Photovoltaic power station identification method and system based on unmanned aerial vehicle

Abstract

The invention discloses a photovoltaic power station identification method and system based on an unmanned aerial vehicle, relates to the technical field of photovoltaic power station identification, and carefully monitors dust physical characteristics of a target photovoltaic power station through the unmanned aerial vehicle to obtain an atmospheric dust matrix index and a dust attachment potential energy index so as to accurately analyze dust washing complexity. The data driving mode can quantify the dust condition of the target photovoltaic power station, the extensive mode that the cleaning difficulty is judged only according to experience in the past is abandoned, the most suitable cleaning scheme can be accurately matched, it is ensured that the cleaning mode is highly matched with the dust characteristics in a dry-type whisk sweeping or electrostatic adsorption or chemical spraying cleaning scheme, and the cleaning efficiency is improved. The problem that the photovoltaic panel is damaged or is not cleaned thoroughly due to an improper cleaning mode is solved, the cleaning efficiency is effectively improved, and the service life of the photovoltaic panel is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power station identification, and particularly relates to a method and system for identifying a photovoltaic power station based on an unmanned aerial vehicle (UAV). Background Art

[0002] With the increasing global demand for clean energy, photovoltaic power stations, as key facilities for solar energy utilization, have developed rapidly. However, photovoltaic power stations face many challenges during operation, which has given rise to the method for identifying photovoltaic power stations based on UAVs. The environments where photovoltaic power stations are located are complex and diverse, ranging from the dry and dusty desert edges to the areas near industrial pollution zones and even the coastal areas with much wind and sand. The components and characteristics of dust vary greatly. Different dusts have different acid-base properties, chemical compositions, and adhesion abilities, which pose great difficulties to the traditional unified cleaning and maintenance mode.

[0003] An existing technology, such as an invention application patent with the publication number of CN118505633A, discloses a photovoltaic panel cleaning system based on UAV analysis, which relates to the technical field of photovoltaic panel cleaning. This application includes a dirt and power generation data acquisition module, a dirt and power generation data analysis module, a cleaning environment data acquisition and analysis module, a cleaning execution module, and a cleaning detection module. By using a UAV to acquire a picture set of each photovoltaic panel, and then analyzing the picture set of each photovoltaic panel to obtain the dirt data of each photovoltaic panel, thereby analyzing the dirt data of each photovoltaic panel and combining with the corresponding power generation data for analysis, it solves the limitation problems existing in the current feasibility analysis process of photovoltaic panel cleaning. At the same time, by analyzing the environmental data, it realizes the feasibility and objectivity analysis of photovoltaic panel cleaning. Then, by analyzing the cleaning mode of each photovoltaic panel, a standard cleaning mode is formulated for the photovoltaic panel. Finally, the cleaning result is detected to ensure the comprehensiveness of photovoltaic panel cleaning.

[0004] Regarding the above solution, the inventors of the present application found that the above technology has at least the following technical problems: 1. Although the existing technology obtains a picture set of photovoltaic panels through a UAV to analyze the dirt data, it only stays at the surface dirt presentation and does not delve into the physical properties of the dust. The lack of accurate detection of the atmospheric dust matrix index means that in the face of corrosive or potentially damaging dust in a special environment, the risk cannot be predicted in advance, and there is still blindness when choosing a cleaning method, which may damage the photovoltaic panel. This makes the formulated cleaning mode difficult to adapt to different dust adhesion characteristics, and the cleaning effect is likely to be greatly reduced.

[0005] 2. Existing technologies mainly rely on the dirtiness data of photovoltaic panels combined with power generation data for analysis, without comprehensively considering the key dimension of photoluminescence characteristics. Photoluminescence characteristics can reflect the carrier recombination and energy level structure changes after the photovoltaic panel is corroded by dust at the microscopic level, and are more sensitive to early corrosion. Relying only on dirtiness and power generation data, it is easy to miss the initial signs of microscopic corrosion and miss the best maintenance node, resulting in the accelerated deterioration of subsequent photovoltaic panel performance. Even if the power generation efficiency is found to be reduced, it is difficult to accurately judge the degree of corrosion due to the lack of multi-dimensional photoluminescence characteristic parameter analysis. Subsequent maintenance measures lack pertinence and cannot effectively prevent further development of corrosion. At the same time, it lacks dynamic adaptability. It does not flexibly adjust the cleaning scheme according to real-time data such as dust physical properties and corrosion level like the photovoltaic power station identification method based on drones. Whether it is dry sweeping, electrostatic adsorption or chemical spray cleaning scheme, it can be switched on demand. This makes it possible that the cleaning mode may not be able to effectively cope with different degrees and types of dirt in complex and changing environments, affecting the cleaning effect and the life of photovoltaic panels. In the long run, it may cause excessive or insufficient cleaning, wasting resources while failing to ensure the continuous and efficient power generation of photovoltaic panels.

[0006] 3. Existing technologies lack real-time monitoring and statistics of the overall erosion of photovoltaic panels, such as the number of severely corroded photovoltaic panels and their proportion. When encountering sudden severe environments or large-scale erosion incidents, it is impossible to quickly detect the extent of the crisis, and it is difficult to initiate emergency plans in a timely manner like the drone-based photovoltaic power station identification method to ensure the stable operation of the power station. It has also not established a complete alarm notification mechanism, and information transmission is not smooth in emergency situations. Once a problem occurs, it is impossible to quickly inform the operation and maintenance team, management and external relevant departments, and the parties cannot quickly work together, which can easily delay emergency repairs, increase repair costs, and even cause economic losses and social impacts caused by power outages. Summary of the invention

[0007] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a photovoltaic power station identification method and system based on drones.

[0008] To solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a photovoltaic power station identification method based on drones, including: Step 1, identification of physical properties of dust: sending drones to the target photovoltaic power station for monitoring, thereby analyzing the complexity of dust cleaning corresponding to the target photovoltaic power station, and then analyzing the cleaning plan corresponding to the target photovoltaic power station.

[0009] Step 2: Identification of photoluminescence characteristics: When the target photovoltaic power station is cleaned according to the corresponding cleaning plan, the drone will collect the photoluminescence characteristics of each photovoltaic panel, and then judge and record the photovoltaic panels eroded by dust as photovoltaic panels to be identified.

[0010] Step 3. Erosion grading analysis: Analyze the erosion grades corresponding to each photovoltaic panel to be identified, and make a maintenance decision based on the erosion grades corresponding to each photovoltaic panel to be identified.

