Photovoltaic power generation equipment operation and maintenance management method and device

Abstract

The invention discloses an operation and maintenance management method and device for photovoltaic power generation equipment, and relates to the technical field of operation and maintenance management. Path deviation values, photovoltaic panel cleaning effect values and water spraying abnormal values of a high-pressure water spraying system of all unmanned aerial vehicles in the photovoltaic panel cleaning process are calculated, and fault values of all the unmanned aerial vehicles are calculated; judging whether the unmanned aerial vehicle can execute the remaining cleaning tasks according to the fault value and a preset fault value threshold value; when the unmanned aerial vehicles cannot execute the remaining cleaning tasks, the number of photovoltaic panels to be cleaned of all the unmanned aerial vehicles is obtained, and whether a standby unmanned aerial vehicle needs to be dispatched for photovoltaic panel cleaning operation and maintenance management or not is judged; therefore, if a certain unmanned aerial vehicle or a cleaning system of the unmanned aerial vehicle has a cleaning fault, the cleaning operation and maintenance management work of the unmanned aerial vehicle can be stopped in time, the possibility that the fault of the unmanned aerial vehicle is further deepened is reduced, and greater cost loss is avoided; and meanwhile, whether a new standby unmanned aerial vehicle is dispatched for photovoltaic panel cleaning management or not can be judged according to actual conditions, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management, and particularly to a method and device for operation and maintenance management of photovoltaic power generation equipment. Background Art

[0002] With the promotion of renewable energy worldwide, photovoltaic power generation has become an important energy supply method. Photovoltaic power generation equipment, especially photovoltaic panels, as its core components, directly affect the power generation efficiency and operation cost of the power station; therefore, efficient and accurate operation and maintenance management of photovoltaic power generation equipment has become a key factor in ensuring the long-term stable operation of the photovoltaic power generation system; the operation and maintenance management of photovoltaic power generation equipment mainly includes the following aspects: equipment detection and monitoring, fault diagnosis and repair, performance evaluation and optimization, cleaning and maintenance, etc.; especially the cleaning work of photovoltaic panels, the surface of photovoltaic panels accumulates dirt due to environmental factors (such as dust, bird droppings, leaves, etc.), which will significantly affect the photoelectric conversion efficiency of photovoltaic panels, and then reduce the overall power generation performance; therefore, regular cleaning and maintenance of photovoltaic panels are important links in the operation and maintenance cost of photovoltaic panels.

[0003] Traditional cleaning methods of photovoltaic panels usually rely on manual or mechanical cleaning equipment, and there are problems such as low cleaning efficiency, high cost, and great difficulty. In order to improve cleaning efficiency and reduce labor costs, automated cleaning technology has gradually been introduced into the operation and maintenance of photovoltaic power generation equipment; in recent years, unmanned aerial vehicle (UAV) cleaning technology has gradually been applied to the field of photovoltaic panel cleaning; UAVs have the advantages of high efficiency, flexibility, and low cost, and can quickly inspect and clean vast photovoltaic panel areas; by carrying cleaning devices such as high-pressure water spraying systems, UAVs can clean by spraying high-pressure water flows without contacting the photovoltaic panels, thereby effectively removing dirt such as dust and bird droppings and restoring the photoelectric conversion efficiency of photovoltaic panels.

[0004] In the process of cleaning and maintaining photovoltaic panels by UAVs, before each UAV departs, the corresponding number of photovoltaic panels to be cleaned and the corresponding travel path are set, and multiple UAVs work together to achieve the operation and maintenance of photovoltaic panel cleaning; however, in the actual cleaning process, if a certain UAV or the cleaning system of the UAV has a cleaning failure, if the UAV is still used for photovoltaic panel cleaning, it may cause the failure of the UAV to deepen further or result in greater cost losses; but if new standby UAVs are blindly dispatched for photovoltaic panel cleaning, it may increase the operation and maintenance cost. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and provide a method and device for operation and maintenance management of photovoltaic power generation equipment.

[0006] In the first aspect of the implementation of the present invention, a method for operation and maintenance management of photovoltaic power generation equipment is first proposed. The method includes:

[0007] Obtain the actual flight path and the preset flight path of each unmanned aerial vehicle (UAV) during the process of cleaning the photovoltaic panel, and calculate the path deviation value, which is used to measure the degree of deviation of the flight path of the UAV during the process of cleaning the photovoltaic panel;

[0008] Obtain the pixel values of the image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image, and calculate the cleaning effect value, which is used to measure the cleaning effect of the UAV cleaning the photovoltaic panel;

[0009] Obtain the water spray amount and water spray pressure of each UAV, and calculate the water spray anomaly value according to the water spray amount and water spray pressure, which is used to measure the degree of water spray anomaly of the high-pressure water spray system carried by the UAV;

[0010] Calculate the fault value of each UAV according to the path deviation value, the cleaning effect value and the water spray anomaly value, compare the fault value with the preset fault value threshold, and judge whether the UAV can perform the remaining cleaning tasks according to the comparison result;

[0011] When the UAV cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the UAV and the number of photovoltaic panels to be cleaned by the other UAVs that can perform the remaining cleaning tasks, and judge whether it is necessary to dispatch a standby UAV for the cleaning operation and maintenance management of the photovoltaic panel.

