Intelligent inspection method and system for photovoltaic power station

Abstract

The invention discloses an intelligent polling method and system for a photovoltaic power station, and relates to the technical field of intelligent polling of the photovoltaic power station. The working condition of the photovoltaic power station is analyzed when the weather is abnormal, so that a data basis and a judgment basis are provided for subsequent polling work; when abnormal weather causes foreign matters to cover a photovoltaic panel and further influence the work of the photovoltaic power station, the unmanned aerial vehicle obtains each abnormal area and analyzes the abnormal area to obtain an optimal inspection route, and then the inspection robot is dispatched to analyze the foreign matter type and the cleaning mode of each abnormal area through the optimal inspection route. And finally, secondary cleaning is carried out on the area where cleaning is not completed so as to guarantee normal work of the photovoltaic power station. Foreign matter covering the photovoltaic panel of the photovoltaic power station is cleaned through the intelligent inspection robot when the weather is abnormal; the problems that at present, cleaning of the photovoltaic power station is not emphasized enough, the cleaning difficulty is large, and the cleaning efficiency is low are solved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent inspection of photovoltaic power stations, and in particular to an intelligent inspection method and system for photovoltaic power stations. Background Art

[0002] With the development of society and science and technology, the application of photovoltaic technology is becoming more and more extensive. However, due to the complex working conditions of equipment in photovoltaic power stations and the large area of ​​photovoltaic panels, traditional cleaning methods are time-consuming, labor-intensive and inefficient. Especially when the weather is abnormal, traditional manual cleaning is more difficult. Therefore, this application proposes an intelligent inspection method and system for photovoltaic power stations.

[0003] The prior art is a photovoltaic inspection method based on drones disclosed in the invention application patent with announcement number: CN113031634A. The photovoltaic inspection method based on drones uses drones equipped with infrared pan-tilt cameras for inspection, and the specific steps are as follows: Step S1, when the photovoltaic power station is generating electricity, determine the height, horizontal distance and angle of the initial point of the infrared camera; Step S2, start the drone inspection; Step S3, scan along the "bow" shape, shoot and store infrared video; Step S4, use a computer to process the image data and flight data in the storage medium, and generate the damaged photovoltaic panel location data; Compared with related technologies, this method is more efficient and intelligent.

[0004] There are at least the following technical problems with the above solution: 1. The above technology mainly uses drones to analyze the location of damaged photovoltaic panels, and the main focus is on fault analysis. It lacks analysis of the cleanliness of photovoltaic power stations. After abnormal weather, a large amount of foreign matter will accumulate on the photovoltaic panels. The existing technology ignores this aspect, which will lead to a decline in the working performance of photovoltaic power stations, and then lead to a large waste of resources.

[0005] 2. After the above technology analyzes the location of the faulty photovoltaic panels of the photovoltaic power station, it lacks analysis of the subsequent repair methods of the faulty photovoltaic panels. Failure to repair the faulty photovoltaic panels in time will cause failures in the operation of the photovoltaic power station, and thus the completeness and rationality of the inspection method cannot be guaranteed. Summary of the invention

[0006] The purpose of this application is to provide a photovoltaic power station intelligent inspection method and system to solve the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present application adopts the following technical solutions: In the first aspect, the present application provides a method for intelligent inspection of photovoltaic power stations. Step one, data analysis: obtaining the working information of the photovoltaic power station when the weather is abnormal, and analyzing whether there is any abnormality in the operation of the photovoltaic power station.

[0008] Step 2: Route planning: When the photovoltaic power station is working abnormally, the drone is used to obtain the abnormal areas and then obtain the optimal inspection route.

[0009] Step 3: Foreign matter cleaning: The inspection robot inspects each abnormal area through the optimal inspection route, obtains foreign matter information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area.

[0010] Step 4: Secondary cleaning: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

[0011] Preferably, the abnormal areas are obtained by drones, and the specific acquisition method is as follows: the photovoltaic power station is divided into block areas of specified length, and images and videos of each area are captured by drones equipped with high-definition cameras, and the captured images and videos are pre-processed and compared with the stored images and videos of each corresponding area, and each area covered by foreign objects is recorded as an abnormal area, and each area not covered by foreign objects is recorded as a normal area.