[0011] Preferably, the analysis of the cleaning plan corresponding to the target photovoltaic power station is as follows: C1. Compare the dust removal intractability complexity corresponding to the target photovoltaic power station with the dust removal intractability complexity intervals corresponding to each cleaning plan in the database. If the dust removal intractability complexity corresponding to the target photovoltaic power station is within the dust removal intractability complexity interval corresponding to a certain cleaning plan in the database, then use the cleaning plan in the database as the cleaning plan corresponding to the target photovoltaic power station. The cleaning plans include a dry dusting cleaning plan, an electrostatic adsorption cleaning plan, and a chemical spray cleaning plan.

[0012] C2. If the cleaning plan corresponding to the target photovoltaic power station is a dry dusting cleaning plan, use the duster device carried by the drone. Starting from the upwind side of each photovoltaic panel in the photovoltaic power station according to the planned "zigzag" dusting path, the drone operates at a set low-altitude flight height and flight speed to clean the photovoltaic panels row by row.

[0013] C3. If the cleaning plan corresponding to the target photovoltaic power station is an electrostatic adsorption cleaning plan, compare the dust removal intractability complexity corresponding to the target photovoltaic power station with the dust removal intractability complexity intervals corresponding to the parameters of each electrostatic adsorption device in the database. If the dust removal intractability complexity corresponding to the target photovoltaic power station is within the dust removal intractability complexity interval corresponding to the parameters of a certain electrostatic adsorption device in the database, then set the parameters of the electrostatic adsorption device in the database as the parameters of the electrostatic adsorption device corresponding to the target photovoltaic power station. At the same time, lift the drone to 3 - 5 cm above the photovoltaic panel and turn on the electrostatic adsorption device. The electrostatic adsorption device scans the photovoltaic panel column by column according to the set parameters of the electrostatic adsorption device.

[0014] C4. If the cleaning plan corresponding to the target photovoltaic power station is a chemical spray cleaning plan, compare the dust removal intractability complexity corresponding to the target photovoltaic power station with the dust removal intractability complexity intervals corresponding to the concentration ratio parameters of each cleaning agent in the database. If the dust removal intractability complexity corresponding to the target photovoltaic power station is within the dust removal intractability complexity interval corresponding to the concentration ratio parameter of a certain cleaning agent in the database, then set the concentration ratio parameter of the cleaning agent in the database as the concentration ratio parameter of the cleaning agent corresponding to the target photovoltaic power station. At the same time, lift the drone to 8 - 10 cm above the photovoltaic panel and turn on the pump-in spray device. The pump-in spray device scans the photovoltaic panel column by column according to the set concentration ratio parameter of the cleaning agent.

[0015] Preferably, the process of determining whether each photovoltaic panel is eroded by dust is as follows: D1. The unmanned aerial vehicle (UAV) collects the photoluminescence characteristics corresponding to each photovoltaic panel in the target photovoltaic power station, and sets a number of collection points, so that the control photoluminescence imaging device installed inside the UAV continuously collects the photoluminescence characteristics corresponding to each collection point in each photovoltaic panel. The photoluminescence characteristics include the coefficient of variation of luminescence uniformity, the spectral peak wavelength shift, the spectral full width at half maximum change rate, and the proportion of the shortening of the luminescence decay time. The coefficient of variation of luminescence uniformity, the spectral peak wavelength shift, the spectral full width at half maximum change rate, and the proportion of the shortening of the luminescence decay time corresponding to each collection point in each photovoltaic panel are input into the dust erosion evaluation value analysis model, and the dust erosion evaluation value results corresponding to each photovoltaic panel are output.

[0016] D2. The dust erosion evaluation value results include values of 1 and -1. When the dust erosion evaluation value result corresponding to a certain photovoltaic panel is 1, it indicates that the photovoltaic panel is not eroded by dust. On the contrary, when the dust erosion evaluation value result corresponding to a certain photovoltaic panel is -1, it indicates that the photovoltaic panel is eroded by dust. In this way, it is determined whether each photovoltaic panel is eroded by dust.

[0017] Preferably, the process of analyzing the erosion level corresponding to each photovoltaic panel to be identified is as follows: The dust erosion evaluation value corresponding to each photovoltaic panel to be identified is compared with the dust erosion evaluation value intervals corresponding to each erosion level in the database. If the dust erosion evaluation value corresponding to a certain photovoltaic panel to be identified is within the dust erosion evaluation value interval corresponding to a certain erosion level in the database, then the erosion level in the database is recorded as the erosion level corresponding to the photovoltaic panel to be identified. In this way, the erosion levels corresponding to each photovoltaic panel to be identified are analyzed. The erosion levels include a slight erosion level, a moderate erosion level, and a severe erosion level.

[0018] Preferably, the maintenance decision is made according to the erosion level corresponding to each photovoltaic panel to be identified. The specific decision-making process is as follows: E1. If the erosion level corresponding to a certain photovoltaic panel to be identified is a slight erosion level, within one month after cleaning, a light cleaning is arranged every two weeks. Use a soft, anti-static cleaning cloth to gently wipe the surface of the photovoltaic panel to remove a small amount of newly attached dust, then dip a clean wet cloth in a small amount of mild cleaner and gently wipe, and finally rinse with clean water and dry.

[0019] E2. If the erosion level corresponding to a certain photovoltaic panel to be identified is a moderate erosion level, within one month after cleaning, cleaning operations are carried out weekly. First, use a high-pressure water gun to rinse the surface of the photovoltaic panel to wash away most of the dust and stains, and then adopt a combination of chemical cleaning and physical cleaning. First, soak the surface of the photovoltaic panel with a chemical cleaner for 3 - 5 minutes to fully dissolve stubborn stains, then use a high-pressure water gun to rinse, then carefully brush with a soft brush and clean water, and finally rinse with clean water multiple times and dry naturally.

[0020] E3. If the erosion level corresponding to a photovoltaic panel to be identified is a severe erosion level, quickly organize a professional team for assessment to determine whether it is a partial repair or a replacement of the entire photovoltaic panel, and implement the corresponding measures as soon as possible. At the same time, adjust the operation strategy of the power station according to the current performance status of the photovoltaic panel. If it is determined to be a partial repair, adopt repair techniques or improve the performance of the photovoltaic panel, apply a new anti-erosion coating, and optimize the internal structure of the solar cells.

[0021] E4. Count the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station. If the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station is greater than or equal to the set number of photovoltaic panels to be identified corresponding to the severe erosion level, an emergency alarm is issued.

