce the interference of unstable lighting on the temperature distribution of the photovoltaic panel, shooting is conducted during periods of relatively stable light intensity, such as early morning or evening, avoiding abnormal temperature fluctuations caused by direct sunlight or sudden changes in lighting that could affect the validity of the data. Finally, the drone is operated to photograph the photovoltaic panel along a preset flight path (ensuring complete coverage of the area to be identified with no blind spots) to obtain high-resolution infrared images.

[0055] Furthermore, a temperature sensor can be installed near the photovoltaic panel to be identified to collect the ambient temperature in real time. .

[0056] Furthermore, multiple irradiance sensors can be installed on the surface of the photovoltaic panel to be identified (avoiding shaded areas and selecting a location with uniform light reception) or at suitable locations around it (ensuring that the irradiance collected by the sensors represents the actual light intensity received by the photovoltaic panel surface) to obtain irradiance values ​​at different locations on the photovoltaic panel surface. .

[0057] Furthermore, the time of each infrared image capture, ambient temperature data acquisition, and irradiance data acquisition is precisely recorded. .

[0058] Step S202: Analyze the infrared image and determine the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as the multiple region validity values ​​of multiple pixels in the infrared image.

[0059] In this context, pixels represent the basic building blocks of infrared images, with each pixel corresponding to a tiny area on the surface of the photovoltaic panel; HSV color represents a spatial model used to describe color, which can include hue. saturation And the three components of brightness Furthermore, there is a mapping relationship between the HSV color information of each pixel and the temperature of the corresponding tiny region.

[0060] Furthermore, multiple color contribution values ​​represent the quantitative values ​​of the influence of each HSV color component (hue, saturation, brightness) of a pixel in an infrared image on the temperature of the corresponding photovoltaic panel, which can accurately reflect the correlation between color information and temperature.

[0061] Furthermore, the region validity value is used to characterize whether a pixel is within the valid region of the image of the photovoltaic panel to be identified. When a pixel is within the valid region (i.e., the photovoltaic panel body region, excluding invalid regions such as background and support), the region validity value is 1; otherwise, it is 0.

[0062] Specifically, from the acquired infrared images, on the one hand, by analyzing the correlation between each component of the HSV color space and temperature, the color contribution value reflecting the influence of color on temperature is quantified, providing support for establishing a precise correspondence between temperature and image color and calculating the temperature value of minimal areas; on the other hand, by determining whether a pixel is within the effective area of ​​the photovoltaic panel, the region validity value is extracted, which can eliminate the interference of invalid pixels such as background pixels, ensuring that subsequent temperature analysis is based only on the effective data of the photovoltaic panel itself.

[0063] Step S203: Based on the time dataset and irradiance dataset, the data are processed by the time correction function and the irradiance function respectively to obtain multiple time contribution values ​​and multiple irradiance contribution values.

[0064] Among them, the time correction function Based on the S-curve function, by adjusting the parameters and Quantification of time ( The impact of photovoltaic panel temperature and health status assessment on the photovoltaic panel is shown in the following equation (1):

[0065] (1)

[0066] In the formula: Indicates the shooting time; and The parameters representing the characteristics of the adjustment function can be determined according to actual needs. They are used to quantify the impact of time on the temperature and health status assessment of photovoltaic panels. By adjusting these two parameters, the performance characteristics of different photovoltaic panels can be adapted to changes over time.

[0067] Furthermore, the irradiance function Using an S-curve function, by adjusting parameters and Quantitative irradiance The degree of influence on the calculation of photovoltaic panel temperature is shown in the following relationship (2):

[0068] (2)

[0069] In the formula: Represented as coordinates Irradiance value at the location; and The parameter representing the characteristics of the adjustment function can be determined according to actual needs and is used to quantify the degree of influence of irradiance on temperature.

[0070] Specifically, the time dataset is input into the aforementioned time correction function. Multiple time contribution values ​​can be calculated.

[0071] Furthermore, the performance of photovoltaic panels changes over time during long-term operation, a factor often overlooked by traditional methods. Therefore, this embodiment introduces a time correction function to account for the impact of time on the temperature and health status assessment of photovoltaic panels, thereby enabling the method to more accurately reflect the actual condition of the photovoltaic panels and providing a more reliable basis for long-term operation and maintenance.

[0072] Furthermore, the irradiance dataset is input into the aforementioned irradiance function. Multiple irradiance contribution values ​​reflecting the influence of irradiance on the temperature of photovoltaic panels can be calculated.

[0073] Furthermore, irradiance has a significant impact on the temperature of photovoltaic panels, but the degree of impact varies under different light intensities. Therefore, by introducing an irradiance function in this embodiment, the weight of irradiance in temperature calculation can be adjusted according to actual conditions, accurately reflecting the relationship between irradiance and temperature, and improving the accuracy of temperature calculation.

[0074] Step S204: Based on the ambient temperature dataset and the irradiance dataset, the data are processed by the ambient temperature influence function and the irradiance correction function respectively to obtain multiple environmental influence values ​​and multiple irradiance influence values.

[0075] Among them, the influence function of ambient temperature Based on the characteristics of the S-curve function, by adjusting the parameters... and This allows it to accurately reflect the impact of ambient temperature on the temperature and health status assessment of photovoltaic panels, as shown in the following equation (3):

[0076] (3)

[0077] In the formula: Indicates ambient temperature; and The parameters representing the characteristics of the adjustment function can be determined according to actual needs and are used to accurately reflect the effect of ambient temperature on the actual operating temperature of photovoltaic panels.

[0078] Furthermore, the irradiance correction function introduces a correction factor based on the irradiance function to further adjust the degree of influence of the average irradiance on the photovoltaic panel surface on the health status assessment, so as to more accurately consider the irradiance factor, as shown in the following relationship (4):

[0079] (4)

[0080] In the formula: This represents the average irradiance, which can be calculated from multiple irradiance values ​​in the irradiance dataset. This represents the correction factor, which can be determined based on actual needs and adjusted accordingly. It allows for more precise adjustment of the impact of irradiance on the health status assessment of photovoltaic panels.

