Unmanned aerial vehicle inspection method and system based on photovoltaic power generation data

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to an unmanned aerial vehicle inspection system based on photovoltaic power generation data, which comprises a power generation acquisition unit, an inspection judgment unit, an inspection unmanned aerial vehicle and an inspection analysis unit. According to the invention, the power generation data of the plurality of photovoltaic power generation areas are acquired by setting the power generation acquisition unit, and whether the inspection instruction is sent out is determined by the inspection determination unit according to the power generation data of any photovoltaic power generation area, so that the positioning accuracy of the photovoltaic inspection of the unmanned aerial vehicle is improved, the use amount of the unmanned aerial vehicle is reduced, and the inspection efficiency is improved; the inspection analysis unit is used for preprocessing real-time images and position coordinates of photovoltaic panels in a photovoltaic power generation area collected by the inspection unmanned aerial vehicle, preliminarily judging a fault photovoltaic panel and the position of the fault photovoltaic panel, and calculating the inspection deviation degree according to the calculated theoretical power generation amount and the collected total power generation amount of the detection period and outputting the inspection deviation degree. And the photovoltaic inspection efficiency of the unmanned aerial vehicle is improved while the data volume of the inspection of the unmanned aerial vehicle is reduced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a drone inspection method and system based on photovoltaic power generation data. Background Technology

[0002] With societal development, electricity has gradually become a primary energy source for people's lives. Wind power, thermal power, geothermal power, nuclear power, and other sources continuously provide electricity to meet people's needs for daily life and production. After electricity is generated, it needs to be transmitted through a high-voltage power grid. For safety reasons, high-voltage power grids are mostly installed at high altitudes. Moreover, the installation environment of high-voltage power grids is even more complex in valleys, hills, and other areas. This makes it very inconvenient to inspect the power grid, resulting in problems not being detected in time. This can easily lead to major problems causing power grid outages and resulting in significant economic losses. Photovoltaic power stations are characterized by large land areas, a large number of photovoltaic panels, and dense distribution of photovoltaic panels. This actually results in low efficiency and high cost for photovoltaic panel inspection. In particular, manual inspection is extremely reliant on manpower, and given the huge area of ​​photovoltaic power stations, manual inspection is costly, inefficient, and has a low safety factor, making it inconvenient for people to use.

[0003] Chinese Patent Publication No. CN113867400A discloses a method and system for inspecting and processing photovoltaic power generation equipment based on drones. This method calculates the number of photovoltaic panels that can be inspected by obtaining the drone's flight distance based on its power level and the size of each photovoltaic panel. However, it is evident that in existing photovoltaic power generation inspection technologies, the determination of inspection targets is often unreasonable, frequently involving a complete inspection. Furthermore, the drone inspection results cannot be preliminarily processed, relying solely on manual analysis of the large amount of collected data. This not only results in a lack of accuracy in drone inspections but also leads to complex and unfavorable data collection, hindering the troubleshooting and repair of faulty photovoltaic modules. Summary of the Invention

[0004] To address this issue, the present invention provides a drone inspection method and system based on photovoltaic power generation data, which overcomes the problem of complex data collection caused by the lack of accuracy in existing drone photovoltaic inspections.

[0005] To achieve the above objectives, the present invention provides a drone inspection system based on photovoltaic power generation data, comprising: A power generation acquisition unit is connected to several external photovoltaic power generation areas to collect power generation data from each of the photovoltaic power generation areas. The power generation data includes the total power generation during the detection period and the power generation per unit time within each unit detection time during the detection period. The inspection and judgment unit is connected to the power generation acquisition unit. The inspection and judgment unit can judge the power generation data of any photovoltaic power generation area collected by the power generation acquisition unit, and determine whether to output an inspection command for the photovoltaic power generation area based on the judgment result. An inspection drone is connected to the inspection judgment unit. The inspection drone can perform inspections in the corresponding photovoltaic power generation area according to the inspection instructions output by the inspection judgment unit, and collect inspection data of each photovoltaic panel in the corresponding photovoltaic power generation area. The inspection data includes real-time images of each photovoltaic panel and the position coordinates corresponding to each real-time image. The inspection analysis unit is connected to the inspection judgment unit and the inspection drone. The inspection analysis unit can process the real-time images of each photovoltaic panel to form a real-time grayscale image. It then judges the real-time grayscale of each pixel in the real-time grayscale image based on the grayscale difference between the standard photovoltaic panel and the foreign object, to determine whether a foreign object area exists on the corresponding photovoltaic panel. When no foreign object area exists on the photovoltaic panel, the inspection analysis unit calculates the theoretical power generation of the corresponding photovoltaic panel based on the dust grayscale difference and a preset power generation value. When a foreign object area exists on the photovoltaic panel, the inspection analysis unit determines whether to mark the photovoltaic panel with coordinates based on a maximum foreign object area, and calculates the theoretical power generation of the photovoltaic panel area excluding the foreign object area for unmarked photovoltaic panels. After judging the real-time grayscale images of all photovoltaic panels within the photovoltaic power generation area, the inspection analysis unit calculates the inspection deviation based on the calculated theoretical power generation and the total power generation during the detection cycle, and outputs the inspection deviation and the corresponding position coordinates of each marked photovoltaic panel as the inspection result for that photovoltaic power generation area.

