Photovoltaic power generation efficiency monitoring system and method, computer device, computer-readable storage medium, and computer program product

Abstract

The present application relates to the technical field of photovoltaic power generation, and discloses a photovoltaic power generation efficiency monitoring system and method, a computer device, a computer-readable storage medium, and a computer program product. The system comprises: a monitoring center, and a photovoltaic module data acquisition module, an environmental data acquisition module, a power generation anomaly analysis module, a power generation anomaly processing module, an energy yield processing module, and a power generation efficiency visualization module which are connected to the monitoring center. The power generation anomaly analysis module is separately connected to the photovoltaic module data acquisition module and the environmental data acquisition module. The power generation anomaly analysis module, the power generation anomaly processing module, the energy yield processing module, and the power generation efficiency visualization module are connected in sequence. The present application achieves accurate monitoring of the power generation efficiency of photovoltaic modules, and can learn about the operational status of power generation devices within a photovoltaic power generation system in a timely manner, thereby ensuring the stable operation of the photovoltaic power generation system.

Description

A photovoltaic power generation efficiency monitoring system, method, computer equipment, computer-readable storage medium, and computer program product.

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411195923.7, filed on August 28, 2024, entitled "A Photovoltaic Power Generation Efficiency Monitoring System and Method", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of photovoltaic power generation technology, specifically to a photovoltaic power generation efficiency monitoring system, method, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0004] With continuous technological advancements and the increasing demand for new energy sources, solar energy has become a renewable energy power generation method due to its abundant, inexhaustible, clean, and safe nature. Currently, large-scale and distributed grid-connected photovoltaic power plants are widely used. The power generation of a photovoltaic power generation system directly affects the economic benefits of the power plant, and the healthy operating status of each power generation device within the system is crucial to ensuring power generation and efficient system operation. The operating efficiency of each power generation device is a characteristic quantity that directly reflects the operating status of the power generation device.

[0005] Currently, during the operation of photovoltaic power generation systems, only some basic parameters of photovoltaic modules are monitored and measured. However, there is no real-time acquisition and calculation method for the power generation efficiency of photovoltaic modules. This results in the inability to know the operating status of the power generation equipment in the photovoltaic power generation system in a timely manner, which in turn affects the stable operation of the photovoltaic power generation system. Summary of the Invention

[0006] In view of this, this application provides a photovoltaic power generation efficiency monitoring system, method, computer equipment, computer-readable storage medium, and computer program product to solve the problem that there is no real-time acquisition and calculation method for the power generation efficiency of photovoltaic modules during the operation of photovoltaic power generation systems, which makes it impossible to know the operating status of power generation equipment in photovoltaic power generation systems in a timely manner.

[0007] In a first aspect, this application provides a photovoltaic power generation efficiency monitoring system, which includes: a monitoring center, and a photovoltaic module data acquisition module, an environmental data acquisition module, a power generation anomaly analysis module, a power generation anomaly processing module, a power generation processing module, and a power generation efficiency visualization module connected to the monitoring center; the power generation anomaly analysis module is connected to the photovoltaic module data acquisition module and the environmental data acquisition module respectively; the power generation anomaly analysis module, the power generation anomaly processing module, the power generation processing module, and the power generation efficiency visualization module are connected in sequence;

[0008] The photovoltaic module data acquisition module is used to collect basic information about photovoltaic modules.

[0009] The environmental data acquisition module is used to collect environmental information from photovoltaic modules.

[0010] The power generation anomaly analysis module is used to classify anomalies based on the basic information and environmental information of photovoltaic modules to obtain the power generation anomaly type of the abnormal photovoltaic module; among which, the power generation anomaly type includes surface anomaly and internal anomaly;

[0011] The power generation anomaly processing module is used to determine the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module if the power generation anomaly type is surface anomaly.

[0012] The power generation processing module is used to determine the abnormal power generation of the surface based on the abnormal power generation index of the surface anomaly, and to determine the abnormal power generation of the internal anomaly based on the abnormal power generation of the surface anomaly.

[0013] The power generation efficiency visualization module is used to construct a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of surface abnormal loss, and the power generation of internal abnormal loss, and to visualize the photovoltaic power generation efficiency visualization map.

[0014] This embodiment provides a photovoltaic power generation efficiency monitoring system that classifies anomalies based on the basic information and environmental information of photovoltaic modules to obtain the power generation anomaly type of the abnormal photovoltaic modules. Based on the power generation anomaly type of the abnormal photovoltaic modules, it determines the abnormal power generation index of surface anomalies, determines the surface abnormal power loss based on the abnormal power generation index of surface anomalies, and determines the internal abnormal power loss based on the surface abnormal power loss. Then, it constructs and visualizes a photovoltaic power generation efficiency visualization chart, realizing accurate monitoring of the power generation efficiency of photovoltaic modules. It can promptly obtain the operating status of the power generation equipment in the photovoltaic power generation system, ensuring the stable operation of the photovoltaic power generation system.

[0015] In one optional implementation, the power generation anomaly analysis module includes:

[0016] The first calculation unit is used to determine the dynamic rated power generation of the photovoltaic module based on the environmental information of the photovoltaic module, and to calculate the actual power generation efficiency of the photovoltaic module based on the actual power generation and dynamic rated power generation of the photovoltaic module in the basic information of the photovoltaic module.

[0017] The first comparison unit is used to compare the actual power generation efficiency with the power generation efficiency threshold. If the actual power generation efficiency is less than the power generation efficiency threshold, the photovoltaic module corresponding to the actual power generation efficiency is marked as an abnormal photovoltaic module.

[0018] The first determining unit is used to determine the grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a texture feature set based on the grayscale image of the abnormal photovoltaic module, and determine the power generation anomaly type of the surface anomaly based on the texture feature set; wherein, the power generation anomaly type of the surface anomaly includes crack anomaly, dirt anomaly and corrosion anomaly.