[0081] Specifically, ambient temperature is a crucial factor affecting the actual operating temperature of photovoltaic panels, but the relationship between ambient temperature and photovoltaic panel temperature is not simply linear. Therefore, this embodiment introduces an ambient temperature influence function. It can simulate this complex relationship, quantify the role of ambient temperature in assessing the temperature and health status of photovoltaic panels, and improve the accuracy of the assessment.

[0082] Furthermore, considering only the effect of irradiance on temperature is insufficient to fully assess its impact on the health of photovoltaic panels. Therefore, this embodiment introduces a correction factor. It can adjust the impact of irradiance on health status assessment more flexibly and accurately according to the actual characteristics of photovoltaic panels and operating environment, and improve the algorithm's adaptability to different photovoltaic panels and operating conditions.

[0083] Step S205: Calculate the temperature variation coefficient of the photovoltaic panel to be identified based on multiple color contribution values, multiple regional validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental impact values, and multiple irradiance impact values.

[0084] Among them, the temperature variation coefficient value This refers to the core parameters that quantify the uniformity of temperature distribution on the surface of a photovoltaic panel after comprehensively considering multiple factors such as the temperature dispersion of a very small area of ​​the photovoltaic panel to be identified, ambient temperature, and irradiance.

[0085] Specifically, by extracting multiple color contribution values, multiple regional validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental impact values, and multiple irradiance impact values, the temperature-related data of the photovoltaic panel surface to be identified can be converted into temperature variation coefficient values ​​that can directly characterize the health status. This ensures that the results accurately reflect the temperature distribution characteristics of the photovoltaic panel itself, rather than false anomalies caused by external environment or data errors.

[0086] Step S206: Based on the temperature variation coefficient value, identify the problem of the photovoltaic panel to be identified, and obtain the target problem identification result of the photovoltaic panel to be identified.

[0087] Specifically, when the temperature coefficient of variation value A reading close to 0 indicates that, after comprehensively considering factors such as ambient temperature, irradiance, and time, the surface temperature distribution of the photovoltaic panel is relatively uniform, and the photovoltaic panel is likely in a healthy state. With... An increase in the value indicates a greater dispersion in the surface temperature of the photovoltaic panel, and combined with various influencing factors, this increases the likelihood of problems with the photovoltaic panel. For example, when... When the value exceeds a certain empirical threshold (such as 0.2, which can be determined based on a large amount of experimental data), a detailed inspection of the photovoltaic panel is required to determine whether there are faults such as hot spots or microcracks.

[0088] The photovoltaic panel problem identification method provided in this embodiment solves the problem of single data dimension in traditional monitoring methods (manual inspection, single electrical parameter monitoring) by acquiring infrared images, ambient temperature datasets, irradiance datasets, and time datasets of the photovoltaic panel to be identified. Furthermore, by analyzing the infrared images and extracting multiple color contribution values, it can capture temperature changes in extremely small areas on the photovoltaic panel surface, achieving higher resolution. Simultaneously, invalid pixels are eliminated through region validity values, ensuring the accuracy of the data source for subsequent temperature analysis and improving temperature calculation accuracy. Furthermore, the influence of time on temperature is quantified using a time correction function, and the effect of local illumination on temperature is quantified using an irradiance function, eliminating time and local irradiance interference and making the temperature analysis more closely reflect the actual operating conditions of the photovoltaic panel. Furthermore, the nonlinear effects of ambient temperature and average irradiance on temperature and health status are quantified using ambient temperature influence functions and irradiance correction functions, respectively, eliminating assessment errors caused by environmental interference, making the temperature variation coefficient calculation more realistic, and avoiding misjudgments of faults due to environmental factors. Furthermore, by integrating multi-dimensional data and calculating the temperature variation coefficient, the surface temperature uniformity of photovoltaic panels can be accurately characterized, providing a core quantitative indicator for health assessment and avoiding subjective judgment errors. Moreover, the temperature variation coefficient can effectively identify potential / manifest defects, avoiding misjudgments and omissions, providing clear fault information for operation and maintenance, and reducing power generation efficiency losses due to faults. Therefore, by implementing this invention, and by comprehensively considering factors such as ambient temperature, irradiance, time, and image color information, the limitations of traditional methods in dealing with complex and variable operating conditions are overcome. It can more accurately identify the non-uniformity of temperature distribution on the surface of photovoltaic panels, thereby determining whether there are potential problems such as hot spots and microcracks, providing a more scientific and accurate decision-making basis for the operation and maintenance of photovoltaic power plants, and improving the reliability and power generation efficiency of photovoltaic power plants.

[0089] This embodiment provides a method for identifying photovoltaic panel problems, which can be used in the aforementioned mobile terminals, such as mobile phones and tablets. Figure 3 This is a flowchart of a photovoltaic panel problem identification method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0090] Step S301: Obtain the infrared image of the photovoltaic panel to be identified, the ambient temperature dataset, the irradiance dataset, and the time dataset. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0091] Step S302: Analyze the infrared image and determine the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as the multiple region validity values ​​of multiple pixels in the infrared image.

[0092] Specifically, step S302 includes:

[0093] Step S3021: Obtain multiple hue values, multiple saturation values, and multiple brightness values ​​of multiple pixels in the infrared image.

[0094] Specifically, each pixel in an infrared image has a hue in the HSV color space. saturation and brightness value.

[0095] Step S3021: Analyze multiple hue values, multiple saturation values ​​and multiple brightness values ​​respectively, and determine the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified.

[0096] Specifically, by conducting in-depth analysis of the HSV color space information (hue value, saturation value, and brightness value) of image pixels, an accurate correspondence between temperature and image color can be further established.

[0097] In some optional implementations, step S3021 above includes:

[0098] Step a1: Determine the average hue of the infrared image based on multiple hue values.

[0099] Step a2: Using a preset error function, normalize the deviation between each hue value and the average hue value to obtain multiple hue contribution values.

[0100] Step a3: Use the Gaussian function to analyze and process multiple saturation values ​​and determine multiple saturation contribution values.

[0101] Step a4: Analyze and process multiple brightness values ​​using an exponential function and determine multiple brightness contribution values.