[0012] Preferably, the specific acquisition process of obtaining the optimal inspection route is as follows: A1. Obtain the abnormal area closest to the starting point of the inspection robot and record it as the first target area on the first side, and record the abnormal area located on the same side of the first target area on the first side and with the closest straight-line distance as the second target area on the first side; repeat the above steps until all abnormal areas on the first side are marked.

[0013] A2. Obtain the abnormal area adjacent to the turning point of the first side and closest to the last target area on the first side in a straight line distance, and record it as the first target area on the second side; obtain the abnormal area on the same side of the first target area on the second side and closest to the straight line distance and record it as the second target area on the second side; repeat the above steps until all abnormal areas on the second side are marked.

[0014] A3. Repeat the above steps until all abnormal areas are marked.

[0015] A4. Connect the first target area on the first side as the starting point to the second target area on the first side, and repeat the above steps until the last target area on the first side is connected; then connect the last target area on the first side to the first target area on the second side, and then repeat the above steps with the first target area on the second side as the starting point until all target areas are connected, and finally record the obtained route as the optimal inspection route.

[0016] Preferably, the foreign matter information of each abnormal area is obtained, and the specific acquisition process is as follows: S1, an infrared thermal image of each abnormal area is obtained by using an infrared thermal imager equipped with a patrol robot, and the infrared thermal image is divided into grids to obtain sub-areas.

[0017] S2. Compare the infrared thermal images of each sub-region of each abnormal region with the stored standard infrared thermal images of each sub-region, record the sub-regions that are inconsistent with the standard infrared thermal images as abnormal sub-regions, and extract the foreign matter information features in each abnormal sub-region through infrared thermal imaging technology, and then obtain the type of foreign matter in each abnormal sub-region.

[0018] Preferably, the corresponding cleaning method is obtained to clean each abnormal area, and each secondary cleaning area is obtained. The specific cleaning process is as follows: if the analysis shows that the type of foreign matter in a certain abnormal sub-area is dust and sand, the rotating cleaning brush loaded on the inspection robot is used to clean the abnormal sub-area.

[0019] If the analysis shows that the type of foreign matter in a certain abnormal sub-area is rain, snow or hail, the low-pressure water gun mounted on the inspection robot is used to melt the ice and snow covering the photovoltaic panel, and then a cleaning brush is used to clean the abnormal sub-area.

[0020] If the type of foreign matter in a certain abnormal sub-area is analyzed to be salt particles, the low-pressure water gun mounted on the inspection robot is used to spray the photovoltaic panel with light salt water, and then a cleaning brush is used to clean the photovoltaic panel in the sub-area.

[0021] After cleaning is completed, the infrared thermal images of each abnormal sub-area are obtained again, and based on the infrared thermal image technology, it is analyzed whether there are still foreign objects covering each abnormal sub-area, and then it is determined whether each abnormal sub-area is cleaned successfully, and each sub-area that has not been cleaned successfully is recorded as a secondary cleaning area.

[0022] Preferably, the secondary cleaning is performed on each secondary cleaning area, and the specific cleaning process is as follows: for each secondary cleaning area covered with dust and sand, each area is cleaned with a high-pressure water gun and then cleaned with a cleaning brush.

[0023] For each secondary cleaning area covered with rain, snow and hail, use the designated photovoltaic panel cleaning fluid to melt the rain, snow and hail and then use a cleaning brush to clean them.

[0024] For each secondary cleaning area covered with salt particles, use the designated photovoltaic panel cleaning fluid to dissolve the salt particles on the photovoltaic panel and then use a cleaning brush to clean it.

[0025] In a second aspect, the present application provides an intelligent inspection system for photovoltaic power stations, including: a data analysis module for obtaining working information of photovoltaic power stations during abnormal weather conditions, and analyzing whether there is any abnormality in the operation of the photovoltaic power station.

[0026] Route planning module: When the photovoltaic power station is working abnormally, the abnormal areas are obtained through drones, and then the optimal inspection route is obtained.

