A cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a drone swarm

Abstract

The present application relates to photovoltaic power generation technology field, specifically disclose a kind of cleaning method of photovoltaic panel in mountain photovoltaic base station based on unmanned aerial vehicle group, comprising: S101 with specified first photovoltaic panel as initial point, if the number of first photovoltaic panel adjacent and same cleaning mode reaches first preset threshold N1, the first photovoltaic panel and the first photovoltaic panel adjacent and same cleaning mode are divided into a region, so as to obtain several regions;S103 calculate the density of the cleaning mode corresponding to the scattered multiple second photovoltaic panels between any specified region and its adjacent region, and according to density and quantity, second photovoltaic panel is divided;S105 is configured with the unmanned aerial vehicle of corresponding cleaning mode for each region;S107 is scanned after completing cleaning, obtains the target to be cleaned twice, and carries out secondary cleaning.The present application is aimed at the characteristics that photovoltaic panel is more scattered in mountain photovoltaic base station, and adjacent photovoltaic panel is integrated into a region by similar "clustering" method with same cleaning mode, and the unmanned aerial vehicle is arranged for cleaning.

Description

[0001] Divisional application

[0002] The present application is a divisional application of the Chinese invention patent application filed on January 24, 2025, with the application number 202510115623.1 and the title "A cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a group of unmanned aerial vehicles". TECHNICAL FIELD

[0003] The present application belongs to the technical field of photovoltaic power generation, and particularly relates to a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a group of unmanned aerial vehicles BACKGROUND

[0004] Photovoltaic panels are composed of a series of solar panels in series and parallel connection. Due to the obstruction of dirt and other stains, the power generation efficiency of photovoltaic panels will be affected, and even photovoltaic hot spots will be generated, damaging the photovoltaic panel components. Nowadays, photovoltaic power stations exist in various forms, such as photovoltaic building integrated type, large-scale photovoltaic power stations in northwest desert areas, etc. The solar panels of these photovoltaic power stations are easily affected by dirt, bird droppings, limescale, oil stains and other stains, resulting in a significant decrease in power generation efficiency and causing economic losses. Therefore, the cleaning of photovoltaic panels is essential.

[0005] The prior art Chinese patent application CN202210411513.6 discloses a photovoltaic cleaning method combined with unmanned aerial vehicles, including the following steps: step one, dividing the photovoltaic power station into several areas, each area is allocated a set of cleaning devices; step two, using unmanned aerial vehicles to collect images of photovoltaic panels, using cloud platforms to identify stains and set cleaning trajectories; step three, using unmanned aerial vehicles to release photovoltaic cleaning robots, the photovoltaic cleaning robots clean along the planned path; step four, the unmanned aerial vehicles finally transport the photovoltaic cleaning robots to the energy storage charging warehouse.

[0006] The above prior art proposes a specific method of cleaning photovoltaic panels by unmanned aerial vehicles. However, the cleaning method is relatively fixed and has certain limitations. Due to the installation angle and environment of solar photovoltaic panels, dust is not uniformly distributed. The fixed cleaning method not only requires high cleaning cost, but also cannot guarantee the cleaning efficiency of solar photovoltaic panels.

[0007] Another prior art Chinese patent application CN202310330164.X discloses a solar photovoltaic panel cleaning operation monitoring and control system, which includes a photovoltaic panel basic information acquisition module, a photovoltaic panel dust monitoring module, a photovoltaic panel contamination monitoring module, a photovoltaic panel cleaning mode analysis module, a photovoltaic panel cleaning rule analysis module, a photovoltaic panel cleaning control scheme generation module and a cleaning operation information library.

[0008] The prior art monitors the dust concentration and dirt information of each solar photovoltaic panel laid in the designated area, analyzes the cleaning demand, cleaning mode and cleaning rule, and then performs cleaning regulation. However, the above-mentioned prior art does not conduct corresponding research on unmanned aerial vehicle photovoltaic panel cleaning, so that the technical field is still weak. SUMMARY

[0009] The purpose of the present application is to provide a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a group of unmanned aerial vehicles, which partially solves or alleviates the above-mentioned deficiencies in the prior art, and can configure corresponding types of unmanned aerial vehicles for cleaning according to the dirt type of the photovoltaic panel area.