[0102] Specifically, in establishing the correspondence between temperature and image color, the range and distribution of hue values ​​may differ for different photovoltaic panels. Therefore, this embodiment introduces a preset error function. Furthermore, by utilizing its characteristic of normalizing deviations, the deviation between the hue value and the mean of the pixels in the infrared image of the photovoltaic panel is standardized so that it can be analyzed under a uniform scale, as shown in the following relationship (5):

[0103] (5)

[0104] in:

[0105] (6)

[0106] In the formula: The independent variable is used to highlight the effect of hue deviation on temperature calculation; Representing coordinates The hue value of the infrared image pixel of the photovoltaic panel in the HSV color space is an important color component for establishing the correspondence between temperature and image color, and its value is related to temperature. The average hue value of the entire photovoltaic panel infrared image is used as a benchmark for analyzing hue value deviation and is used in error function calculation to measure the degree of hue deviation. This represents the standard deviation of the hue values ​​in the entire photovoltaic panel infrared image. It is used to reflect the degree of dispersion of the hue values ​​and is used in the error function to standardize the hue deviation.

[0107] Furthermore, by normalizing the deviation between each hue value and the average hue value using the aforementioned preset error function, multiple hue contribution values ​​can be obtained.

[0108] Furthermore, by normalizing the hue deviation using a preset error function, the impact of hue changes on temperature calculation can be accurately quantified, avoiding inaccurate temperature calculations caused by differences in hue values.

[0109] Furthermore, saturation values ​​in an image reflect the vibrancy of colors, and saturation variations in different regions are correlated with temperature. Therefore, this embodiment introduces a Gaussian function. Based on its ability to describe data distribution in statistics, it is used to characterize the distribution characteristics of pixel saturation values ​​in infrared images of photovoltaic panels, so as to analyze the influence of saturation on temperature perception, as shown in the following relationship (7):

[0110] (7)

[0111] In the formula: Used to analyze the saturation values ​​of pixels in the HSV color space of infrared images of photovoltaic panels. Distribution; Indicates the saturation value; This represents the expected value of the saturation. The standard deviation of the saturation value is used to account for the influence of saturation on temperature perception. Representing coordinates The saturation values ​​of the pixels in the infrared image of the photovoltaic panel in the HSV color space are used to analyze the impact of pixel color vibrancy on temperature calculation.

[0112] Furthermore, by inputting multiple saturation values ​​into the Gaussian function shown in the above relation (7), the corresponding multiple saturation contribution values ​​can be calculated.

[0113] Furthermore, by introducing the Gaussian function, this distribution relationship can be accurately described, which helps to more accurately consider the contribution of saturation factors to temperature calculation and improve the accuracy of temperature calculation.

[0114] Furthermore, while lightness value significantly influences temperature perception in the relationship between temperature and color, this influence is not linear. Therefore, this embodiment introduces an exponential function. By utilizing the nonlinear characteristics of the exponential function, the brightness values ​​of pixels in the infrared image of a photovoltaic panel are processed to further refine the relationship between temperature and color. The following relation (8) is shown:

[0115] (8)

[0116] In the formula: This represents the result after standardizing and quadratic transformation of the deviation between the pixel brightness value and the mean brightness value of the entire image; Representing coordinates The brightness value of the pixels in the infrared image of the photovoltaic panel in the HSV color space affects the perception of temperature in the correspondence between temperature and color. This represents the expected value of the brightness of all pixels in the infrared image of the photovoltaic panel, and represents the average level of brightness values, used to adjust the impact of brightness on temperature calculation. This represents the standard deviation of the brightness values ​​of all pixels in the infrared image of the photovoltaic panel, reflecting the degree of dispersion of brightness values ​​and influencing the strength of the effect of brightness on temperature calculation.

[0117] Furthermore, multiple brightness values ​​are input into the aforementioned exponential function. Multiple corresponding brightness contribution values ​​can be calculated.

[0118] Furthermore, by introducing an exponential function, the complex relationship between lightness value and temperature can be better simulated, making temperature calculations more consistent with reality.

[0119] Step S3023: Using a rectangular function, determine whether multiple pixels are within the valid area of ​​the image of the photovoltaic panel to be identified, and obtain multiple area validity values.

[0120] Specifically, when processing infrared images of photovoltaic panels, the images may contain some invalid pixels (such as background areas), which can interfere with temperature calculations. Therefore, this embodiment introduces a rectangular function. Based on its simple and effective region-limiting characteristics, by setting specific conditions, the calculation area is limited to the effective range of the photovoltaic panel image. Among these, Related to the image coordinates, the following relationship (9) is shown:

[0121] (9)

[0122] In the formula: Indicates the center of the effective area of ​​the photovoltaic panel coordinate; Indicates the horizontal width of the effective area.

[0123] Furthermore, utilizing It can be used to determine whether a pixel is within the valid region: when hour, ,otherwise .

[0124] Furthermore, through the aforementioned rectangle function This allows us to calculate the validity values ​​for multiple corresponding regions.

[0125] Furthermore, by introducing a rectangular function, the influence of invalid pixels can be effectively eliminated, ensuring that calculations are performed only within the effective area of ​​the photovoltaic panel image, thus improving the accuracy of the calculations.

[0126] Step S303: Based on the time dataset and irradiance dataset, the data is processed using a time correction function and an irradiance function, respectively, to obtain multiple time contribution values ​​and multiple irradiance contribution values. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0127] Step S304: Based on the ambient temperature dataset and the irradiance dataset, the data are processed by the ambient temperature influence function and the irradiance correction function respectively to obtain multiple environmental influence values ​​and multiple irradiance influence values.

[0128] Specifically, step S304 includes:

[0129] Step S3041: Based on the ambient temperature dataset, the influence of ambient temperature on the temperature and health status assessment of the photovoltaic panel to be identified is quantified using the ambient temperature influence function to obtain multiple environmental influence values.

[0130] Specifically, by substituting the ambient temperatures in the ambient temperature dataset into the ambient temperature influence function shown in the above relation (3), the influence of ambient temperature on the temperature and health status assessment of the photovoltaic panel to be identified can be quantified, thereby obtaining multiple quantified values, i.e. multiple environmental influence values.