[0019] This embodiment provides a photovoltaic power generation efficiency monitoring system that accurately obtains the actual power generation efficiency of photovoltaic modules by comparing the actual power generation and dynamic rated power generation of the photovoltaic modules. Furthermore, by comparing the actual power generation efficiency with the power generation efficiency threshold, a texture feature set is constructed based on the grayscale image of the abnormal photovoltaic modules. Based on the texture feature set, the power generation anomaly type of the surface anomaly is determined, thus achieving accurate classification of abnormal photovoltaic modules and accurate determination of the power generation anomaly type.

[0020] In one optional implementation, the first determining unit includes:

[0021] A sub-unit is constructed to obtain the standard grayscale image of the abnormal photovoltaic module, determine the actual grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a binary image based on the actual grayscale image and the standard grayscale image, and determine the abnormal region based on the binary image;

[0022] Determine sub-units for constructing gray-level co-occurrence matrices based on abnormal regions, and determine the texture feature set of abnormal regions based on the gray-level co-occurrence matrices;

[0023] The comparison sub-unit is used to compare the texture feature set of the abnormal region with the texture feature set corresponding to the crack abnormality, the dirt abnormality, and the corrosion abnormality, respectively, to obtain the first texture feature similarity between the abnormal region and the crack abnormality, the second texture feature similarity between the abnormal region and the dirt abnormality, and the third texture feature similarity between the abnormal region and the corrosion abnormality.

[0024] The first comparison subunit is used to compare the first texture feature similarity, the second texture feature similarity, and the third texture feature similarity with a preset similarity threshold, and determine the power generation anomaly type of the surface anomaly based on the comparison results.

[0025] This embodiment provides a photovoltaic power generation efficiency monitoring system that utilizes the spatial correlation characteristics of gray levels in anomaly region images to construct a gray-level co-occurrence matrix, and uses the gray-level co-occurrence matrix to describe texture features, thereby determining the texture feature set. This achieves accurate extraction of texture features. Furthermore, by comparing the texture feature set of the anomaly region with the texture feature sets corresponding to crack anomalies, dirt anomalies, and corrosion anomalies, respectively, the system achieves accurate determination of the power generation anomaly type of surface anomalies.

[0026] In one optional implementation, the power generation anomaly processing module is specifically used to determine the area of ​​the abnormal region based on the power generation anomaly type of the surface anomaly, and to calculate the abnormal power generation index of the surface anomaly based on the area of ​​the abnormal region, if the power generation anomaly type is surface anomaly.

[0027] In one optional implementation, the power generation processing module includes:

[0028] The second calculation unit is used to determine the average light intensity and light duration of abnormal photovoltaic modules based on the environmental information of the photovoltaic modules, and to calculate the surface abnormal loss power generation based on the average light intensity and light duration of abnormal photovoltaic modules and the abnormal power generation index of the surface abnormality.

[0029] The third calculation unit is used to determine the dynamic rated power generation of the abnormal photovoltaic module based on the average light intensity and light duration of the abnormal photovoltaic module, determine the actual power generation of the abnormal photovoltaic module based on the basic information of the photovoltaic module, and calculate the current abnormal loss power generation based on the dynamic rated power generation and the actual power generation of the abnormal photovoltaic module.

[0030] The second comparison subunit is used to determine the internal abnormal loss power generation based on the surface abnormal loss power generation and the current abnormal loss power generation, and compare the internal abnormal loss power generation with the abnormal loss power generation threshold. If the internal abnormal loss power generation is greater than the abnormal loss power generation threshold, the surface abnormal loss power generation and the internal abnormal loss power generation are sent to the power generation efficiency visualization module.

[0031] This embodiment provides a photovoltaic power generation efficiency monitoring system that calculates the current abnormal loss power generation based on the dynamic rated power generation and the actual power generation of the abnormal photovoltaic module. Then, it determines the internal abnormal loss power generation based on the surface abnormal loss power generation and the current abnormal loss power generation, thus achieving accurate calculation of the internal abnormal loss power generation. By comparing the internal abnormal loss power generation with the abnormal loss power generation threshold, it achieves accurate judgment of internal abnormalities of the photovoltaic module.

[0032] In one alternative implementation, the power generation efficiency visualization module includes:

[0033] The construction unit is used to determine the surface abnormal loss coefficient and the internal abnormal loss coefficient based on the surface abnormal loss power generation and the internal abnormal loss power generation, respectively, and to construct a photovoltaic power generation efficiency visualization chart based on the actual power generation efficiency, the surface abnormal loss coefficient and the internal abnormal loss coefficient.

[0034] The second comparison unit is used to compare the surface abnormal loss coefficient with the surface abnormal loss range to determine the severity level of the surface abnormal loss coefficient, and to compare the internal abnormal loss coefficient with the internal abnormal loss range to determine the severity level of the internal abnormal loss coefficient.

[0035] The second determining unit is used to determine the comprehensive severity level of the photovoltaic module based on the severity level of the surface abnormal loss coefficient and the severity level of the internal abnormal loss coefficient, and to determine the maintenance priority of the photovoltaic module based on the comprehensive severity level of the photovoltaic module.

[0036] The display unit is used to visualize the photovoltaic power generation efficiency and the maintenance priority of photovoltaic modules.

[0037] This embodiment provides a photovoltaic power generation efficiency monitoring system that constructs a photovoltaic power generation efficiency visualization map by using actual power generation efficiency, surface abnormal loss coefficient, and internal abnormal loss coefficient. By adding maintenance priorities to the photovoltaic power generation efficiency visualization map, maintenance personnel can maintain the photovoltaic modules according to the maintenance priorities of abnormal photovoltaic modules in the power generation efficiency visualization map, thus ensuring timely maintenance and stable operation of the photovoltaic power generation system.

[0038] Secondly, this application provides a method for monitoring photovoltaic power generation efficiency, the method comprising:

[0039] The photovoltaic module data acquisition module collects basic information about the photovoltaic module, and the environmental data acquisition module collects environmental information about the photovoltaic module.