[0027] Foreign body cleaning module: The inspection robot inspects each abnormal area through the optimal inspection route, obtains the foreign body information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area.

[0028] Secondary cleaning module: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

[0029] The beneficial effects of the present application are as follows: 1. The present application provides an intelligent inspection method and system for photovoltaic power stations, which analyzes the working conditions of photovoltaic power stations during abnormal weather conditions, and provides a data basis and judgment basis for subsequent inspection work. When abnormal weather causes foreign objects to cover photovoltaic panels and thus affect the operation of photovoltaic power stations, each abnormal area is acquired through a drone and analyzed to obtain the optimal inspection route, and then an inspection robot is dispatched to analyze the type of foreign objects and cleaning methods in each abnormal area through the optimal inspection route. Finally, a secondary cleaning is performed on the unfinished areas to ensure the normal operation of the photovoltaic power station. The present application uses an intelligent inspection robot to clean up foreign objects covering the photovoltaic panels of photovoltaic power stations during abnormal weather conditions, thereby solving the current problems of insufficient attention to the cleaning of photovoltaic power stations, high cleaning difficulty, and low cleaning efficiency.

[0030] 2. This application analyzes the working performance of photovoltaic power stations during abnormal weather and normal weather, and then analyzes the performance abnormality index of photovoltaic power stations, which provides a basis for judgment for subsequent intelligent inspections of photovoltaic power stations.

[0031] 3. This application analyzes the abnormal areas of the photovoltaic power station and then obtains the optimal inspection route, thereby maximizing the efficiency of the inspection robot, avoiding the waste of resources caused by overly complex inspection routes, and laying the foundation for the subsequent work of the inspection robot.

[0032] 4. This application analyzes the foreign matter information of each abnormal area and obtains the corresponding cleaning method, which makes up for the problem of insufficient attention to the cleaning of photovoltaic power stations in the prior art, greatly improves the cleaning efficiency, ensures the accuracy of foreign matter cleaning, reduces the waste of resources, and ensures the intelligent inspection effect of photovoltaic power stations.

[0033] 5. This application analyzes the initial cleaning situation of the inspection robot and then performs secondary cleaning on the areas that have not been cleaned, thereby avoiding the problem of decreased working performance of the photovoltaic power station due to incomplete cleaning. At the same time, it also ensures the work completion rate of the inspection robot, thereby greatly improving the perfection and rationality of the intelligent inspection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 The present invention is a flowchart of the steps for implementing the present application method.

[0036] Figure 2 This is a schematic diagram of the system structure connection for this application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] Reference Figure 1 As shown, the present application provides a first aspect of a photovoltaic power station intelligent inspection method, comprising the following steps: Step 1, data analysis: obtaining the working information of the photovoltaic power station when the weather is abnormal, and analyzing whether there is any abnormality in the operation of the photovoltaic power station.

[0039] It should be noted that abnormal weather is obtained from the meteorological center, including rain, snow, hail, sandstorms or typhoons.

[0040] In a specific example, the working information of the photovoltaic power station when the weather is abnormal is obtained, and whether the working of the photovoltaic power station is abnormal is analyzed. The specific analysis process is as follows: the total amount of solar radiation in a cycle, the total amount of electric energy actually output, the working temperature of the photovoltaic panel and the total area of ​​the photovoltaic panel receiving sunlight are extracted from the power generation information corresponding to the photovoltaic power station when the weather is abnormal, and then the working performance evaluation coefficient of the photovoltaic power station when the weather is abnormal is obtained based on the analysis. .

[0041] The total solar radiation in each cycle, the actual total electric energy output, the working temperature of the photovoltaic panels and the total area of ​​the photovoltaic panels receiving sunlight are extracted from the power generation information of the photovoltaic power station when the weather is normal, and then the working performance evaluation coefficient of the photovoltaic power station when the weather is normal is obtained based on this analysis. .

[0042] According to the calculation formula The abnormal performance index of photovoltaic power station is obtained through analysis ,when When , it is judged that there is no abnormality in the operation of the photovoltaic power station; when When the PV power station is abnormal, it is judged that the PV power station is working abnormally.