[0012] Optionally, the steps of obtaining the actual flight path and the preset flight path of each UAV during the process of cleaning the photovoltaic panel and calculating the path deviation value are as follows:

[0013] For each UAV, obtain the number of photovoltaic panels that the UAV has cleaned, and sort the cleaned photovoltaic panels in the cleaning order to obtain a set of cleaned photovoltaic panels;

[0014] For two adjacent photovoltaic panels in the set, obtain the actual flight path of the UAV when flying between these two photovoltaic panels, compare the actual flight path with the preset flight path, calculate the path distance of the coincidence of the two, and calculate the flight path deviation ratio of the UAV between the corresponding two photovoltaic panels according to the preset flight path distance. The calculation formula is: In the formula, SD is the flight path deviation ratio, bn is the preset flight path distance, and bm is the path distance of the coincidence;

[0015] Calculate the flight path deviation ratios between all adjacent two photovoltaic panels in the set, compare the flight path deviation ratio with the minimum value of the preset flight path deviation ratio. If the flight path deviation ratio is less than the minimum value of the preset flight path deviation ratio, record the corresponding flight path deviation ratio as the abnormal flight path deviation ratio, and divide the total number of abnormal flight path deviation ratios by the total number of flight path deviation ratios to obtain the path deviation value of the corresponding UAV.

[0016] Optionally, the steps of obtaining the cleaning effect value by calculating the pixel values of the image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image are as follows:

[0017] For each drone, obtain the images of each photovoltaic panel cleaned by the corresponding drone, and perform the same preprocessing on the images of each photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image to obtain the processed image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image;

[0018] Calculate the similarity value between the processed image x of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image y. The calculation formula is:

[0019]

[0020] In the formula, SA(x,y) is the similarity value between the processed image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image, μ x and μ y respectively represent the mean pixel values of images x and y; σ x 2 and σ y 2 respectively represent the variances of the pixel values of images x and y; σ xy is the covariance of the pixel values of images x and y; C 1 and C 2 are two constants used to prevent the denominator from being zero:

[0021] Add up the similarity values between each processed image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image to obtain the cleaning effect value of the drone.

[0022] Optionally, the steps of calculating the water spraying anomaly value according to the water spraying amount and the water spraying pressure are as follows:

[0023] Obtain the actual water spraying amount each time the high-pressure water spraying system sprays water during the process of the drone cleaning the photovoltaic panel, calculate the absolute difference between the actual water spraying amount and the set water spraying amount of the drone, and divide the absolute difference by the set number of water sprayings of the drone to obtain the water spraying amount anomaly ratio;

[0024] Obtain the actual water spraying pressure during the actual water spraying process each time the high-pressure water spraying system sprays water during the process of the drone cleaning the photovoltaic panel, calculate the absolute difference between the actual water spraying pressure and the set water spraying pressure of the drone, and divide the absolute difference of the water spraying pressure by the set water spraying pressure of the drone to obtain the water spraying pressure anomaly ratio;

[0025] Calculate the water spraying anomaly value of the drone according to the water spraying amount anomaly ratio and the water spraying pressure anomaly ratio.

[0026] Optionally, the steps for calculating the fault value of each drone based on the path deviation value, cleaning effect value, and water spraying anomaly value of the drone are as follows:

[0027]

[0028] In the formula, WER is the fault value, DER, DSW, and HY are the path deviation value, cleaning effect value, and water spraying anomaly value respectively, and b1, b2, and b3 are the preset proportional values of DER, DSW, and HY respectively, and b1, b2, and b3 are all greater than 0.

[0029] Optionally, judging whether the drone can perform the remaining cleaning tasks according to the comparison result includes:

[0030] Compare the fault value with the preset fault value threshold. If the fault value is not less than the preset fault value threshold, the corresponding drone cannot perform the remaining cleaning tasks;

[0031] If the fault value is less than the preset fault value threshold, the corresponding drone can perform the remaining cleaning tasks.

[0032] Optionally, the steps for obtaining the number of photovoltaic panels to be cleaned by the drone, as well as the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and judging whether to dispatch a spare drone for the cleaning operation and maintenance management of the photovoltaic panels are as follows:

[0033] For the drones that cannot perform the remaining cleaning tasks, obtain the images of each photovoltaic panel cleaned by the drone, and perform the same preprocessing on the images of each photovoltaic panel after cleaning and the preset standard images of photovoltaic panel cleaning to obtain the preprocessed images of the photovoltaic panel after cleaning and the preset standard images of photovoltaic panel cleaning;

[0034] Calculate the similarity value between the preprocessed image of the photovoltaic panel after cleaning and the preset standard image of photovoltaic panel cleaning, and compare the similarity value with the preset similarity value threshold. If the similarity value is less than the preset minimum similarity value threshold, mark the corresponding photovoltaic panel as a photovoltaic panel to be re-cleaned;

[0035] Obtain the number of remaining photovoltaic panels to be cleaned, the number of photovoltaic panels to be re-cleaned corresponding to the drones that cannot perform the remaining cleaning tasks, and the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and calculate their sum to obtain the total number of photovoltaic panels to be cleaned;

[0036] Obtain the sum of the remaining stored water volumes of all drones that can perform the remaining cleaning tasks to obtain the total stored water volume, and divide the total stored water volume by the total number of photovoltaic panels to be cleaned to obtain the average available water volume;

[0037] Obtain the maximum average available water volume for cleaning the photovoltaic panels from the historical cleaning records, and compare the average available water volume with the maximum average available water volume. If the average available water volume is not less than the maximum average available water volume, there is no need to dispatch standby drones for the cleaning operation and maintenance management of the photovoltaic panels; if it is less, standby drones need to be dispatched for the cleaning operation and maintenance management of the photovoltaic panels.

[0038] In the second aspect of the implementation of the present invention, a device for operation and maintenance management of a photovoltaic power generation device is proposed. The device includes:

[0039] Path deviation module: Obtain the actual flight path and the preset flight path of each drone during the process of cleaning the photovoltaic panels, and calculate the path deviation value, which is used to measure the degree of deviation of the flight path of the drone during the process of cleaning the photovoltaic panels;

[0040] Cleaning effect module: Obtain the pixel values of the image of the photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard image, and calculate the cleaning effect value, which is used to measure the cleaning effect of the drone cleaning the photovoltaic panels;

[0041] Water spraying anomaly module: Obtain the water spraying volume and water spraying pressure of each drone, and calculate the water spraying anomaly value according to the water spraying volume and water spraying pressure, which is used to measure the degree of water spraying anomaly of the high-pressure water spraying system carried by the drone;

[0042] Judgment module: Calculate the fault value of each drone according to the path deviation value, cleaning effect value and water spraying anomaly value, compare the fault value with the preset fault value threshold, and judge whether the drone can perform the remaining cleaning tasks according to the comparison result;

[0043] Operation and maintenance management module: When the drone cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and judge whether it is necessary to dispatch standby drones for the cleaning operation and maintenance management of the photovoltaic panels.