[0040] The power generation anomaly analysis module classifies anomalies based on the basic information and environmental information of photovoltaic modules to obtain the power generation anomaly type of the abnormal photovoltaic module; among which, the power generation anomaly type includes surface anomaly and internal anomaly;

[0041] If the power generation anomaly type is surface anomaly, the power generation anomaly processing module determines the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module.

[0042] The power generation processing module determines the abnormal power generation of the surface based on the abnormal power generation index of the surface anomaly, and determines the abnormal power generation of the internal anomaly based on the abnormal power generation of the surface anomaly.

[0043] The power generation efficiency visualization module constructs a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of surface abnormal loss, and the power generation of internal abnormal loss, and then displays the photovoltaic power generation efficiency visualization map in a visual way.

[0044] Thirdly, this application provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power generation efficiency monitoring method of the second aspect described above.

[0045] Fourthly, this application provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic power generation efficiency monitoring method described in the second aspect.

[0046] Fifthly, this application provides a computer program product, including computer instructions for causing a computer to execute the photovoltaic power generation efficiency monitoring method described in the second aspect above. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 is a structural block diagram of a photovoltaic power generation efficiency monitoring system according to an embodiment of this application;

[0049] Figure 2 is a flowchart illustrating the specific working steps of the photovoltaic power generation efficiency monitoring system according to an embodiment of this application;

[0050] Figure 3 is a flowchart illustrating a photovoltaic power generation efficiency monitoring method according to an embodiment of this application;

[0051] Figure 4 is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] This embodiment provides a photovoltaic power generation efficiency monitoring system, as shown in Figure 1, including: a monitoring center 101, and photovoltaic module data acquisition module 102, environmental data acquisition module 103, power generation anomaly analysis module 104, power generation anomaly processing module 105, power generation processing module 106, and power generation efficiency visualization module 107 connected to the monitoring center 101; the power generation anomaly analysis module 104 is connected to the photovoltaic module data acquisition module 102 and the environmental data acquisition module 103 respectively; the power generation anomaly analysis module 104, the power generation anomaly processing module 105, the power generation processing module 106, and the power generation efficiency visualization module 107 are connected in sequence;

[0054] The photovoltaic module data acquisition module 102 is used to collect basic information about photovoltaic modules.

[0055] Specifically, the basic information of photovoltaic modules includes image data of the photovoltaic module surface, actual power generation, data collection cycle, and surface area.

[0056] The environmental data acquisition module 103 is used to collect environmental information of photovoltaic modules.

[0057] Specifically, the environmental information of the photovoltaic module includes the average light intensity and duration of light exposure during the collection period.

[0058] The power generation anomaly analysis module 104 is used to classify anomalies based on the basic information and environmental information of the photovoltaic module to obtain the power generation anomaly type of the abnormal photovoltaic module; among which, the power generation anomaly type includes surface anomaly and internal anomaly.

[0059] The power generation anomaly processing module 105 is used to determine the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module if the power generation anomaly type is surface anomaly.

[0060] Specifically, the power generation anomaly processing module 105 is used to determine the area of ​​the abnormal region based on the power generation anomaly type of the surface anomaly, and to calculate the abnormal power generation index of the surface anomaly based on the area of ​​the abnormal region when the power generation anomaly type is surface anomaly.

[0061] Optionally, the types of power generation anomalies caused by surface abnormalities include crack anomalies, dirt anomalies, and corrosion anomalies; wherein, when the power generation anomaly type is a crack anomaly, the crack area L is obtained, and the crack anomaly power generation index C is calculated based on the crack area, and the calculation formula is as follows: C=w×L 2 +b (1)

[0062] Where w is the weight of the crack area on the crack abnormal power generation index, and b is the bias term; where, by using the least squares method, the crack area L is used as the independent variable and the crack abnormal power generation index C is used as the dependent variable, a fitting operation is performed, and the corresponding curve or straight line is fitted. The intercept of the fitted curve on the Y-axis is the bias term b.

[0063] Optionally, when the power generation anomaly type of surface anomaly does not include crack anomaly, the crack anomaly power generation index is 0.

[0064] Optionally, when the power generation anomaly type of the surface anomaly is a fouling anomaly, the fouling area S is obtained, and the fouling anomaly power generation index I is calculated based on the fouling area S, as shown in the following formula:

[0065] Where k is the first coefficient, γ is the fouling deviation coefficient, and n is the index of the influence of fouling area on the abnormal power generation index. The larger the fouling area S, the higher the abnormal power generation index I, reflecting the degree of influence of fouling on abnormal power generation.

[0066] Optionally, when the power generation anomaly type of surface anomaly does not include fouling anomaly, the fouling anomaly power generation index is 0.

[0067] Optionally, when the power generation anomaly type of the surface anomaly is corrosion anomaly, the corrosion area M is obtained, and the corrosion anomaly power generation index Q is calculated based on the corrosion area M. The calculation formula is as follows:

[0068] Where m is the second coefficient and τ is the corrosion deviation coefficient.

[0069] Optionally, when the power generation anomaly type of surface anomaly does not include corrosion anomaly, the corrosion anomaly power generation index is 0.

[0070] Optionally, when the abnormal power generation index of the surface anomaly is 0, there is no corresponding anomaly type.

[0071] The power generation processing module 106 is used to determine the abnormal power generation of the surface based on the abnormal power generation index of the surface anomaly, and to determine the abnormal power generation of the internal anomaly based on the abnormal power generation of the surface anomaly.

[0072] The power generation efficiency visualization module 107 is used to construct a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of surface abnormal loss and the power generation of internal abnormal loss, and to visualize the photovoltaic power generation efficiency visualization map.