[0131] Step S3042: Based on the irradiance dataset, the influence of irradiance on the photovoltaic panel temperature is quantified using the irradiance correction function to obtain multiple irradiance influence values.

[0132] Specifically, the irradiance values ​​in the irradiance dataset are substituted sequentially into the irradiance correction function shown in the above relationship (4), and the average irradiance on the photovoltaic panel surface is further considered. The impact on health status assessment is then used to obtain multiple irradiance impact values ​​that reflect the effect of irradiance on photovoltaic panel temperature, which are then corrected accordingly.

[0133] Step S305: Calculate the temperature variation coefficient of the photovoltaic panel to be identified based on multiple color contribution values, multiple area validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental impact values, and multiple irradiance impact values. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0134] Step S306: Based on the temperature variation coefficient value, identify problems with the photovoltaic panel to be identified, and obtain the target problem identification result for the photovoltaic panel to be identified. For details, please refer to... Figure 2 Step S206 of the illustrated embodiment will not be described again here.

[0135] The photovoltaic panel problem identification method provided in this embodiment accurately quantifies the impact of hue changes on temperature calculation by normalizing hue deviation through a preset error function, avoiding inaccurate temperature calculations caused by hue value differences. Furthermore, the Gaussian function accurately characterizes the distribution features of saturation values ​​of pixels in the photovoltaic panel's infrared image, thus more precisely considering the contribution of saturation factors to temperature calculation and improving accuracy. Further, utilizing the nonlinear characteristics of the exponential function to process the brightness values ​​of pixels in the photovoltaic panel's infrared image better simulates the complex relationship between brightness values ​​and temperature, making temperature calculations more consistent with reality. Further, the rectangular function effectively eliminates the influence of invalid pixels, ensuring calculations are performed only within the effective area of ​​the photovoltaic panel image, improving accuracy. Further, the ambient temperature influence function simulates the complex relationship between ambient temperature and photovoltaic panel temperature, quantifying the role of ambient temperature in assessing photovoltaic panel temperature and health status, improving assessment accuracy. Further, the irradiance correction function allows for more flexible and accurate adjustment of the impact of irradiance on health status assessment based on the actual characteristics of the photovoltaic panel and its operating environment, thereby improving adaptability to different photovoltaic panels and operating conditions.

[0136] This embodiment provides a method for identifying photovoltaic panel problems, which can be used in the aforementioned mobile terminals, such as mobile phones and tablets. Figure 4 This is a flowchart of a photovoltaic panel problem identification method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0137] Step S401: Obtain the infrared image of the photovoltaic panel to be identified, the ambient temperature dataset, the irradiance dataset, and the time dataset. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0138] Step S402 involves analyzing the infrared image and determining the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as the multiple region validity values ​​of multiple pixels in the infrared image. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0139] Step S403: Based on the time dataset and irradiance dataset, the data is processed using a time correction function and an irradiance function, respectively, to obtain multiple time contribution values ​​and multiple irradiance contribution values. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0140] Step S404: Based on the ambient temperature dataset and irradiance dataset, the data are processed using the ambient temperature influence function and irradiance correction function, respectively, to obtain multiple environmental influence values ​​and multiple irradiance influence values. For details, please refer to [link to relevant documentation]. Figure 3 Step S304 of the illustrated embodiment will not be described again here.

[0141] Step S405: Calculate the temperature variation coefficient of the photovoltaic panel to be identified based on multiple color contribution values, multiple area validity values, multiple time contribution values, multiple irradiance contribution values, multiple environmental impact values, and multiple irradiance impact values.

[0142] Specifically, step S405 includes:

[0143] Step S4051: Calculate multiple initial temperature values ​​for multiple target regions in the infrared image based on multiple color contribution values, multiple irradiance contribution values, multiple region validity values, and multiple time contribution values.

[0144] The multiple target regions are defined as numerous non-overlapping minimal regions divided from infrared images. Furthermore, these minimal regions can be divided using a uniform grid, and their specific dimensions can be determined based on actual requirements.

[0145] Specifically, multiple initial temperature values ​​of multiple target regions in an infrared image can be calculated by combining multiple hue contribution values, multiple saturation contribution values, multiple brightness contribution values, multiple irradiance contribution values, multiple region validity values, and multiple time contribution values, using the following relationship (10):

[0146] (10)

[0147] In the formula: This represents the index of the smallest region in the photovoltaic panel. It is used to traverse each smallest region during the calculation process to obtain temperature and other parameter information related to that region, thereby completing the analysis of the entire photovoltaic panel. Indicates the first The initial temperature value of a tiny region can reflect the temperature condition of the corresponding tiny region.

[0148] Furthermore, in calculation At that time, for the image in From 0 to , From 0 to Within a given range, considering the relationship between each component of the pixel in the HSV color space and relevant statistics (quantified using error functions, Gaussian functions, exponential functions, etc.), and combining factors such as region limitation (rectangular function), irradiance influence (irradiance function), and time correction (time correction function), the temperature value of each minima is obtained through integral calculation. .

[0149] Step S4052: Calculate the temperature weighted average value based on multiple initial temperature values.

[0150] Specifically, combining the calculated initial temperature values ​​of all minima... Calculate the temperature-weighted average. .

[0151] Furthermore, temperature-weighted average It can represent the overall average temperature level of photovoltaic panels and is used to measure the degree of temperature dispersion in the calculation of temperature variation coefficient, serving as a benchmark for evaluating the uniformity of temperature distribution.

[0152] Step S4053: Normalize multiple initial temperature values ​​using a normalization function to obtain multiple target temperature values.

[0153] Specifically, the temperature values ​​in different microregions may differ in magnitude and distribution due to measurement errors, environmental factors, etc., and direct calculation will affect the accuracy of the results. Therefore, this embodiment introduces a normalization function, which uses its characteristic of converting different values ​​into comparable values. By normalizing the temperature values, the influence caused by differences in the magnitude and distribution of temperature values ​​is eliminated, as shown in the following relationship (11):

[0154] (11)

[0155] In the formula: This represents the normalization function, used to normalize different temperature values. (This is) Converting these values ​​into comparable numerical values ​​can eliminate the impact of differences in magnitude and other factors on different temperature values. Represents the expectation of the initial set of temperature values; It represents the standard deviation.

