Photovoltaic power generation efficiency evaluation method, device, equipment and storage medium

Abstract

The present application discloses a method, device, equipment and storage medium for evaluating the power generation efficiency of photovoltaic power generation, which relates to the technical field of renewable energy, and includes: obtaining multi-source photovoltaic power generation data in real time, wherein the multi-source photovoltaic power generation data includes electricity meter data, panoramic image data, thermal imaging data, irradiance data and ambient temperature data; performing image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result, and determining abnormal photovoltaic panels according to the image recognition result; calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result; obtaining the historical power generation efficiency, and performing time series analysis according to the historical power generation efficiency and the real-time power generation efficiency to obtain an efficiency analysis result; generating an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panels. The present application can achieve accurate evaluation of the power generation efficiency of photovoltaic power stations.

Description

Technical Field

[0001] This application relates to the technical field of renewable energy, and particularly to a method, device, equipment and storage medium for evaluating the power generation efficiency of photovoltaic power generation. Background Art

[0002] In the current field of photovoltaic power generation, there are certain limitations in the evaluation of the power generation efficiency of photovoltaic power stations. Traditional evaluation methods usually rely on inverter data or electricity meter readings as the only data sources. Although these data can reflect the overall power generation situation, they lack detailed component-level information. Relying solely on inverter data and electricity meter readings, it is impossible to obtain the specific performance data of individual photovoltaic panels or components, and thus it is impossible to identify local problems, such as the failure or performance degradation of individual photovoltaic panels, resulting in inaccurate overall evaluation results. That is to say, due to the lack of detailed component-level data, the accuracy of evaluating the power generation efficiency of photovoltaic power stations is limited.

[0003] The above content is only used to assist in understanding the technical solution of this application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for evaluating the power generation efficiency of photovoltaic power generation, aiming to solve the technical problem of inaccurate evaluation results of the power generation efficiency of photovoltaic power stations.

[0005] To achieve the above purpose, this application proposes a method for evaluating the power generation efficiency of photovoltaic power generation, and the method for evaluating the power generation efficiency of photovoltaic power generation includes:

[0006] Real-time acquisition of multi-source photovoltaic power generation data, where the multi-source photovoltaic power generation data includes electricity meter data, panoramic image data, thermal imaging data, irradiance data and ambient temperature data;

[0007] Perform image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result, and determine abnormal photovoltaic panels according to the image recognition result;

[0008] Calculate the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result;

[0009] Obtain the historical power generation efficiency, and perform time series analysis according to the historical power generation efficiency and the real-time power generation efficiency to obtain an efficiency analysis result;

[0010] Generate an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panels.

[0011] In one embodiment, the step of performing image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result includes:

[0012] Perform object detection on the photovoltaic panels in the panoramic image data to determine each photovoltaic panel in the panoramic image data and the model of each photovoltaic panel;

[0013] Perform thermal map analysis on the thermal imaging data to generate a temperature distribution map of the photovoltaic power station, and determine the temperature range of each photovoltaic panel according to each photovoltaic panel in the panoramic image data and the temperature distribution map of the photovoltaic power station;

[0014] Calculate the total number of the photovoltaic panels, and calculate the total area of the photovoltaic panels of the photovoltaic power station according to the total number and model of the photovoltaic panels;

[0015] Take the temperature range of the photovoltaic panels and the total area of the photovoltaic panels as the image analysis results.

[0016] In one embodiment, the step of determining abnormal photovoltaic panels according to the image analysis results includes:

[0017] Determine the first photovoltaic panels with abnormal temperature according to the temperature range of each photovoltaic panel;

[0018] Detect the surface condition of each photovoltaic panel in the panoramic image data, and determine the photovoltaic panels with abnormal surface conditions as the second photovoltaic panels;

[0019] Determine the first photovoltaic panels and the second photovoltaic panels as abnormal photovoltaic panels.

[0020] In one embodiment, the step of calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis results includes:

[0021] Preprocess the electricity meter data to obtain the actual power generation of the photovoltaic power station;

[0022] Calculate the photoelectric conversion efficiency of the photovoltaic power station based on the actual power generation, the irradiance data, and the total area of the photovoltaic panels;

[0023] Calculate the theoretical power generation based on the irradiance data, the total area of the photovoltaic panels, the photoelectric conversion efficiency, and the ambient temperature data;

[0024] Determine the real-time power generation efficiency of the photovoltaic power station according to the actual power generation and the theoretical power generation.

[0025] In one embodiment, before the step of generating an efficiency evaluation report according to the efficiency analysis results and the abnormal photovoltaic panels, it further includes:

[0026] Real-time obtain the voltage value, current value, and photovoltaic panel temperature value of each photovoltaic panel, and calculate the real-time power generation of each photovoltaic panel according to the voltage value and the current value;

[0027] Perform clustering processing on each of the photovoltaic panels according to the real-time power generation amount, the photovoltaic panel temperature value, and the irradiance data of each photovoltaic panel to obtain the clustering result of each photovoltaic panel;

[0028] Identify the significant features of each of the photovoltaic panels according to the clustering results of each photovoltaic panel, and generate an efficiency evaluation report based on the significant features of each photovoltaic panel.

[0029] In one embodiment, after the step of calculating the real-time power generation amount of each photovoltaic panel according to the voltage value and the current value, the following steps are further included:

[0030] Determine the current performance data according to the real-time power generation amount and the photovoltaic panel temperature value of each photovoltaic panel;

[0031] Obtain the historical performance data of each photovoltaic panel, and calculate the historical performance deviation value according to the historical performance data and the current performance data;

[0032] Determine the adjacent photovoltaic panels of each photovoltaic panel, and calculate the adjacent performance deviation value according to the current performance data of the photovoltaic panel and the current performance data of the adjacent photovoltaic panel;

[0033] Obtain the preset standard temperature, and calculate the temperature anomaly degree according to the current performance data of the photovoltaic panel and the preset standard temperature;

[0034] Perform weighted summation on the historical performance deviation value, the adjacent performance deviation value, and the temperature anomaly degree to obtain an anomaly score;

[0035] Generate an efficiency evaluation report according to the anomaly scores of each photovoltaic panel.