[0006] Furthermore, the inspection and determination unit is equipped with a first preset power generation Q1 and a second preset power generation Q2, wherein Q1 < Q2. The power generation acquisition unit is equipped with a detection period T and a unit detection time t, where T = t × K, and K is the number of segmented detections within the detection period T. The inspection and determination unit can obtain the total power generation Qz of any photovoltaic power generation area collected by the power generation acquisition unit during the detection period, and compare the total power generation Qz during the detection period with the first preset power generation Q1 and the second preset power generation Q2. When Qz < Q1, the inspection and judgment unit determines that the total power generation Qz in the detection period T is lower than the first preset power generation Q1. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection instruction for the photovoltaic power generation area. When Q1≤Qz≤Q2, the inspection judgment unit determines that the total power generation Qz in the detection period T is between the first preset power generation Q1 and the second preset power generation Q2. The inspection judgment unit will obtain the power generation per unit time in each unit detection time in the detection period T and calculate the real-time power generation fluctuation difference of the corresponding photovoltaic power generation area to determine whether to output an inspection command to the photovoltaic power generation area. When Qz > Q2, the inspection judgment unit determines that the total power generation Qz within the detection period T is higher than the second preset power generation Q2. The inspection judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is normal and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area.

[0007] Furthermore, when the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit obtains the power generation per unit time t of each unit detection duration in the corresponding photovoltaic power generation area within the detection period T, sorts the power generation per unit time duration from largest to smallest, and calculates the real-time power generation fluctuation difference ΔQs, ΔQs=Qta-Qtz, where Qta is the maximum power generation per unit time duration and Qtz is the minimum power generation per unit time duration.

[0008] Furthermore, the inspection and judgment unit is equipped with a standard power generation fluctuation difference ΔQb. When the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit compares the calculated real-time power generation fluctuation difference ΔQs with the standard power generation fluctuation difference ΔQb. When ΔQs≤ΔQb, the inspection judgment unit determines that the real-time power generation fluctuation difference ΔQs does not exceed the standard power generation fluctuation difference ΔQb, and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area. When ΔQs > ΔQb, the inspection and judgment unit determines that the real-time power generation fluctuation difference ΔQs has exceeded the standard power generation fluctuation difference ΔQb. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection command for the photovoltaic power generation area.

[0009] Furthermore, the inspection judgment unit outputs an inspection command, and the inspection drone performs an inspection in the corresponding photovoltaic power generation area according to the inspection command, and collects real-time images of each photovoltaic panel in the photovoltaic power generation area, and transmits each real-time image to the inspection analysis unit. The inspection analysis unit performs grayscale processing on the real-time images of each photovoltaic panel to form a real-time grayscale image of each photovoltaic panel. The inspection analysis unit will judge the real-time grayscale image of any photovoltaic panel to determine whether to mark the photovoltaic panel as abnormal.

[0010] Furthermore, the inspection and analysis unit is equipped with a standard photovoltaic panel grayscale value Gb and a foreign object grayscale difference ΔGy. When the inspection and analysis unit judges the real-time grayscale image of any photovoltaic panel, it acquires the real-time grayscale value Gs of any pixel in the real-time grayscale image and calculates the real-time grayscale difference ΔGs, where ΔGs = |Gb - Gs|. The inspection and analysis unit compares the real-time grayscale difference ΔGs with the foreign object grayscale difference ΔGy and acquires the real-time grayscale value of the next pixel in the real-time grayscale image. The inspection and analysis unit repeats the above operation of calculating the real-time grayscale difference and comparing it with the foreign object grayscale difference until all pixels in the real-time grayscale image have been compared. If the inspection and analysis unit determines that the real-time grayscale difference of all pixels in the real-time grayscale image does not exceed the grayscale difference of foreign objects, i.e. ΔGs≤ΔGy, the inspection and analysis unit determines that the photovoltaic panel corresponding to the real-time grayscale image has no foreign object area. The inspection and analysis unit determines the average grayscale of the real-time grayscale image in order to calculate the theoretical power generation of the photovoltaic panel. If the inspection and analysis unit determines that the real-time grayscale difference of any pixel in the real-time grayscale image exceeds the grayscale difference of the foreign object, i.e. ΔGs>ΔGy, the inspection and analysis unit determines that there is a foreign object area in the photovoltaic panel corresponding to the real-time grayscale image. The inspection and analysis unit will determine the area of ​​the foreign object area of ​​the photovoltaic panel to determine whether to mark the coordinates of the foreign object area.

[0011] Furthermore, the inspection and analysis unit is equipped with a dust grayscale difference ΔGh and a preset power generation Qy, wherein ΔGh < ΔGy. When the inspection and analysis unit determines that there is no foreign object in the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the photovoltaic panel dust area Sh of the part where the real-time grayscale difference ΔGs of the photovoltaic panel is higher than the dust grayscale difference ΔGh, and calculates the theoretical power generation Qu of the photovoltaic panel, Qu = Qy × [(So - Sh) / So], where So is the total area of ​​the photovoltaic panel.

[0012] Furthermore, the inspection and analysis unit is equipped with a maximum foreign object area Sg. When the inspection and analysis unit determines that there is a foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the foreign object area Se of the photovoltaic panel and compares the foreign object area Se with the maximum foreign object area Sg. When Se < Sg, the inspection and analysis unit determines that the area of ​​the foreign object region Se of the photovoltaic panel is lower than the maximum foreign object area Sg. The inspection and analysis unit will repeat the above operation of determining the dust area of ​​the photovoltaic panel based on the dust grayness difference ΔGh and calculating the theoretical power generation of the photovoltaic panel. The theoretical power generation Qu1 is calculated for the photovoltaic panel area other than the foreign object region area Se. Qu1 = Qy × (Sr / So) × [(Sr-Sh) / Sr], where Sr = So-Se, and Sr is the power generation area of ​​the photovoltaic panel. When Se≥Sg, the inspection and analysis unit determines that the foreign object area Se of the photovoltaic panel has reached the maximum foreign object area Sg. The inspection and analysis unit does not calculate the theoretical power generation of the photovoltaic panel, marks the coordinates of the photovoltaic panel, and judges the real-time grayscale image of the next photovoltaic panel.