[0043] In a specific example, the analysis obtains the working performance evaluation coefficient of the photovoltaic power station when the weather is abnormal. The specific analysis process is as follows: the total solar radiation obtained by the photovoltaic power station in the previous cycle, the total amount of electric energy actually output, the working temperature of the photovoltaic panel and the total area of ​​the photovoltaic panel receiving sunlight are recorded as , , and , according to the calculation formula: Get the working performance evaluation coefficient of the photovoltaic power station ,in and They are respectively represented by the error value of the working temperature of the photovoltaic panels in the set photovoltaic power station and the error value of the total area of ​​the photovoltaic panels receiving sunlight. , and They are respectively represented by the energy conversion efficiency standard value of the photovoltaic power station, the photovoltaic panel operating temperature standard value and the total area standard value of the photovoltaic panel receiving sunlight. , and They are respectively expressed as the weight factor corresponding to the energy conversion efficiency of the photovoltaic power station, the weight factor corresponding to the operating temperature of the photovoltaic panel, and the weight factor corresponding to the total area of ​​the photovoltaic panel receiving sunlight.

[0044] It should be noted that the error value of the working temperature of the photovoltaic panels and the error value of the total area of ​​the photovoltaic panels receiving sunlight in the photovoltaic power station are set by the staff, and are expressed as the difference between the maximum and minimum values ​​of the working temperature of the photovoltaic panels detected within the period corresponding to the maximum number of detections when the weather is normal and the difference between the maximum and minimum values ​​of the total area of ​​the photovoltaic panels receiving sunlight.

[0045] It should be noted that the standard value of the energy conversion efficiency of photovoltaic power stations, the standard value of the operating temperature of photovoltaic panels and the standard value of the total area of ​​photovoltaic panels receiving sunlight are set by the staff, and are respectively expressed as the average value of the energy conversion efficiency of the photovoltaic power station after removing the minimum value, the average value of the operating temperature of the photovoltaic panels after removing the minimum value and the average value of the total area of ​​the photovoltaic panels receiving sunlight after removing the minimum value, obtained during the period corresponding to the maximum number of detections when the weather is normal.

[0046] It should be noted that , , , .

[0047] It should be noted that the weight factors corresponding to the energy conversion efficiency of the photovoltaic power station, the weight factors corresponding to the operating temperature of the photovoltaic panel, and the weight factors corresponding to the total area of ​​the photovoltaic panel receiving sunlight are obtained through factor analysis. First, the information of the spatial path attenuation of the energy conversion efficiency of the photovoltaic power station, the operating temperature of the photovoltaic panel, and the total area of ​​the photovoltaic panel receiving sunlight is condensed, and then the variance explanation rate after rotation is obtained, and the weight is obtained by dividing the cumulative variance explanation rate.

[0048] It should be noted that factor analysis is a well-known technology. It is a multivariate statistical analysis method that starts from studying the internal dependencies of variables and reduces some variables with intricate relationships to a few comprehensive factors; information concentration is expressed as calculating the median; the variance explanation rate is the amount of information extracted by the factor, and the variance explanation rate = characteristic root / total number of analysis items; the variance explanation rate after rotation is expressed as the variance explanation rate of the factor after maximum variance rotation.

[0049] In a specific example, the analysis obtains the working performance evaluation coefficient of the photovoltaic power station when the weather is normal. The specific analysis process is as follows: the number of detections of the photovoltaic power station corresponding to each historical detection cycle when the weather is normal is recorded as , Indicates the number corresponding to each historical detection cycle, , filter out the maximum number of detections of photovoltaic power stations The corresponding historical detection period is recorded as the target detection period, and the total solar radiation of the photovoltaic power station detected in the target detection period, the total amount of electric energy actually output, the working temperature of the photovoltaic panel, and the total area of ​​the photovoltaic panel receiving sunlight are recorded as , , and , The number indicating the number of detections within the target detection cycle. , according to the calculation formula: The standard value of the working performance evaluation coefficient of the photovoltaic power station is obtained by analysis ,in , , and They are respectively represented as the minimum total solar radiation of the photovoltaic power station within the target detection period, the minimum total amount of electric energy actually output, the minimum operating temperature of the photovoltaic panel, and the minimum total area of ​​the photovoltaic panel receiving sunlight.