[0010] In order to solve the above-mentioned technical problems, the present application specifically adopts the following technical solutions:

[0011] The first aspect of the present application is to provide a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a group of unmanned aerial vehicles, comprising the steps of:

[0012] S101 takes a designated first photovoltaic panel as the initial point, if the number of first photovoltaic panels adjacent to it and having the same cleaning mode reaches a first preset threshold N1, the first photovoltaic panel and the first photovoltaic panels adjacent to it and having the same cleaning mode are divided into one area, thereby obtaining a plurality of areas, and step S103 is executed; the cleaning mode is one of ordinary cleaning mode, strong cleaning mode or mixed cleaning mode;

[0013] S103 calculates the density Pi of the cleaning mode corresponding to the scattered second photovoltaic panels between any designated area and its adjacent area; the density Pi is the percentage of the number ni of second photovoltaic panels of any cleaning mode in the sum of the number of second photovoltaic panels of three cleaning modes and N, wherein i=1, 2, 3;

[0014] If N is greater than or equal to a second preset threshold N2, and the density Pi of any cleaning mode is greater than or equal to 70%, each of the second photovoltaic panels scattered between the designated area and its adjacent area is divided into one area, and step S105 is executed;

[0015] If N is greater than or equal to a second preset threshold N2, and the density of all cleaning modes is less than 70%, each of the second photovoltaic panels is divided into the nearest area with the same cleaning mode according to its cleaning mode, and step S105 is executed;

[0016] If N is less than the second preset threshold N2, all the second photovoltaic panels are divided into the corresponding area according to the cleaning mode with the maximum density among the scattered second photovoltaic panels, and step S105 is executed;

[0017] S105 configures the unmanned aerial vehicle with the corresponding cleaning mode for each region; wherein, for the region with a single cleaning mode, the unmanned aerial vehicle with the corresponding cleaning mode is configured to clean;

[0018] S107 performs region scanning after completing the cleaning, obtains the target for secondary cleaning, and performs secondary cleaning.

[0019] As an improvement, when a region absorbs photovoltaic panels of other cleaning modes, the step S105 of configuring the unmanned aerial vehicle with the corresponding cleaning mode for each region includes:

[0020] S10511 if the density of the first cleaning mode is greater than the first preset density threshold P0, and the sum of the densities of the remaining two cleaning modes is less than the second preset density threshold P0', a plurality of unmanned aerial vehicles with the first cleaning mode are configured, and step S107 is executed; the first cleaning mode is any one of the normal cleaning mode, the strong cleaning mode and the mixed cleaning mode;

[0021] S10512 if the density of the first cleaning mode is greater than the third preset density threshold P0'', and the density of the second cleaning mode is greater than the fourth preset density threshold P0''', a plurality of unmanned aerial vehicles with the first cleaning mode and a plurality of second unmanned aerial vehicles with the second cleaning mode are configured; the first cleaning mode is any one of the normal cleaning mode, the strong cleaning mode and the mixed cleaning mode, and the second cleaning mode is any one of the remaining two cleaning modes other than the first cleaning mode; the third preset density threshold is less than the first preset density threshold, and the fourth preset density threshold is greater than the second preset density threshold, and step S107 is executed;

[0022] S10513 if the difference between the densities of the three cleaning modes is less than the preset difference threshold, a plurality of unmanned aerial vehicles with the normal cleaning mode, a plurality of unmanned aerial vehicles with the strong cleaning mode and a plurality of unmanned aerial vehicles with the mixed cleaning mode are respectively configured, and step S107 is executed.

[0023] As a further improvement, in step S10511, when the first cleaning mode is the normal cleaning mode or the strong cleaning mode, the unmanned aerial vehicle with the corresponding cleaning mode is configured to clean, and the remaining part is to be cleaned secondarily;

[0024] In the case where the first cleaning mode is the mixed cleaning mode, the unmanned aerial vehicle with the mixed cleaning mode is configured to clean the entire region.

[0025] As a further improvement, in step S10512, in the case where the first cleaning mode and the second cleaning mode are both one of the normal cleaning mode or the intensive cleaning mode, the drone with the corresponding cleaning mode is configured to clean; and the remaining part is cleaned by the drone with the normal cleaning mode and the drone with the intensive cleaning mode respectively.

[0026] In the case where the first cleaning mode or the second cleaning mode is the mixed cleaning mode, the drone with the corresponding cleaning mode is configured to clean; and the remaining part is cleaned twice.

[0027] As an improvement, when the plurality of the second photovoltaic panels are divided into the designated areas, step S105 is a step of configuring the drone with the corresponding cleaning mode for each area, including:

[0028] In the case where the cleaning mode of the designated area is the mixed cleaning mode, the drone with the mixed cleaning mode is configured to clean the entire designated area;

[0029] In the case where the cleaning mode of the designated area is the normal cleaning mode or the intensive cleaning mode, the drone with the corresponding cleaning mode is configured to clean, and the remaining part is cleaned twice.