[0013] Furthermore, when the inspection analysis unit completes the judgment of the real-time grayscale image of all photovoltaic panels in the photovoltaic power generation area, it sums up the calculated theoretical power generation to obtain the power generation Qf of the photovoltaic power generation area in the analysis period, and calculates the inspection deviation B, B=(Qf / Qz)×100%. The inspection analysis unit outputs the inspection deviation B of the photovoltaic power generation area and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result.

[0014] A drone inspection method for use in any of the above-mentioned drone inspection systems based on photovoltaic power generation data includes, Step S1: The power generation acquisition unit collects the total power generation of the photovoltaic power generation area during the detection period and the power generation per unit time within each unit detection time of the detection period. Step S2: The inspection judgment unit judges the data collected by the power generation acquisition unit based on the fluctuation difference between the first preset power generation, the second preset power generation, and the standard power generation, and determines whether to output an inspection command to the inspection drone. Step S3: The inspection drone collects real-time images of each photovoltaic panel in the photovoltaic power generation area and obtains the position coordinates corresponding to each real-time image. Step S4: The inspection and analysis unit performs grayscale processing on the real-time images of each photovoltaic panel, and determines whether there is a foreign object area on the corresponding photovoltaic panel based on the real-time grayscale of each pixel in the real-time grayscale image. Based on the area of ​​the foreign object area, the photovoltaic panel is marked with coordinates or the theoretical power generation is calculated. Step S5: The inspection analysis unit calculates the inspection deviation based on the calculated theoretical power generation and the total power generation during the detection cycle, and outputs the inspection deviation and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result of the photovoltaic power generation area.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: by setting up a power generation acquisition unit to collect power generation data from several photovoltaic power generation areas, and by setting up an inspection judgment unit to determine whether to issue an inspection command based on the power generation data of any photovoltaic power generation area, the positioning accuracy of UAV photovoltaic inspection is improved, the number of inspection UAVs used is greatly reduced, and the inspection efficiency is effectively improved. Furthermore, by setting up an inspection analysis unit to preprocess the real-time images and position coordinates of photovoltaic panels in the photovoltaic power generation area collected by the inspection UAV, the faulty photovoltaic panels and their positions are initially determined by the threshold set in the inspection analysis unit, and the inspection deviation is calculated based on the calculated theoretical power generation and the total power generation collected in the detection cycle. The inspection deviation and the position coordinates of the faulty photovoltaic panels are then output. This not only reduces the amount of data for UAV inspection, but also realizes intelligent analysis and preprocessing of inspection data, ensuring the accuracy of inspection results while improving the efficiency of UAV photovoltaic inspection.

[0016] Furthermore, by setting a first preset power generation and a second preset power generation within the inspection and judgment unit, the normal power generation range of the photovoltaic power generation area is determined. When the total power generation of a certain photovoltaic power generation area during the detection period is lower than the first preset power generation, the inspection and judgment unit directly determines that the power generation of the photovoltaic power generation area is abnormal and directly outputs an inspection command. When the total power generation of a certain photovoltaic power generation area during the detection period is between the first preset power generation and the second preset power generation, the real-time power generation fluctuation difference of the photovoltaic power generation area is calculated to determine whether there is a power generation abnormality in the photovoltaic power generation area. When the total power generation of a certain photovoltaic power generation area during the detection period is higher than the second preset power generation, it indicates that the power generation data of the photovoltaic power generation area is normal, so there is no need to inspect the photovoltaic power generation area, which improves the accuracy of the UAV photovoltaic inspection and judgment and improves the efficiency of photovoltaic inspection.

[0017] In particular, the inspection and judgment unit obtains the power generation per unit time t of each unit detection duration in the corresponding photovoltaic power generation area within the detection period T, and calculates the real-time power generation fluctuation difference. The real-time power generation fluctuation difference is compared with the standard power generation fluctuation difference set in the inspection and judgment unit. When the real-time power generation fluctuation difference does not exceed the standard power generation fluctuation difference, it indicates that the power generation of the photovoltaic power generation area is stable, so no inspection command is output. When the real-time power generation fluctuation difference exceeds the standard power generation fluctuation difference, it indicates that the power generation fluctuation within each unit detection duration is large, so the inspection and judgment unit outputs the inspection command for the photovoltaic power generation area to conduct an inspection of the photovoltaic power generation area. This further improves the accuracy of the drone photovoltaic inspection and judgment, and reduces the use of drones while ensuring normal inspection of photovoltaic power generation areas.

[0018] Furthermore, by setting a standard difference between the grayscale of the photovoltaic panel and the grayscale of foreign objects in the inspection and analysis unit, the grayscale of each pixel in the converted real-time grayscale image is judged. This allows for the rapid determination of foreign objects and light spots on the surface of the photovoltaic panel. When the inspection and analysis unit determines that there is no foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, it will determine the surface dust condition of the photovoltaic panel based on the real-time grayscale image. When the inspection and analysis unit determines that there is a foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, it will determine whether the area of ​​the foreign object area affects the normal power generation of the photovoltaic panel based on the area of ​​the foreign object area. This achieves intelligent preprocessing of the collected data, improving the efficiency of inspection data processing while ensuring the accuracy of inspection data judgment.