[0073] This embodiment provides a photovoltaic power generation efficiency monitoring system that classifies anomalies based on the basic information and environmental information of photovoltaic modules to obtain the power generation anomaly type of the abnormal photovoltaic modules. Based on the power generation anomaly type of the abnormal photovoltaic modules, it determines the abnormal power generation index of surface anomalies, determines the surface abnormal power loss based on the abnormal power generation index of surface anomalies, and determines the internal abnormal power loss based on the surface abnormal power loss. Then, it constructs and visualizes a photovoltaic power generation efficiency visualization chart, realizing accurate monitoring of the power generation efficiency of photovoltaic modules. It can promptly obtain the operating status of the power generation equipment in the photovoltaic power generation system, ensuring the stable operation of the photovoltaic power generation system.

[0074] In some alternative implementations, the power generation anomaly analysis module 104 includes:

[0075] The first calculation unit 1041 is used to determine the dynamic rated power generation of the photovoltaic module based on the environmental information of the photovoltaic module, and to calculate the actual power generation efficiency of the photovoltaic module based on the actual power generation and dynamic rated power generation of the photovoltaic module in the basic information of the photovoltaic module.

[0076] Specifically, based on the average irradiance and irradiance duration of each photovoltaic module in the current data collection period, the dynamic rated power generation of each photovoltaic module in the current data collection period is set. The specific steps include: constructing a dynamic rated power generation lookup table, mapping the average irradiance and irradiance duration of each photovoltaic module in the current data collection period to the dynamic rated power generation lookup table, thereby obtaining the dynamic rated power generation of the photovoltaic modules; wherein, the dynamic rated power generation lookup table contains the dynamic rated power generation of each photovoltaic module under different irradiance and irradiance duration conditions.

[0077] Optionally, the actual power generation efficiency δ 实际 The calculation formula is as follows: ： δ 实际 =E 实际 / E 动态额定 (4)

[0078] Among them, E 实际 E represents the actual power generation of a photovoltaic module. 动态额定 This indicates the dynamic rated power generation of the photovoltaic module.

[0079] The first comparison unit 1042 is used to compare the actual power generation efficiency with the power generation efficiency threshold. If the actual power generation efficiency is less than the power generation efficiency threshold, the photovoltaic module corresponding to the actual power generation efficiency is marked as an abnormal photovoltaic module.

[0080] Specifically, when the actual power generation efficiency is less than the power generation efficiency threshold, the photovoltaic module has a power generation abnormality, and the photovoltaic module corresponding to the actual power generation efficiency is marked as an abnormal photovoltaic module; when the actual power generation efficiency is greater than or equal to the power generation efficiency threshold, the photovoltaic module does not have an abnormality.

[0081] The first determining unit 1043 is used to determine the grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a texture feature set based on the grayscale image of the abnormal photovoltaic module, and determine the power generation anomaly type of the surface anomaly based on the texture feature set; wherein, the power generation anomaly type of the surface anomaly includes crack anomaly, dirt anomaly and corrosion anomaly.

[0082] This embodiment provides a photovoltaic power generation efficiency monitoring system that accurately obtains the actual power generation efficiency of photovoltaic modules by comparing the actual power generation and dynamic rated power generation of the photovoltaic modules. Furthermore, by comparing the actual power generation efficiency with the power generation efficiency threshold, a texture feature set is constructed based on the grayscale image of the abnormal photovoltaic modules. Based on the texture feature set, the power generation anomaly type of the surface anomaly is determined, thus achieving accurate classification of abnormal photovoltaic modules and accurate determination of the power generation anomaly type.

[0083] In some alternative implementations, the first determining unit 1043 includes:

[0084] Subunit 10431 is constructed to obtain the standard grayscale image of the abnormal photovoltaic module, determine the actual grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a binary image based on the actual grayscale image and the standard grayscale image, and determine the abnormal region based on the binary image.

[0085] Specifically, the image data of the abnormal photovoltaic module is preprocessed by denoising and grayscale to generate the actual grayscale image of the abnormal photovoltaic module; standard image data of the abnormal photovoltaic module is obtained, and the standard image data is preprocessed by denoising and grayscale to generate the standard grayscale image of the abnormal photovoltaic module; then, the grayscale values ​​of corresponding pixels in the standard grayscale image and the actual grayscale image are subtracted to obtain the difference value, and a binary image is constructed based on the difference value; the difference value of each pixel in the binary image is obtained, a difference threshold is set, and the regions of pixels in the binary image with difference values ​​greater than or equal to the difference threshold are selected and marked as abnormal regions.

[0086] Subunit 10432 is defined to construct a gray-level co-occurrence matrix based on the abnormal region and to determine the texture feature set of the abnormal region based on the gray-level co-occurrence matrix.

[0087] Specifically, the gray values ​​of the abnormal region are quantized into discrete gray levels, and fixed parameters of the gray-level co-occurrence matrix are set, including the relative positions of pixel pairs and the spacing between adjacent pixel pairs, thereby constructing the gray-level co-occurrence matrix of the abnormal region, and obtaining the texture feature set of the abnormal region based on the gray-level co-occurrence matrix.

[0088] Optionally, the steps for constructing the gray-level co-occurrence matrix of the abnormal region include: quantizing the gray levels of the abnormal region into discrete gray levels, dividing the gray value range into several levels, for example, an 8-bit image can be divided into 16, 32, and 64 levels, and defining the parameters required for the gray-level co-occurrence matrix, including distance (d) and orientation (θ). Then, based on the calculated gray-level co-occurrence matrix, a series of texture feature values ​​are extracted to construct a texture feature set. The texture feature values ​​in the texture feature set include, but are not limited to:

[0089] (1) Contrast: A statistical feature describing the contrast of pixels at different gray levels in an image. It is a feature that measures the roughness of the image texture and reflects the clarity of the image and the depth of the texture grooves. The deeper the texture grooves, the greater the contrast and the clearer the visual effect. Conversely, the lower the contrast, the shallower the grooves and the blurrier the effect.

[0090] (2) Energy: describes the uniformity of pixel gray distribution in an image, measures the randomness contained in the image, and represents the complexity of the image. Entropy is maximized when all values ​​of the co-occurrence matrix are equal or the pixel values ​​exhibit the greatest randomness.