[0050] Step 2: Route planning: When the photovoltaic power station is working abnormally, the drone is used to obtain the abnormal areas and then obtain the optimal inspection route.

[0051] In a specific example, the abnormal areas are obtained by using a drone, and the specific acquisition method is as follows: the photovoltaic power station is divided into block areas of specified length, and images and videos of each area are captured by a drone equipped with a high-definition camera. The captured images and videos are pre-processed and compared with the stored images and videos of each corresponding area, and each area covered by foreign objects is recorded as an abnormal area, and each area not covered by foreign objects is recorded as a normal area.

[0052] It should be noted that image preprocessing includes performing vignetting correction, distortion correction and light correction on the image.

[0053] It should be noted that the stored images and videos of the corresponding areas are images and videos of the areas taken when the photovoltaic panels of the photovoltaic power station are not covered by foreign objects during normal weather.

[0054] In a specific example, the optimal inspection route is obtained, and the specific acquisition process is as follows: A1. Obtain the abnormal area closest to the starting point of the inspection robot and record it as the first target area on the first side, and record the abnormal area on the same side of the first target area on the first side and the closest in straight line distance as the second target area on the first side; repeat the above steps until all abnormal areas on the first side are marked.

[0055] A2. Obtain the abnormal area adjacent to the turning point of the first side and closest to the last target area on the first side in a straight line distance, and record it as the first target area on the second side; obtain the abnormal area on the same side of the first target area on the second side and closest to the straight line distance and record it as the second target area on the second side; repeat the above steps until all abnormal areas on the second side are marked.

[0056] A3. Repeat the above steps until all abnormal areas are marked.

[0057] A4. Connect the first target area on the first side as the starting point to the second target area on the first side, and repeat the above steps until the last target area on the first side is connected; then connect the last target area on the first side to the first target area on the second side, and then repeat the above steps with the first target area on the second side as the starting point until all target areas are connected, and finally record the obtained route as the optimal inspection route.

[0058] It should be noted that when there is no abnormal area on the side adjacent to the inflection point of the first side, the adjacent side at the other inflection point of the adjacent side is obtained, and its abnormal area is recorded as the second side target area.

[0059] It should be noted that the starting point of the optimal inspection route is the first target area on the first side, and the end point is the last target area on the last side.

[0060] Step 3: Foreign matter cleaning: The inspection robot inspects each abnormal area through the optimal inspection route, obtains foreign matter information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area.

[0061] In a specific example, the foreign matter information of each abnormal area is obtained, and the specific acquisition process is as follows: S1, an infrared thermal image of each abnormal area is obtained by using an infrared thermal imager equipped with a patrol robot, and the infrared thermal image is divided into grids to obtain sub-areas.

[0062] S2. Compare the infrared thermal images of each sub-region of each abnormal region with the stored standard infrared thermal images of each sub-region, record the sub-regions that are inconsistent with the standard infrared thermal images as abnormal sub-regions, and extract the foreign matter information features in each abnormal sub-region through infrared thermal imaging technology, and then obtain the type of foreign matter in each abnormal sub-region.

[0063] It should be noted that types of foreign matter include dust, sand, rain, snow, hail and salt particles.

[0064] It should be noted that the stored standard infrared thermal images of each sub-area are infrared thermal images of each sub-area acquired when the weather is normal and the photovoltaic power station operates normally.

[0065] In a specific example, the corresponding cleaning method is obtained to clean each abnormal area, and each secondary cleaning area is obtained. The specific cleaning process is as follows: if the analysis shows that the type of foreign matter in a certain abnormal sub-area is dust and sand, the rotating cleaning brush loaded on the inspection robot is used to clean the abnormal sub-area.

[0066] If the analysis shows that the type of foreign matter in a certain abnormal sub-area is rain, snow or hail, the low-pressure water gun mounted on the inspection robot is used to melt the ice and snow covering the photovoltaic panel, and then a cleaning brush is used to clean the abnormal sub-area.