[0030] As an improvement, S111 when the remaining power of any drone reaches a preset power threshold, the to-be-processed task list of the any drone is obtained, and it is judged whether the to-be-processed task list is empty, if not empty, step S113 is executed, if empty, step S115 is executed; the preset power threshold can only support the drone to return to the starting point from the current position;

[0031] S113 obtains the remaining power and the to-be-processed task list of a plurality of other drones near the any drone, and executes step S117; wherein, the other drones within a preset radius area with the any drone as the center are the other drones adjacent to the any drone;

[0032] S115 controls the any drone to return to the starting point from the current position;

[0033] S117 judges whether the other drones can add new cleaning tasks according to the remaining power and the number of to-be-processed tasks in the to-be-processed task list of the other drones near; if can add, step S119 is executed; if cannot add, step S201 is executed;

[0034] S119 selecting at least one other drone from all other drones, which can add a number of cleaning tasks greater than a preset threshold, and evenly distributing the to-be-processed tasks in the to-be-processed task list of the any drone to the selected at least one other drone.

[0035] As an improvement, when at least one other drone is selected, a recommendation index P of each other drone is calculated: P = K1 * D i，j + K2 * T i，j ; wherein D i，j is the distance between the other drone and the any drone, T i，j is the length of time the other drone needs to wait to perform the newly added task; K1 and K2 are empirical constants.

[0036] The to-be-processed tasks in the front row are preferentially distributed to other drones with small recommendation indexes.

[0037] As an improvement, before step S113 is performed, the method further comprises the step of:

[0038] S201 determining whether the number of to-be-processed tasks in the to-be-processed task list of the any drone is greater than or equal to a preset to-be-processed quantity threshold, and if yes, performing step S203; if no, performing step S113.

[0039] S203 reassigning a new drone to the region.

[0040] As an improvement, step S115 specifically comprises the step of:

[0041] S114 determining whether the current task is completed, and if yes, controlling the any drone to return to the departure point; if no, distributing the current task to the nearest other drone.

[0042] As an improvement, the method further comprises the step of:

[0043] S401 calculating a secondary cleaning target quantity of each region, and determining whether the secondary cleaning target quantity is greater than or equal to the amount of to-be-processed tasks that can be added by all drones that can work again in the region, and if yes, performing step S403, otherwise, performing step S405.

[0044] S403 deploying drones that are idle and can work again in adjacent regions.

[0045] S405 evenly distributing each of the secondary cleaning targets in the region to all drones that can work again in the region.

[0046] The present application has the advantages that:

[0047] The present application is directed to the feature that photovoltaic panels are scattered in the mountain photovoltaic base station, and the photovoltaic panels with the same cleaning mode and adjacent or indirectly adjacent are integrated into a region to arrange the unmanned aerial vehicle for cleaning by the method similar to clustering.

[0048] For the scattered photovoltaic panels between regions, the present application is processed according to three situations:

[0049] The scattered photovoltaic panels reaching a certain order of magnitude and having relatively concentrated cleaning mode are integrated into a separate region. For the scattered photovoltaic panels reaching a certain order of magnitude but having relatively dispersed cleaning mode, the surrounding regions are absorbed according to the cleaning mode. For the few scattered photovoltaic panels, they are directly included in a designated region.

[0050] For some composite regions (i.e. regions including multiple cleaning modes), the present application also provides corresponding unmanned aerial vehicle configuration strategies, which can greatly improve the cleaning efficiency and reduce the loss of unmanned aerial vehicles in the journey.

[0051] In addition, the present application can also deploy the unmanned aerial vehicles according to the power of the unmanned aerial vehicles. When the remaining power of a certain unmanned aerial vehicle reaches the power threshold, the unmanned aerial vehicle returns, avoiding the problem that the unmanned aerial vehicle cannot return due to the low remaining power during the work caused by the fact that the photovoltaic base station is relatively vast.

[0052] Meanwhile, the present application also finds a replacement for the returning unmanned aerial vehicle to take over the unfinished task, ensuring the execution of the cleaning task. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference signs. In the drawings, each element or part is not necessarily drawn according to the actual proportion. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0054] Figure 1 The flowchart of the first embodiment of the present application;

[0055] Figure 2 The flowchart of the second embodiment of the present application;

[0056] Figure 3 The flowchart of the third embodiment of the present application. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0058] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0059] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0060] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0061] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0062] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0063] Example 1: As Figure 1 As shown, this embodiment provides a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm. This method targets photovoltaic panels that are relatively scattered, such as those used in recreational photovoltaic base stations on rugged terrain like mountains. Compared to plains like deserts, mountainous areas have more complex terrain, making it unsuitable for installing photovoltaic panels on all terrains. This results in a more scattered installation, making it unsuitable to divide the area into equal zones.

[0064] The embodiment aims to provide a method for preset region division for a mountainous photovoltaic base station under construction, so as to improve the cleaning efficiency of the unmanned aerial vehicle. The specific steps include:

[0065] In step S101, a specified first photovoltaic panel is taken as an initial point. If the number of first photovoltaic panels adjacent to the first photovoltaic panel and having the same cleaning mode reaches a first preset threshold N1, the first photovoltaic panel and the first photovoltaic panels adjacent thereto and having the same cleaning mode are divided into one region, so as to obtain a plurality of regions, and step S103 is performed. The cleaning mode is one of a normal cleaning mode, a strong cleaning mode, or a mixed cleaning mode.