[0044] The beneficial effects of the present invention:

[0045] The present invention provides a method and device for operation and maintenance management of photovoltaic power generation equipment. By calculating the path deviation value, the photovoltaic panel cleaning effect value, and the water spraying abnormality value of the high-pressure water spraying system during the process of cleaning the photovoltaic panels by each unmanned aerial vehicle (UAV), and calculating the fault value of each UAV according to the path deviation value, the cleaning effect value, and the water spraying abnormality value, and comparing the fault value with a preset fault value threshold, and judging whether the UAV can perform the remaining cleaning tasks according to the comparison result; when the UAV cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the UAV, and the number of photovoltaic panels to be cleaned by the other UAVs that can perform the remaining cleaning tasks, and judge whether it is necessary to dispatch a standby UAV for the cleaning operation and maintenance management of the photovoltaic panels; in this way, during the process of cleaning and maintaining the photovoltaic panels by the UAV, if a certain UAV or the cleaning system of the UAV has a cleaning fault, the cleaning operation and maintenance management work of the UAV can be stopped in time, reducing the possibility of further deepening the fault of the UAV and avoiding greater cost losses; at the same time, it can judge whether to dispatch a new standby UAV for the cleaning management of the photovoltaic panels according to the actual situation, reducing the operation and maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 is a flowchart of a method for operation and maintenance management of a photovoltaic power generation equipment;

[0048] Figure 2 is a framework diagram of a device for operation and maintenance management of a photovoltaic power generation equipment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] The embodiments of the present invention provide a method for operation and maintenance management of a photovoltaic power generation equipment. Refer to Figure 1 , Figure 1 is a flowchart of a method for operation and maintenance management of a photovoltaic power generation equipment provided by an embodiment of the present invention. The method includes the following steps:

[0052] Obtain the actual flight path and the preset flight path of each drone during the process of cleaning the photovoltaic panel, and calculate the path deviation value, which is used to measure the degree of deviation of the flight path of the drone during the process of cleaning the photovoltaic panel;

[0053] Obtain the pixel values of the photovoltaic panel image after cleaning and the preset photovoltaic panel cleaning standard image, and calculate the cleaning effect value, which is used to measure the cleaning effect of the drone on the photovoltaic panel;

[0054] Obtain the water spray amount and water spray pressure of each drone, and calculate the water spray anomaly value according to the water spray amount and water spray pressure, which is used to measure the degree of water spray anomaly of the high-pressure water spray system carried by the drone;

[0055] Calculate the fault value of each drone according to the path deviation value, cleaning effect value and water spray anomaly value, compare the fault value with the preset fault value threshold, and judge whether the drone can perform the remaining cleaning tasks according to the comparison result;

[0056] When the drone cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and judge whether it is necessary to dispatch a spare drone for the cleaning operation and maintenance management of the photovoltaic panel.

[0057] Based on a method for operation and maintenance management of a photovoltaic power generation device provided by an embodiment of the present invention, in the above manner, during the process of cleaning and maintaining the photovoltaic panel by the drone, if a cleaning failure occurs in a certain drone or the cleaning system of the drone, the cleaning operation and maintenance management work of the drone can be stopped in time, reducing the possibility of further deepening of the drone's failure and avoiding greater cost losses; at the same time, it can judge whether to dispatch a new spare drone for photovoltaic panel cleaning management according to the actual situation, reducing the operation and maintenance cost.

[0058] In one embodiment, the steps of obtaining the actual flight path and the preset flight path of each drone during the process of cleaning the photovoltaic panel and calculating the path deviation value are as follows:

[0059] For each drone, obtain the number of photovoltaic panels that the drone has cleaned, and sort the cleaned photovoltaic panels in the cleaning order to obtain a set of cleaned photovoltaic panels;

[0060] For two adjacent photovoltaic panels in the set, obtain the actual flight path of the drone when flying between these two photovoltaic panels, compare the actual flight path with the preset flight path, calculate the path distance of the coincidence of the two, and calculate the flight path deviation ratio of the drone between the corresponding two photovoltaic panels according to the preset flight path distance. The calculation formula is: In the formula, SD is the flight path deviation ratio, bn is the preset flight path distance, and bm is the coincident path distance.

[0061] The flight path deviation ratio between all two adjacent photovoltaic panels in the set is calculated, and the flight path deviation ratio is compared with the preset minimum flight path deviation ratio. If the flight path deviation ratio is less than the preset minimum flight path deviation ratio, the corresponding flight path deviation ratio is recorded as the abnormal flight path deviation ratio. The total number of abnormal flight path deviation ratios is divided by the total number of flight path deviation ratios to obtain the path deviation value of the corresponding UAV.

[0062] It should be noted that the number of photovoltaic panels cleaned by the drone and the order in which they are cleaned can be tracked by the drone control system or task scheduling platform; the flight path data of the drone, which includes the actual flight trajectory data and the preset flight path obtained through the GPS positioning system or flight management system; the calculation of the overlapping path distance is calculated by comparing the actual flight path with the preset path, and using the path overlap algorithm to obtain the path length of the overlapping part; the preset flight path distance data is usually pre-set by the drone control system before the flight, and the factory is planned by professionals based on the layout and tasks of the photovoltaic panels; the flight path deviation ratio data is calculated by calculating the flight path difference between adjacent photovoltaic panels. If the deviation ratio exceeds the preset threshold, it is marked as an abnormal path and included in the statistics, and finally the path deviation value of the drone is obtained. The accurate acquisition and analysis of these data help evaluate the operating status of the drone and provide a basis for subsequent fault diagnosis.