[0036] In one embodiment, the step of performing time series analysis on the historical power generation efficiency and the real-time power generation efficiency to obtain an efficiency analysis result includes:

[0037] Input the historical power generation efficiency into a preset prediction model to obtain the predicted efficiency output by the model;

[0038] Compare the predicted efficiency with the real-time power generation efficiency to determine whether the real-time power generation efficiency is within the preset deviation range of the predicted efficiency;

[0039] If the real-time power generation efficiency is outside the preset deviation range of the predicted efficiency, determine that the real-time power generation efficiency is an abnormal power generation efficiency, and determine the efficiency analysis result based on the fact that the real-time power generation efficiency is an abnormal power generation efficiency.

[0040] In addition, to achieve the above object, the present application further provides a photovoltaic power generation efficiency evaluation device, which includes:

[0041] A data acquisition module, configured to acquire multi-source photovoltaic power generation data in real time, where the multi-source photovoltaic power generation data includes electricity meter data, panoramic image data, thermal imaging data, irradiance data, and ambient temperature data;

[0042] An image analysis module, configured to perform image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result, and determine abnormal photovoltaic panels according to the image recognition result;

[0043] An efficiency calculation module, configured to calculate the real-time power generation efficiency of a photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result;

[0044] A time analysis module, configured to obtain the historical power generation efficiency, perform time series analysis according to the historical power generation efficiency and the real-time power generation efficiency, and obtain an efficiency analysis result;

[0045] A report generation module, configured to generate an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panels.

[0046] In addition, to achieve the above object, the present application further provides a photovoltaic power generation efficiency evaluation device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the photovoltaic power generation efficiency evaluation method as described above.

[0047] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the photovoltaic power generation efficiency evaluation method as described above are implemented.

[0048] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the photovoltaic power generation efficiency evaluation method as described above are implemented.

[0049] This application obtains various types of multi-source photovoltaic power generation data in real time, including electricity meter data, panoramic image data, thermal imaging data, irradiance data, and ambient temperature data; performs image analysis on the panoramic image data and thermal imaging data to obtain image analysis results, and determines abnormal photovoltaic panels based on the image recognition results. Through image analysis technology, it automatically detects abnormal conditions of photovoltaic panels, including surface dirt, cracks, heat generation, etc., thereby quickly locating the problematic photovoltaic panels; calculates the real-time power generation efficiency of the photovoltaic power station based on the multi-source photovoltaic power generation data and the image analysis results, calculates the power generation efficiency of the photovoltaic power station in real time, and immediately conducts performance evaluation; obtains the historical power generation efficiency, conducts time series analysis based on the historical power generation efficiency and the real-time power generation efficiency to obtain efficiency analysis results, identifies the change trend and abnormal fluctuations of the power generation efficiency, and provides long-term performance evaluation and prediction; generates an efficiency evaluation report based on the efficiency analysis results and the abnormal photovoltaic panels, including the overall performance of the photovoltaic power station, the location and type of the abnormal photovoltaic panels, the efficiency change trend, and improvement suggestions. This application can collect various types of data in real time through multi-sensor collaborative processing, provide immediate performance evaluation for the photovoltaic power station, not only improve the management efficiency of the photovoltaic power station, but also achieve real-time and accurate evaluation of the power generation efficiency of the photovoltaic power station, which helps to improve the overall performance and maintenance efficiency of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the photovoltaic power generation efficiency evaluation method of this application;

[0053] Figure 2 It is a stage flowchart of the photovoltaic power generation efficiency evaluation method provided for Embodiment 1 of this application;

[0054] Figure 3 It is a schematic brief flowchart of the photovoltaic power generation efficiency evaluation method provided for Embodiment 1 of this application;

[0055] Figure 4 It is a schematic module structure diagram of the photovoltaic power generation efficiency evaluation device for the embodiments of this application;

[0056] Figure 5It is a schematic diagram of the device structure of the hardware operating environment involved in the photovoltaic power generation efficiency evaluation method in the embodiments of the present application.

[0057] The implementation, functional features, and advantages of the present application will be further described with reference to the accompanying drawings in combination with the embodiments. Specific embodiments

[0058] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0059] To better understand the technical solutions of the present application, the following will be described in detail in combination with the accompanying drawings of the specification and specific embodiments.

[0060] Based on this, the embodiments of the present application provide a photovoltaic power generation efficiency evaluation method, referring to Figure 1 , Figure 1 It is a flowchart of the first embodiment of the photovoltaic power generation efficiency evaluation method of the present application.

[0061] In this embodiment, the photovoltaic power generation efficiency evaluation method includes steps S10 to S50:

[0062] Step S10, obtaining multi-source photovoltaic power generation data in real time, where the multi-source photovoltaic power generation data includes electricity meter data, panoramic image data, thermal imaging data, irradiance data, and ambient temperature data.

[0063] In a feasible implementation manner, step S10 may include steps S11 to S13:

[0064] Step S11, establishing a communication connection with a handheld infrared device, and obtaining the electricity meter data collected by the handheld infrared device based on the communication connection.

[0065] It should be noted that the handheld infrared device is used to collect the real-time power generation data during the detection of the photovoltaic power station. The handheld infrared device uses a high-precision and low-power infrared sensor, which can accurately capture the data display of the electricity meter, support the collection of electricity meter data conforming to the remote communication 645 protocol, and can collect key parameters including the combined positive active total electric energy.