[0066] The dirt on the photovoltaic panel mainly includes dust, water stains, oil stains, and bird droppings. The cleaning of dust and water stains is relatively simple, so the cleaning mode of the unmanned aerial vehicle for cleaning dust and water stains is summarized as a normal mode in the present application. The cleaning of oil stains and bird droppings is relatively complex, so the cleaning mode of the unmanned aerial vehicle for cleaning oil stains and bird droppings is summarized as a strong mode in the present application. The cleaning agent and cleaning tool carried by the unmanned aerial vehicle are different in the two modes, which leads to the fact that the two cleaning modes can only be used for their own type of dirt and cannot cover other dirt. In actual use, the dirt on some photovoltaic panels is not of only one type, so the cleaning mode of the unmanned aerial vehicle in the present application also includes a mixed cleaning mode, which can perform normal cleaning and strong cleaning. It can be predicted that the types of cleaning agents and cleaning tools carried by the unmanned aerial vehicle in the mixed cleaning mode are more, which will greatly reduce the endurance time of the unmanned aerial vehicle, so the mixed cleaning mode cannot be used as a regular mode.

[0067] In this step, the cleaning mode of each photovoltaic panel in the entire region to be cleaned can be obtained in advance, that is, the type of dirt on each photovoltaic panel is matched with the corresponding cleaning mode. The specific method for obtaining the type of dirt can refer to the prior art CN202310330164.X in the background art, which will not be described here.

[0068] The purpose of this step is to divide the preset cleaning region in order to better arrange the cleaning of the unmanned aerial vehicle. In view of the fact that the photovoltaic panels in the mountainous area are relatively scattered (of course, the cleaning modes of the photovoltaic panels in adjacent regions are generally similar), the photovoltaic panels having the same cleaning type and being close to each other need to be “clustered” first, that is, a specified photovoltaic panel is taken as the center, and the same type of photovoltaic panels adjacent thereto (including indirectly adjacent) are divided into the same preset cleaning region. Of course, the number of photovoltaic panels in a cleaning region should not be too small, so the first preset threshold N1 is set in the present embodiment, that is, the same type of photovoltaic panels need to reach the first preset threshold N1 in number to be a preset region. The purpose is to prevent the preset cleaning region from being too scattered.

[0069] S103 calculates the density Pi of the cleaning mode corresponding to the scattered second photovoltaic panels between any designated area and its adjacent area; the density Pi is the percentage of the number ni of the second photovoltaic panels of any cleaning mode in the number of the second photovoltaic panels of the three cleaning modes and N, wherein i=1, 2, 3.

[0070] The density of various cleaning modes can be calculated according to the density of photovoltaic panels of various types of dirt contained. Simply speaking, it is analyzed that there are 80% of ordinary dirt, 15% of strong dirt and 5% of mixed dirt in a certain preset area, and the corresponding cleaning mode densities are P1=80%, P2=15% and P3=5%, and so on.

[0071] The method in step S101 can integrate photovoltaic panels of the same type which are relatively concentrated into a preset cleaning area. After the integration through this step, there will still be some scattered photovoltaic panels (second photovoltaic panels) not included in the preset cleaning area. Therefore, the purpose of this step is to integrate the above scattered photovoltaic panels, which includes three cases:

[0072] 1. independently form a new cleaning area; 2. respectively be absorbed by the surrounding cleaning area; 3. all be absorbed by a certain preset cleaning area.

[0073] First, any designated area is specified in the already divided area, and the scattered photovoltaic panels between the area and its adjacent area are divided.

[0074] Suppose the number of the above scattered photovoltaic panels is N.

[0075] If N is greater than or equal to the second preset threshold N2, and the density Pi of any cleaning mode is greater than or equal to the partition preset density threshold Pd, the scattered second photovoltaic panels between the designated area and its adjacent area are divided into one area, and step S105 is executed.

[0076] That is, the number of scattered photovoltaic panels is greater than or equal to N2 (for example, 500), and the density of any cleaning mode is greater than Pd (70%), so these scattered photovoltaic panels can independently form an area. That is, the scattered photovoltaic panels are relatively concentrated as much as possible under the condition of meeting a certain number requirement.

[0077] If N is greater than or equal to the second preset threshold N2, and the density of all cleaning modes is less than the partition preset density threshold Pd, each second photovoltaic panel is divided into the nearest area with the same cleaning mode according to its cleaning mode, and step S105 is executed.