[0022] Preferably, when the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station is greater than or equal to the set number of photovoltaic panels to be identified corresponding to the severe erosion level, an emergency alarm is issued. The specific alarm process is as follows: Through the in-station broadcast system, the alarm information is played in a loop at the maximum volume to ensure that all operation and maintenance personnel on the power station site can hear it. At the same time, send text messages with the highest priority identification to the mobile phones of the power station operation and maintenance team and management level, and push a prominent pop-up notification on the operation and maintenance management APP. The pop-up window is continuously displayed until the personnel manually confirm it, and a detailed alarm content link is attached. Clicking on it can view all information.

[0023] In the second aspect, the present invention provides a photovoltaic power station identification system based on an unmanned aerial vehicle, including: A dust physical property identification module: used to send the unmanned aerial vehicle to the target photovoltaic power station for monitoring, thereby analyzing the complexity of dust removal for the target photovoltaic power station, and further analyzing the cleaning plan corresponding to the target photovoltaic power station.

[0024] A photoluminescence property identification module: used to collect the photoluminescence properties of each photovoltaic panel by the unmanned aerial vehicle after the target photovoltaic power station is cleaned according to the corresponding cleaning plan, and then judge, and record each photovoltaic panel affected by dust erosion as each photovoltaic panel to be identified.

[0025] An erosion grading analysis module: used to analyze the erosion level corresponding to each photovoltaic panel to be identified, and execute a maintenance decision according to the erosion level corresponding to each photovoltaic panel to be identified.

[0026] The beneficial effects of the present invention are as follows: 1. In the embodiment of the present invention, the physical properties of dust in the target photovoltaic power station are carefully monitored by drones, and the atmospheric dust matrix index and dust adhesion potential index are obtained, so as to accurately analyze the complexity of dust cleaning. This data-driven approach can quantify the dust condition of the target photovoltaic power station, and bid farewell to the previous extensive mode of judging the difficulty of cleaning based on experience alone. In areas with strong sandstorms and chemical companies around, the data collected by drones can accurately reflect the corrosiveness and strong adhesion of dust, providing a solid basis for the subsequent selection of cleaning solutions. According to the comparison of the complexity of dust cleaning with the database, the most suitable cleaning solution can be accurately matched. Whether it is dry sweeping, electrostatic adsorption or chemical spray cleaning solution, it ensures that the cleaning method is highly compatible with the dust characteristics, avoiding damage to the photovoltaic panels or incomplete cleaning caused by improper cleaning methods, effectively improving the cleaning efficiency and extending the service life of the photovoltaic panels.

[0027] 2. In the embodiment of the present invention, the photoluminescence imaging device carried by the drone is used to collect the photoluminescence characteristics, and multiple collection points are set to fully cover the photovoltaic panels. The multi-dimensional parameters such as the coefficient of variation of the luminescence uniformity, the displacement of the spectral peak wavelength, the change rate of the spectral half-height width, and the shortening ratio of the luminescence decay time are used as the basis for judgment. The dust erosion assessment value analysis model is used to accurately judge whether the photovoltaic panel is eroded by dust. Compared with the traditional method of relying only on appearance observation or single parameter detection, this method can detect potential erosion problems earlier and more accurately, take countermeasures in time, and reduce the decline in power generation efficiency caused by dust erosion. The erosion level is finely divided, and different maintenance decisions are corresponding to different levels, realizing differentiated management of photovoltaic panel maintenance. For minor erosion, mild cleaning and maintenance measures are adopted; for moderate erosion, the cleaning intensity and detection frequency are increased; for severe erosion, professional evaluation and emergency response are quickly initiated to ensure that the photovoltaic power station always maintains a high level of power generation efficiency.

[0028] 3. In the embodiment of the present invention, the number of photovoltaic panels to be identified corresponding to the severe erosion level is counted in real time, and an emergency alarm is triggered immediately when the set threshold is reached. Once the number of severely corroded photovoltaic panels exceeds the standard, personnel from all parties can respond quickly to avoid greater losses due to information lags. The emergency alarm prompts all parties to quickly work together, and the operation and maintenance personnel can promptly suspend the operation of the problem area and organize emergency repairs; the management can coordinate resources and deploy manpower and material resources; the superior departments and cooperative units can also make preparations in advance to ensure the safe and stable operation of the power station to the greatest extent, reduce the risk of power outages, and reduce economic losses. Accurate cleaning solutions and corrosion identification and response mechanisms reduce unnecessary excessive cleaning and maintenance operations, and reduce the cost of cleaning materials, manpower, and replacement of photovoltaic panels. For example, the appropriate detergent concentration ratio is selected according to the characteristics of the dust to avoid detergent waste; simple and effective maintenance measures are taken for slightly corroded photovoltaic panels to delay the need for replacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0030] Figure 1 This is the flowchart of the implementation steps of the method of the present invention.

[0031] Figure 2 This is the schematic diagram of the connection of the system modules of the present invention. Specific implementation manners

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0033] An embodiment of the present invention is as Figure 1 shown. A method for identifying a photovoltaic power station based on an unmanned aerial vehicle includes: Step 1: Identification of the physical characteristics of dust: Sending the unmanned aerial vehicle to the target photovoltaic power station for monitoring, thereby analyzing the complexity of dust cleaning corresponding to the target photovoltaic power station, and further analyzing the cleaning plan corresponding to the target photovoltaic power station.

[0034] In a specific embodiment, the analysis of the complexity of dust cleaning corresponding to the target photovoltaic power station is as follows: A1. The unmanned aerial vehicle flies over the target photovoltaic power station, and collects the atmospheric dust matrix index and the dust adhesion potential energy index of each photovoltaic panel in the target photovoltaic power station. The atmospheric dust matrix index includes the corresponding acidity and alkalinity, the proportion of each chemical component, and the content of various compounds. The dust adhesion potential energy index includes the minimum cleaning external force, the critical wind speed of dust adhesion, and the dust viscosity. Then, the atmospheric dust matrix evaluation value and the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station are analyzed, and the atmospheric dust matrix evaluation value and the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station are respectively denoted as Q y and W y , where y represents the number corresponding to each photovoltaic panel, y = 1, 2...... g, and g is any integer greater than 2.