[0066] Step S12, obtaining the panoramic image data and thermal imaging data of the photovoltaic power station collected by the cruise unmanned aerial vehicle.

[0067] It should be noted that the cruise drone can collect panoramic images and thermal imaging data of photovoltaic power stations from a high-altitude perspective. The main body of the drone adopts an industrial-grade drone with high endurance and strong wind resistance. It needs to have a high-definition camera with a resolution of no less than 4K, support optical zoom, and be equipped with a thermal imaging device with a temperature measurement accuracy of ±2°C and a resolution of no less than 640×480. It has an RTK module and can provide centimeter-level positioning accuracy to ensure the accuracy of the flight trajectory. The drone supports preset flight paths, can realize automatic inspections, supports multi-spectral imaging, and can identify tiny defects in photovoltaic panels.

[0068] Step S13, obtaining irradiance data and ambient temperature data collected by the irradiator.

[0069] It should be noted that the irradiance meter can measure irradiance data and temperature values ​​at the same time. The high-precision irradiance sensor has a measurement range of 0-2000W / m² and an accuracy of ≤±2%. The temperature sensor can measure the ambient temperature and the temperature of the photovoltaic panel backplane. It supports wired and wireless communication methods, can monitor solar irradiance, ambient temperature and photovoltaic panel temperature in real time, supports multi-point layout, and can capture the irradiance distribution differences of large power stations.

[0070] Step S20, performing image analysis on the panoramic image data and the thermal imaging data to obtain image analysis results, and determining abnormal photovoltaic panels based on the image recognition results.

[0071] It should be noted that for panoramic image data, image analysis may include: edge detection, using the Canny edge detection algorithm or the Sobel operator to extract the edge information of the photovoltaic panel; color segmentation, using color space (such as HSV) to perform color segmentation to distinguish the photovoltaic panel from other backgrounds; morphological operations, using open and close operations to remove small noise areas and retain the main photovoltaic panel outlines. For thermal imaging data, image analysis may include: temperature distribution, extracting the temperature value of each pixel and generating a temperature distribution map; hot spot detection, identifying areas with abnormally high temperatures, which may be fault points; temperature gradient, calculating the temperature gradient, and identifying areas with drastic temperature changes.

[0072] In a feasible implementation, step S20 may include steps A10 to A40:

[0073] Step A10, performing target detection on the photovoltaic panels in the panoramic image data, and determining each photovoltaic panel and the model of each photovoltaic panel in the panoramic image data.

[0074] It should be noted that object detection uses machine learning or deep learning models to locate and classify objects in images. Specifically, for photovoltaic panel detection, region-based convolutional neural networks (R-CNNs), single-stage detectors (such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), which directly predict object bounding boxes and class probabilities and are faster), or instance segmentation (if more precise segmentation of the edges of photovoltaic panels is required, models such as Mask R-CNN can be used) can be adopted.

[0075] Once the positions of the photovoltaic panels are detected, the specific models of each photovoltaic panel need to be identified next. If reference pictures of different model photovoltaic panels are known, template matching algorithms can be used, or specialized trained classifiers, such as CNNs, can be used to identify the models based on the visual features of the photovoltaic panels.

[0076] Step A20: Perform a heat map analysis on the thermal imaging data to generate a temperature distribution map of the photovoltaic power station, and determine the temperature range of each photovoltaic panel based on each photovoltaic panel in the panoramic image data and the temperature distribution map of the photovoltaic power station.

[0077] It should be noted that by generating a temperature distribution map from the thermal imaging data, the temperature distribution of the photovoltaic power station can be intuitively displayed. The specific steps can include: converting the pixel values in the thermal imaging data into actual temperature values, using color coding to represent different temperature ranges, and generating a colored temperature distribution map. Then, using the position information of the photovoltaic panels detected in the panoramic image data, for each photovoltaic panel, determine the temperature range or other statistics within its coverage area. Among them, in addition to the temperature range of the photovoltaic panels, temperature sensors can also be separately set for each photovoltaic panel in this application to detect the temperature value of each photovoltaic panel to obtain more accurate temperature data than the temperature range of the photovoltaic panels.

[0078] Step A30: Calculate the total number of photovoltaic panels, and calculate the total area of the photovoltaic panels in the photovoltaic power station based on the total number of photovoltaic panels and their models.

[0079] It should be noted that computer vision techniques (such as deep learning-based object detection algorithms) can be used to automatically detect and count each photovoltaic panel from the panoramic image data. Next, the area corresponding to each photovoltaic panel model needs to be known. Using the total number of photovoltaic panels and the area of each photovoltaic panel, the total area of the photovoltaic panels in the photovoltaic power station can be calculated, usually by multiplying the area of each photovoltaic panel by its quantity and then adding them up.

[0080] Step A40: Take the temperature range of the photovoltaic panels and the total area of the photovoltaic panels as the image analysis results.

[0081] Through a comprehensive analysis of panoramic image data and thermal imaging data, this embodiment can not only accurately detect and identify the position and model of each photovoltaic panel, but also generate a detailed temperature distribution map, and calculate the total number and total area of the photovoltaic panels. Finally, these information are integrated into the image analysis results, providing reliable data support for subsequent power generation efficiency evaluation.

[0082] In a feasible embodiment, step S30 may further include steps A50 to A80:

[0083] Step A50, determining the first photovoltaic panels with abnormal temperature according to the temperature range of each photovoltaic panel.

[0084] It should be noted that before detecting temperature anomalies, reasonable temperature thresholds need to be set. These thresholds can be determined based on industry standards, historical data or expert experience. The temperature thresholds can include high temperature thresholds, low temperature thresholds and temperature difference thresholds.