[0091] (3) Entropy: describes the degree of uncertainty of image texture, measures the randomness contained in the image, and represents the complexity of the image. The entropy is the largest when all values ​​of the co-occurrence matrix are equal or the pixel values ​​exhibit the greatest randomness.

[0092] (4) Inverse variance: reflects the clarity and regularity of the texture; the texture is clear and has strong regularity.

[0093] The comparison subunit 10433 is used to compare the texture feature set of the abnormal region with the texture feature set corresponding to the crack abnormality, the dirt abnormality, and the corrosion abnormality, respectively, to obtain the first texture feature similarity between the abnormal region and the crack abnormality, the second texture feature similarity between the abnormal region and the dirt abnormality, and the third texture feature similarity between the abnormal region and the corrosion abnormality.

[0094] Specifically, based on the texture feature sets corresponding to crack anomalies, dirt anomalies, and corrosion anomalies, the contrast, energy, entropy, and inverse variance of crack anomalies are obtained and denoted as X. b Z b J b and N bThe contrast, energy, entropy, and inverse variance of the dirt abnormality are denoted as X. a Z a J a and N a The contrast, energy, entropy, and inverse variance of the corrosion anomaly are denoted as X. c Z c J c and N c The contrast, energy, entropy, and inverse variance of the anomalous region are denoted as X, Z, J, and N, respectively. The formula for calculating the similarity K1 of the first texture feature between the anomalous region and the crack anomaly is as follows:

[0095] In the above formula, a1, a2, a3, and a4 represent weighting factors.

[0096] The formula for calculating the similarity K2 of the second texture feature between the abnormal region and the dirt abnormality is as follows:

[0097] The formula for calculating the similarity K3 of the third texture feature between the abnormal region and the corrosion anomaly is as follows:

[0098] The first comparison subunit 10434 is used to compare the first texture feature similarity, the second texture feature similarity and the third texture feature similarity with a preset similarity threshold, and determine the power generation anomaly type of the surface anomaly based on the comparison results.

[0099] Specifically, the preset similarity thresholds include a crack anomaly similarity threshold, a dirt anomaly similarity threshold, and a corrosion anomaly similarity threshold. When the first texture feature similarity K1 is greater than the crack anomaly similarity threshold, the anomaly type of the abnormal photovoltaic module is a crack anomaly, and the area of ​​the abnormal region corresponding to the crack anomaly is obtained and marked as the crack area. When the second texture feature similarity K2 is greater than the dirt anomaly similarity threshold, the anomaly type of the abnormal photovoltaic module is a dirt anomaly, and the area of ​​the abnormal region corresponding to the dirt anomaly is obtained and marked as the dirt area. When the third texture feature similarity K3 is greater than the corrosion anomaly similarity threshold, the anomaly type of the abnormal photovoltaic module is a corrosion anomaly, and the area of ​​the abnormal region corresponding to the corrosion anomaly is obtained and marked as the corrosion area.

[0100] Optionally, when the similarity of the first texture feature, the similarity of the second texture feature, and the similarity of the third texture feature are all less than or equal to the similarity thresholds for crack anomalies, dirt anomalies, and corrosion anomalies, then there are no surface anomalies.

[0101] This embodiment provides a photovoltaic power generation efficiency monitoring system that utilizes the spatial correlation characteristics of gray levels in anomaly region images to construct a gray-level co-occurrence matrix, and uses the gray-level co-occurrence matrix to describe texture features, thereby determining the texture feature set. This achieves accurate extraction of texture features. Furthermore, by comparing the texture feature set of the anomaly region with the texture feature sets corresponding to crack anomalies, dirt anomalies, and corrosion anomalies, respectively, the system achieves accurate determination of the power generation anomaly type of surface anomalies.

[0102] In some alternative implementations, the power generation processing module 106 includes:

[0103] The second calculation unit 1061 is used to determine the average light intensity and light duration of the abnormal photovoltaic module based on the environmental information of the photovoltaic module, and to calculate the surface abnormal loss power generation based on the average light intensity and light duration of the abnormal photovoltaic module and the abnormal power generation index of the surface abnormality.

[0104] Specifically, the surface abnormal loss power generation E 表面损耗 The calculation formula is as follows: E 表面损耗 =C×G×H×P R +I×G×H×P R +Q×G×H×P R (8)

[0105] Where G represents the average irradiance of the abnormal photovoltaic module during the current acquisition period, H represents the irradiance duration of the abnormal photovoltaic module during the current acquisition period, and P... R P represents the performance ratio of a photovoltaic module. R ∈(0.15,0.25).

[0106] The third calculation unit 1062 is used to determine the dynamic rated power generation of the abnormal photovoltaic module based on the average light intensity and light duration of the abnormal photovoltaic module, determine the actual power generation of the abnormal photovoltaic module based on the basic information of the photovoltaic module, and calculate the current abnormal loss power generation based on the dynamic rated power generation and the actual power generation of the abnormal photovoltaic module.

[0107] Specifically, the actual power generation of the abnormal photovoltaic module collected at the end timestamp of the current collection period is compared with the dynamic rated power generation corresponding to the current collection period to obtain the difference result, and the difference result is used as the current abnormal loss power generation.

[0108] The second comparison subunit 1063 is used to determine the internal abnormal loss power generation based on the surface abnormal loss power generation and the current abnormal loss power generation, and compare the internal abnormal loss power generation with the abnormal loss power generation threshold. If the internal abnormal loss power generation is greater than the abnormal loss power generation threshold, the surface abnormal loss power generation and the internal abnormal loss power generation are sent to the power generation efficiency visualization module 107.

[0109] Specifically, the internal abnormal loss power generation E 内部损耗 The calculation formula is as follows: E 内部损耗 =E 异常损耗 -E 表面损耗 (9)

[0110] Among them, E 异常损耗 E represents the current abnormal power generation. 表面损耗 This indicates the amount of electricity generated due to abnormal surface loss.

