Cleaning method for photovoltaic panel in mountain photovoltaic base station based on unmanned aerial vehicle group

Abstract

The invention relates to the technical field of photovoltaic power generation, and particularly discloses a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on an unmanned aerial vehicle group, which comprises the following steps: S101, taking a specified first photovoltaic panel as an initial point, and if the number of adjacent first photovoltaic panels with the same cleaning mode reaches a first preset threshold value N1, judging whether the first photovoltaic panel is adjacent to the specified first photovoltaic panel or not; if yes, dividing the first photovoltaic panel and the adjacent first photovoltaic panel with the same cleaning mode as the first photovoltaic panel into a region, so as to obtain a plurality of regions; s103, calculating the density of the cleaning modes corresponding to a plurality of scattered second photovoltaic panels between any specified area and the adjacent area, and dividing the second photovoltaic panels according to the density and the number; s105, configuring an unmanned aerial vehicle in a corresponding cleaning mode for each area; and S107, after cleaning is completed, area scanning is carried out, a target to be cleaned for the second time is obtained, and secondary cleaning is carried out. According to the characteristic that photovoltaic panels in a mountain photovoltaic base station are distributed in a scattered mode, adjacent photovoltaic panels with the same cleaning mode are integrated into one area through a similar clustering method, and an unmanned aerial vehicle is configured for overall cleaning.

Description

Technical Field

[0001] The invention belongs to the field of photovoltaic power generation technology, and in particular relates to a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm. Background Art Photovoltaic panels are composed of a series of solar panels connected in series and parallel. Due to the obstruction of dust and other stains, the power generation efficiency of photovoltaic panels will be affected, and even photovoltaic hot spots will be generated, damaging photovoltaic panel components. Nowadays, photovoltaic power stations exist in various forms, such as photovoltaic building integrated type, large photovoltaic power stations in the northwest desert area, etc. The solar panels of these photovoltaic power stations are easily affected by dust, bird droppings, scale, oil stains and other stains, which greatly reduce the power generation efficiency and cause economic losses. Therefore, cleaning of photovoltaic panels is essential.

[0002] Prior art Chinese patent application CN202210411513.6 discloses a photovoltaic cleaning method combined with a drone, comprising the following steps: Step 1, dividing the photovoltaic power station into several areas, each area is assigned a set of cleaning devices; Step 2, using the drone to capture images of the photovoltaic panels, using the cloud platform to identify stains and set the cleaning trajectory; Step 3, using the drone to deploy the photovoltaic cleaning robot, and the photovoltaic cleaning robot cleans along the planned path; Step 4, the drone finally transports the photovoltaic cleaning robot to the energy storage charging warehouse.

[0003] The above-mentioned prior art proposes a specific method of cleaning photovoltaic panels by drones. However, the cleaning method is relatively fixed and has certain limitations. Due to the installation angle and environmental issues of solar photovoltaic panels, dust is not evenly distributed. The fixed cleaning method not only consumes a lot of cleaning costs, but also cannot guarantee the cleaning efficiency of solar photovoltaic panels.

[0004] 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; The above-mentioned prior art monitors the dust concentration and dirt information of each solar photovoltaic panel laid in a specified area, thereby analyzing the cleaning needs, cleaning mode and cleaning rules, and then performing cleaning control. However, the above-mentioned prior art does not conduct corresponding research on drone photovoltaic panel cleaning, making this technical field still relatively weak. Summary of the invention

[0005] The purpose of the present invention is to provide a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, which partially solves or alleviates the above-mentioned deficiencies in the prior art and can configure corresponding types of drones for cleaning according to the type of contamination in the photovoltaic panel area.