[0085] For each photovoltaic panel, its temperature range needs to be determined. According to the set temperature thresholds, it is judged whether each photovoltaic panel has temperature anomalies. Specifically, if the temperature of the photovoltaic panel exceeds the set high temperature threshold or low temperature threshold, or the temperature difference exceeds the temperature difference threshold, it is marked as the first photovoltaic panel with temperature anomalies.

[0086] Step A60, detecting the surface condition of each photovoltaic panel in the panoramic image data, and determining the photovoltaic panels with abnormal surface conditions as the second photovoltaic panels.

[0087] It should be noted that in order to detect the surface condition of the photovoltaic panel, features need to be extracted from the image. These features can include edges, textures, colors, etc. In order to detect the surface condition of the photovoltaic panel, features need to be extracted from the image. These features can include edges, textures, colors, etc. Methods such as local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) are used to analyze the texture of the photovoltaic panel surface. Then, using the extracted features, an anomaly detection algorithm is applied to identify abnormal conditions on the surface of the photovoltaic panel. For example, using a pre-trained convolutional neural network (CNN) model, such as ResNet, VGG, etc., to classify the photovoltaic panel images and identify abnormal conditions, and using rule-based methods, such as template matching, morphological operations, etc., to detect specific types of defects.

[0088] Step A70, determining the first photovoltaic panels and the second photovoltaic panels as abnormal photovoltaic panels.

[0089] This embodiment realizes the comprehensive identification of abnormal photovoltaic panels in a photovoltaic power station. It can not only accurately detect photovoltaic panels with abnormal temperatures, but also identify photovoltaic panels with abnormal surface conditions, and combine these two types of abnormal photovoltaic panels to form a final list of abnormal photovoltaic panels.

[0090] Step S30: Calculate the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis results.

[0091] In a feasible embodiment, step S30 may include steps S31 to S34:

[0092] Step S31: Preprocess the meter data to obtain the actual power generation of the photovoltaic power station.

[0093] It should be noted that the original power generation data is read from the meter or other data sources, and data cleaning is performed on it, including removing outliers, missing values, and noise data, and unit conversion is carried out. The actual power generation is usually the total power generation within a certain time period.

[0094] Step S32: Calculate the photoelectric conversion efficiency of the photovoltaic power station based on the actual power generation, the irradiance data, and the total area of the photovoltaic panels.

[0095] It should be noted that the photoelectric conversion efficiency is the ratio of the actual power generation to the theoretical maximum power generation. The theoretical maximum power generation refers to the maximum electrical energy that the photovoltaic power station can generate under ideal conditions. This value can be calculated by the following formula:

[0096] Theoretical maximum power generation = irradiance × total area of photovoltaic panels × conversion efficiency under standard test conditions.

[0097] Among them, the conversion efficiency under standard test conditions usually refers to the conversion efficiency of the photovoltaic panel measured under standard test conditions (STC, Standard Test Conditions). The STC conditions are usually defined as: temperature: 25°C, irradiance: 1000 W / m², air mass: AM1.5.

[0098] Step S33: Calculate the theoretical power generation based on the irradiance data, the total area of the photovoltaic panels, the photoelectric conversion efficiency, and the ambient temperature data.

[0099] The theoretical power generation Q_theoretical can be calculated by the following formula:

[0100] Q_theoretical = ∑(G_i * A * η * Δt_i * (1 - β(T_i - T_STC))), where G_i is the irradiance in the i-th time period, A is the total area of the photovoltaic panels, η is the photoelectric conversion efficiency, Δt_i is the time interval, β is the temperature coefficient, T_i is the actual temperature, and T_STC is the temperature under standard test conditions (usually 25°C).

[0101] It should be noted that the irradiance data is the solar irradiance data at the location of the photovoltaic power station, usually in watts per square meter (W / m²). The total area of the photovoltaic panels is the total area of all the photovoltaic panels in the photovoltaic power station, usually in square meters (m²). The photoelectric conversion efficiency is the photoelectric conversion efficiency obtained from step S32. The ambient temperature data is the ambient temperature data at the location of the photovoltaic power station, usually in degrees Celsius (°C). The output power of the photovoltaic panels is affected by temperature. Generally, an increase in temperature will cause a decrease in the output power of the photovoltaic panels. Therefore, a temperature correction factor (1 - β(T_i - T_STC)) needs to be introduced to adjust the calculation of the theoretical power generation. Among them, the temperature coefficient represents the percentage decrease in output power for every 1°C increase in temperature (for example, -0.5% per degree).

[0102] Step S34, determine the real-time power generation efficiency of the photovoltaic power station according to the actual power generation and the theoretical power generation.

[0103] It should be noted that the formula for calculating the power generation efficiency is: real-time power generation efficiency = theoretical power generation / actual power generation. Considering the influence of factors such as dust and shadows, a correction factor k can be introduced, and the value of k is dynamically adjusted through historical data analysis and machine learning algorithms.

[0104] This embodiment effectively calculates the actual power generation, photoelectric conversion efficiency, theoretical power generation, and real-time power generation efficiency of the photovoltaic power station, ensuring an accurate assessment of the performance of the photovoltaic power station.

[0105] Step S40, obtain the historical power generation efficiency, and perform time series analysis based on the historical power generation efficiency and the real-time power generation efficiency to obtain the efficiency analysis result.

[0106] In a feasible embodiment, step S40 may include steps S41 to S43:

[0107] Step S41, input the historical power generation efficiency into a preset prediction model to obtain the predicted efficiency output by the model.