[0067] If the type of foreign matter in a certain abnormal sub-area is analyzed to be salt particles, the low-pressure water gun mounted on the inspection robot is used to spray the photovoltaic panel with light salt water, and then a cleaning brush is used to clean the photovoltaic panel in the sub-area.

[0068] After cleaning is completed, the infrared thermal images of each abnormal sub-area are obtained again, and based on the infrared thermal image technology, it is analyzed whether there are still foreign objects covering each abnormal sub-area, and then it is determined whether each abnormal sub-area is cleaned successfully, and each sub-area that has not been cleaned successfully is recorded as a secondary cleaning area.

[0069] Step 4: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

[0070] In a specific example, the secondary cleaning of each secondary cleaning area is performed, and the specific cleaning process is as follows: for each secondary cleaning area covered with dust and sand, each area is cleaned with a high-pressure water gun and then cleaned with a cleaning brush.

[0071] For each secondary cleaning area covered with rain, snow and hail, use the designated photovoltaic panel cleaning fluid to melt the rain, snow and hail and then use a cleaning brush to clean them.

[0072] For each secondary cleaning area covered with salt particles, use the designated photovoltaic panel cleaning fluid to dissolve the salt particles on the photovoltaic panel and then use a cleaning brush to clean it.

[0073] It should be noted that the designated photovoltaic panel cleaning fluid is a diluted neutral detergent.

[0074] It should be noted that the inspection robot is connected to the monitoring terminal, and during the inspection and cleaning process, images and videos of each abnormal area are uploaded and stored in real time.

[0075] It should be noted that in addition to the intelligent inspection of photovoltaic power stations when the weather is abnormal, photovoltaic power stations are also inspected according to fixed inspection cycles when the weather is normal.

[0076] It should be noted that after cleaning each secondary cleaning area, it is analyzed whether the cleaning is successful. If the cleaning is still unsuccessful, an early warning will be given to the relevant staff, and manual cleaning will be performed on each area that was not successfully cleaned.

[0077] In a second aspect, the present application provides an intelligent inspection system for photovoltaic power stations, including: a data analysis module: obtaining working information of the photovoltaic power station when the weather is abnormal, and analyzing whether there is any abnormality in the operation of the photovoltaic power station.

[0078] Route planning module: When the photovoltaic power station is working abnormally, the abnormal areas are obtained through drones, and then the optimal inspection route is obtained.

[0079] Foreign body cleaning module: The inspection robot inspects each abnormal area through the optimal inspection route, obtains the foreign body information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area.

[0080] Secondary cleaning module: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

[0081] The present application provides an intelligent inspection method and system for photovoltaic power stations. By analyzing the working conditions of photovoltaic power stations during abnormal weather, a data basis and judgment basis are provided for subsequent inspection work. When abnormal weather causes foreign objects to cover photovoltaic panels and thus affect the operation of photovoltaic power stations, each abnormal area is acquired through a drone and analyzed to obtain the optimal inspection route. Then, an inspection robot is dispatched to analyze the type of foreign objects and cleaning methods in each abnormal area through the optimal inspection route. Finally, a secondary cleaning is performed on the unfinished areas to ensure the normal operation of the photovoltaic power station. The present application uses an intelligent inspection robot to clean foreign objects covering the photovoltaic panels of photovoltaic power stations during abnormal weather, thereby solving the current problems of insufficient attention to the cleaning of photovoltaic power stations, high cleaning difficulty, and low cleaning efficiency.

[0082] The above contents are merely examples and explanations of the concept of the present application. The technical personnel in the relevant technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in the present application, they should all fall within the scope of protection of the present application.