[0035] It should be noted that the drone is equipped with a special micro pH sensor, which adopts the principle of ion selective electrode. When the drone flies close to the surface of the photovoltaic panel, the sensor comes into contact with dust particles. Hydrogen ions or hydroxide ions in the dust will react with the surface of the electrode, generating a potential difference. Through the built-in high-precision potential measurement module, the potential difference is converted into the corresponding pH value. Equipped with a micro laser induced breakdown spectroscopy analyzer, when the drone flies low over the photovoltaic panel, it emits high-energy laser pulses that focus on the dust particles, instantly vaporizing and ionizing the dust to form a plasma. The plasma emits optical radiation containing characteristic spectral lines of various elements. The spectral acquisition system in the LIBS analyzer captures this optical radiation and converts it into spectral data. By comparing with the built-in element spectral database, various chemical components in the dust are identified, and the proportion of each chemical component and the content of various compounds are obtained based on the spectral line intensity.

[0036] It should also be noted that the drone carries a retractable micro manipulator, and a special micro duster is installed at the end of the manipulator. The material of the duster has a certain elasticity and a known surface roughness. When the drone hovers at a specific height above the photovoltaic panel, the manipulator extends to make the duster contact the dust on the surface of the photovoltaic panel, and pushes the duster with a slowly and uniformly increasing force. At the same time, the magnitude of the thrust is monitored in real time through the force sensor on the manipulator. When it is observed that the dust starts to be cleared by the duster, record the thrust value at this moment, which is the minimum external force for dust removal at this position. The drone is equipped with a micro wind tunnel experimental device, which consists of a small fan, an air duct, and an air velocity measuring instrument. When the drone is located above the photovoltaic panel, the fan is started, and a controllable air flow is formed at the outlet of the air duct and blows towards the dust on the surface of the photovoltaic panel. The air velocity measuring instrument monitors the air velocity at the outlet of the air duct in real time, and at the same time, the high-definition camera on the drone is used to observe the movement state of the dust particles under the action of the air flow. Starting from a low air velocity and gradually increasing the air velocity, when it is found that the dust particles start to detach from the surface of the photovoltaic panel on a large scale, record the air velocity value at this moment, which is the critical air velocity for dust adhesion. The drone carries a test device with a micro-nano probe. The tip of the probe has been specially treated and has a surface energy similar to that of the dust particles. When the drone approaches the dust on the surface of the photovoltaic panel, the probe contacts the dust particles, and by measuring the force required for the probe to pull up the dust particles, the adhesion force between the dust particles and the surface of the photovoltaic panel, that is, the dust viscosity, is obtained.

[0037] A2. Substitute the atmospheric dust matrix evaluation value and the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station into the calculation formula: Among them, the dust removal intractable complexity γ corresponding to the target photovoltaic power station is obtained, where Q' and W' are respectively the standard atmospheric dust matrix evaluation value and the standard dust adhesion potential evaluation value corresponding to the set photovoltaic power station, Λ1 and Λ2 are respectively the weight factors corresponding to the atmospheric dust matrix evaluation value and the dust adhesion potential evaluation value of the set photovoltaic power station, and K1 and K2 are respectively the adjustment factors corresponding to the atmospheric dust matrix evaluation value and the dust adhesion potential evaluation value of the set photovoltaic power station.

[0038] It should be noted that both K1 and K2 are greater than 0 and less than 1.

[0039] It should also be noted that first, widely collect the dust data of photovoltaic power stations in different environments, such as the pH value, chemical composition, etc. in cities, deserts, industrial areas, etc. The sources include on-site monitoring, scientific research results, etc. Classification determination: Analyze the data and classify them according to the environment. For example, in a weakly acidic environment, the corrosion of the photovoltaic panel is small, and the corresponding sub-item evaluation value is set lower; in a strongly acidic (pH < 4) or strongly alkaline (pH > 10) environment, the damage is large, and the sub-item value is set higher. Then, rank the damage degree of the photovoltaic panel according to the chemical composition. For example, in the case of a large amount of heavy metals, the weight is increased, and the standard values of different environments are comprehensively obtained. Simulate different photovoltaic panel surfaces and wind speed conditions in the laboratory, put in dust to measure parameters such as the critical adhesion wind speed, and at the same time test the real data at the on-site power station, and compare to find the differences. Set as needed: According to the meteorology of the area where the power station is located, the design life of the power station, and the maintenance cycle, in areas with strong winds and sand, in order to ensure that the power generation is not greatly affected within the three-month maintenance period, set strict standard values; in clean areas, appropriately relax, and comprehensively determine the standard values. Study the operation and maintenance data of a large number of power stations to see the impact of the atmospheric dust matrix and the dust adhesion potential on the power generation efficiency and the cleaning difficulty. In areas where the chemical composition of the dust causes many problems with the battery chips, the weight factor of the former is high; in areas where the dust is difficult to remove, the weight factor of the latter is increased. Dynamic adaptation: Consider the operation stage of the power station. When it is newly built, the photovoltaic panels are new, and the weight factor of the dust adhesion potential can be low; when it has been in operation for a long time and is aging, it is adjusted higher. If the power station is in areas with strong ultraviolet rays and large temperature differences between day and night at high altitudes, ultraviolet rays affect the dust, and the temperature difference affects the viscosity of the dust and the photovoltaic panel, then adjust the corresponding adjustment factors accordingly to make the calculation fit the actual situation. Follow up on technology: Introduce new anti-dust coatings and equipment. If it reduces the cleaning difficulty in a certain aspect, such as strong anti-adhesion makes the cleaning related to the dust viscosity easier, then reduce the dust adhesion potential adjustment factor to reflect the technical advantages.

[0040] In another specific embodiment, the analysis to obtain the atmospheric dust matrix evaluation value and the dust adhesion potential evaluation value corresponding to the target photovoltaic power station is as follows: B1. Input the pH value, the proportion of each chemical component, and the content of various compounds corresponding to each photovoltaic panel in the target photovoltaic power station into the atmospheric dust matrix evaluation value analysis model, and output the atmospheric dust matrix evaluation value corresponding to the target photovoltaic power station.

[0041] It should be noted that the analysis process of the atmospheric dust matrix evaluation value corresponding to the target photovoltaic power station is as follows: The pH value, the proportion of each chemical component, and the content of various types of compounds corresponding to each photovoltaic panel in the target photovoltaic power station are normalized, and the pH value, the proportion of each chemical component, and the content of various types of compounds corresponding to each photovoltaic panel in the target photovoltaic power station after processing are respectively denoted as q y and are substituted into the analysis formula

[0042] whereby the atmospheric dust matrix evaluation value Q corresponding to the target photovoltaic power station is obtained y t represents the serial number corresponding to each chemical component, t = 1, 2......b, t is a positive integer, and t is also

[0043] the sum of the proportions of each chemical component. l represents the serial number corresponding to various types of compounds, l = 1, 2......v, v is a positive integer, and v is also the sum of various types of compounds.