[0108] It should be noted that, first of all, historical data for training the prediction model needs to be collected and prepared, including the actual power generation of the photovoltaic power station over a period of time in the past, and the theoretical power generation calculated based on environmental conditions such as irradiance and temperature, such as irradiance (solar radiation intensity), temperature, humidity, etc. Then, a suitable prediction model is selected, such as a time series model, a machine learning model, or a deep learning model, and the selected model is trained using the historical data. The trained model is used to predict the data for the current time period to obtain the predicted power generation efficiency.

[0109] Step S42: Compare the predicted efficiency with the real-time power generation efficiency to determine whether the real-time power generation efficiency is within the preset deviation range of the predicted efficiency.

[0110] It should be noted that, first of all, a reasonable deviation range is set. This range can be determined based on empirical values of historical data or industry standards. For example, if historical data shows that the actual power generation efficiency usually fluctuates within ±5% of the predicted value, then ±5% can be set as the preset deviation range. Calculate the deviation between the real-time power generation efficiency and the predicted efficiency, and judge whether the real-time power generation efficiency is within the reasonable range according to the set preset deviation range. If the deviation is less than or equal to the preset deviation range, the real-time power generation efficiency is considered normal; otherwise, the real-time power generation efficiency is considered abnormal.

[0111] Step S43: If the real-time power generation efficiency is outside the preset deviation range of the predicted efficiency, determine that the real-time power generation efficiency is an abnormal power generation efficiency, and determine the efficiency analysis result based on the fact that the real-time power generation efficiency is an abnormal power generation efficiency.

[0112] It should be noted that the efficiency analysis result may include, in addition to the real-time power generation efficiency and the predicted efficiency, information such as the deviation between the real-time power generation efficiency and the predicted efficiency, whether it exceeds the preset deviation range, the reason for the abnormality, and the recommended measures proposed for the reason for the abnormality.

[0113] This embodiment ensures the accurate assessment of the real-time power generation efficiency of the photovoltaic power station, and provides important performance indicators and an abnormal detection mechanism, which can effectively determine whether the real-time power generation efficiency of the photovoltaic power station is abnormal based on the historical power generation efficiency and the real-time power generation efficiency.

[0114] Step S50: Generate an efficiency evaluation report based on the efficiency analysis result and the abnormal photovoltaic panels.

[0115] Exemplarily, Figure 2 A stage flowchart is provided, such as Figure 2As shown in the figure, the evaluation system provided in this embodiment includes five modules: handheld infrared data collection, drone cruise analysis, image recognition, irradiance measurement, data processing and evaluation, and five execution stages: data collection stage, data transmission stage, data processing stage, efficiency evaluation stage, and result analysis and report generation stage. In the data collection stage, the user holds the infrared electricity meter to continuously collect electricity meter data and record the initial value, usually one cycle every 5 minutes, for a total of 30 minutes. The drone cruises in the air to inspect the photovoltaic power station according to the preset path, collecting panoramic images and thermal imaging data, that is, two types of photos: visible light and non-visible light. At the same time, the photos will be processed to calculate the installed capacity of the photovoltaic power station. The irradiance meter records the solar irradiance and temperature data in real time; in the data transmission stage, all the collected data is transmitted to the cloud platform in real time through a secure channel, and a data integrity check is performed to ensure that all necessary data has been collected; in the data processing stage, the images collected by the drone are processed through image recognition to identify the ground station data, the number of photovoltaic panels, and abnormal situations, and all the collected data is cleaned and preprocessed; in the efficiency evaluation stage, the actual power generation efficiency is calculated based on the processed data, the actual efficiency is compared with the theoretical efficiency, and the efficiency abnormal areas are identified; in the result analysis and report generation stage, the reasons for the efficiency abnormality are analyzed, which may include equipment failures, environmental factors, etc., and a comprehensive report including the efficiency evaluation results, abnormal analysis, and optimization suggestions is generated.

[0116] This embodiment obtains various types of multi-source photovoltaic power generation data in real time, including electricity meter data, panoramic image data, thermal imaging data, irradiance data, and environmental temperature data; performs image analysis on the panoramic image data and thermal imaging data to obtain the image analysis results, and determines the abnormal photovoltaic panels according to the image recognition results. Through image analysis technology, the abnormal situations of the photovoltaic panels are automatically detected, including surface dirt, cracks, heating, etc., so as to quickly locate the problem photovoltaic panels; calculates the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis results, calculates the power generation efficiency of the photovoltaic power station in real time, and immediately conducts performance evaluation; obtains the historical power generation efficiency, conducts time series analysis based on the historical power generation efficiency and the real-time power generation efficiency to obtain the efficiency analysis results, identifies the change trend and abnormal fluctuations of the power generation efficiency, and provides long-term performance evaluation and prediction; generates an efficiency evaluation report according to the efficiency analysis results and the abnormal photovoltaic panels, including the overall performance of the photovoltaic power station, the location and type of the abnormal photovoltaic panels, the efficiency change trend, and improvement suggestions. This embodiment can collect various types of data in real time through multi-sensor collaborative processing, provide immediate performance evaluation for the photovoltaic power station, not only improve the management efficiency of the photovoltaic power station, but also achieve real-time and accurate evaluation of the power generation efficiency of the photovoltaic power station, which helps to improve the overall performance and maintenance efficiency of the photovoltaic power station.

[0117] Exemplarily, to facilitate understanding of the implementation process of the photovoltaic power generation efficiency evaluation method obtained by combining this embodiment with the above-mentioned first embodiment, please refer to Figure 3 , Figure 3 which provides a schematic diagram of the brief process of a photovoltaic power generation efficiency evaluation method. Specifically:

[0118] The power generation efficiency evaluation system proposed in this embodiment includes the following five modules: handheld infrared data collection, drone cruise analysis, image recognition, irradiance measurement, data processing and evaluation. The five modules execute in parallel, and the modules are connected and data-exchanged through a data bus and the software system supporting this embodiment, forming a complete information collection, transmission, processing, and analysis system. Among them, 1-6 represent 6 steps. 1 is to collect the electricity meter data on the electricity meter by a handheld infrared device, 2 is to perform a cruise thermal imaging analysis on the photovoltaic modules by a drone, 3 is to perform image recognition on the photovoltaic modules, 4 is to measure the irradiance by an irradiance meter, 5 is to perform data processing and evaluation by a cloud platform, and 6 is to generate a report.