[0063] It should be noted that the larger the path deviation value of the drone, the greater the possibility of drone failure. If the drone is still used for photovoltaic panel cleaning, the drone failure may be further aggravated or cause greater cost losses. The reason is that when the path deviation value of the drone is larger, it means that the drone deviates from the preset flight path during flight, and there may be problems such as insufficient navigation accuracy, flight control system failure or external environmental factors. If the path deviation of the drone is too large, it means that its flight system may be abnormal, causing it to fail to follow the predetermined route when performing the cleaning task, and some photovoltaic panels may be missed or not cleaned in place, affecting the cleaning effect. In addition, an excessively deviated flight path may cause additional pressure on the battery consumption and mechanical parts of the drone, increasing the risk of failure. If such a drone is continued to be used for cleaning, it may cause the aircraft failure to worsen, such as excessive battery consumption, flight control system failure, etc., further affecting the cleaning progress and effect, and may even cause the drone to be grounded, thereby increasing the operation and maintenance costs and delaying the cleaning task, causing greater losses and delays. Therefore, timely detection and troubleshooting, and reasonable dispatch of spare drones are the key to avoiding the spread of faults and ensuring the smooth completion of photovoltaic panel cleaning tasks.

[0064] In one embodiment, the steps of obtaining the pixel values ​​of the cleaned photovoltaic panel image and the preset photovoltaic panel cleaning standard image to calculate the cleaning effect value are:

[0065] For each unmanned aerial vehicle (UAV), obtain the images of each photovoltaic panel cleaned corresponding to the UAV, and perform the same preprocessing on both the images of the photovoltaic panels after cleaning and the preset standard images of the photovoltaic panel cleaning to obtain the processed images of the photovoltaic panels after cleaning and the preset standard images of the photovoltaic panel cleaning;

[0066] Calculate the similarity value between the processed image x of the photovoltaic panel after cleaning and the preset standard image y of the photovoltaic panel cleaning. The calculation formula is:

[0067]

[0068] In the formula, SA(x, y) is the similarity value between the processed image of the photovoltaic panel after cleaning and the preset standard image of the photovoltaic panel cleaning, μ x , μ y respectively represent the average pixel values of images x and y; the average value reflects the brightness information of the image; σ x 2 and σ y 2 respectively represent the variances of the pixel values of images x and y; the variance is used to describe the contrast information of the image; σ xy is the covariance of the pixel values of images x and y, indicating the degree of co - variation between images x and y, and reflecting whether the two images have similar structures; C 1 and C 2 are two constants used to prevent the denominator from being zero and to stabilize the calculation, usually taking very small values: μ x 2 +μ y 2 +C 1 is the weighted sum of the brightness differences between the images, aiming to measure the brightness similarity between the two images; σ x 2 +σ y 2 +C 2 is the weighted sum of the brightness differences between the images, aiming to measure the brightness similarity between the two images; 2σ x σ y +C 1 and 2σ xy +C 2 are the weighted measures of the structural similarity between the images, measuring the structural similarity between the two images;

[0069] Add up the similarity values between each processed image of the photovoltaic panel after cleaning and the preset standard image of the photovoltaic panel cleaning to obtain the cleaning effect value of the UAV.

[0070] It should be noted that C 1 and C 2Used to avoid the situation where the denominator in the calculation is zero. Their values are usually related to the dynamic range L of the image, and a standard constant value is often selected according to different applications or image types. The value of this constant is usually set as: C 1 =(K 1 L) 2 , where L is the dynamic range of the pixel values of the image, usually 255 (for 8-bit images); K 1 is a small constant, usually set as K 1 =0.01; therefore, the value of C 1 is usually C 1 =(0.01×255) 2 ≈6.5025; C 2 =(K 2 L) 2 , where K 1 is also a small constant. For example, it is usually set as K 2 =0.03; therefore, the value of C 2 is usually C 2 =(0.03×255) 2 ≈58.5225; These constant values are usually used in the calculation of 8-bit images. For different image depths (such as 16-bit images or floating-point images), the calculation method and specific values of the constants may be adjusted, and the specific values are set by professionals according to the actual situation, and will not be specifically limited and elaborated here.

[0071] It should be noted that preprocessing usually includes operations such as image normalization, noise removal, brightness and contrast adjustment, etc., in order to ensure that the images have the same scale and quality when comparing; in addition, each drone will capture images of the cleaned photovoltaic panels through the high-definition camera carried during the cleaning task, and the image acquisition process is usually recorded in real time through an automated data acquisition system to ensure that the images of each cleaning point can be accurately captured; secondly, the preset standard images of the photovoltaic panel cleaning are reference images of the cleaning effect formed through standardized tests or historical data accumulation. These images are usually taken by manual cleaning or other optimal cleaning solutions and stored as benchmarks. Therefore, both the cleaned images and the standard images need to go through the same preprocessing steps to ensure that they are in the same format and conditions when comparing images, so as to obtain effective and accurate similarity calculation results.

[0072] It should be noted that the smaller the cleaning effect value of the drone, the greater the possibility of drone failure. If the drone is still used for cleaning the photovoltaic panels, it may lead to further deepening of the drone's failure or cause greater cost losses. The reason is that: when the cleaning effect value of the drone is smaller, it usually means that the drone fails to achieve the expected cleaning effect during the cleaning task, and there may be potential problems such as cleaning equipment failure, flight path problems, or low efficiency of the cleaning system. For example, if the water spraying system of the drone malfunctions, it may result in incomplete cleaning, and the cleaning effect of the image is quite different from the standard image, thus leading to a lower cleaning effect value. This situation indicates an increased failure risk of the drone, because running in an unqualified state for a long time may exacerbate the occurrence of failures, such as increased battery loss, reduced stability of the flight control system, and even flight safety accidents. If the drone continues to be used for cleaning the photovoltaic panels, it will not only further deepen the loss and failure risk of the drone, but also may cause greater economic losses due to the unsatisfactory cleaning effect. In addition, if the drone continues to perform the cleaning task, it may cause other normally operating drones to be forced to undertake additional tasks, increasing the operation and maintenance costs, and may delay the cleaning progress of the entire photovoltaic power station, thus affecting the power generation efficiency and the overall operation of the power station.