[0078] In other words, if the number of scattered photovoltaic panels is greater than or equal to N2 (e.g., 500), but the density of all clean types is lower than Pd (70%), then the scattered photovoltaic panels are of mixed types and are not suitable for forming a separate area. In this case, these scattered photovoltaic panels can be integrated into the surrounding area according to their type.

[0079] If N is less than the second preset threshold N2, all the second photovoltaic panels are divided into the designated area, and step S105 is executed.

[0080] In other words, if the number of scattered photovoltaic panels is less than N2 (e.g., 500), it indicates a small scale, and they can be directly included in the designated area. Of course, a better approach is to include them in the corresponding area according to the cleaning mode with the highest density.

[0081] The S105 is equipped with drones that are configured with the appropriate cleaning mode for each area.

[0082] The purpose of this step is to configure the drones according to the areas defined in step S103. It can be understood that for areas with a single cleaning mode, simply configure the drone with the corresponding cleaning mode for cleaning. For some complex areas (i.e., areas including multiple cleaning modes), this step also provides corresponding configuration strategies. The term "complex" mainly refers to the situation where multiple second photovoltaic panels are divided into a single area. Of course, this method can also be applied to other areas that incorporate photovoltaic panels with other cleaning modes.

[0083] Specifically, when multiple second photovoltaic panels are divided into a region, the step S105 of configuring a drone with a corresponding cleaning mode for each region includes:

[0084] S10511 If the density of the first cleaning mode is greater than the first preset density threshold P0, and the sum of the densities of the other two cleaning modes is less than the second preset density threshold P0', configure multiple drones with the first cleaning mode and execute step S107; the first cleaning mode is any one of the normal cleaning mode, the powerful cleaning mode and the mixed cleaning mode.

[0085] The previous step calculated the density of each cleaning mode within a certain area. However, matching drones solely based on density is unreasonable in some situations. For example, if the density of a certain cleaning mode is over 90%, while the combined density of other modes is less than 5% (the remaining 5% does not require cleaning), a drone using a cleaning mode with a density of only 5% may have a round-trip time and time spent hopping within the area that exceeds the cleaning time. This situation is considered uneconomical.

[0086] Therefore, in this step, when the density of a certain cleaning mode (for example, the normal cleaning mode) is greater than the first preset density threshold P0 (for example, 90%), and the sum of the densities of the remaining two cleaning modes is less than the second preset density threshold P0' (5%), only the drone with the normal cleaning mode is configured to go.

[0087] The mode with a density greater than the preset density threshold P0 can be one of the normal cleaning mode, the strong cleaning mode, and the mixed cleaning mode.

[0088] In the case where the first cleaning mode is the normal cleaning mode or the strong cleaning mode, the drone with the normal cleaning mode or the strong cleaning mode is configured to clean the 90% corresponding area, and the remaining 5% will be cleaned by the drone in subsequent secondary cleaning.

[0089] In the case where the first cleaning mode is the mixed cleaning mode, the drone with the mixed cleaning mode is configured to clean the entire area. The drone with the mixed cleaning mode can clean all types of dirt, and since the total amount of normal + strong dirt in the area is also less than 5%, directly configuring the drone with the mixed cleaning mode to clean is more economical and efficient.

[0090] S10512 If the density of the first cleaning mode is greater than a third preset density threshold P0'', and the density of the second cleaning mode is greater than a fourth preset density threshold P0''', a plurality of drones with the first cleaning mode and a plurality of second drones with the second cleaning mode are configured; the first cleaning mode is any one of the normal cleaning mode, the strong cleaning mode, and the mixed cleaning mode, and the second cleaning mode is any one of the remaining two cleaning modes other than the first cleaning mode; the third preset density threshold is less than the first preset density threshold, and the fourth preset density threshold is greater than the second preset density threshold, and step S107 is performed.

[0091] Based on the analysis of the previous step, when the density of a certain cleaning mode in a certain preset area is too small, it is not economical to configure a drone alone. However, when the density of a certain type of dirt reaches a certain threshold, a drone of the corresponding type needs to be configured for cleaning.

[0092] For example, the density of the first cleaning mode in a certain area is greater than the third preset density threshold P0'' (50%), and the density of the second cleaning mode is greater than the fourth preset density threshold P0''' (for example, 40%), so drones with the above two cleaning modes should be configured for targeted cleaning.

[0093] In the case that the first cleaning mode and the second cleaning mode are one of the ordinary cleaning mode or the strong cleaning mode, the corresponding unmanned aerial vehicle is configured to clean. The remaining 10% (of course, it can be less than 10% because some photovoltaic panels do not need to be cleaned) can be cleaned by the unmanned aerial vehicle with the ordinary cleaning mode and the unmanned aerial vehicle with the strong cleaning mode respectively, without the need to separately configure the unmanned aerial vehicle with the mixed cleaning mode to clean.