[0044] B2. Input the minimum cleaning external force, the critical wind speed for dust adhesion, and the dust viscosity corresponding to each photovoltaic panel in the target photovoltaic power station into the dust adhesion potential energy evaluation value analysis model, and output the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station.

[0045] It should be noted that the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station is obtained by analyzing according to the analysis process of the atmospheric dust matrix evaluation value corresponding to the target photovoltaic power station.

[0046] In another specific embodiment, the analysis of the cleaning plan corresponding to the target photovoltaic power station is as follows: C1. Compare the dust cleaning intractability complexity corresponding to the target photovoltaic power station with the dust cleaning intractability complexity intervals corresponding to each cleaning plan in the database. If the dust cleaning intractability complexity corresponding to the target photovoltaic power station is within the dust cleaning intractability complexity interval corresponding to a certain cleaning plan in the database, then take the cleaning plan in the database as the cleaning plan corresponding to the target photovoltaic power station. The cleaning plans include a dry dusting cleaning plan, an electrostatic adsorption cleaning plan, and a chemical spraying cleaning plan.

[0047] C2. If the cleaning plan corresponding to the target photovoltaic power station is a dry dusting cleaning plan, then use the duster equipment carried by the drone. According to the planned "zigzag" dusting path, the drone starts working from the upwind side of each photovoltaic panel in the photovoltaic power station and sweeps each row of photovoltaic panels according to the set low-altitude flight height and flight speed.

[0048] C3. If the cleaning solution corresponding to the target PV power station is the electrostatic adsorption cleaning solution, compare the dust removal intractability complexity corresponding to the target PV power station with the dust removal intractability complexity intervals corresponding to the parameters of each electrostatic adsorption device in the database. If the dust removal intractability complexity corresponding to the target PV power station is within the dust removal intractability complexity interval corresponding to the parameters of a certain electrostatic adsorption device in the database, set the parameters of the electrostatic adsorption device in the database as the parameters of the electrostatic adsorption device corresponding to the target PV power station. At the same time, fly the drone to a position 3 - 5 cm above the PV panel, turn on the electrostatic adsorption device, and the electrostatic adsorption device scans the PV panel column by column according to the set parameters of the electrostatic adsorption device.

[0049] C4. If the cleaning solution corresponding to the target PV power station is the chemical spray cleaning solution, compare the dust removal intractability complexity corresponding to the target PV power station with the dust removal intractability complexity intervals corresponding to the concentration ratio parameters of each cleaning agent in the database. If the dust removal intractability complexity corresponding to the target PV power station is within the dust removal intractability complexity interval corresponding to the concentration ratio parameters of a certain cleaning agent in the database, set the concentration ratio parameters of the cleaning agent in the database as the concentration ratio parameters of the cleaning agent corresponding to the target PV power station. At the same time, fly the drone to a position 8 - 10 cm above the PV panel, turn on the pump-in spray device, and the pump-in spray device scans the PV panel column by column according to the set concentration ratio parameters of the cleaning agent.

[0050] Step 2. Identification of photoluminescence characteristics: After the target PV power station is cleaned according to the corresponding cleaning solution, the drone collects the photoluminescence characteristics corresponding to each PV panel, and then judges, and marks each PV panel affected by dust erosion as each PV panel to be identified.

[0051] In a specific embodiment, the process of judging whether each PV panel is affected by dust erosion is as follows: D1. The drone collects the photoluminescence characteristics corresponding to each PV panel in the target PV power station, and sets several collection points, so that the control photoluminescence imaging device installed inside the drone continuously collects the photoluminescence characteristics corresponding to each collection point in each PV panel. The photoluminescence characteristics include the coefficient of variation of luminescence uniformity, the spectral peak wavelength shift, the spectral full width at half maximum change rate, and the proportion of shortened luminescence decay time. Input the coefficient of variation of luminescence uniformity, the spectral peak wavelength shift, the spectral full width at half maximum change rate, and the proportion of shortened luminescence decay time corresponding to each collection point in each PV panel into the dust erosion evaluation value analysis model, and output the dust erosion evaluation value results corresponding to each PV panel.

[0052] D2. The results of the dust erosion evaluation value include the values of 1 and -1. When the result of the dust erosion evaluation value corresponding to a certain photovoltaic panel is 1, it indicates that the photovoltaic panel is not eroded by dust. On the contrary, when the result of the dust erosion evaluation value corresponding to a certain photovoltaic panel is -1, it indicates that the photovoltaic panel is eroded by dust. In this way, it is judged whether each photovoltaic panel is eroded by dust.

[0053] In another specific embodiment, the expression of the dust erosion evaluation value analysis model is:

[0054] In the formula, where, and respectively represent the coefficient of variation of luminous uniformity, the spectral peak wavelength shift, the change rate of spectral full width at half maximum, and the reduction ratio of luminous decay time corresponding to the f-th acquisition point in the y-th photovoltaic panel. y represents the number corresponding to each photovoltaic panel, y = 1, 2......g, where g is any integer greater than 2, and f represents the number corresponding to each acquisition point, f = 1, 2......a, where a is any integer greater than 2, Λ y represents the result of the dust erosion evaluation value corresponding to the y-th photovoltaic panel. Ι′ is the set threshold of the dust erosion evaluation value. Among them, S′, H′, V′, M′ are respectively the standard coefficient of variation of luminous uniformity, the standard spectral peak wavelength shift, the standard change rate of spectral full width at half maximum, and the standard reduction ratio of luminous decay time corresponding to the set photovoltaic panel. X1, X2, X3, X4 are respectively the weight factors corresponding to the coefficient of variation of luminous uniformity, the spectral peak wavelength shift, the change rate of spectral full width at half maximum, and the reduction ratio of luminous decay time of the set photovoltaic panel. Ν1, Ν2, Ν3, Ν4 are respectively the adjustment factors corresponding to the coefficient of variation of luminous uniformity, the spectral peak wavelength shift, the change rate of spectral full width at half maximum, and the reduction ratio of luminous decay time of the set photovoltaic panel. e represents the natural constant.

[0055] It should be noted that X1, X2, X3, and X4 are all greater than 0 and less than 1.