[0019] Furthermore, the inspection and analysis unit calculates the theoretical power generation of photovoltaic panels without foreign objects based on the dust area and preset power generation. This effectively eliminates the impact of dust deposition on power generation, making the calculated theoretical power generation more accurate and improving the accuracy of inspection data processing.

[0020] Furthermore, the inspection and analysis unit determines the area of ​​the foreign object zone on the photovoltaic panel where there is a foreign object, and determines whether the photovoltaic panel can generate electricity normally based on the set maximum foreign object area. When the area of ​​the foreign object zone on the photovoltaic panel is lower than the maximum foreign object area, it is determined that the photovoltaic panel can generate electricity normally. The theoretical power generation of the photovoltaic panel is calculated based on the power-generating area of ​​the photovoltaic panel. When the area of ​​the foreign object zone on the photovoltaic panel has reached the maximum foreign object area, it is determined that the photovoltaic panel has a fault. By marking the coordinates of the photovoltaic panel, the actual location of the photovoltaic panel is determined, which facilitates on-site inspection and maintenance.

[0021] Furthermore, by summing the calculated theoretical power generation and calculating the inspection deviation of the photovoltaic power generation area with the total power generation of the detection cycle, and outputting the inspection deviation and the position coordinates of each photovoltaic panel with coordinate markers as the inspection result, intelligent preprocessing of photovoltaic inspection data is realized, and the accuracy of inspection data is improved. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the structure of the drone inspection system based on photovoltaic power generation data described in this embodiment; Figure 2 This is a flowchart of the drone inspection method based on photovoltaic power generation data described in this embodiment. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 The diagram shown is a structural schematic of the drone inspection system based on photovoltaic power generation data described in this embodiment. This paper discloses a drone inspection system based on photovoltaic power generation data, comprising: A power generation acquisition unit is connected to several external photovoltaic power generation areas to collect power generation data from each of the photovoltaic power generation areas. The power generation data includes the total power generation during the detection period and the power generation per unit time within each unit detection time during the detection period. The inspection and judgment unit is connected to the power generation acquisition unit. The inspection and judgment unit can judge the power generation data of any photovoltaic power generation area collected by the power generation acquisition unit, and determine whether to output an inspection command for the photovoltaic power generation area based on the judgment result. An inspection drone is connected to the inspection judgment unit. The inspection drone can perform inspections in the corresponding photovoltaic power generation area according to the inspection instructions output by the inspection judgment unit, and collect inspection data of each photovoltaic panel in the corresponding photovoltaic power generation area. The inspection data includes real-time images of each photovoltaic panel and the position coordinates corresponding to each real-time image. The inspection analysis unit is connected to the inspection judgment unit and the inspection drone. The inspection analysis unit can process the real-time images of each photovoltaic panel to form a real-time grayscale image. It then judges the real-time grayscale of each pixel in the real-time grayscale image based on the grayscale difference between the standard photovoltaic panel and the foreign object, to determine whether a foreign object area exists on the corresponding photovoltaic panel. When no foreign object area exists on the photovoltaic panel, the inspection analysis unit calculates the theoretical power generation of the corresponding photovoltaic panel based on the dust grayscale difference and a preset power generation value. When a foreign object area exists on the photovoltaic panel, the inspection analysis unit determines whether to mark the photovoltaic panel with coordinates based on a maximum foreign object area, and calculates the theoretical power generation of the photovoltaic panel area excluding the foreign object area for unmarked photovoltaic panels. After judging the real-time grayscale images of all photovoltaic panels within the photovoltaic power generation area, the inspection analysis unit calculates the inspection deviation based on the calculated theoretical power generation and the total power generation during the detection cycle, and outputs the inspection deviation and the corresponding position coordinates of each marked photovoltaic panel as the inspection result for that photovoltaic power generation area.

[0028] By setting up a power generation acquisition unit to collect power generation data from several photovoltaic power generation areas, and by setting up an inspection judgment unit to determine whether to issue an inspection command based on the power generation data of any photovoltaic power generation area, the positioning accuracy of UAV photovoltaic inspection is improved, the number of inspection UAVs used is greatly reduced, and the inspection efficiency is effectively improved. By setting up an inspection analysis unit to preprocess the real-time images and position coordinates of photovoltaic panels in the photovoltaic power generation area collected by the inspection UAV, the faulty photovoltaic panels and their positions are initially determined by the threshold set in the inspection analysis unit. The inspection deviation is calculated based on the calculated theoretical power generation and the total power generation of the detection cycle, and the inspection deviation and the position coordinates of the faulty photovoltaic panels are output. This not only reduces the amount of data of UAV inspection, but also realizes intelligent analysis and preprocessing of inspection data, ensuring the accuracy of inspection results while improving the efficiency of UAV photovoltaic inspection.

[0029] Specifically, the inspection and judgment unit is equipped with a first preset power generation Q1 and a second preset power generation Q2, where Q1 < Q2. The power generation acquisition unit is equipped with a detection period T and a unit detection time t, where T = t × K, and K is the number of segmented detections within the detection period T. The inspection and judgment unit can obtain the total power generation Qz of any photovoltaic power generation area collected by the power generation acquisition unit during the detection period, and compare the total power generation Qz during the detection period with the first preset power generation Q1 and the second preset power generation Q2. When Qz < Q1, the inspection and judgment unit determines that the total power generation Qz in the detection period T is lower than the first preset power generation Q1. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection instruction for the photovoltaic power generation area. When Q1≤Qz≤Q2, the inspection judgment unit determines that the total power generation Qz in the detection period T is between the first preset power generation Q1 and the second preset power generation Q2. The inspection judgment unit will obtain the power generation per unit time in each unit detection time in the detection period T and calculate the real-time power generation fluctuation difference of the corresponding photovoltaic power generation area to determine whether to output an inspection command to the photovoltaic power generation area. When Qz > Q2, the inspection judgment unit determines that the total power generation Qz within the detection period T is higher than the second preset power generation Q2. The inspection judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is normal and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area.