[0111] Optionally, an internal anomaly exists when the internal abnormal power loss exceeds the abnormal power loss threshold; an internal anomaly does not exist when the internal abnormal power loss is less than or equal to the abnormal power loss threshold.

[0112] This embodiment provides a photovoltaic power generation efficiency monitoring system that calculates the current abnormal loss power generation based on the dynamic rated power generation and the actual power generation of the abnormal photovoltaic module. Then, it determines the internal abnormal loss power generation based on the surface abnormal loss power generation and the current abnormal loss power generation, thus achieving accurate calculation of the internal abnormal loss power generation. By comparing the internal abnormal loss power generation with the abnormal loss power generation threshold, it achieves accurate judgment of internal abnormalities of the photovoltaic module.

[0113] In some alternative implementations, the power generation efficiency visualization module 107 includes:

[0114] The construction unit 1071 is used to determine the surface abnormal loss coefficient and the internal abnormal loss coefficient based on the surface abnormal loss power generation and the internal abnormal loss power generation, respectively, and to construct a photovoltaic power generation efficiency visualization chart based on the actual power generation efficiency, the surface abnormal loss coefficient and the internal abnormal loss coefficient.

[0115] Specifically, the dynamic rated power generation E 额定 and surface abnormal loss power generation E 表面损耗 Calculate the surface abnormal loss coefficient. The calculation formula is as follows:

[0116] Optionally, the dynamic rated power generation E 额定 and internal abnormal loss coefficient E 内部损耗Calculate the internal abnormal loss coefficient, ε. 内部损耗 The formula for calculating ε is shown below: 内部损耗 =E 内部损耗 / E 额定 (11)

[0117] optional ， Based on actual power generation efficiency δ 实际 Surface abnormal loss coefficient Internal abnormal loss coefficient ε 内部损耗 And to construct a visualization of photovoltaic power generation efficiency using the data acquisition period T, specifically including: constructing a two-dimensional coordinate system with the data acquisition period T as the X-axis and power generation efficiency as the Y-axis, and plotting the actual power generation efficiency δ 实际 Surface abnormal loss coefficient Internal abnormal loss coefficient ε 内部损耗 By fitting the data in a two-dimensional coordinate system, a fitted curve is obtained, which in turn yields a visualization of photovoltaic power generation efficiency.

[0118] The second comparison unit 1072 is used to compare the surface abnormal loss coefficient with the surface abnormal loss range to determine the severity level of the surface abnormal loss coefficient, and to compare the internal abnormal loss coefficient with the internal abnormal loss range to determine the severity level of the internal abnormal loss coefficient.

[0119] Specifically, a surface abnormal loss interval is set, and within this interval, threshold points are selected to divide the area into sub-intervals of different severity levels. The surface abnormal loss coefficient is then compared with the surface abnormal loss interval to obtain the severity level of the surface abnormal loss coefficient. For example, when the surface abnormal loss coefficient... When the surface abnormal loss coefficient is within the range of (0, 0.25], the severity level of the surface abnormal loss coefficient is Level 1 surface abnormality; when the surface abnormal loss coefficient... When the surface abnormal loss coefficient is within the range of (0.25, 0.5], the severity level of the surface abnormal loss coefficient is level two surface abnormality; when the surface abnormal loss coefficient... When the surface abnormal loss coefficient is within the range of (0.5, 0.75), the severity level of the surface abnormal loss coefficient is level three surface abnormality; when the surface abnormal loss coefficient... When the surface abnormal loss range is within (0.75, 1], the severity level of the surface abnormal loss coefficient is level four surface abnormality.

[0120] Optionally, an internal abnormal loss interval is set, and within this interval, a threshold point is selected to divide the interval into sub-intervals of different severity levels. The internal abnormal loss coefficient is then compared with the internal abnormal loss interval to obtain the severity level of the internal abnormal loss coefficient. For example, when the internal abnormal loss coefficient ε... 内部损耗When the internal abnormal loss coefficient ε is within the range of (0, 0.25], the severity level of the internal abnormal loss coefficient is Level 1 internal abnormality; when the internal abnormal loss coefficient ε is within the range of (0.25, 0.5], the severity level of the internal abnormal loss coefficient is Level 2 internal abnormality; when the internal abnormal loss coefficient ε is within the range of (0.5, 0.75], the severity level of the internal abnormal loss coefficient is Level 3 internal abnormality; when the internal abnormal loss coefficient ε 内部损耗 When the internal abnormal loss range is within (0.75, 1], the severity level of the internal abnormal loss coefficient is level four internal abnormality.

[0121] The second determining unit 1073 is used to determine the comprehensive severity level of the photovoltaic module based on the severity level of the surface abnormal loss coefficient and the severity level of the internal abnormal loss coefficient, and to determine the maintenance priority of the photovoltaic module based on the comprehensive severity level of the photovoltaic module.

[0122] Specifically, based on the surface anomaly loss coefficient Severity level T1 and internal abnormal loss coefficient ε 内部损耗 The severity level T2 determines the overall severity level of the photovoltaic module. The formula for calculating the overall severity level T3 of the photovoltaic module is as follows: T3=α×T1+β×T2 (12 )

[0123] Where α and β represent the severity levels of the surface anomaly loss coefficient and the weighting coefficients corresponding to the severity levels of the internal anomaly loss coefficient, respectively.

[0124] Optionally, the maintenance priority of each photovoltaic module is determined based on the overall severity level of each photovoltaic module, and the maintenance priority is displayed in the power generation efficiency visualization chart. Maintenance personnel perform maintenance on the photovoltaic modules according to the maintenance priority of the abnormal photovoltaic modules in the power generation efficiency visualization chart.

[0125] Display unit 1074 is used to visualize the photovoltaic power generation efficiency graph and the maintenance priority of photovoltaic modules.