[0056] It should also be noted that a large number of samples of the same type of photovoltaic panels in normal operating conditions and not significantly eroded by dust are collected. These samples cover photovoltaic panels of different production batches and different installation environments but all maintain good power generation performance. Using high-precision photoluminescence detection equipment, under standard test conditions, photoluminescence characteristic data is collected at multiple sampling points for each sample to obtain the original data of the coefficient of variation of luminescence uniformity, the shift amount of the spectral peak wavelength, the change rate of the spectral full width at half maximum, and the shortening ratio of the luminescence decay time. Statistical processing is performed on the large amount of collected original data, and statistical quantities such as the average value and standard deviation of each index are calculated. The average value is used as the corresponding standard value. Combining with the actual experience of long-term operation and maintenance of photovoltaic power stations, maintenance personnel in different regions feedback that certain parameters are more critical for judging erosion in the local environment. For example, in high-humidity regions, due to the special influence of water vapor on the recombination process of photo-generated carriers, the change rate of the spectral full width at half maximum becomes an important index for judging erosion, and the weight factor is appropriately increased; in regions with strong sunlight and much sand and dust, the weight factor of the coefficient of variation of luminescence uniformity is increased. Considering these factors, the weight factor is finely adjusted, taking into account the special environmental factors where the photovoltaic panel is located. For example, in high-altitude regions, due to the thin air and strong ultraviolet rays, the photoluminescence process is affected, and the adjustment factor corresponding to the coefficient of variation of luminescence uniformity is adjusted to compensate for the interference of the environment on the measurement accuracy of this parameter; in coastal regions, the salt mist environment may accelerate the surface aging of the photovoltaic panel and affect the spectral characteristics, and the adjustment factor of the shift amount of the spectral peak wavelength is correspondingly adjusted.

[0057] Step 3: Analysis of erosion grading: Analyze the erosion grade corresponding to each photovoltaic panel to be identified, and perform a maintenance decision according to the erosion grade corresponding to each photovoltaic panel to be identified.

[0058] In a specific embodiment, the analysis of the erosion grade corresponding to each photovoltaic panel to be identified is as follows: Compare the dust erosion evaluation value corresponding to each photovoltaic panel to be identified with the dust erosion evaluation value interval corresponding to each erosion grade in the database. If the dust erosion evaluation value corresponding to a certain photovoltaic panel to be identified is within the dust erosion evaluation value interval corresponding to a certain erosion grade in the database, then record the erosion grade in the database as the erosion grade corresponding to the photovoltaic panel to be identified. In this way, the erosion grade corresponding to each photovoltaic panel to be identified is analyzed. The erosion grades include mild erosion grade, moderate erosion grade, and severe erosion grade.

[0059] In another specific embodiment, the maintenance decision is made according to the erosion grade corresponding to each photovoltaic panel to be identified as follows: E1. If the erosion grade corresponding to a certain photovoltaic panel to be identified is the mild erosion grade, within one month after cleaning, arrange a light cleaning every two weeks. Gently wipe the surface of the photovoltaic panel with a soft, anti-static cleaning cloth to remove a small amount of newly attached dust, then gently wipe it with a clean wet cloth dipped in a small amount of mild detergent, and finally rinse it with clean water and dry it.

[0060] E2. If the erosion level corresponding to a photovoltaic panel to be identified is a moderate erosion level, then within one month after cleaning, cleaning operations shall be carried out weekly. First, use a high-pressure water gun to rinse the surface of the photovoltaic panel to wash away most of the dust and stains. Then, adopt a combination of chemical cleaning and physical cleaning. First, soak the surface of the photovoltaic panel with a chemical cleaner for 3 - 5 minutes to fully dissolve stubborn stains, then rinse with a high-pressure water gun. After that, carefully brush with a soft brush and clean water, and finally rinse with clean water multiple times and dry naturally.

[0061] E3. If the erosion level corresponding to a photovoltaic panel to be identified is a severe erosion level, then quickly organize a professional team for assessment to determine whether it is local repair or replacement of the entire photovoltaic panel, and implement corresponding measures as soon as possible. At the same time, according to the current performance status of the photovoltaic panel, adjust the operation strategy of the power station. If it is determined to be local repair, then adopt repair techniques or improve the performance of the photovoltaic panel, apply new anti-erosion coatings, and optimize the internal structure of the battery cells.

[0062] E4. Count the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station. If the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station is greater than or equal to the set number of photovoltaic panels to be identified corresponding to the severe erosion level, then an emergency alarm shall be issued.

[0063] In another specific embodiment, if the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station is greater than or equal to the set number of photovoltaic panels to be identified corresponding to the severe erosion level, then an emergency alarm shall be issued. The specific alarm process is as follows: Through the in-station broadcast system, the alarm information shall be played in a loop at the maximum volume to ensure that all operation and maintenance personnel at the power station site can hear it. At the same time, send text messages with the highest priority identification to the mobile phones of the power station operation and maintenance team and management level, and push prominent pop-up notifications on the operation and maintenance management APP. The pop-up window shall be continuously displayed until manually confirmed by the personnel, and a detailed alarm content link shall be attached. Clicking on it can view all information.

[0064] As shown in the embodiments of the present invention Figure 2 A photovoltaic power station identification system based on an unmanned aerial vehicle includes: a dust physical property identification module, a photoluminescence property identification module, an erosion grading analysis module, and a database.

[0065] It should be noted that the database is used to store the dust cleaning intractability complexity intervals corresponding to each cleaning plan, the dust cleaning intractability complexity intervals corresponding to the parameters of each electrostatic adsorption device, and the dust cleaning intractability complexity intervals corresponding to the concentration ratio parameters of each cleaner. The database is also used to store the dust erosion evaluation value intervals corresponding to each erosion level.

[0066] Dust physical property identification module: It is used to dispatch drones to the target photovoltaic power station for monitoring, so as to analyze the complexity of dust washing in the target photovoltaic power station, and then analyze the corresponding cleaning plan for the target photovoltaic power station.

[0067] Photoluminescence property identification module: It is used to collect the photoluminescence properties of each photovoltaic panel by the drone after the target photovoltaic power station is cleaned according to the corresponding cleaning plan, and then judge, and mark each photovoltaic panel affected by dust erosion as each photovoltaic panel to be identified.

[0068] Erosion grading analysis module: It is used to analyze the erosion grade corresponding to each photovoltaic panel to be identified, and execute maintenance decisions according to the erosion grade corresponding to each photovoltaic panel to be identified.

[0069] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all fall within the protection scope of the present invention.