[0030] By setting a first preset power generation value and a second preset power generation value within the inspection and judgment unit, the normal power generation range of the photovoltaic power generation area is determined. When the total power generation of a certain photovoltaic power generation area during the detection period is lower than the first preset power generation value, the inspection and judgment unit directly determines that the power generation of the photovoltaic power generation area is abnormal and directly outputs an inspection command. When the total power generation of a certain photovoltaic power generation area during the detection period is between the first preset power generation value and the second preset power generation value, the real-time power generation fluctuation difference of the photovoltaic power generation area is calculated to determine whether there is a power generation abnormality in the photovoltaic power generation area. When the total power generation of a certain photovoltaic power generation area during the detection period is higher than the second preset power generation value, it indicates that the power generation data of the photovoltaic power generation area is normal, so there is no need to inspect the photovoltaic power generation area, which improves the accuracy of the UAV photovoltaic inspection and judgment and improves the efficiency of photovoltaic inspection.

[0031] Specifically, when the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit obtains the power generation per unit time t within each unit detection duration of the corresponding photovoltaic power generation area in the detection period T, sorts the power generation per unit time from largest to smallest, and calculates the real-time power generation fluctuation difference ΔQs, where ΔQs = Qta - Qtz, and Qta is the maximum power generation among the power generation per unit time, and Qtz is the minimum power generation among the power generation per unit time.

[0032] Specifically, the inspection and judgment unit is equipped with a standard power generation fluctuation difference ΔQb. When the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit compares the calculated real-time power generation fluctuation difference ΔQs with the standard power generation fluctuation difference ΔQb. When ΔQs≤ΔQb, the inspection judgment unit determines that the real-time power generation fluctuation difference ΔQs does not exceed the standard power generation fluctuation difference ΔQb, and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area. When ΔQs > ΔQb, the inspection and judgment unit determines that the real-time power generation fluctuation difference ΔQs has exceeded the standard power generation fluctuation difference ΔQb. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection command for the photovoltaic power generation area.

[0033] The inspection and judgment unit obtains the power generation per unit time t of each unit detection duration in the corresponding photovoltaic power generation area within the detection period T, and calculates the real-time power generation fluctuation difference. The real-time power generation fluctuation difference is compared with the standard power generation fluctuation difference set in the inspection and judgment unit. When the real-time power generation fluctuation difference does not exceed the standard power generation fluctuation difference, it indicates that the power generation of the photovoltaic power generation area is stable, so no inspection command is output. When the real-time power generation fluctuation difference exceeds the standard power generation fluctuation difference, it indicates that the power generation fluctuation within each unit detection duration is large, so the inspection and judgment unit outputs an inspection command for the photovoltaic power generation area to conduct an inspection of the photovoltaic power generation area. This further improves the accuracy of the drone photovoltaic inspection and judgment, and reduces the use of drones while ensuring normal inspection of photovoltaic power generation areas.

[0034] Specifically, the inspection judgment unit outputs an inspection command, and the inspection drone performs an inspection in the corresponding photovoltaic power generation area according to the inspection command, and collects real-time images of each photovoltaic panel in the photovoltaic power generation area, and transmits each real-time image to the inspection analysis unit. The inspection analysis unit performs grayscale processing on the real-time images of each photovoltaic panel to form a real-time grayscale image of each photovoltaic panel. The inspection analysis unit will judge the real-time grayscale image of any photovoltaic panel to determine whether to mark the photovoltaic panel as abnormal.

[0035] Specifically, the inspection and analysis unit is equipped with a standard photovoltaic panel grayscale value Gb and a foreign object grayscale difference ΔGy. When the inspection and analysis unit judges the real-time grayscale image of any photovoltaic panel, it acquires the real-time grayscale value Gs of any pixel in the real-time grayscale image and calculates the real-time grayscale difference ΔGs, where ΔGs = |Gb - Gs|. The inspection and analysis unit compares the real-time grayscale difference ΔGs with the foreign object grayscale difference ΔGy and acquires the real-time grayscale value of the next pixel in the real-time grayscale image. The inspection and analysis unit repeats the above operation of calculating the real-time grayscale difference and comparing it with the foreign object grayscale difference until all pixels in the real-time grayscale image have been compared. If the inspection and analysis unit determines that the real-time grayscale difference of all pixels in the real-time grayscale image does not exceed the grayscale difference of foreign objects, i.e. ΔGs≤ΔGy, the inspection and analysis unit determines that the photovoltaic panel corresponding to the real-time grayscale image has no foreign object area. The inspection and analysis unit determines the average grayscale of the real-time grayscale image in order to calculate the theoretical power generation of the photovoltaic panel. If the inspection and analysis unit determines that the real-time grayscale difference of any pixel in the real-time grayscale image exceeds the grayscale difference of the foreign object, i.e. ΔGs>ΔGy, the inspection and analysis unit determines that there is a foreign object area in the photovoltaic panel corresponding to the real-time grayscale image. The inspection and analysis unit will determine the area of ​​the foreign object area of ​​the photovoltaic panel to determine whether to mark the coordinates of the foreign object area.