[0126] This embodiment provides a photovoltaic power generation efficiency monitoring system that constructs a photovoltaic power generation efficiency visualization map by using actual power generation efficiency, surface abnormal loss coefficient, and internal abnormal loss coefficient. By adding maintenance priorities to the photovoltaic power generation efficiency visualization map, maintenance personnel can maintain the photovoltaic modules according to the maintenance priorities of abnormal photovoltaic modules in the power generation efficiency visualization map, thus ensuring timely maintenance and stable operation of the photovoltaic power generation system.

[0127] The following specific example illustrates the working process of a photovoltaic power generation efficiency monitoring system.

[0128] Example 1:

[0129] As shown in Figure 2, the working process of a photovoltaic power generation efficiency monitoring system includes the following steps:

[0130] Step S1: Obtain basic information and environmental information for each photovoltaic module, analyze the basic information and environmental information to obtain the actual power generation efficiency, and judge the actual power generation efficiency;

[0131] Step S2: Based on the judgment result of the actual power generation efficiency, determine whether there are abnormal photovoltaic modules and obtain the power generation abnormality types contained in the abnormal photovoltaic modules. The power generation abnormality types of abnormal photovoltaic modules include surface abnormalities and internal abnormalities.

[0132] Step S3: Based on the type of photovoltaic module power generation anomaly being surface anomaly, obtain the corresponding surface anomaly power generation index;

[0133] Step S4: Based on the abnormal power generation index of the corresponding surface abnormality, obtain the surface abnormal loss power generation of the abnormal photovoltaic module, and based on the surface abnormal loss power generation, obtain the internal abnormal loss power generation.

[0134] Step S5: Obtain the surface abnormal loss coefficient and the internal abnormal loss coefficient based on the power generation of surface abnormal loss and the power generation of internal abnormal loss; and construct a photovoltaic power generation efficiency visualization map based on the actual power generation efficiency, basic information, surface abnormal loss coefficient and internal abnormal loss coefficient; set the surface abnormal loss threshold range and the internal abnormal loss threshold range, and then determine the severity level of the obtained surface abnormal loss coefficient and internal abnormal loss coefficient to obtain the maintenance priority, and display it on the corresponding photovoltaic power generation efficiency visualization map for visualization output.

[0135] According to an embodiment of this application, a method for monitoring photovoltaic power generation efficiency 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.

[0136] This embodiment provides a photovoltaic power generation efficiency monitoring method, which can be used in the aforementioned photovoltaic power generation efficiency monitoring system. Figure 3 is a flowchart of a photovoltaic power generation efficiency monitoring method according to an embodiment of this application. As shown in Figure 3, the process includes the following steps:

[0137] Step S301: Collect basic information of photovoltaic modules through photovoltaic module data acquisition module, and collect environmental information of photovoltaic modules through environmental data acquisition module.

[0138] Step S302: The power generation anomaly analysis module classifies anomalies based on the basic information and environmental information of the photovoltaic module to obtain the power generation anomaly type of the abnormal photovoltaic module; among which, the power generation anomaly type includes surface anomaly and internal anomaly.

[0139] Step S303: If the power generation anomaly type is surface anomaly, the power generation anomaly processing module determines the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module.

[0140] In step S304, the power generation processing module determines the surface abnormal loss power generation based on the abnormal power generation index of the surface abnormality, and determines the internal abnormal loss power generation based on the surface abnormal loss power generation.

[0141] Step S305: The power generation efficiency visualization module constructs a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of surface abnormal loss and the power generation of internal abnormal loss, and displays the photovoltaic power generation efficiency visualization map.

[0142] The photovoltaic power generation efficiency monitoring method of this embodiment is applied to a photovoltaic power generation efficiency monitoring system as shown in the embodiment of Figure 1. Therefore, the specific implementation of steps S301 and S305 can be referred to the corresponding description in the embodiment section shown in Figure 1 above, and will not be repeated here.

[0143] It is understood that the function and beneficial effects of the method in this embodiment correspond to the function and beneficial effects of a photovoltaic power generation efficiency monitoring system in the embodiment shown in Figure 1, and will not be repeated here.

[0144] This application also provides a computer device having a photovoltaic power generation efficiency monitoring system as shown in FIG1 above.

[0145] Please refer to Figure 4, which is a schematic diagram of the structure of a computer device provided in an optional embodiment of this application. As shown in Figure 4, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses and can be installed on a common motherboard or otherwise as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 uses one processor 10 as an example.

[0146] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Optionally, processor 10 may also include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPRS), or any combination thereof.

[0147] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0148] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0150] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means; Figure 4 shows an example of a connection via a bus.

[0151] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0152] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently 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.; optionally, the storage medium may 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, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0153] A portion of this application 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 this application 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.

[0154] Although embodiments of this application 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 this application, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A photovoltaic power generation efficiency monitoring system, characterized in that, The system includes: a monitoring center, and photovoltaic module data acquisition module, environmental data acquisition module, power generation anomaly analysis module, power generation anomaly processing module, power generation processing module, and power generation efficiency visualization module connected to the monitoring center; the power generation anomaly analysis module is connected to the photovoltaic module data acquisition module and the environmental data acquisition module respectively; the power generation anomaly analysis module, the power generation anomaly processing module, the power generation processing module, and the power generation efficiency visualization module are connected in sequence; The photovoltaic module data acquisition module is used to collect basic information about the photovoltaic module; The environmental data acquisition module is used to collect environmental information of the photovoltaic modules; The power generation anomaly analysis module is used to classify anomalies based on the basic information and environmental information of the photovoltaic module to obtain the power generation anomaly type of the abnormal photovoltaic module; wherein, the power generation anomaly type includes surface anomaly and internal anomaly; The power generation anomaly processing module is used to determine the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module if the power generation anomaly type is surface anomaly. The power generation processing module is used to determine the abnormal power generation loss of the surface based on the abnormal power generation index of the surface anomaly, and to determine the abnormal power generation loss of the interior based on the abnormal power generation loss of the surface. The power generation efficiency visualization module is used to construct a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of the surface abnormal loss and the power generation of the internal abnormal loss, and to visualize and display the photovoltaic power generation efficiency visualization map.