[0073] In one implementation method, the advantage of calculating the cleaning effect value of the drone in the above manner is that it not only considers the brightness and contrast of the image, but also integrates the structural information of the image, which makes the evaluation of the cleaning effect more in line with the actual effect perceived by the human eye, so as to accurately identify the areas with incomplete cleaning or abnormalities. In addition, by adding multiple image similarity values to obtain a comprehensive cleaning effect value, the limitation of relying only on a single index can be avoided, ensuring a more comprehensive and accurate evaluation of the cleaning effect.

[0074] In one embodiment, the steps of obtaining the water spraying amount and water spraying pressure of each drone and calculating the water spraying abnormality value according to the water spraying amount and water spraying pressure are as follows:

[0075] Obtain the actual water spraying amount each time the high-pressure water spraying system sprays water during the process of the drone cleaning the photovoltaic panel, calculate the absolute difference between the actual water spraying amount and the set water spraying amount of the drone, and divide the absolute difference by the set number of water sprayings of the drone to obtain the water spraying amount abnormality ratio;

[0076] Obtain the actual water spraying pressure during the actual water spraying process each time the high-pressure water spraying system sprays water during the process of the drone cleaning the photovoltaic panel, calculate the absolute difference between the actual water spraying pressure and the set water spraying pressure of the drone, and divide the absolute difference of the water spraying pressure by the set water spraying pressure of the drone to obtain the water spraying pressure abnormality ratio;

[0077] Calculate the water spraying abnormality value of the drone according to the water spraying amount abnormality ratio and the water spraying pressure abnormality ratio.

[0078] It should be noted that the data of the actual water spraying volume and the actual water spraying pressure can be obtained through the sensors of the high-pressure water spraying system of the drone. These sensors will record the water flow rate and pressure changes in real time during each water spraying; in addition, the set water spraying volume and the set water spraying pressure of the drone are determined by analyzing the images of the photovoltaic panels taken by the drone. Before the cleaning task starts, the drone takes images of the photovoltaic panels through the on-board high-definition camera, and uses image recognition technology to analyze the pollution degree and dirt distribution of the photovoltaic panels; according to the image analysis results combined with the historical cleaning records, the high-pressure water spraying system inside the drone will dynamically adjust the water spraying volume and the water spraying pressure to ensure the best cleaning effect; the specific set water spraying volume and the set water spraying pressure of the drone are determined according to the actual situation and will not be limited and elaborated.

[0079] It should be noted that the calculation formula for the water spraying anomaly value of the drone according to the water spraying volume anomaly ratio and the water spraying pressure anomaly ratio is as follows: In the formula, HY is the water spraying anomaly value, mb and mu are the water spraying volume anomaly ratio and the water spraying pressure anomaly ratio respectively, i is the sequence number of the drone's water spraying times, n is the total number of the drone's water spraying times, a1 and a2 are the preset ratio values of mb and mu respectively, and both a1 and a2 are greater than 0; in addition, a1 and a2 are set by professionals according to the actual situation. Generally, the sum of a1 and a2 is 1. For example, a1 and a2 can be 0.5 and 0.5 respectively, or other numbers, and no specific limitation is made.

[0080] It should be noted that the smaller the water spraying anomaly value of the drone, the greater the possibility of drone failure. If the drone is still used for cleaning the photovoltaic panels, it may cause the drone's failure to deepen further or result in greater cost losses. The reason is that: when the water spraying anomaly value of the drone is larger, it indicates that the difference between the actual water spraying volume and the water spraying pressure and the set value is larger, which may indicate that there is a fault or performance degradation in the high-pressure water spraying system of the drone. For example, if the actual water spraying volume is insufficient, it may lead to incomplete cleaning of the photovoltaic panels, resulting in low cleaning effect of the photovoltaic panels; if the water spraying pressure is too high, it may damage the surface of the photovoltaic panels or cause excessive load on the equipment, thereby increasing the risk of failure. These anomalies may also indicate problems with the sensors or control systems, resulting in the inability to accurately adjust the water spraying parameters, further affecting the cleaning efficiency. Continuing to use the drone in this state for cleaning will not only delay the cleaning progress, cause the dirt on the photovoltaic panels not to be removed in time, and then affect the power generation efficiency of the photovoltaic panels, but also may cause more serious equipment damage. After the equipment is damaged, the cost of repair or replacement will increase significantly, and the overall service life of the drone may be shortened, resulting in greater economic losses. Therefore, timely discovery and repair of these anomalies not only helps to improve the cleaning effect, but also can avoid further damage to the drone and reduce unnecessary cost expenditures.

[0081] In one embodiment, the steps of calculating the fault value of each drone according to the path deviation value, cleaning effect value, and water spraying abnormality value of the drone are as follows:

[0082]

[0083] In the formula, WER is the fault value, DER, DSW, and HY are the path deviation value, cleaning effect value, and water spraying abnormality value respectively, b1, b2, and b3 are the preset proportional values of DER, DSW, and HY respectively, and b1, b2, and b3 are all greater than 0; in addition, b1, b2, and b3 are set by professionals according to the actual situation. Generally, the sum of b1, b2, and b3 is 1. For example, b1, b2, and b3 can be 0.3, 0.3, and 0.4 respectively, or other numbers, and no specific limitation is made.