[0036] By setting a standard photovoltaic panel grayscale difference and a foreign object grayscale difference in the inspection analysis unit, the grayscale of each pixel in the converted real-time grayscale image is judged, which can quickly determine the situation of foreign objects and light spots on the photovoltaic panel surface. When the inspection analysis unit determines that there is no foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, it will determine the surface dust situation of the photovoltaic panel based on the real-time grayscale image. When the inspection analysis unit determines that there is a foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, it will determine whether it affects the normal power generation of the photovoltaic panel based on the area of ​​the foreign object area. This realizes intelligent preprocessing of the collected data, which improves the efficiency of inspection data processing while ensuring the accuracy of inspection data judgment.

[0037] Specifically, the inspection and analysis unit is equipped with a dust grayscale difference ΔGh and a preset power generation Qy, where ΔGh < ΔGy. When the inspection and analysis unit determines that there is no foreign object in the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the photovoltaic panel dust area Sh of the part where the real-time grayscale difference ΔGs of the photovoltaic panel is higher than the dust grayscale difference ΔGh, and calculates the theoretical power generation Qu of the photovoltaic panel, Qu = Qy × [(So - Sh) / So], where So is the total area of ​​the photovoltaic panel.

[0038] The inspection and analysis unit calculates the theoretical power generation of photovoltaic panels without foreign objects based on the dust area and preset power generation. This effectively eliminates the impact of dust accumulation on the power generation, making the calculated theoretical power generation more accurate and improving the accuracy of inspection data processing.

[0039] Specifically, the inspection and analysis unit is equipped with a maximum foreign object area Sg. When the inspection and analysis unit determines that there is a foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the foreign object area Se of the photovoltaic panel and compares the foreign object area Se with the maximum foreign object area Sg. When Se < Sg, the inspection and analysis unit determines that the area of ​​the foreign object region Se of the photovoltaic panel is lower than the maximum foreign object area Sg. The inspection and analysis unit will repeat the above operation of determining the dust area of ​​the photovoltaic panel based on the dust grayness difference ΔGh and calculating the theoretical power generation of the photovoltaic panel. The theoretical power generation Qu1 is calculated for the photovoltaic panel area other than the foreign object region area Se. Qu1 = Qy × (Sr / So) × [(Sr-Sh) / Sr], where Sr = So-Se, and Sr is the power generation area of ​​the photovoltaic panel. When Se≥Sg, the inspection and analysis unit determines that the foreign object area Se of the photovoltaic panel has reached the maximum foreign object area Sg. The inspection and analysis unit does not calculate the theoretical power generation of the photovoltaic panel, marks the coordinates of the photovoltaic panel, and judges the real-time grayscale image of the next photovoltaic panel.

[0040] The inspection and analysis unit determines the area of ​​the foreign object zone on the photovoltaic panel and determines whether the photovoltaic panel can generate electricity normally based on the set maximum foreign object area. When the area of ​​the foreign object zone on the photovoltaic panel is lower than the maximum foreign object area, the photovoltaic panel is judged to be able to generate electricity normally. The theoretical power generation of the photovoltaic panel is calculated based on the power-generating area of ​​the photovoltaic panel. When the area of ​​the foreign object zone on the photovoltaic panel has reached the maximum foreign object area, the photovoltaic panel is judged to have a fault. By marking the coordinates of the photovoltaic panel, the actual location of the photovoltaic panel is determined, which facilitates on-site inspection and maintenance.

[0041] Specifically, when the inspection analysis unit completes the judgment of the real-time grayscale image of all photovoltaic panels in the photovoltaic power generation area, it sums up the calculated theoretical power generation to obtain the power generation Qf of the photovoltaic power generation area in the analysis period, and calculates the inspection deviation B, B=(Qf / Qz)×100%. The inspection analysis unit outputs the inspection deviation B of the photovoltaic power generation area and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result.

[0042] By summing the calculated theoretical power generation and calculating the inspection deviation of the photovoltaic power generation area with the total power generation of the detection cycle, and outputting the inspection deviation and the position coordinates of each photovoltaic panel with coordinate markers as the inspection result, intelligent preprocessing of photovoltaic inspection data is realized, and the accuracy of inspection data is improved.

[0043] Please refer to Figure 2, which is a flowchart of the drone inspection method based on photovoltaic power generation data described in this embodiment. This embodiment also discloses a drone inspection method applied to any of the above-mentioned drone inspection systems based on photovoltaic power generation data, including: Step S1: The power generation acquisition unit collects the total power generation of the photovoltaic power generation area during the detection period and the power generation per unit time within each unit detection time of the detection period. Step S2: The inspection judgment unit judges the data collected by the power generation acquisition unit based on the fluctuation difference between the first preset power generation, the second preset power generation, and the standard power generation, and determines whether to output an inspection command to the inspection drone. Step S3: The inspection drone collects real-time images of each photovoltaic panel in the photovoltaic power generation area and obtains the position coordinates corresponding to each real-time image. Step S4: The inspection and analysis unit performs grayscale processing on the real-time images of each photovoltaic panel, and determines whether there is a foreign object area on the corresponding photovoltaic panel based on the real-time grayscale of each pixel in the real-time grayscale image. Based on the area of ​​the foreign object area, the photovoltaic panel is marked with coordinates or the theoretical power generation is calculated. Step S5: The inspection analysis unit calculates the inspection deviation based on the calculated theoretical power generation and the total power generation during the detection cycle, and outputs the inspection deviation and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result of the photovoltaic power generation area.