2. The system according to claim 1, characterized in that, The power generation anomaly analysis module includes: The first calculation unit is used to determine the dynamic rated power generation of the photovoltaic module based on the environmental information of the photovoltaic module, and to calculate the actual power generation efficiency of the photovoltaic module based on the actual power generation of the photovoltaic module in the basic information of the photovoltaic module and the dynamic rated power generation. The first comparison unit is used to compare the actual power generation efficiency with the power generation efficiency threshold. If the actual power generation efficiency is less than the power generation efficiency threshold, the photovoltaic module corresponding to the actual power generation efficiency is marked as an abnormal photovoltaic module. The first determining unit is configured to determine the grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a texture feature set based on the grayscale image of the abnormal photovoltaic module, and determine the power generation anomaly type of the surface anomaly based on the texture feature set; wherein, the power generation anomaly type of the surface anomaly includes crack anomaly, dirt anomaly and corrosion anomaly.

3. The system according to claim 2, characterized in that, The first determining unit includes: A sub-unit is constructed to acquire a standard grayscale image of an abnormal photovoltaic module, determine the actual grayscale image of the abnormal photovoltaic module based on the basic information of the photovoltaic module, construct a binary image based on the actual grayscale image and the standard grayscale image, and determine the abnormal region based on the binary image. A sub-unit is defined for constructing a gray-level co-occurrence matrix based on the abnormal region, and for determining the texture feature set of the abnormal region based on the gray-level co-occurrence matrix; The comparison subunit is used to compare the texture feature set of the abnormal region with the texture feature set corresponding to the crack abnormality, the dirt abnormality, and the corrosion abnormality, respectively, to obtain the first texture feature similarity between the abnormal region and the crack abnormality, the second texture feature similarity between the abnormal region and the dirt abnormality, and the third texture feature similarity between the abnormal region and the corrosion abnormality. The first comparison subunit is used to compare the first texture feature similarity, the second texture feature similarity, and the third texture feature similarity with a preset similarity threshold, and determine the power generation anomaly type of the surface anomaly based on the comparison results.

4. The system according to claim 1, characterized in that, The power generation anomaly processing module is specifically used to determine the area of ​​the abnormal region based on the power generation anomaly type of the surface anomaly, and to calculate the abnormal power generation index of the surface anomaly based on the area of ​​the abnormal region, if the power generation anomaly type is a surface anomaly.

5. The system according to claim 1, characterized in that, The power generation processing module includes: The second calculation unit is used to determine the average light intensity and light duration of the abnormal photovoltaic module based on the environmental information of the photovoltaic module, and to calculate the surface abnormal loss power generation based on the average light intensity and light duration of the abnormal photovoltaic module and the abnormal power generation index of the surface abnormality. The third calculation unit is used to determine the dynamic rated power generation of the abnormal photovoltaic module based on the average light intensity and light duration of the abnormal photovoltaic module, determine the actual power generation of the abnormal photovoltaic module based on the basic information of the photovoltaic module, and calculate the current abnormal loss power generation based on the dynamic rated power generation of the abnormal photovoltaic module and the actual power generation of the abnormal photovoltaic module. The second comparison subunit is used to determine the internal abnormal loss power generation based on the surface abnormal loss power generation and the current abnormal loss power generation, and compare the internal abnormal loss power generation with the abnormal loss power generation threshold. If the internal abnormal loss power generation is greater than the abnormal loss power generation threshold, the surface abnormal loss power generation and the internal abnormal loss power generation are sent to the power generation efficiency visualization module.

6. The system according to claim 2, characterized in that, The power generation efficiency visualization module includes: A construction unit is used to determine the surface abnormal loss coefficient and the internal abnormal loss coefficient based on the surface abnormal loss power generation and the internal abnormal loss power generation, respectively, and to construct the photovoltaic power generation efficiency visualization chart based on the actual power generation efficiency, the surface abnormal loss coefficient and the internal abnormal loss coefficient. The second comparison unit is used to compare the surface abnormal loss coefficient with the surface abnormal loss range to determine the severity level of the surface abnormal loss coefficient, and to compare the internal abnormal loss coefficient with the internal abnormal loss range to determine the severity level of the internal abnormal loss coefficient. The second determining unit is used to determine the overall severity level of the photovoltaic module based on the severity level of the surface abnormal loss coefficient and the severity level of the internal abnormal loss coefficient, and to determine the maintenance priority of the photovoltaic module based on the overall severity level of the photovoltaic module. The display unit is used to visualize the photovoltaic power generation efficiency graph and the maintenance priority of the photovoltaic module.

7. A method for monitoring photovoltaic power generation efficiency, characterized in that, The method includes: The photovoltaic module data acquisition module collects basic information about the photovoltaic module, and the environmental data acquisition module collects environmental information about the photovoltaic module. The power generation anomaly analysis module classifies anomalies based on the basic information and environmental information of the photovoltaic module to obtain the power generation anomaly type of the abnormal photovoltaic module; wherein, the power generation anomaly type includes surface anomaly and internal anomaly; If the power generation anomaly type is surface anomaly, the power generation anomaly processing module determines the abnormal power generation index of the surface anomaly based on the power generation anomaly type of the abnormal photovoltaic module. The power generation processing module determines the abnormal power generation loss of the surface based on the abnormal power generation index of the surface anomaly, and determines the abnormal power generation loss of the interior based on the abnormal power generation loss of the surface. The power generation efficiency visualization module constructs a photovoltaic power generation efficiency visualization map based on the basic information of the photovoltaic module, the power generation of the surface abnormal loss, and the power generation of the internal abnormal loss, and then displays the photovoltaic power generation efficiency visualization map.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power generation efficiency monitoring method of claim 7.

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 power generation efficiency monitoring method of claim 7.

10. A computer program product, characterized in that, It includes computer instructions, which are used to cause the computer to execute the photovoltaic power generation efficiency monitoring method of claim 7.

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