Claims

1. A photovoltaic power station identification method based on drones, characterized in that: include: Step 1: Identification of the physical characteristics of dust: Send drones to the target photovoltaic power station for monitoring, so as to analyze the complexity of dust cleaning corresponding to the target photovoltaic power station, and then analyze the cleaning plan corresponding to the target photovoltaic power station; Step 2: Identification of photoluminescence characteristics: When the target photovoltaic power station is cleaned according to the corresponding cleaning plan, the drone collects the photoluminescence characteristics of each photovoltaic panel, and then judges and records the photovoltaic panels corroded by dust as photovoltaic panels to be identified; Step 3: Analysis of corrosion classification: Analyze the corrosion level corresponding to each photovoltaic panel to be identified, and make maintenance decisions based on the corrosion level corresponding to each photovoltaic panel to be identified.

2. A method for identifying a photovoltaic power station based on a drone as claimed in claim 1, characterized in that: The above analysis is conducted on the complexity of dust cleaning corresponding to the target photovoltaic power station. The specific analysis process is as follows: A1. The drone flies over the target photovoltaic power station and collects the atmospheric dust matrix index and dust adhesion potential index of each photovoltaic panel in the target photovoltaic power station. The atmospheric dust matrix index includes the corresponding pH, the proportion of each chemical component and the content of various types of compounds. The dust adhesion potential index includes the minimum removal force, the critical wind speed for dust adhesion and the dust viscosity. Then, the atmospheric dust matrix evaluation value and dust adhesion potential evaluation value corresponding to the target photovoltaic power station are obtained by analysis, and the atmospheric dust matrix evaluation value and dust adhesion potential evaluation value corresponding to the target photovoltaic power station are recorded as Q y and W y , where y represents the serial number of each photovoltaic panel, y=1,2...g, g is any integer greater than 2; A2. Substitute the atmospheric dust matrix assessment value and dust adhesion potential assessment value corresponding to the target photovoltaic power station into the calculation formula: The dust cleaning complexity γ corresponding to the target PV power station is obtained, where Q′ and W′ are the standard atmospheric dust matrix evaluation value and standard dust attachment potential evaluation value corresponding to the set PV power station, Λ1 and Λ2 are the weight factors corresponding to the atmospheric dust matrix evaluation value and dust attachment potential evaluation value of the set PV power station, respectively; K1 and K2 are the adjustment factors corresponding to the atmospheric dust matrix evaluation value and dust attachment potential evaluation value of the set PV power station, respectively.

3. A photovoltaic power station identification method based on drone as claimed in claim 2, characterized in that: The analysis obtains the atmospheric dust matrix evaluation value and dust adhesion potential evaluation value corresponding to the target photovoltaic power station. The specific analysis process is as follows: B1. Input the pH value, chemical composition ratio and content of various types of compounds corresponding to each photovoltaic panel in the target photovoltaic power station into the atmospheric dust matrix assessment value analysis model, and output the atmospheric dust matrix assessment value corresponding to the target photovoltaic power station; B2. Input the minimum external removal force, dust adhesion critical wind speed and dust viscosity corresponding to each photovoltaic panel in the target photovoltaic power station into the dust adhesion potential energy evaluation value analysis model, and output the dust adhesion potential energy evaluation value corresponding to the target photovoltaic power station.

4. A method for identifying a photovoltaic power station based on a drone as claimed in claim 3, characterized in that: The cleaning scheme corresponding to the target photovoltaic power station is analyzed, and the specific analysis process is as follows: C1. Compare the dust cleaning complexity corresponding to the target photovoltaic power station with the dust cleaning complexity intervals corresponding to each cleaning scheme in the database. If the dust cleaning complexity corresponding to the target photovoltaic power station is within the dust cleaning complexity interval corresponding to a cleaning scheme in the database, then use the cleaning scheme in the database as the cleaning scheme corresponding to the target photovoltaic power station. The cleaning schemes include dry sweeping cleaning scheme, electrostatic adsorption cleaning scheme and chemical spray cleaning scheme. C2. If the cleaning solution for the target PV power station is a dry dusting cleaning solution, the drone will use the duster equipment carried by the drone. According to the planned zigzag dusting path, the drone will start from the upwind side of each PV panel of the PV power station and clean the PV panels row by row at the set low-altitude flight altitude and flight speed. C3. If the cleaning scheme corresponding to the target photovoltaic power station is the electrostatic adsorption cleaning scheme, the dust cleaning complexity corresponding to the target photovoltaic power station is compared with the dust cleaning complexity interval corresponding to each electrostatic adsorption device parameter in the database. If the dust cleaning complexity corresponding to the target photovoltaic power station is within the dust cleaning complexity interval corresponding to a certain electrostatic adsorption device parameter in the database, the electrostatic adsorption device parameter in the database is set as the electrostatic adsorption device parameter corresponding to the target photovoltaic power station. At the same time, the drone is taken off to 3-5 cm above the photovoltaic panel, and the electrostatic adsorption device is turned on. The electrostatic adsorption device scans the photovoltaic panel column by column according to the set electrostatic adsorption device parameters. C4. If the cleaning scheme corresponding to the target photovoltaic power station is a chemical spray cleaning scheme, the cleaning complexity corresponding to the target photovoltaic power station is compared with the cleaning complexity intervals corresponding to the detergent concentration ratio parameters in the database. If the cleaning complexity corresponding to the target photovoltaic power station is within the cleaning complexity interval corresponding to a detergent concentration ratio parameter in the database, the detergent concentration ratio parameter in the database is set as the detergent concentration ratio parameter corresponding to the target photovoltaic power station. At the same time, the drone is taken off to 8-10 cm above the photovoltaic panel, and the pump spray device is turned on. The pump spray device scans the photovoltaic panels column by column according to the set detergent concentration ratio parameters.

5. A method for identifying a photovoltaic power station based on a drone as claimed in claim 4, characterized in that: The specific process of judging whether each photovoltaic panel is corroded by dust is as follows: D1. The drone collects the photoluminescence characteristics corresponding to each photovoltaic panel in the target photovoltaic power station, and sets a number of collection points so that the control photoluminescence imaging device installed inside the drone continuously collects the photoluminescence characteristics corresponding to each collection point in each photovoltaic panel. The photoluminescence characteristics include the coefficient of variation of luminescence uniformity, the displacement of the spectral peak wavelength, the change rate of the spectral half-height width, and the shortening ratio of the luminescence decay time. The coefficient of variation of luminescence uniformity, the displacement of the spectral peak wavelength, the change rate of the spectral half-height width, and the shortening ratio of the luminescence decay time corresponding to each collection point in each photovoltaic panel are input into the dust erosion assessment value analysis model, and the dust erosion assessment value results corresponding to each photovoltaic panel are output; D2. The dust erosion assessment value results include values ​​of 1 and -1. When the dust erosion assessment value result corresponding to a photovoltaic panel is 1, it indicates that the photovoltaic panel is not corroded by dust. Conversely, when the dust erosion assessment value result corresponding to a photovoltaic panel is -1, it indicates that the photovoltaic panel is corroded by dust. In this way, it is determined whether each photovoltaic panel is corroded by dust.