[0084] In one embodiment, comparing the fault value with the preset fault value threshold and judging whether the drone can perform the remaining cleaning tasks according to the comparison result includes:

[0085] Compare the fault value with the preset fault value threshold. If the fault value is not less than the preset fault value threshold, the corresponding drone cannot perform the remaining cleaning tasks;

[0086] If the fault value is less than the preset fault value threshold, the corresponding drone can perform the remaining cleaning tasks.

[0087] It should be noted that the preset fault value threshold is set by professionals according to the actual situation, and no specific limitation or elaboration is made.

[0088] In one implementation manner, the preset fault value threshold is a critical value set according to the normal working range of the drone and empirical data, and is used to judge whether the drone has a fault risk. When the fault value of the drone is greater than or equal to the preset threshold, it indicates that the path deviation, cleaning effect, or water spraying abnormality degree of the drone exceeds the normal range, and there may be a fault or potential problem. Continuing to perform the cleaning task may cause further damage to the equipment or fail to effectively complete the cleaning task. Therefore, this drone will be judged as unable to perform the remaining cleaning tasks; on the contrary, if the fault value is less than the preset fault value threshold, it means that the working states of the drone are within the normal range, and there is no significant fault or abnormality in the equipment, and it can continue to perform the photovoltaic panel cleaning task; therefore, this drone will be judged as able to perform the remaining cleaning tasks. Through this comparison and judgment, it is possible to effectively prevent work interruption and higher maintenance costs caused by equipment failures, thereby improving the efficiency and safety of the cleaning tasks.

[0089] In one embodiment, when the drone is unable to execute the remaining cleaning tasks, the steps of obtaining the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the remaining drones that can execute the remaining cleaning tasks, and determining whether to dispatch a standby drone for the cleaning operation and maintenance management of the photovoltaic panels are as follows:

[0090] For the drone that is unable to execute the remaining cleaning tasks, obtain the images of each photovoltaic panel after cleaning by the drone, and perform the same preprocessing on both the images of the photovoltaic panels after cleaning and the preset standard images of photovoltaic panel cleaning to obtain the preprocessed images of the photovoltaic panels after cleaning and the preset standard images of photovoltaic panel cleaning;

[0091] Calculate the similarity value between the preprocessed images of the photovoltaic panels after cleaning and the preset standard images of photovoltaic panel cleaning, and compare the similarity value with the preset similarity threshold. If the similarity value is less than the preset minimum similarity threshold, mark the corresponding photovoltaic panel as a photovoltaic panel to be cleaned again;

[0092] Obtain the number of remaining photovoltaic panels to be cleaned corresponding to the drone that is unable to execute the remaining cleaning tasks, the number of photovoltaic panels to be cleaned again, and the number of photovoltaic panels to be cleaned by the remaining drones that can execute the remaining cleaning tasks, and calculate their sum to obtain the total number of photovoltaic panels to be cleaned;

[0093] Obtain the sum of the remaining stored water volumes of all drones that can execute the remaining cleaning tasks to obtain the total stored water volume, and divide the total stored water volume by the total number of photovoltaic panels to be cleaned to obtain the average available water volume;

[0094] Obtain the maximum average available water volume for photovoltaic panel cleaning from the historical cleaning records, and compare the average available water volume with the maximum average available water volume. If the average available water volume is not less than the maximum average available water volume, there is no need to dispatch a standby drone for the cleaning operation and maintenance management of the photovoltaic panels; if it is less, a standby drone needs to be dispatched for the cleaning operation and maintenance management of the photovoltaic panels.

[0095] It should be noted that the preset similarity value threshold is set by professionals according to the actual situation, and specific details are not limited and will not be elaborated here. In addition, the remaining number of photovoltaic panels to be cleaned by the drones that cannot execute the remaining cleaning tasks, the number of photovoltaic panels to be re-cleaned, and the number of photovoltaic panels to be cleaned by the drones that can execute tasks can all be obtained through real-time monitoring by the drone cleaning system. In addition, the stored water volume data of the drones that can execute tasks is obtained through sensors on the drones, and the sensors will record the water usage of each drone in real time, so as to calculate the total stored water volume. The acquisition of the maximum average available water volume in the historical cleaning records depends on the cleaning management system; this system will record the resource consumption, cleaning effect, and task completion status in each cleaning task during the previous photovoltaic panel cleaning process. Based on these historical data, the maximum average available water volume during the photovoltaic panel cleaning process can be calculated to serve as the basis for determining whether to dispatch backup drones.

[0096] In addition, the calculation of the similarity value between the preprocessed image of the cleaned photovoltaic panel and the preset standard image of the photovoltaic panel cleaning has been described in the above embodiments, and specific details are not limited and will not be elaborated here.

[0097] In one implementation method, by conducting a detailed analysis of the drones that cannot execute the remaining cleaning tasks and verifying the cleaning effect in combination with image recognition technology, it is not only possible to accurately evaluate the cleaning status of each photovoltaic panel, but also to promptly discover the photovoltaic panels that need to be re-cleaned, thereby avoiding subsequent problems caused by incomplete cleaning. In addition, calculating the remaining water volume and cleaning burden of all drones that can execute tasks can ensure that each drone continues to work with sustainable water volume, avoiding task interruption or substandard cleaning effects caused by insufficient water volume. By comparing with the maximum average available water volume in the historical data, the system can dynamically evaluate whether to dispatch backup drones, so as to supplement the cleaning force in a timely manner in case of insufficient water volume, ensure the smooth completion of the cleaning task, and avoid delays caused by drone failures or insufficient resources; at the same time, it reduces additional cost losses. This process not only improves the cleaning efficiency, but also ensures the cleaning quality of the photovoltaic panels and the stability of the continuous operation and maintenance management.