[0156] Furthermore, by normalizing multiple initial temperature values ​​using the aforementioned normalization function, these temperature values ​​can be analyzed under a unified standard, thus improving the reliability of the temperature variation coefficient calculation.

[0157] Step S4054: Calculate the temperature variation coefficient of the photovoltaic panel to be identified based on multiple initial temperature values, temperature weighted average values, multiple target temperature values, multiple environmental impact values, and multiple irradiance impact values.

[0158] Specifically, the temperature variation coefficient of the photovoltaic panel to be identified can be calculated by combining multiple initial temperature values, temperature-weighted average values, multiple target temperature values, multiple environmental impact values, and multiple irradiance impact values ​​using the following relationship (12):

[0159] (12)

[0160] In the formula: This represents the temperature variation coefficient value, which is used to comprehensively assess the health status of photovoltaic panels, and the value reflects the likelihood of problems with the photovoltaic panels. This represents the total number of tiny regions obtained after segmenting the photovoltaic panel to be identified, thus determining the scale of the number of regions involved in the calculation.

[0161] Step S406: Based on the temperature variation coefficient value, identify the problem of the photovoltaic panel to be identified, and obtain the target problem identification result of the photovoltaic panel to be identified.

[0162] Specifically, it can be based on the temperature coefficient of variation value. The numerical value is used to identify problems with the photovoltaic panel to be identified, which may specifically include:

[0163] 1. Low Value (close to 0): When A value close to 0 indicates that, after comprehensively considering factors such as ambient temperature, irradiance, and time, the surface temperature distribution of the photovoltaic panel is relatively uniform, and the photovoltaic panel is likely in a healthy state. In this case, the monitoring frequency can be appropriately reduced, but regular routine inspections are still necessary to ensure the continuous and stable operation of the photovoltaic panel. For example, the monitoring cycle can be shortened from daily to weekly or monthly, while also paying attention to other operating parameters, such as power generation efficiency and electrical performance, to comprehensively assess the health status of the photovoltaic panel.

[0164] 2. Medium Value: If A value at a moderate level (the specific range can be determined based on extensive experimental data and practical experience, for example, 0.1-0.2) indicates that the photovoltaic panel may have some potential problems, but these have not yet seriously affected its normal operation. In this case, it is necessary to increase the monitoring frequency and pay close attention. The changing trends and fluctuations of various related parameters. For example, monitoring is conducted multiple times a day, and... Ambient temperature Irradiance Detailed analysis of the data is required. Additionally, other detection methods, such as electroluminescence (EL) detection and thermal imaging, can be used to further investigate potential faults.

[0165] 3. High Value: When When the value exceeds a certain empirical threshold (e.g., 0.2), it indicates an increased dispersion in the surface temperature of the photovoltaic panel. Considering various influencing factors, the likelihood of a problem with the photovoltaic panel is high. In this case, a comprehensive inspection of the photovoltaic panel must be conducted immediately, including visual inspection, electrical performance testing, and internal structural inspection. Based on the inspection results, appropriate repair or replacement measures should be taken. For example, if uneven temperature distribution is found to be caused by localized shading, the installation angle of the photovoltaic panel can be adjusted or the obstruction can be removed; if it is determined to be a quality problem with the photovoltaic panel itself, such as damaged cells, the damaged photovoltaic module needs to be replaced.

[0166] For example, The range of values ​​is non-negative real numbers. Theoretically, 0 represents absolute uniformity, and the larger the value, the greater the temperature dispersion. Generally, based on experience and a large amount of experimental data, it can be roughly divided into three intervals: low (e.g., 0-0.1), medium (0.1-0.2), and high (greater than 0.2).

[0167] Furthermore, problem identification can be performed on the photovoltaic panels to be identified in different regions, which may specifically include:

[0168] 1. Low Value cases (0-0.1): When Within this range, it indicates that the surface temperature distribution of the photovoltaic panel is relatively uniform, and the overall health condition is good. From the above relationship (11), it can be seen that the minimum regions... Values ​​and weighted averages near, The sum of squares is small, after , , Even after the function is corrected, it remains at a low level. For example, right The normalization process makes the temperatures in different regions comparable and the differences between them small; and The effect of the function on the temperature variation coefficient is also within a relatively small range. At this point, the photovoltaic panels can be maintained according to the routine maintenance plan, with a suitable reduction in monitoring frequency.

[0169] 2. Medium Value case (0.1-0.2): If This range indicates that there may be potential problems with the photovoltaic panels. In the above relationship (11), The relatively large sum of squares indicates that temperature differences between the smallest regions are beginning to emerge. This could be due to the ambient temperature. fluctuations through The function affected the temperature or irradiance of a certain area. Uneven distribution, after The effect of the function causes an increase in the coefficient of temperature variation. In this case, it is necessary to increase the monitoring frequency and closely monitor the situation. By analyzing trends and combining them with other detection methods, such as EL testing, potential faults can be identified.

[0170] 3. High Value case (greater than 0.2): when If the value is greater than 0.2, the photovoltaic panel likely has a significant problem. In the above relationship (11), the temperature difference in each minimum region is significant. The sum of squares is very large, and even after applying various correction functions, it still results in... The value is high. This could be due to structural damage inside the photovoltaic panel, leading to abnormal localized temperatures. , The same function is calculated Significant deviations may occur over time; or long-term abnormal environmental temperature and radiation conditions may lead to these deviations. and The function has a significant impact on the coefficient of temperature variation. In this case, a comprehensive inspection of the photovoltaic panels must be conducted immediately, and repair or replacement measures should be taken based on the inspection results.

[0171] In some optional implementations, the above method further includes:

[0172] Step b1: Based on multiple initial temperature values, identify problems with the photovoltaic panel to be identified and obtain initial problem identification results.

[0173] Specifically, based on multiple initial temperature values, the identification of problems with the photovoltaic panel to be identified includes identification of normal conditions and identification of abnormal conditions.