[0094] In the case that the first cleaning mode or the second cleaning mode is the mixed cleaning mode, the corresponding unmanned aerial vehicle is configured to clean. The remaining 10% (which can be the ordinary cleaning mode or the strong cleaning mode) is cleaned by the corresponding unmanned aerial vehicle in the subsequent secondary cleaning.

[0095] S10513If the difference between the densities of the three cleaning modes is less than the preset difference threshold, the unmanned aerial vehicle with the ordinary cleaning mode, the unmanned aerial vehicle with the strong cleaning mode, and the unmanned aerial vehicle with the mixed cleaning mode are respectively configured, and step S107 is executed.

[0096] It can be predicted that if the densities of the three modes are too small, it is not economical to clean by substitution (i.e., using the unmanned aerial vehicle with the ordinary cleaning mode and the unmanned aerial vehicle with the strong cleaning mode to clean the mixed dirt, or using the unmanned aerial vehicle with the mixed cleaning mode to clean the ordinary dirt or the strong dirt).

[0097] Therefore, in this step, when the difference between the densities of the three cleaning modes is less than the preset difference threshold (for example, 10%), the corresponding unmanned aerial vehicle can be directly configured to clean.

[0098] In addition, when the plurality of second photovoltaic panels are divided into the designated area, the step S105 of configuring the unmanned aerial vehicle with the corresponding cleaning mode for each area includes:

[0099] In the case that the cleaning mode of the designated area is the mixed cleaning mode, the unmanned aerial vehicle with the mixed cleaning mode is configured to clean the entire designated area.

[0100] After the scattered photovoltaic panels (second photovoltaic panels) are divided into the designated area, it is possible that the designated area will have photovoltaic panels with different cleaning modes. Since the number of scattered photovoltaic panels is small, adding them to the area itself is a mixed cleaning area, so the unmanned aerial vehicle with the mixed cleaning mode can be directly used to clean all photovoltaic panels in the entire area.

[0101] In the case that the cleaning mode of the designated area is the ordinary cleaning mode or the strong cleaning mode, the unmanned aerial vehicle with the corresponding cleaning mode is configured to clean, and the remaining part is cleaned in the secondary cleaning.

[0102] If the original area is not a hybrid cleaning mode photovoltaic panel, the corresponding unmanned aerial vehicle is configured according to the original cleaning mode to clean the cleaning mode photovoltaic panel, and the remaining uncleaned photovoltaic panel is cleaned again.

[0103] When the scattered photovoltaic panel is included in the surrounding area according to the self-cleaning mode, it is configured according to the original cleaning mode of the surrounding area.

[0104] S107 completes cleaning and performs area scanning to obtain a secondary cleaning target and performs secondary cleaning.

[0105] After cleaning in the above step, some scattered photovoltaic panels are still not cleaned, so area scanning is needed to obtain the position of the uncleaned photovoltaic panel, and the unmanned aerial vehicle is organized to clean again.

[0106] Embodiment two:

[0107] This embodiment is based on embodiment one, and the power of the unmanned aerial vehicle is allocated, and the specific steps include:

[0108] S111 When the remaining power of any unmanned aerial vehicle reaches a preset power threshold, the to-be-processed task list of the unmanned aerial vehicle is obtained, and it is judged whether the to-be-processed task list is empty. If not, step S113 is executed, and if empty, step S115 is executed; the preset power threshold can only support the unmanned aerial vehicle to return to the starting point from the current position.

[0109] Generally, photovoltaic stations are relatively vast, and cleaning work consumes a lot of power of the unmanned aerial vehicle, so the power of the unmanned aerial vehicle needs to be monitored at all times to avoid failure to return.

[0110] In this step, the remaining power of the unmanned aerial vehicle is monitored in real time, and once the remaining power reaches the power threshold, the unmanned aerial vehicle should return.

[0111] When the unmanned aerial vehicle reaches the preset power, it may still be executing a task or have a task to be executed. In the above case, a task successor needs to be found.

[0112] S113 obtains the remaining power and to-be-processed task list of a plurality of other unmanned aerial vehicles near the any unmanned aerial vehicle, and executes step S117; wherein the other unmanned aerial vehicles within a preset radius region with the any unmanned aerial vehicle as the center are other unmanned aerial vehicles adjacent to the any unmanned aerial vehicle.

[0113] When a certain unmanned aerial vehicle that is still executing a task or has a task to be executed reaches the preset power, a task successor needs to be found. Therefore, the current position of the unmanned aerial vehicle is taken as the center and a preset radius is searched to find adjacent unmanned aerial vehicles, and the remaining power and to-be-processed task list of the adjacent unmanned aerial vehicles are obtained.

[0114] S115 controls the any drone to return to the starting point from the current position.