[0119] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the above-mentioned first embodiment can be referred to the above introduction and will not be elaborated hereinafter. On this basis, before step S50, the photovoltaic power generation efficiency evaluation method further includes steps B10 to B30:

[0120] Step B10, obtain the voltage value, current value, and photovoltaic panel temperature value of each photovoltaic panel in real time, and calculate the real-time power generation amount of each photovoltaic panel according to the voltage value and the current value.

[0121] It should be noted that a voltage sensor and a current sensor are installed on each photovoltaic panel for real-time monitoring of voltage and current. The data acquisition system (such as a SCADA system, a data recorder, etc.) periodically or continuously collects data from the sensors and transmits the data to the cloud platform or data center by wired or wireless means. The real-time power generation amount of each photovoltaic panel can be calculated using the formula P = IV, where P is the real-time power generation amount, V is the voltage, and I is the current.

[0122] Step B20, perform clustering processing on each photovoltaic panel according to the real-time power generation amount, the photovoltaic panel temperature value, and the irradiance data of each photovoltaic panel to obtain the clustering result of each photovoltaic panel.

[0123] It should be noted that the temperature range of the photovoltaic panel here can be replaced by the temperature of the photovoltaic panel measured by the temperature sensor arranged in each photovoltaic panel. Compared with the temperature range of the photovoltaic panel, its temperature is more accurate. In this application, the real-time power generation, the temperature range of the photovoltaic panel, and the irradiance data are used as the features for clustering. A clustering algorithm is selected, and the selected clustering algorithm is applied to cluster the data. According to the clustering results, the clustering category to which each photovoltaic panel belongs can be determined. Among them, the fineness of clustering can be controlled by controlling the number of clusters of clustering.

[0124] Step B30: Identify the significant features of each photovoltaic panel according to the clustering results of each photovoltaic panel, and generate an efficiency evaluation report according to the significant features of each photovoltaic panel.

[0125] In a feasible implementation manner, after step B10, steps C10 to C60 may further be included:

[0126] Step C10: Determine the current performance data according to the real-time power generation and the photovoltaic panel temperature value of each photovoltaic panel.

[0127] It should be noted that the current performance data is a combination of the real-time power generation and the photovoltaic panel temperature value.

[0128] Step C20: Obtain the historical performance data of each photovoltaic panel, and calculate the historical performance deviation value according to the historical performance data and the current performance data.

[0129] The historical performance deviation value includes the historical performance deviation value of the real-time power generation and the historical performance deviation value of the photovoltaic panel temperature value.

[0130] Step C30: Determine the adjacent photovoltaic panels of each photovoltaic panel, and calculate the adjacent performance deviation value according to the current performance data of the photovoltaic panel and the current performance data of the adjacent photovoltaic panels.

[0131] It should be noted that in the previous image recognition, each photovoltaic panel has been recognized from the panoramic image data. Therefore, the adjacent photovoltaic panels of each photovoltaic panel can be directly determined. The adjacent performance deviation value also includes the adjacent performance deviation value of the real-time power generation and the adjacent performance deviation value of the photovoltaic panel temperature value.

[0132] Step C40: Obtain the preset standard temperature, and calculate the temperature anomaly degree according to the current performance data of the photovoltaic panel and the preset standard temperature.

[0133] Calculate the temperature deviation between the photovoltaic panel temperature value in the current performance data and the preset standard temperature, and determine the temperature anomaly degree according to the temperature deviation. The temperature anomaly degree can directly be the absolute temperature deviation, or the ratio of the temperature deviation to the standard temperature.

[0134] Step C50: Perform a weighted sum of the historical performance deviation value, the adjacent performance deviation value, and the degree of temperature anomaly to obtain an anomaly score.

[0135] It should be noted that according to the actual situation and experience, define the weights of the historical performance deviation value, the adjacent performance deviation value, and the degree of temperature anomaly, and merge the data of the historical performance deviation value, the adjacent performance deviation value, and the degree of temperature anomaly into a dataset.

[0136] Step C60: Generate an efficiency evaluation report based on the anomaly scores of each photovoltaic panel.

[0137] In the process of this embodiment for a more in-depth analysis of the performance of photovoltaic panels, by combining real-time data, historical data, and data of adjacent photovoltaic panels to evaluate the performance of each photovoltaic panel and generate an efficiency evaluation report, it ensures a comprehensive evaluation of the performance of each photovoltaic panel in the photovoltaic power station.

[0138] In this embodiment, by real-time monitoring the voltage value and current value of each photovoltaic panel, calculating its real-time power generation, and performing clustering processing on the photovoltaic panels based on these data, and finally generating an anomaly score and an efficiency evaluation report according to the clustering results, it can effectively perform real-time monitoring, clustering processing, and anomaly scoring on each photovoltaic panel in the photovoltaic power station, thereby generating a detailed efficiency evaluation report, ensuring a comprehensive evaluation of the performance of each photovoltaic panel in the photovoltaic power station, and providing important performance indicators and an anomaly detection mechanism.

[0139] It should be noted that the above examples are only for understanding this application and do not constitute a limitation to the photovoltaic power generation efficiency evaluation method of this application. Based on this technical concept, more forms of simple transformation are within the protection scope of this application.