[0098] It should be noted that when it is necessary to dispatch spare drones for the cleaning, operation and maintenance management of photovoltaic panels, the specific number of dispatched spare drones can be dispatched based on the difference between the average available water volume and the maximum average available water volume. This difference represents the additional water volume required for the current cleaning task. According to this difference, a certain number of spare drones can be reasonably dispatched to ensure that they carry sufficient water volume. For example, on the premise that the average available water volume is increased to a level not lower than the historical maximum average available water volume, the corresponding minimum number of spare drones can be used as the number of dispatched spare drones. The advantage of doing this is that by reasonably dispatching spare drones according to the difference between the average available water volume and the historical maximum average available water volume, it can be ensured that the cleaning task is completed with sufficient resources. By calculating the required additional water volume and dispatching the minimum number of spare drones, the resource utilization efficiency can be maximized, unnecessary intervention of spare drones can be avoided, and thus unnecessary resource waste can be reduced. This method not only improves the accuracy and flexibility of the cleaning, operation and maintenance management, but also effectively avoids task delays, ensures the smooth progress of the cleaning task, reduces the operation cost at the same time, and improves the overall operation efficiency.

[0099] Based on the same inventive concept, an embodiment of the present invention further provides an operation and maintenance management device for a photovoltaic power generation device. Refer to Figure 2 , Figure 2 which is a framework diagram of an operation and maintenance management device for a photovoltaic power generation device provided by an embodiment of the present invention. The device includes:

[0100] Path deviation module: Obtain the actual flight path and the preset flight path of each drone during the process of cleaning the photovoltaic panel, and calculate the path deviation value, which is used to measure the degree of deviation of the flight path of the drone during the process of cleaning the photovoltaic panel;

[0101] Cleaning effect module: Obtain the pixel values of the photovoltaic panel image after cleaning and the preset photovoltaic panel cleaning standard image, and calculate the cleaning effect value, which is used to measure the cleaning effect of the drone on the photovoltaic panel;

[0102] Water spraying abnormality module: Obtain the water spraying volume and water spraying pressure of each drone, and calculate the water spraying abnormality value according to the water spraying volume and water spraying pressure, which is used to measure the degree of water spraying abnormality of the high-pressure water spraying system carried by the drone;

[0103] Judgment module: Calculate the fault value of each drone according to the path deviation value, cleaning effect value and water spraying abnormality value, compare the fault value with the preset fault value threshold, and judge whether the drone can perform the remaining cleaning tasks according to the comparison result;

[0104] Operation and maintenance management module: When the drone cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the drone, as well as the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and judge whether it is necessary to dispatch spare drones for the cleaning, operation and maintenance management of the photovoltaic panels.

[0105] Based on a photovoltaic power generation device operation and maintenance management device provided by an embodiment of the present invention, through the above method, during the process of cleaning and maintaining photovoltaic panels by drones, if a cleaning failure occurs in a certain drone or the cleaning system of the drone, the cleaning and maintenance management work of the drone can be stopped in a timely manner, reducing the possibility of further deepening the failure of the drone and avoiding greater cost losses; at the same time, it can judge whether to dispatch a new spare drone for photovoltaic panel cleaning management according to the actual situation, reducing the operation and maintenance costs.

[0106] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be artificially used to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A photovoltaic power generation equipment operation and maintenance management method, characterized in that: The following steps are involved: The actual flight path of each UAV during the process of cleaning the photovoltaic panels and the preset flight path are obtained to calculate the path deviation value, which is used to measure the degree of flight path deviation of the UAV during the process of cleaning the photovoltaic panels; Obtain the pixel values ​​of the cleaned photovoltaic panel image and the preset photovoltaic panel cleaning standard image to calculate the cleaning effect value, which is used to measure the cleaning effect of the drone cleaning photovoltaic panel; The water spraying volume and pressure of each UAV are obtained, and the water spraying abnormality value is calculated according to the water spraying volume and pressure, which is used to measure the degree of abnormality of water spraying of the high-pressure water spraying system carried by the UAV; The fault value of each drone is calculated based on the path deviation value, cleaning effect value and water spray abnormality value, and the fault value is compared with the preset fault value threshold, and whether the drone can perform the remaining cleaning task is determined based on the comparison result; When the drone cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the remaining drones that can perform the remaining cleaning tasks, and determine whether it is necessary to dispatch a spare drone to perform the cleaning and maintenance management of the photovoltaic panels.

2. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: The steps for obtaining the actual flight path of each drone during the process of cleaning the photovoltaic panels and calculating the path deviation value from the preset flight path are as follows: For each drone, the number of photovoltaic panels that have been cleaned by the drone is obtained, and the cleaned photovoltaic panels are sorted according to the cleaning order to obtain a set of cleaned photovoltaic panels; For two adjacent photovoltaic panels in the set, the actual flight path of the drone when flying between the two photovoltaic panels is obtained, the actual flight path is overlapped and compared with the preset flight path, the overlapped path length of the two is calculated, and the flight path deviation ratio of the drone between the two photovoltaic panels is calculated according to the preset flight path length. The calculation formula is: Where, SD is the flight path deviation ratio, bn is the preset flight path distance, and bm is the overlapped path distance; The flight path deviation ratio between all two adjacent photovoltaic panels in the set is calculated, and the flight path deviation ratio is compared with the preset minimum flight path deviation ratio. If the flight path deviation ratio is less than the preset minimum flight path deviation ratio, the corresponding flight path deviation ratio is recorded as the abnormal flight path deviation ratio. The total number of abnormal flight path deviation ratios is divided by the total number of flight path deviation ratios to obtain the path deviation value of the corresponding UAV.

3. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: The steps of obtaining the pixel values ​​of the cleaned photovoltaic panel image and the preset photovoltaic panel cleaning standard image to calculate the cleaning effect value are as follows: For each drone, an image of each photovoltaic panel cleaned by each drone is obtained, and the image of each photovoltaic panel cleaned and the preset photovoltaic panel cleaning standard image are subjected to the same preprocessing to obtain a processed photovoltaic panel cleaned image and a preset photovoltaic panel cleaning standard image; The similarity value between the preprocessed photovoltaic panel cleaned image x and the preset photovoltaic panel cleaned standard image y is calculated using the following formula: Where SA(x,y) is the similarity value between the pre-processed cleaned photovoltaic panel image and the preset photovoltaic panel clean standard image, μ x , μ y Respectively represent the mean pixel values ​​of images x and y; σ x 2 and σ y 2 Respectively represent the variance of the pixel values ​​of images x and y; σ xy is the covariance of the pixel values ​​of images x and y; C1 and C2 are two constants used to prevent the denominator from being zero: The similarity values ​​between each processed photovoltaic panel cleaned image and the preset photovoltaic panel cleaning standard image are added to obtain the cleaning effect value of the drone.

4. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: The steps for calculating the water spray abnormal value based on the water spray volume and water spray pressure are as follows: Obtain the actual amount of water sprayed each time the high-pressure water spraying system sprays water when the drone is cleaning the photovoltaic panels, calculate the absolute difference between the actual amount of water sprayed and the amount of water sprayed set by the drone, and divide the absolute difference by the number of water sprays set by the drone to obtain the abnormal ratio of water spraying; The actual water spraying pressure during the actual water spraying process is obtained when the high-pressure water spraying system sprays water each time when the drone is cleaning the photovoltaic panels, and the absolute difference between the actual water spraying pressure and the water spraying pressure set by the drone is calculated, and the absolute difference in the water spraying pressure is divided by the water spraying pressure set by the drone to obtain the water spraying pressure abnormality ratio; The water spray abnormal value of the UAV is calculated based on the water spray volume abnormal ratio and the water spray pressure abnormal ratio.

5. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: The steps for calculating the fault value of each drone based on the drone's path deviation value, cleaning effect value, and water spray anomaly value are as follows: Where WER is the fault value, DER, DSW and HY are the path deviation value, cleaning effect value and water spray abnormality value respectively, b1, b2 and b3 are the preset proportion values ​​of DER, DSW and HY respectively, and b1, b2 and b3 are all greater than 0.

6. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: Judging whether the drone can perform the remaining cleaning tasks based on the comparison results include: The fault value is compared with the preset fault value threshold. If the fault value is not less than the preset fault value threshold, the corresponding drone cannot perform the remaining cleaning tasks. If the fault value is less than the preset fault value threshold, the corresponding drone can perform the remaining cleaning task.

7. A photovoltaic power generation equipment operation and maintenance management method according to claim 1, characterized in that: The steps to obtain the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the remaining drones that can perform the remaining cleaning tasks, and to determine whether it is necessary to dispatch a spare drone to perform the cleaning and maintenance management of the photovoltaic panels are as follows: For the drone that cannot perform the remaining cleaning tasks, obtain the images of each photovoltaic panel cleaned by the drone after cleaning, and perform the same preprocessing on the images of each photovoltaic panel after cleaning and the preset photovoltaic panel cleaning standard images to obtain the preprocessed photovoltaic panel after cleaning images and the preset photovoltaic panel cleaning standard images; Calculate the similarity value between the pre-processed photovoltaic panel cleaned image and the preset photovoltaic panel clean standard image, and compare the similarity value with the preset similarity value threshold. If the similarity value is less than the preset similarity value minimum threshold, the corresponding photovoltaic panel is marked as a re-cleaned photovoltaic panel. Obtain the number of remaining photovoltaic panels to be cleaned, the number of re-cleaned photovoltaic panels, and the number of photovoltaic panels to be cleaned of the remaining drones that can perform the remaining cleaning tasks, and calculate their sum to obtain the total number of photovoltaic panels to be cleaned; The sum of the remaining storage water of all drones that can perform the remaining cleaning tasks is obtained to obtain the total storage water volume, and the total storage water volume is divided by the total number of photovoltaic panels to be cleaned to obtain the average available water volume; The maximum average available water volume for cleaning photovoltaic panels is obtained from historical cleaning records, and the average available water volume is compared with the maximum average available water volume. If the average available water volume is not less than the maximum average available water volume, there is no need to dispatch backup drones for cleaning and maintenance management of photovoltaic panels. If it is less than that, a backup drone will need to be dispatched to carry out cleaning, operation and maintenance management of the photovoltaic panels.

8. A photovoltaic power generation equipment operation and maintenance management device, used to implement a photovoltaic power generation equipment operation and maintenance management method as described in any one of claims 1 to 7, characterized in that: The device comprises: Path deviation module: obtains the actual flight path of each drone during the process of cleaning photovoltaic panels and the preset flight path to calculate the path deviation value, which is used to measure the degree of flight path deviation of the drone during the process of cleaning photovoltaic panels; Cleaning effect module: obtain the cleaned photovoltaic panel image and the pixel value of the preset photovoltaic panel cleaning standard image to calculate the cleaning effect value, which is used to measure the cleaning effect of the drone cleaning photovoltaic panel; Water spray abnormality module: obtains the water spray volume and water spray pressure of each drone, and calculates the water spray abnormality value based on the water spray volume and water spray pressure, which is used to measure the degree of water spray abnormality of the high-pressure water spray system carried by the drone; Judgment module: Calculate the fault value of each drone based on the path deviation value, cleaning effect value and water spray abnormality value, compare the fault value with the preset fault value threshold, and judge whether the drone can perform the remaining cleaning tasks based on the comparison results; Operation and maintenance management module: When the drone cannot perform the remaining cleaning tasks, obtain the number of photovoltaic panels to be cleaned by the drone and the number of photovoltaic panels to be cleaned by the other drones that can perform the remaining cleaning tasks, and determine whether it is necessary to dispatch a spare drone to perform the cleaning and maintenance management of the photovoltaic panels.

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