[0115] Of course, it can be foreseen that in this step, it is also needed to judge whether the current task is completed before returning, if yes, control the drone to return to the starting point; if not, assign the current task to the nearest other drone.

[0116] S117 judges whether the other drone can add a new cleaning task according to the remaining power of the other drone nearby and the number of tasks to be processed in the task list; if yes, execute step S119; if not, execute step S201.

[0117] When looking for a task successor, it is needed to judge whether the drone has enough power to support the subsequent task and the newly added task, so as to judge whether the drone is suitable for adding a new task. It is easy to understand that the drone with sufficient power and less tasks is preferred.

[0118] S119 selects at least one other drone from all other drones which can add a number of cleaning tasks greater than a preset threshold, and evenly distributes the tasks to be processed in the task list of the any drone to the selected at least one other drone.

[0119] In addition to judging whether the drone can add a new task, the distance and waiting time of the two are also needed to be considered. Therefore, in this embodiment, when at least one other drone is selected, the recommendation index of each other drone is calculated: P = K1 * D i，j + K2 * T i，j ; wherein, D i，j is the distance between the other drone and the any drone, T i，j is the waiting time required by the other drone to execute the newly added task; K1 and K2 are empirical constants.

[0120] The tasks to be processed in the front are preferentially assigned to the other drone with a small recommendation index.

[0121] S201 judges whether the number of tasks to be processed in the task list of the any drone is greater than or equal to a preset threshold of tasks to be processed, if yes, execute step S203; if not, execute step S113.

[0122] The number of tasks to be processed in the task list of a certain drone is greater than or equal to the preset threshold of tasks to be processed, which means that the task of the drone has been saturated and cannot take new tasks. When all drones are saturated, a new drone needs to be added.

[0123] S203 reassign a new drone to the area.

[0124] Embodiment Three:

[0125] This embodiment is based on the drone deployment method of Embodiment One and Embodiment Two. In the second cleaning, appropriate drones are selected for cleaning, thereby improving efficiency. The specific steps include:

[0126] S401 calculate the second cleaning target quantity of each area, and determine whether the second cleaning target quantity is greater than or equal to the addable task quantity of all drones that can work again in the area. If yes, execute step S403, otherwise execute step S405.

[0127] In the second cleaning, the remaining uncleaned areas after the first cleaning are integrated and then re-divided. The division rule is still according to the dirt type.

[0128] After the second division of the area, there may be drones in the area that are still performing the last cleaning task. Therefore, it can be determined first whether the drones in the area can meet the current cleaning.

[0129] S403 deploy idle drones that can work again in adjacent areas.

[0130] In the case that the drones in the area cannot meet the second cleaning of the area, drones that can still perform tasks are deployed from adjacent areas to clean. It can be foreseen that if the task lists of the drones in the adjacent areas are all saturated, the drones need to be reconfigured.

[0131] The adjacent area can be an area bordering the target area, or an area with a distance less than a distance threshold from the target area.

[0132] S405 evenly distribute each second cleaning target in the area to all drones that can work again in the area.

[0133] In the case that the drones in the area can already meet the demand, the second cleaning task can be evenly distributed.

[0134] It should be noted that, in this document, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0135] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, also can be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a plurality of instructions to make a computer terminal (may be mobile phone, computer, server, or network equipment, etc.) execute the method described in various embodiments of the present application.

[0136] The embodiments of the present application are described above in conjunction with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative, not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims.