6. A method for identifying a photovoltaic power station based on a drone as claimed in claim 5, characterized in that: The expression of the dust erosion assessment value analysis model is: In the formula, and They are respectively represented as the coefficient of variation of luminous uniformity, the displacement of spectral peak wavelength, the change rate of spectral half-width and the shortening ratio of luminous decay time corresponding to the f-th collection point in the y-th photovoltaic panel, y represents the number corresponding to each photovoltaic panel, y=1,2......g, g is any integer greater than 2, f represents the number corresponding to each collection point, f=1,2......a, a is any integer greater than 2, Λ y represents the dust erosion assessment value result corresponding to the y-th photovoltaic panel, Ι′ is the set dust erosion assessment value threshold, wherein S′, H′, V′, and M′ are respectively the standard luminous uniformity variation coefficient, standard spectral peak wavelength displacement, standard spectral half-width change rate, and standard luminous decay time shortening ratio corresponding to the set photovoltaic panel, X1, X2, X3, and X4 are respectively the weight factors corresponding to the set photovoltaic panel luminous uniformity variation coefficient, the weight factors corresponding to the spectral peak wavelength displacement, the weight factors corresponding to the spectral half-width change rate, and the weight factors corresponding to the luminous decay time shortening ratio, N1, N2, N3, and N4 are respectively the adjustment factors corresponding to the set photovoltaic panel luminous uniformity variation coefficient, the adjustment factors corresponding to the spectral peak wavelength displacement, the adjustment factors corresponding to the spectral half-width change rate, and the adjustment factors corresponding to the luminous decay time shortening ratio, and e represents a natural constant.

7. A method for identifying a photovoltaic power station based on a drone as claimed in claim 6, characterized in that: The corrosion level corresponding to each photovoltaic panel to be identified is analyzed, and the specific analysis process is as follows: The dust erosion assessment value corresponding to each photovoltaic panel to be identified is compared with the dust erosion assessment value interval corresponding to each erosion level in the database. If the dust erosion assessment value corresponding to a photovoltaic panel to be identified is within the dust erosion assessment value interval corresponding to a certain erosion level in the database, the erosion level in the database is recorded as the erosion level corresponding to the photovoltaic panel to be identified. In this way, the erosion level corresponding to each photovoltaic panel to be identified is analyzed, and the erosion levels include slight erosion level, severe erosion level and severe erosion level.

8. A method for identifying a photovoltaic power station based on a drone as claimed in claim 7, characterized in that: The maintenance decision is executed according to the corrosion level corresponding to each photovoltaic panel to be identified. The specific decision-making process is as follows: E1. If the erosion level of a photovoltaic panel to be identified is a slight erosion level, a light cleaning should be arranged every two weeks within one month after cleaning. Use a soft, anti-static cleaning cloth to gently wipe the surface of the photovoltaic panel to remove a small amount of newly attached dust, then use a clean wet cloth dipped in a small amount of mild detergent to gently wipe it, and finally rinse it with clean water and dry it; E2. If the erosion level of a photovoltaic panel to be identified is moderate erosion, it should be cleaned every week within one month after cleaning. First, use a high-pressure water gun to wash the surface of the photovoltaic panel to wash away most of the dust and stains. Then, use a combination of chemical cleaning and physical cleaning. First, soak the surface of the photovoltaic panel with a chemical cleaner for 3-5 minutes to fully dissolve stubborn stains. Then, use a high-pressure water gun to rinse it. Then, use a soft brush with clean water to carefully scrub it. Finally, rinse it with clean water several times and let it dry naturally. E3. If the erosion level of a photovoltaic panel to be identified is severe erosion, a professional team will be quickly organized to conduct an assessment to determine whether to perform a partial repair or replace the entire photovoltaic panel, and implement corresponding measures as soon as possible. At the same time, the operation strategy of the power station will be adjusted according to the current performance of the photovoltaic panel. If it is determined to be a partial repair, repair technology will be used or the performance of the photovoltaic panel will be improved, a new anti-erosion coating will be applied, and the internal structure of the battery cell will be optimized; E4. Count the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station. If the number of photovoltaic panels to be identified corresponding to the severe erosion level of the target photovoltaic power station is greater than or equal to the number of photovoltaic panels to be identified corresponding to the set severe erosion level, an emergency alarm is issued.

9. A method for identifying a photovoltaic power station based on a drone as claimed in claim 8, characterized in that: If the number of photovoltaic panels to be identified corresponding to the severe corrosion level of the target photovoltaic power station is greater than or equal to the number of photovoltaic panels to be identified corresponding to the set severe corrosion level, an emergency alarm is issued. The specific alarm process is as follows: The alarm information is played in a loop at maximum volume through the station broadcasting system to ensure that all operation and maintenance personnel at the power plant site can hear it. At the same time, a text message notification with the highest priority mark is sent to the mobile phones of the power plant operation and maintenance team and management, and a prominent pop-up notification is pushed on the operation and maintenance management APP. The pop-up window continues to display until the personnel manually confirms it, and it is accompanied by a detailed alarm content link, which can be clicked to view all the information.

10. A photovoltaic power station identification system based on a drone that executes the photovoltaic power station identification method based on a drone according to any one of claims 1 to 9, characterized in that: include: Dust physical characteristics identification module: used to send drones to the target photovoltaic power station for monitoring, so as to analyze the complexity of dust cleaning corresponding to the target photovoltaic power station, and then analyze the cleaning plan corresponding to the target photovoltaic power station; Photoluminescence characteristic identification module: used to collect the photoluminescence characteristics of each photovoltaic panel by the drone after the target photovoltaic power station is cleaned according to the corresponding cleaning plan, and then judge and record the photovoltaic panels corroded by dust as photovoltaic panels to be identified; Erosion classification analysis module: used to analyze the erosion level corresponding to each photovoltaic panel to be identified, and execute maintenance decisions based on the erosion level corresponding to each photovoltaic panel to be identified.

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