[0044] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0045] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A drone inspection system based on photovoltaic power generation data, characterized in that, include, A power generation acquisition unit is connected to several external photovoltaic power generation areas to collect power generation data from each of the photovoltaic power generation areas. The power generation data includes the total power generation during the detection period and the power generation per unit time within each unit detection time during the detection period. The inspection and judgment unit is connected to the power generation acquisition unit. The inspection and judgment unit can judge the power generation data of any photovoltaic power generation area collected by the power generation acquisition unit, and determine whether to output an inspection command for the photovoltaic power generation area based on the judgment result. An inspection drone is connected to the inspection judgment unit. The inspection drone can perform inspections in the corresponding photovoltaic power generation area according to the inspection instructions output by the inspection judgment unit, and collect inspection data of each photovoltaic panel in the corresponding photovoltaic power generation area. The inspection data includes real-time images of each photovoltaic panel and the position coordinates corresponding to each real-time image. The inspection and analysis unit is connected to the inspection and judgment unit and the inspection drone. The inspection and analysis unit can perform grayscale processing on the real-time images of each photovoltaic panel to form a real-time grayscale image. It can also judge the real-time grayscale of each pixel in the real-time grayscale image based on the grayscale difference between the standard photovoltaic panel and the foreign object, so as to determine whether there is a foreign object area in the corresponding photovoltaic panel. When there is no foreign object area in the photovoltaic panel, the inspection and analysis unit calculates the theoretical power generation of the corresponding photovoltaic panel based on the dust grayscale difference and the preset power generation. When there is a foreign object area in the photovoltaic panel, the inspection and analysis unit determines whether to mark the photovoltaic panel with coordinates based on the maximum foreign object area. It also calculates the theoretical power generation of the photovoltaic panel area other than the foreign object area for the unmarked photovoltaic panels. When the inspection analysis unit completes the judgment on the real-time grayscale image of all photovoltaic panels in the photovoltaic power generation area, it calculates the inspection deviation based on the calculated theoretical power generation and the total power generation in the detection cycle, and outputs the inspection deviation and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result of the photovoltaic power generation area.

2. The UAV inspection system based on photovoltaic power generation data according to claim 1, characterized in that, The inspection and judgment unit is equipped with a first preset power generation Q1 and a second preset power generation Q2, where Q1 < Q2. The power generation acquisition unit is equipped with a detection period T and a unit detection time t, where T = t × K, and K is the number of segmented detections within the detection period T. The inspection and judgment unit can obtain the total power generation Qz of any photovoltaic power generation area collected by the power generation acquisition unit during the detection period, and compare the total power generation Qz during the detection period with the first preset power generation Q1 and the second preset power generation Q2. When Qz < Q1, the inspection and judgment unit determines that the total power generation Qz in the detection period T is lower than the first preset power generation Q1. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection instruction for the photovoltaic power generation area. When Q1≤Qz≤Q2, the inspection judgment unit determines that the total power generation Qz in the detection period T is between the first preset power generation Q1 and the second preset power generation Q2. The inspection judgment unit will obtain the power generation per unit time in each unit detection time in the detection period T and calculate the real-time power generation fluctuation difference of the corresponding photovoltaic power generation area to determine whether to output an inspection command to the photovoltaic power generation area. When Qz > Q2, the inspection judgment unit determines that the total power generation Qz within the detection period T is higher than the second preset power generation Q2. The inspection judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is normal and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area.

3. The UAV inspection system based on photovoltaic power generation data according to claim 2, characterized in that, When the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit obtains the power generation per unit time t of each unit detection duration in the corresponding photovoltaic power generation area within the detection period T, sorts the power generation per unit time duration from largest to smallest, and calculates the real-time power generation fluctuation difference ΔQs, ΔQs=Qta-Qtz, where Qta is the maximum power generation per unit time duration and Qtz is the minimum power generation per unit time duration.

4. The UAV inspection system based on photovoltaic power generation data according to claim 3, characterized in that, The inspection and judgment unit is equipped with a standard power generation fluctuation difference ΔQb. When the total power generation Qz within the detection period T is between the first preset power generation Q1 and the second preset power generation Q2, the inspection and judgment unit compares the calculated real-time power generation fluctuation difference ΔQs with the standard power generation fluctuation difference ΔQb. When ΔQs≤ΔQb, the inspection judgment unit determines that the real-time power generation fluctuation difference ΔQs does not exceed the standard power generation fluctuation difference ΔQb, and does not output an inspection command. The inspection judgment unit will judge the power generation data of the next photovoltaic power generation area collected by the power generation acquisition unit to determine whether to carry out drone inspection in the corresponding photovoltaic power generation area. When ΔQs > ΔQb, the inspection and judgment unit determines that the real-time power generation fluctuation difference ΔQs has exceeded the standard power generation fluctuation difference ΔQb. The inspection and judgment unit determines that the power generation data of the corresponding photovoltaic power generation area is abnormal, and the inspection and judgment unit outputs the inspection command for the photovoltaic power generation area.

5. The UAV inspection system based on photovoltaic power generation data according to claim 4, characterized in that, The inspection judgment unit outputs an inspection command, and the inspection drone performs an inspection in the corresponding photovoltaic power generation area according to the inspection command, and collects real-time images of each photovoltaic panel in the photovoltaic power generation area, and transmits each real-time image to the inspection analysis unit. The inspection analysis unit performs grayscale processing on the real-time images of each photovoltaic panel to form a real-time grayscale image of each photovoltaic panel. The inspection analysis unit will judge the real-time grayscale image of any photovoltaic panel to determine whether to mark the photovoltaic panel as abnormal.