[0140] This application also provides a photovoltaic power generation efficiency evaluation device. Please refer to Figure 4 , the photovoltaic power generation efficiency evaluation device includes:

[0141] A data acquisition module 10 for real-time acquisition of multi-source photovoltaic power generation data, where the multi-source photovoltaic power generation data includes electricity meter data, panoramic image data, thermal imaging data, irradiance data, and ambient temperature data;

[0142] An image analysis module 20 for performing image analysis on the panoramic image data and the thermal imaging data to obtain an image analysis result and determining abnormal photovoltaic panels according to the image recognition result;

[0143] An efficiency calculation module 30 for calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result;

[0144] A time analysis module 40, configured to obtain historical power generation efficiency, perform time series analysis based on the historical power generation efficiency and the real-time power generation efficiency, and obtain an efficiency analysis result;

[0145] A report generation module 50, configured to generate an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panel.

[0146] The photovoltaic power generation efficiency evaluation device provided by the present application adopts the photovoltaic power generation efficiency evaluation method in the above embodiment, and can solve the technical problem that the evaluation result of the power generation efficiency of the photovoltaic power station is not accurate enough. Compared with the prior art, the beneficial effects of the photovoltaic power generation efficiency evaluation device provided by the present application are the same as those of the photovoltaic power generation efficiency evaluation method provided by the above embodiment, and other technical features in the photovoltaic power generation efficiency evaluation device are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0147] The present application provides a photovoltaic power generation efficiency evaluation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the photovoltaic power generation efficiency evaluation method in the first embodiment above.

[0148] Next, refer to Figure 5 , which shows a schematic structural diagram of a photovoltaic power generation efficiency evaluation device suitable for implementing the embodiment of the present application. The photovoltaic power generation efficiency evaluation device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs and desktop computers. Figure 5 The photovoltaic power generation efficiency evaluation device shown is only an example, and should not bring any limitation to the functions and usage scope of the embodiment of the present application.

[0149] As Figure 5As shown, the photovoltaic power generation efficiency evaluation device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the photovoltaic power generation efficiency evaluation device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the photovoltaic power generation efficiency evaluation device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a photovoltaic power generation efficiency evaluation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be alternatively implemented or had.

[0150] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0151] The photovoltaic power generation efficiency evaluation device provided by the present application adopts the photovoltaic power generation efficiency evaluation method in the above embodiment, and can solve the technical problem that the evaluation result of the power generation efficiency of the photovoltaic power station is not accurate enough. Compared with the prior art, the beneficial effects of the photovoltaic power generation efficiency evaluation device provided by the present application are the same as those of the photovoltaic power generation efficiency evaluation method provided by the above embodiment, and the other technical features in the photovoltaic power generation efficiency evaluation device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0152] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0153] As described above, the above is only the specific embodiment of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0154] This application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the photovoltaic power generation efficiency evaluation method in the above embodiments.

[0155] The readable storage medium provided by this application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above photovoltaic power generation efficiency evaluation method, which can solve the technical problem that the evaluation result of the power generation efficiency of a photovoltaic power station is not accurate enough. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the photovoltaic power generation efficiency evaluation method provided by the above embodiments, and will not be elaborated here.

[0156] This application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the photovoltaic power generation efficiency evaluation method as described above.

[0157] The computer program product provided by this application can solve the technical problem that the evaluation result of the power generation efficiency of a photovoltaic power station is not accurate enough. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as those of the photovoltaic power generation efficiency evaluation method provided by the above embodiments, and will not be elaborated here.

[0158] The above is only a partial embodiment of this application, and thus does not limit the patent scope of this application. Any equivalent structural transformation made under the technical concept of this application by using the content of the specification and drawings of this application, or directly / indirectly applied in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A photovoltaic power generation efficiency evaluation method, characterized in that: The photovoltaic power generation efficiency evaluation method comprises: Acquire multi-source photovoltaic power generation data in real time, wherein the multi-source photovoltaic power generation data includes electric meter data, panoramic image data, thermal imaging data, irradiance data and ambient temperature data; Performing image analysis on the panoramic image data and the thermal imaging data to obtain image analysis results, and determining abnormal photovoltaic panels according to the image analysis results; Calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result; Acquire historical power generation efficiency, perform time series analysis based on the historical power generation efficiency and the real-time power generation efficiency, and obtain efficiency analysis results; Generate an efficiency evaluation report based on the efficiency analysis result and the abnormal photovoltaic panel; Wherein, the step of performing image analysis on the panoramic image data and the thermal imaging data to obtain image analysis results includes: Performing target detection on the photovoltaic panels in the panoramic image data to determine each photovoltaic panel in the panoramic image data and the model of each photovoltaic panel; Performing a thermal map analysis on the thermal imaging data to generate a temperature distribution map of the photovoltaic power station, and determining a photovoltaic panel temperature range of each photovoltaic panel according to each photovoltaic panel in the panoramic image data and the temperature distribution map of the photovoltaic power station; Calculating the total number of the photovoltaic panels, and calculating the total area of ​​the photovoltaic panels of the photovoltaic power station according to the total number and models of the photovoltaic panels; The photovoltaic panel temperature range and the photovoltaic panel total area are taken as image analysis results; Wherein, the step of calculating the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result includes: Preprocessing the electric meter data to obtain the actual power generation of the photovoltaic power station; Calculating the photoelectric conversion efficiency of the photovoltaic power station based on the actual power generation, the irradiance data and the total area of ​​the photovoltaic panels; Calculating theoretical power generation based on the irradiance data, the total area of ​​the photovoltaic panels, the photoelectric conversion efficiency, and the ambient temperature data; Determining the real-time power generation efficiency of the photovoltaic power station according to the actual power generation and the theoretical power generation; Wherein, before the step of generating an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panel, the method further includes: Acquire the voltage value, current value and temperature value of each photovoltaic panel in real time, and calculate the real-time power generation of each photovoltaic panel according to the voltage value and the current value; Performing clustering processing on each photovoltaic panel according to the real-time power generation, the temperature value of the photovoltaic panel, and the irradiance data of each photovoltaic panel to obtain a clustering result for each photovoltaic panel; The significant features of each photovoltaic panel are identified according to the clustering results of each photovoltaic panel, and an efficiency evaluation report is generated according to the significant features of each photovoltaic panel.