Claims

1. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, characterized in that, Including the following steps: S101. Taking the designated first photovoltaic panel as the initial point, if the number of first photovoltaic panels adjacent to it and with the same cleaning mode reaches the first preset threshold N1, then the first photovoltaic panel and the first photovoltaic panels adjacent to it and with the same cleaning mode are divided into a region, thereby obtaining several regions, and step S103 is executed; the cleaning mode is one of the normal cleaning mode, the powerful cleaning mode, or the mixed cleaning mode. S103 calculates the density Pi of the cleaning mode corresponding to multiple scattered second photovoltaic panels in any specified area and its adjacent areas; the density Pi is the percentage of the number ni of the second photovoltaic panels in any cleaning mode to the total number N of the second photovoltaic panels in the three cleaning modes, where i=1,2,3; If N is greater than or equal to the second preset threshold N2, and the density Pi of any cleaning mode is greater than or equal to 70%, the scattered second photovoltaic panels between the designated area and its adjacent areas are divided into one area, and step S105 is executed. If N is greater than or equal to the second preset threshold N2, and the density of all cleaning modes is less than 70%, then each second photovoltaic panel is assigned to the nearest area with the same cleaning mode according to its cleaning mode, and step S105 is executed. If N is less than the second preset threshold N2, the cleaning mode with the highest density among the scattered second photovoltaic panels is used as the standard, and all the second photovoltaic panels are divided into the corresponding areas, and step S105 is executed. The S105 is equipped with drones that are configured with the appropriate cleaning mode for each area; After S107 completes cleaning, it performs area scanning to obtain the target to be cleaned a second time, and then performs a second cleaning. In step S105, for areas with a single cleaning mode, a drone with the corresponding cleaning mode is configured for cleaning; while for complex areas, the step of configuring a drone with the corresponding cleaning mode for the complex area includes: S10511 If the density of the first cleaning mode is greater than the first preset density threshold P0, and the sum of the densities of the other two cleaning modes is less than the second preset density threshold P0', configure multiple drones with the first cleaning mode and execute step S107; the first cleaning mode is any one of the normal cleaning mode, the powerful cleaning mode and the mixed cleaning mode. S10512 If the density of the first cleaning mode is greater than the third preset density threshold P0'', and the density of the second cleaning mode is greater than the fourth preset density threshold P0''', configure multiple drones with the first cleaning mode and multiple second drones with the second cleaning mode; the first cleaning mode is any one of the normal cleaning mode, the powerful cleaning mode, and the mixed cleaning mode, and the second cleaning mode is any one of the other two cleaning modes besides the first cleaning mode; the third preset density threshold is less than the first preset density threshold, and the fourth preset density threshold is greater than the second preset density threshold, then execute step S107; S10513 If the difference between the densities of the three cleaning modes is less than the preset difference threshold, configure multiple drones with normal cleaning mode, multiple drones with strong cleaning mode and multiple drones with mixed cleaning mode respectively, and execute step S107.

2. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 1, characterized in that: In step S10511, when the first cleaning mode is normal cleaning mode or strong cleaning mode, a drone with the corresponding cleaning mode is configured to perform cleaning, and the remaining part is to be cleaned a second time. When the first cleaning mode is the mixed cleaning mode, a drone with the mixed cleaning mode is configured to clean the entire area.

3. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 1, characterized in that: In step S10512, if both the first cleaning mode and the second cleaning mode are one of the normal cleaning mode or the powerful cleaning mode, a drone with the corresponding cleaning mode is configured to perform cleaning; and the remaining part is cleaned by a drone with the normal cleaning mode and a drone with the powerful cleaning mode respectively. When the first cleaning mode or the second cleaning mode is a mixed cleaning mode, a drone with the corresponding cleaning mode is configured to perform cleaning; and the remaining parts are to be cleaned a second time.

4. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 1, characterized in that, It also includes the following steps: S111 When the remaining power of any drone reaches the preset power threshold, obtain the list of tasks to be processed for any drone, and determine whether the list of tasks to be processed is empty. If it is not empty, execute step S201; if it is empty, execute step S115. The preset power threshold can only support the drone to return from its current location to its starting point; S201 Determine whether the number of pending tasks in the pending task list of any UAV is greater than or equal to a preset pending task threshold. If it is greater than or equal to the preset pending task threshold, execute step S203; if it is less than the preset pending task threshold, execute step S113. S203 re-deploys a new drone to the area; S113 obtains the remaining battery power and pending task list of multiple other drones near any drone, and executes step S117; wherein, other drones within a preset radius area centered on the current location of any drone are other drones adjacent to any drone. S115 controls any of the drones to return from its current location to its starting point; S117 Determines whether the other drones can add a new cleaning task based on their remaining battery power and the number of tasks in the task list; if they can add a task, proceed to step S119; if they cannot add a task, proceed to step S201. S119 Select at least one other drone from all other drones whose number of cleaning tasks that can be added is greater than a preset threshold, and evenly distribute the tasks to be processed in the task list of any drone to the selected at least one other drone.

5. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, as described in claim 4, characterized in that, When at least one other drone is selected, calculate the recommendation index for each other drone: P = K1 * D i，j +K2*T i，j ; where D i，j T represents the distance between the other drones and any one of the drones. i，j The waiting time required for the other drones to perform newly added tasks; K1 and K2 are empirical constants; Among the pending tasks, those ranked higher are preferentially assigned to other drones with lower recommendation indices.

6. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, as described in claim 4, characterized in that, Step S115 specifically includes the following steps: S114 determines whether the current task is completed. If it is completed, it controls any of the drones to return to the starting point; if it is not completed, it assigns the current task to the nearest other drone.

7. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, as described in any one of claims 1 to 6, characterized in that, It also includes the following steps: S401 Calculate the number of secondary cleaning targets in each area and determine whether the number of secondary cleaning targets is greater than or equal to the number of additional pending tasks of all drones that can work again in the area. If yes, proceed to step S403; otherwise, proceed to step S405. S403 allocates idle and operational drones within adjacent areas; S405 evenly distributes each of the secondary cleaning targets within the area to all drones within the area that can be used again.

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