6. The UAV inspection system based on photovoltaic power generation data according to claim 5, characterized in that, The inspection and analysis unit is equipped with a standard photovoltaic panel grayscale value Gb and a foreign object grayscale difference ΔGy. When the inspection and analysis unit judges the real-time grayscale image of any photovoltaic panel, it acquires the real-time grayscale value Gs of any pixel in the real-time grayscale image and calculates the real-time grayscale difference ΔGs, where ΔGs = |Gb - Gs|. The inspection and analysis unit compares the real-time grayscale difference ΔGs with the foreign object grayscale difference ΔGy and acquires the real-time grayscale value of the next pixel in the real-time grayscale image. The inspection and analysis unit repeats the above operation of calculating the real-time grayscale difference and comparing it with the foreign object grayscale difference until all pixels in the real-time grayscale image have been compared. If the inspection and analysis unit determines that the real-time grayscale difference of all pixels in the real-time grayscale image does not exceed the grayscale difference of foreign objects, i.e. ΔGs≤ΔGy, the inspection and analysis unit determines that the photovoltaic panel corresponding to the real-time grayscale image has no foreign object area. The inspection and analysis unit determines the average grayscale of the real-time grayscale image in order to calculate the theoretical power generation of the photovoltaic panel. If the inspection and analysis unit determines that the real-time grayscale difference of any pixel in the real-time grayscale image exceeds the grayscale difference of the foreign object, i.e. ΔGs>ΔGy, the inspection and analysis unit determines that there is a foreign object area in the photovoltaic panel corresponding to the real-time grayscale image. The inspection and analysis unit will determine the area of ​​the foreign object area of ​​the photovoltaic panel to determine whether to mark the coordinates of the foreign object area.

7. The UAV inspection system based on photovoltaic power generation data according to claim 6, characterized in that, The inspection and analysis unit is equipped with a dust grayscale difference ΔGh and a preset power generation Qy, where ΔGh < ΔGy. When the inspection and analysis unit determines that there is no foreign object in the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the photovoltaic panel dust area Sh of the part where the real-time grayscale difference ΔGs of the photovoltaic panel is higher than the dust grayscale difference ΔGh, and calculates the theoretical power generation Qu of the photovoltaic panel, Qu = Qy × [(So - Sh) / So], where So is the total area of ​​the photovoltaic panel.

8. The UAV inspection system based on photovoltaic power generation data according to claim 7, characterized in that, The inspection and analysis unit is equipped with a maximum foreign object area Sg. When the inspection and analysis unit determines that there is a foreign object area on the photovoltaic panel corresponding to the real-time grayscale image, the inspection and analysis unit obtains the foreign object area Se of the photovoltaic panel and compares the foreign object area Se with the maximum foreign object area Sg. When Se < Sg, the inspection and analysis unit determines that the area of ​​the foreign object region Se of the photovoltaic panel is lower than the maximum foreign object area Sg. The inspection and analysis unit will repeat the above operation of determining the dust area of ​​the photovoltaic panel based on the dust grayness difference ΔGh and calculating the theoretical power generation of the photovoltaic panel. The theoretical power generation Qu1 is calculated for the photovoltaic panel area other than the foreign object region area Se. Qu1 = Qy × (Sr / So) × [(Sr-Sh) / Sr], where Sr = So-Se, and Sr is the power generation area of ​​the photovoltaic panel. When Se≥Sg, the inspection and analysis unit determines that the foreign object area Se of the photovoltaic panel has reached the maximum foreign object area Sg. The inspection and analysis unit does not calculate the theoretical power generation of the photovoltaic panel, marks the coordinates of the photovoltaic panel, and judges the real-time grayscale image of the next photovoltaic panel.

9. The UAV inspection system based on photovoltaic power generation data according to claim 8, characterized in that, When the inspection analysis unit completes the judgment of the real-time grayscale image of all photovoltaic panels in the photovoltaic power generation area, it sums up the calculated theoretical power generation to obtain the power generation Qf of the photovoltaic power generation area in the analysis period, and calculates the inspection deviation B, B=(Qf / Qz)×100%. The inspection analysis unit outputs the inspection deviation B of the photovoltaic power generation area and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result.

10. A drone inspection method applied to the drone inspection system based on photovoltaic power generation data as described in any one of claims 1-9, characterized in that, include, Step S1: The power generation acquisition unit collects the total power generation of the photovoltaic power generation area during the detection period and the power generation per unit time within each unit detection time of the detection period. Step S2: The inspection judgment unit judges the data collected by the power generation acquisition unit based on the fluctuation difference between the first preset power generation, the second preset power generation, and the standard power generation, and determines whether to output an inspection command to the inspection drone. Step S3: The inspection drone collects real-time images of each photovoltaic panel in the photovoltaic power generation area and obtains the position coordinates corresponding to each real-time image. Step S4: The inspection and analysis unit performs grayscale processing on the real-time images of each photovoltaic panel, and determines whether there is a foreign object area on the corresponding photovoltaic panel based on the real-time grayscale of each pixel in the real-time grayscale image. Based on the area of ​​the foreign object area, the photovoltaic panel is marked with coordinates or the theoretical power generation is calculated. Step S5: The inspection analysis unit calculates the inspection deviation based on the calculated theoretical power generation and the total power generation during the detection cycle, and outputs the inspection deviation and the position coordinates of each photovoltaic panel with coordinate marks as the inspection result of the photovoltaic power generation area.

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