2. The photovoltaic power generation efficiency evaluation method according to claim 1, characterized in that: The step of determining abnormal photovoltaic panels according to the image analysis results includes: Determine a first photovoltaic panel with abnormal temperature according to the photovoltaic panel temperature range of each photovoltaic panel; detecting the surface condition of each photovoltaic panel in the panoramic image data, and determining a photovoltaic panel with an abnormal surface condition as a second photovoltaic panel; The first photovoltaic panel and the second photovoltaic panel are determined to be abnormal photovoltaic panels.

3. The photovoltaic power generation efficiency evaluation method according to claim 1, characterized in that: After the step of calculating the real-time power generation of each photovoltaic panel according to the voltage value and the current value, the method further includes: Determine current performance data according to the real-time power generation of each photovoltaic panel and the temperature value of the photovoltaic panel; Acquire historical performance data of each of the photovoltaic panels, and calculate a historical performance deviation value based on the historical performance data and the current performance data; Determining adjacent photovoltaic panels of each photovoltaic panel, and calculating adjacent performance deviation values ​​according to current performance data of the photovoltaic panel and current performance data of the adjacent photovoltaic panels; Obtaining a preset standard temperature, and calculating the degree of temperature abnormality according to current performance data of the photovoltaic panel and the preset standard temperature; Performing a weighted summation of the historical performance deviation value, the adjacent performance deviation value, and the temperature anomaly degree to obtain an anomaly score; An efficiency evaluation report is generated according to the abnormality score of each photovoltaic panel.

4. The photovoltaic power generation efficiency evaluation method according to claim 1, characterized in that: The step of performing time series analysis according to the historical power generation efficiency and the real-time power generation efficiency to obtain an efficiency analysis result comprises: Inputting the historical power generation efficiency into a preset prediction model to obtain a predicted efficiency output by the model; Comparing the predicted efficiency with the real-time power generation efficiency to determine whether the real-time power generation efficiency is within a preset deviation range of the predicted efficiency; If the real-time power generation efficiency is outside the preset deviation range of the predicted efficiency, the real-time power generation efficiency is determined to be abnormal power generation efficiency, and an efficiency analysis result is determined based on the real-time power generation efficiency.

5. A photovoltaic power generation efficiency evaluation device, characterized in that: The photovoltaic power generation efficiency evaluation device comprises: A data acquisition module, used for acquiring multi-source photovoltaic power generation data in real time, wherein the multi-source photovoltaic power generation data includes electric meter data, panoramic image data, thermal imaging data, irradiance data and ambient temperature data; An image analysis module, used to perform image analysis on the panoramic image data and the thermal imaging data to obtain image analysis results, and determine abnormal photovoltaic panels according to the image analysis results; An efficiency calculation module, used to calculate the real-time power generation efficiency of the photovoltaic power station according to the multi-source photovoltaic power generation data and the image analysis result; A time analysis module, used to obtain historical power generation efficiency, perform time series analysis based on the historical power generation efficiency and the real-time power generation efficiency, and obtain efficiency analysis results; A report generation module, used to generate an efficiency evaluation report according to the efficiency analysis result and the abnormal photovoltaic panel; Wherein, the image analysis module is specifically used for: Performing target detection on the photovoltaic panels in the panoramic image data to determine each photovoltaic panel in the panoramic image data and the model of each photovoltaic panel; Performing a thermal map analysis on the thermal imaging data to generate a temperature distribution map of the photovoltaic power station, and determining a photovoltaic panel temperature range of each photovoltaic panel according to each photovoltaic panel in the panoramic image data and the temperature distribution map of the photovoltaic power station; Calculating the total number of the photovoltaic panels, and calculating the total area of ​​the photovoltaic panels of the photovoltaic power station according to the total number and models of the photovoltaic panels; The photovoltaic panel temperature range and the photovoltaic panel total area are taken as image analysis results; Wherein, the efficiency calculation module is specifically used for: Preprocessing the electric meter data to obtain the actual power generation of the photovoltaic power station; Calculating the photoelectric conversion efficiency of the photovoltaic power station based on the actual power generation, the irradiance data and the total area of ​​the photovoltaic panels; Calculating theoretical power generation based on the irradiance data, the total area of ​​the photovoltaic panels, the photoelectric conversion efficiency, and the ambient temperature data; Determining the real-time power generation efficiency of the photovoltaic power station according to the actual power generation and the theoretical power generation; Wherein, the photovoltaic power generation efficiency evaluation device is also used for: Acquire the voltage value, current value and temperature value of each photovoltaic panel in real time, and calculate the real-time power generation of each photovoltaic panel according to the voltage value and the current value; Performing clustering processing on each photovoltaic panel according to the real-time power generation, the temperature value of the photovoltaic panel, and the irradiance data of each photovoltaic panel to obtain a clustering result for each photovoltaic panel; The significant features of each photovoltaic panel are identified according to the clustering results of each photovoltaic panel, and an efficiency evaluation report is generated according to the significant features of each photovoltaic panel.

6. A photovoltaic power generation efficiency evaluation device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the photovoltaic power generation efficiency evaluation method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the photovoltaic power generation efficiency evaluation method according to any one of claims 1 to 4 are implemented.

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