Claims

1. A photovoltaic power station intelligent inspection method, characterized in that: include: Step 1: Data analysis: Obtain the working information of the photovoltaic power station during abnormal weather conditions, and analyze whether the working of the photovoltaic power station is abnormal; Step 2: Route planning: When the photovoltaic power station is working abnormally, the drone is used to obtain each abnormal area and then obtain the optimal inspection route; Step 3: Foreign matter cleaning: The inspection robot inspects each abnormal area through the optimal inspection route, obtains foreign matter information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area; Step 4: Secondary cleaning: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

2. A photovoltaic power station intelligent inspection method according to claim 1, characterized in that: The said obtaining the working information of the photovoltaic power station when the weather is abnormal, and analyzing whether the working of the photovoltaic power station is abnormal, the specific analysis process is as follows: The total solar radiation, the actual total electric energy output, the operating temperature of the photovoltaic panels and the total area of ​​the photovoltaic panels receiving sunlight within a detection period are extracted from the power generation information corresponding to the photovoltaic power station during abnormal weather, and then the working performance evaluation coefficient of the photovoltaic power station during abnormal weather is obtained based on this analysis. ; The total solar radiation, the actual total electric energy output, the operating temperature of the photovoltaic panels and the total area of ​​the photovoltaic panels receiving sunlight in each historical detection period are extracted from the power generation information corresponding to the photovoltaic power station when the weather is normal, and then the working performance evaluation coefficient of the photovoltaic power station when the weather is normal is obtained based on this analysis. ; According to the calculation formula The abnormal performance index of photovoltaic power station is obtained through analysis ,when When the PV power station is operating normally, it is determined that there is no abnormality; when When the PV power station is abnormal, it is judged that the PV power station is working abnormally.

3. A photovoltaic power station intelligent inspection method according to claim 2, characterized in that: The analysis results in the working performance evaluation coefficient of the photovoltaic power station when the weather is normal. The specific analysis process is as follows: The number of detections of the photovoltaic power station corresponding to each historical detection cycle when the weather is normal is recorded as , Indicates the number corresponding to each historical detection cycle, , filter out the maximum number of detections of photovoltaic power stations The corresponding historical detection period is recorded as the target detection period, and the total solar radiation of the photovoltaic power station detected in the target detection period, the total amount of electric energy actually output, the working temperature of the photovoltaic panel, and the total area of ​​the photovoltaic panel receiving sunlight are recorded as , , and , The number indicating the number of detections within the target detection cycle. , according to the calculation formula: The standard value of the working performance evaluation coefficient of the photovoltaic power station is obtained by analysis ,in , , and They are respectively represented as the minimum total solar radiation of the photovoltaic power station within the target detection period, the minimum total amount of electric energy actually output, the minimum operating temperature of the photovoltaic panel, and the minimum total area of ​​the photovoltaic panel receiving sunlight.

4. A photovoltaic power station intelligent inspection method according to claim 2, characterized in that: The analysis results in the working performance evaluation coefficient of the photovoltaic power station when the weather is normal. The specific analysis process is as follows: The number of detections of the photovoltaic power station corresponding to each historical detection cycle when the weather is normal is recorded as , Indicates the number corresponding to each historical detection cycle, , filter out the maximum number of detections of photovoltaic power stations The corresponding historical detection period is recorded as the target detection period, and the total solar radiation of the photovoltaic power station detected in the target detection period, the total amount of electric energy actually output, the working temperature of the photovoltaic panel, and the total area of ​​the photovoltaic panel receiving sunlight are recorded as , , and , The number indicating the number of detections within the target detection cycle. , according to the calculation formula: The standard value of the working performance evaluation coefficient of the photovoltaic power station is obtained by analysis ,in , , and They are respectively represented as the minimum total solar radiation of the photovoltaic power station within the target detection period, the minimum total amount of electric energy actually output, the minimum operating temperature of the photovoltaic panel, and the minimum total area of ​​the photovoltaic panel receiving sunlight.

5. A photovoltaic power station intelligent inspection method according to claim 2, characterized in that: The specific method of obtaining each abnormal area by using a drone is as follows: The photovoltaic power station is divided into block areas of specified length. A drone equipped with a high-definition camera is used to capture images and videos of each area. The captured images and videos are pre-processed and compared with the stored images and videos of the corresponding areas. Areas covered by foreign objects are recorded as abnormal areas, and areas not covered by foreign objects are recorded as normal areas.