[0174] Furthermore, the identification of normal state conditions may specifically include:

[0175] 1. Uniform temperature distribution: If the calculated temperature distribution is uniform... The relatively close values ​​indicate a fairly uniform temperature distribution on the photovoltaic panel surface. At this point, the photovoltaic panel may be operating normally. However, the final temperature variation coefficient still needs to be considered. Further confirmation is needed. For example, continuous monitoring over a period of time, if... The fluctuation range is small, and the subsequent calculations The levels are also at a low level, which suggests that the photovoltaic panels are operating normally.

[0176] 2. Within Expected Range: A normal temperature range can be preset based on factors such as the type and material of the photovoltaic panel and the installation environment. When the value falls within this expected range, it indicates that the temperature of the photovoltaic panel meets normal operating conditions. If If the temperature exceeds this range, even if the temperature distribution appears uniform, further analysis is needed to determine the cause, as there may be underlying problems.

[0177] Furthermore, the identification of abnormal states can specifically include:

[0178] 1. Temperature values ​​are abnormally high or low: When some Values ​​significantly higher or lower than the normal range may indicate a problem in a very small area of ​​the photovoltaic panel. For example, excessively high values... A value that is too low may indicate a hot spot effect in the area, which could be caused by factors such as damaged solar cells, partial shading, or circuit failure. The value may indicate a decline in the performance of photovoltaic materials in the area or an abnormal heat dissipation. In this case, it is necessary to combine image information and other monitoring data, such as irradiance distribution and changes in ambient temperature, to further determine the root cause of the problem.

[0179] 2. Uneven temperature distribution: If Significant differences in the values ​​indicate uneven temperature distribution on the photovoltaic panel surface. This unevenness may be due to quality issues with the photovoltaic panel itself, such as poor material consistency, or external factors, such as localized shading or inconsistent ventilation conditions. Uneven temperature distribution can accelerate the aging and damage of the photovoltaic panel, requiring timely investigation and appropriate measures.

[0180] In some alternative embodiments, The value range depends on the actual operating temperature range of the photovoltaic panel, and is generally between the ambient temperature and the highest temperature generated by the photovoltaic panel due to factors such as sunlight. For example, a common range might be... arrive (The coverage may vary depending on the region and the type of photovoltaic panel.)

[0181] Furthermore, problem identification can be performed on photovoltaic panels of different ranges, specifically including:

[0182] 1. Normal Temperature Range: Assuming that the normal temperature range is determined based on the photovoltaic panel specifications and historical operating data, the normal temperature range is as follows: arrive When calculated The values ​​mostly fall within this range, and each The values ​​are not significantly different (e.g., the difference is within a certain range). Within this range, it indicates that this tiny area of ​​the photovoltaic panel is in normal working condition. From a formulaic perspective, the image color parameters at this time ( , , ), Irradiance and time The results of the interaction of factors are as expected, and the functions (such as...) , , The contributions of (etc.) to temperature calculations are within the normal range. For example, The function normalizes the hue deviation, ensuring that the effect of hue on temperature remains within a reasonable range. The saturation distribution reflected by the function also did not show any abnormalities.

[0183] 2. Abnormally high temperature: If The value is higher than the upper limit of the normal range, for example, exceeding... There may be a hot spot problem. Analysis of equation (9) suggests it could be due to irradiance. The abnormally high temperature in this area led to The function value increases, thus significantly improving [the performance]. Or the color tone of the area. with the mean deviation through The function amplifies the impact on temperature calculations. Further investigation is needed to examine the lighting conditions in the area, and whether the photovoltaic panel surface is obstructed.

[0184] 3. Abnormally low temperature: When The value is lower than the lower limit of the normal range, such as below This could be due to abnormal heat dissipation of the photovoltaic panels in the area or a decline in the performance of the photovoltaic materials. In equation (9), it could be due to ambient temperature. Abnormally low, making function pairs The effect is biased towards lower temperatures; it could also be a time correction function. This indicates that the performance of the photovoltaic panels in this area has changed over time, leading to a decrease in temperature. The heat dissipation system and the aging condition of the photovoltaic panels need to be checked.

[0185] Furthermore, the above The upper and lower limits of the normal temperature range, and The low, medium, and high temperature range boundaries can all be considered as thresholds. These thresholds are not fixed values ​​and need to be adjusted based on factors such as the type of photovoltaic panel, installation location, and service life. For example, in high-temperature regions, the upper limit of the normal temperature range for photovoltaic panels may be appropriately increased; for photovoltaic panels with a longer service life, The threshold may need to be relaxed appropriately.

[0186] Furthermore, in actual monitoring, when the calculated value or When a value exceeds a certain threshold, the corresponding processing procedure is triggered. If it exceeds the normal temperature range threshold, further analysis of various influencing factors in that area is required. If the threshold exceeds the medium range, monitoring and investigation of potential problems need to be strengthened. By reasonably setting and applying the threshold, the relationship (11) can be effectively used to quickly identify and handle photovoltaic panel problems.

[0187] In some optional implementations, the above method further includes:

[0188] Step c1: Obtain multiple temperature variation coefficient values ​​of the photovoltaic panel to be identified.

[0189] Step c2 involves analyzing multiple temperature variation coefficient values ​​and determining the trend of health status changes of the photovoltaic panel to be identified.

[0190] Specifically, the temperature variation coefficient value obtained from each calculation The values ​​are recorded in chronological order to form time series data. Then, by analyzing the time series data, we can more accurately understand the changing trends in the health status of photovoltaic panels, specifically including whether the trend is stable or changing.

[0191] Furthermore, a stable trend can specifically include:

[0192] 1. Stable low Value: If If the value remains at a low level with minimal fluctuations over a period of time, it indicates that the photovoltaic panels are in a healthy and stable operating state. In this case, the current operation and maintenance strategy can be maintained, but regular spot checks are still necessary to ensure that the performance of the photovoltaic panels has not undergone any sudden changes.

[0193] 2. Stable to moderate levels Value: When While the photovoltaic panels are not currently experiencing serious malfunctions, their trends need close monitoring once the values ​​stabilize at a moderate level. Data analysis methods, such as moving averages and exponential smoothing, can be used to analyze these trends. Time series data are processed to predict the future. The changes. If the prediction results show... If there is an upward trend, preventative measures should be taken in advance, such as strengthening cleaning and optimizing heat dissipation, to prevent the problem from worsening.

[0194] Furthermore, the specific details of trend changes may include:

[0195] 1. Upward trend: If The value shows a clear upward trend, indicating that the health of the photovoltaic panels is gradually deteriorating. At this point, it is necessary to analyze the reasons in depth, which may include changes in environmental factors (such as prolonged high temperature and high humidity environments), accelerated aging of the photovoltaic panels, or the emergence of new potential faults. Based on the analysis results, the operation and maintenance strategy should be adjusted in a timely manner, the frequency of inspection should be increased, and repair or replacement plans should be arranged in advance to avoid further degradation of photovoltaic panel performance and a significant reduction in power generation efficiency.

[0196] 2. Downward trend: When A downward trend in the value may indicate that previous maintenance measures were effective and the health of the photovoltaic panels is improving. However, it could also be due to accidental changes in external factors, such as recent improved weather conditions, which reduced the adverse effects of environmental factors on the photovoltaic panels. Further observation and analysis, combined with other monitoring data, are needed to make a comprehensive judgment to determine whether the photovoltaic panels have truly recovered to a good condition. If the maintenance measures were indeed effective, experience can be summarized and the operation and maintenance plan optimized; if it is just an accidental factor, vigilance and continuous monitoring are still necessary.

[0197] Furthermore, by promptly identifying and resolving problems with photovoltaic (PV) panels, it is possible to ensure that the panels are always in optimal working condition, thereby improving power generation efficiency. For example, identifying and addressing hot spot issues can significantly improve the power generation efficiency of PV panels. This is because hot spots cause excessively high local temperatures, reducing the photoelectric conversion efficiency of the PV panels and, in severe cases, even damaging them. By using this algorithm to detect and resolve hot spot problems in advance, power generation losses due to PV panel malfunctions are reduced, ensuring the overall power generation efficiency of the PV power plant.

[0198] Furthermore, real-time monitoring and precise assessment of the health status of photovoltaic panels can help optimize the overall operation and management of photovoltaic power plants. Power generation strategies can be adjusted based on the actual condition of the photovoltaic panels, such as adjusting the inverter's operating parameters to better match the photovoltaic panels with the inverter, thereby further improving power generation efficiency.

[0199] The photovoltaic panel problem identification method provided in this embodiment achieves pixel-level to micro-region-level precise temperature extraction by dividing the panel into extremely small regions and combining multiple influence values ​​to calculate the initial temperature, thereby capturing temperature anomalies in minute areas. Furthermore, by normalizing multiple initial temperature values ​​using a normalization function, these temperature values ​​can be analyzed under a unified standard, eliminating the influence of differences in temperature magnitude and distribution, and improving the reliability of temperature variation coefficient calculation. Finally, by integrating multi-dimensional data to calculate the temperature variation coefficient, it can accurately characterize the temperature uniformity of the photovoltaic panel itself after excluding environmental and irradiation interference, providing a scientific and accurate quantitative indicator for problem identification. Furthermore, by directly identifying obvious temperature anomaly areas through initial temperature values, a two-layer identification system of preliminary warning and subsequent precise verification is achieved, giving maintenance personnel earlier time to troubleshoot and preventing further deterioration of problems in abnormal areas. Furthermore, by analyzing multiple temperature variation coefficient values, it helps predict the long-term performance trend of the photovoltaic panel, allowing maintenance personnel to plan maintenance schedules and prepare replacement components in advance based on the prediction results, avoiding serious impacts of sudden failures on the power generation system.

[0200] This embodiment also provides a photovoltaic panel problem identification device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0201] This embodiment provides a photovoltaic panel problem identification device, such as... Figure 5 As shown, the device includes:

[0202] The acquisition module 501 is used to acquire the infrared image, ambient temperature dataset, irradiance dataset, and time dataset of the photovoltaic panel to be identified;

[0203] The analysis and determination module 502 is used to analyze the infrared image and determine multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as multiple region validity values ​​of multiple pixels in the infrared image. The multiple region validity values ​​are used to characterize whether the pixels are within the effective area of ​​the image of the photovoltaic panel to be identified.

[0204] The first processing module 503 is used to process the time dataset and the irradiance dataset respectively through a time correction function and an irradiance function to obtain multiple time contribution values ​​and multiple irradiance contribution values.

[0205] Processing module 504 is used to process the ambient temperature dataset and the irradiance dataset respectively through an ambient temperature influence function and an irradiance correction function to obtain multiple environmental influence values ​​and multiple irradiance influence values;

[0206] Calculation module 505 is used to calculate the temperature variation coefficient value of the photovoltaic panel to be identified based on the multiple color contribution values, the multiple area validity values, the multiple time contribution values, the multiple irradiance contribution values, the multiple environmental impact values, and the multiple irradiance impact values.

[0207] The identification module 506 is used to identify problems in the photovoltaic panel to be identified based on the temperature variation coefficient value, and obtain the target problem identification result of the photovoltaic panel to be identified.

[0208] The photovoltaic panel problem identification device provided in this embodiment of the invention can execute the photovoltaic panel problem identification method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0209] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0210] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0211] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0212] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the photovoltaic panel problem identification method of the embodiments of the present invention.

[0213] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0214] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic panel problem identification method shown in the above embodiments is implemented.