6. A photovoltaic power station intelligent inspection method according to claim 5, characterized in that: The specific process of obtaining the optimal inspection route is as follows: A1. Obtain the abnormal area closest to the departure site of the inspection robot and record it as the first target area on the first side, and record the abnormal area on the same side of the first target area on the first side and the closest in straight line distance as the second target area on the first side; repeat the above steps until all abnormal areas on the first side are marked; A2. Obtain the abnormal area adjacent to the turning point of the first side and closest to the last target area on the first side in a straight line distance, and record it as the first target area on the second side; obtain the abnormal area on the same side of the first target area on the second side and closest to the last target area on the first side in a straight line distance, and record it as the second target area on the second side; repeat the above steps until all abnormal areas on the second side are marked; A3. Repeat the above steps until all abnormal areas are marked; A4. Connect the first target area on the first side as the starting point to the second target area on the first side, and repeat the above steps until the last target area on the first side is connected; then connect the last target area on the first side to the first target area on the second side, and then repeat the above steps with the first target area on the second side as the starting point until all target areas are connected, and finally record the obtained route as the optimal inspection route.

7. A photovoltaic power station intelligent inspection method according to claim 6, characterized in that: The specific process of obtaining the information of each foreign object in each abnormal area is as follows: S1. Obtain infrared thermal images of each abnormal area through an inspection robot equipped with an infrared thermal imager, and divide it into grids to obtain sub-areas; S2. Compare the infrared thermal images of each sub-region of each abnormal region with the stored standard infrared thermal images of each sub-region, record the sub-regions that are inconsistent with the standard infrared thermal images as abnormal sub-regions, and extract the foreign matter information features in each abnormal sub-region through infrared thermal imaging technology, and then obtain the type of foreign matter in each abnormal sub-region.

8. A photovoltaic power station intelligent inspection method according to claim 7, characterized in that: The corresponding cleaning method is obtained to clean each abnormal area, and each secondary cleaning area is obtained. The specific cleaning process is as follows: If the analysis shows that the foreign matter in a certain abnormal sub-area is dust or sand, the abnormal sub-area is cleaned using the rotating cleaning brush mounted on the inspection robot; If the analysis shows that the foreign matter type in a certain abnormal sub-area is rain, snow or hail, the low-pressure water gun mounted on the inspection robot is used to melt the ice and snow covering the photovoltaic panel, and then the cleaning brush is used to clean the abnormal sub-area; If the foreign matter type in a certain abnormal sub-area is analyzed to be salt particles, the low-pressure water gun mounted on the inspection robot is used to spray the photovoltaic panel with salt water, and then the photovoltaic panel in the sub-area is cleaned with a cleaning brush; After cleaning is completed, the infrared thermal images of each abnormal sub-area are obtained again, and based on the infrared thermal image technology, it is analyzed whether there are still foreign objects covering each abnormal sub-area, and then it is determined whether each abnormal sub-area is cleaned successfully, and each sub-area that has not been cleaned successfully is recorded as a secondary cleaning area.

9. A photovoltaic power station intelligent inspection method according to claim 81, characterized in that: The secondary cleaning of each secondary cleaning area is performed, and the specific cleaning process is as follows: For each secondary cleaning area covered with dust and sand, use a high-pressure water gun to clean each area and then use a cleaning brush to clean it; For each secondary cleaning area covered with rain, snow, and hail, use the designated photovoltaic panel cleaning fluid to dissolve the rain, snow, and hail before cleaning with a cleaning brush; For each secondary cleaning area covered with salt particles, use the designated photovoltaic panel cleaning fluid to dissolve the salt particles on the photovoltaic panel and then use a cleaning brush to clean it.

10. A photovoltaic power station intelligent inspection system, used to execute a photovoltaic power station intelligent inspection method according to claims 1-9, characterized in that: include: Data analysis module: obtain the working information of the photovoltaic power station when the weather is abnormal, and analyze whether the working of the photovoltaic power station is abnormal; Route planning module: When the photovoltaic power station is working abnormally, the drone is used to obtain the abnormal areas and then obtain the optimal inspection route; Foreign body cleaning module: The inspection robot inspects each abnormal area through the optimal inspection route, obtains the foreign body information of each abnormal area, and then obtains the corresponding cleaning method to clean each abnormal area, and obtains each secondary cleaning area; Secondary cleaning module: Perform secondary cleaning on each secondary cleaning area until the cleaning is successful.

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