[0215] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0216] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying problems in photovoltaic panels, characterized in that, The method includes: Acquire the infrared image, ambient temperature dataset, irradiance dataset, and time dataset of the photovoltaic panel to be identified; The infrared image is analyzed, and multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified are determined, as well as multiple region validity values ​​of multiple pixels in the infrared image. The multiple region validity values ​​are used to characterize whether the pixels are within the effective area of ​​the image of the photovoltaic panel to be identified. Based on the time dataset and the irradiance dataset, multiple time contribution values ​​and multiple irradiance contribution values ​​are obtained after processing with the time correction function and the irradiance function, respectively. The irradiance function adopts the form of an S-shaped curve function to quantify the degree of influence of irradiance on the calculation of photovoltaic panel temperature. The multiple irradiance contribution values ​​are used to reflect the influence of irradiance on photovoltaic panel temperature. Based on the ambient temperature dataset and the irradiance dataset, multiple environmental impact values ​​and multiple irradiance impact values ​​are obtained by processing with the ambient temperature influence function and the irradiance correction function, respectively. The irradiance correction function is obtained by introducing a correction factor on the basis of the irradiance function, and is used to adjust the degree of influence of the average irradiance on the photovoltaic panel surface on the health status assessment. The multiple irradiance impact values ​​are used to reflect the influence of the average irradiance on the photovoltaic panel surface on the health status assessment. The temperature variation coefficient of the photovoltaic panel to be identified is calculated based on the multiple color contribution values, the multiple area effectiveness values, the multiple time contribution values, the multiple irradiance contribution values, the multiple environmental impact values, and the multiple irradiance impact values. Based on the temperature variation coefficient value, the photovoltaic panel to be identified is identified to obtain the target problem identification result of the photovoltaic panel to be identified.

2. The method according to claim 1, characterized in that, The infrared image is analyzed to determine multiple color contribution values ​​of the HSV color of pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as multiple region validity values ​​of multiple pixels in the infrared image, including: Obtain multiple hue values, multiple saturation values, and multiple brightness values ​​of multiple pixels in the infrared image; The multiple hue values, multiple saturation values, and multiple brightness values ​​are analyzed respectively, and the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified are determined. Using a rectangle function, it is determined whether the plurality of pixels are within the effective area of ​​the image of the photovoltaic panel to be identified, and the effectiveness value of the plurality of areas is obtained.

3. The method according to claim 2, characterized in that, The multiple color contribution values ​​include multiple hue contribution values, multiple saturation contribution values, and multiple brightness contribution values; the multiple hue values, multiple saturation values, and multiple brightness values ​​are analyzed and the multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified are determined, including: The average hue of the infrared image is determined based on the multiple hue values. Using a preset error function, the deviation between each hue value and the average hue value is normalized to obtain the multiple hue contribution values; The Gaussian function is used to analyze and process the multiple saturation values ​​and determine the multiple saturation contribution values; The multiple brightness values ​​are analyzed and processed using an exponential function to determine the multiple brightness contribution values.

4. The method according to claim 1, characterized in that, Based on the ambient temperature dataset and the irradiance dataset, multiple environmental impact values ​​and multiple irradiance impact values ​​are obtained after processing with the ambient temperature influence function and the irradiance correction function, respectively, including: Based on the environmental temperature dataset, the environmental temperature influence function is used to quantify the impact of environmental temperature on the temperature and health status assessment of the photovoltaic panel to be identified, and the multiple environmental influence values ​​are obtained. Based on the irradiance dataset, the influence of irradiance on the photovoltaic panel temperature is quantified using the irradiance correction function to obtain the multiple irradiance influence values.

5. The method according to claim 1, characterized in that, Based on the multiple color contribution values, the multiple regional effectiveness values, the multiple time contribution values, the multiple irradiance contribution values, the multiple environmental impact values, and the multiple irradiance impact values, the temperature variation coefficient value of the photovoltaic panel to be identified is calculated, including: Based on the multiple color contribution values, the multiple irradiance contribution values, the multiple region validity values, and the multiple time contribution values, multiple initial temperature values ​​of multiple target regions in the infrared image are calculated, wherein the multiple target regions are multiple non-overlapping minimal regions divided according to the infrared image; Calculate the temperature-weighted average value based on the multiple initial temperature values; The multiple initial temperature values ​​are normalized using a normalization function to obtain multiple target temperature values; The temperature variation coefficient value of the photovoltaic panel to be identified is calculated based on the multiple initial temperature values, the temperature weighted average value, the multiple target temperature values, the multiple environmental impact values, and the multiple irradiance impact values.

6. The method according to claim 5, characterized in that, The method further includes: Based on the multiple initial temperature values, the photovoltaic panel to be identified is used to identify problems, and the initial problem identification results are obtained.

7. The method according to claim 1, characterized in that, The method further includes: Obtain multiple temperature variation coefficient values ​​of the photovoltaic panel to be identified; The multiple temperature variation coefficient values ​​are analyzed to determine the trend of the health status of the photovoltaic panel to be identified.

8. A photovoltaic panel problem identification device, characterized in that, The device includes: The acquisition module is used to acquire infrared images, ambient temperature datasets, irradiance datasets, and time datasets of the photovoltaic panel to be identified. The analysis and determination module is used to analyze the infrared image and determine multiple color contribution values ​​of the HSV color of the pixels in the infrared image to the temperature of the photovoltaic panel to be identified, as well as multiple region validity values ​​of multiple pixels in the infrared image. The multiple region validity values ​​are used to characterize whether the pixels are within the effective area of ​​the image of the photovoltaic panel to be identified. The first processing module is used to process the time dataset and the irradiance dataset respectively through a time correction function and an irradiance function to obtain multiple time contribution values ​​and multiple irradiance contribution values. The irradiance function adopts an S-shaped curve function form to quantify the degree of influence of irradiance on the photovoltaic panel temperature calculation. The multiple irradiance contribution values ​​are used to reflect the influence of irradiance on the photovoltaic panel temperature. The processing module is used to process the ambient temperature dataset and the irradiance dataset using the ambient temperature influence function and the irradiance correction function, respectively, to obtain multiple environmental influence values ​​and multiple irradiance influence values. The irradiance correction function is obtained by introducing a correction factor on the basis of the irradiance function, and is used to adjust the degree of influence of the average irradiance on the photovoltaic panel surface on the health status assessment. The multiple irradiance influence values ​​are used to reflect the influence of the average irradiance on the photovoltaic panel surface on the health status assessment. The calculation module is used to calculate the temperature variation coefficient value of the photovoltaic panel to be identified based on the multiple color contribution values, the multiple area validity values, the multiple time contribution values, the multiple irradiance contribution values, the multiple environmental impact values, and the multiple irradiance impact values. The identification module is used to identify problems in the photovoltaic panel to be identified based on the temperature variation coefficient value, and obtain the target problem identification result of the photovoltaic panel to be identified.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic panel problem identification method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the photovoltaic panel problem identification method according to any one of claims 1 to 7.

Images