[0006] In order to solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions: The first aspect of the present invention is to provide a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, comprising the steps of: S101 takes the designated first photovoltaic panel as the initial point, and if the number of first photovoltaic panels adjacent to the designated first photovoltaic panel and having the same cleaning mode reaches a first preset threshold value N1, the first photovoltaic panel and the first photovoltaic panels adjacent to the designated first photovoltaic panel and having the same cleaning mode are divided into one area, thereby obtaining a plurality of areas, and executing step S103; the cleaning mode is one of a normal cleaning mode, a strong cleaning mode or a mixed cleaning mode; S103 calculates the density Pi of the cleaning modes corresponding to the plurality of scattered second photovoltaic panels between any designated area and its adjacent areas; the density Pi=the number ni of the second photovoltaic panels in any cleaning mode / the sum of the numbers of the second photovoltaic panels in the three cleaning modes N, wherein i=1, 2, 3; If N is greater than or equal to a second preset threshold value N2, and the density Pi of any cleaning mode is greater than or equal to a partition preset density threshold value Pd, each of the second photovoltaic panels scattered between the designated area and its adjacent areas is 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 the partition preset density threshold Pd, each of the second photovoltaic panels is divided into the closest 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, all the second photovoltaic panels are divided into the designated area, and step S105 is executed; S105 configures drones with corresponding cleaning modes for each area; After the cleaning is completed, S107 performs a regional scan to obtain the target to be cleaned a second time, and performs a second cleaning.

[0007] As an improvement, when the plurality of second photovoltaic panels are divided into one area, the step of configuring a drone with a corresponding cleaning mode for each area in step S105 includes: S10511 If the density of the first cleaning mode is greater than the first preset density threshold value P0, and the sum of the densities of the other two cleaning modes is less than the second preset density threshold value P0', configure multiple drones with the first cleaning mode and execute step S107; the first cleaning mode is any one of the ordinary cleaning mode, the strong cleaning mode and the mixed cleaning mode; S10512: If the density of the first cleaning mode is greater than the third preset density threshold value P0'', and the density of the second cleaning mode is greater than the fourth preset density threshold value P0''', configure a plurality of drones with the first cleaning mode, and a plurality of second drones with the second cleaning mode; the first cleaning mode is any one of the ordinary 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 value is less than the first preset density threshold value, and the fourth preset density threshold value is greater than the second preset density threshold value, and execute step S107; S10513 If the difference between the densities of the three cleaning modes is less than the preset difference threshold, multiple drones with ordinary cleaning mode, multiple drones with strong cleaning mode and multiple drones with mixed cleaning mode are configured respectively, and step S107 is executed.

[0008] As a further improvement, in step S10511, when the first cleaning mode is the normal cleaning mode or the 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, the drone with the mixed cleaning mode is configured to clean the entire area.

[0009] As a further improvement, in step S10512, when the first cleaning mode and the second cleaning mode are both one of the ordinary cleaning mode and the strong 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 ordinary cleaning mode and a drone with the strong cleaning mode respectively; When the first cleaning mode or the second cleaning mode is a mixed cleaning mode, a drone with a corresponding cleaning mode is configured to perform cleaning; and the remaining part is to be cleaned a second time.

[0010] As an improvement, when a plurality of the second photovoltaic panels are divided into the designated areas, the step S105 of configuring a drone with a corresponding cleaning mode for each area includes: When the cleaning mode of the designated area is a mixed cleaning mode, a drone with the mixed cleaning mode is configured to clean the entire designated area; When the cleaning mode of the designated area is the normal cleaning mode or the 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.

[0011] As an improvement, when the remaining power of any UAV reaches a preset power threshold in S111, a list of pending tasks of any UAV is obtained, and it is determined whether the list of pending tasks is empty. If it is not empty, step S113 is executed; if it is empty, step S115 is executed; the preset power threshold can only support the UAV to return from the current position to the starting point; S113 obtains the remaining power and pending task lists of multiple other drones near the any drone, and executes step S117; wherein, with the current location of the any drone as the center, the other drones within the preset radius are other drones adjacent to the any drone; S115 controls any one of the drones to return from the current position to the starting point; S117 determines whether the other drones nearby can add new cleaning tasks according to the remaining power of the other drones and the number of pending tasks in the pending task list; if they can be added, execute step S119; if they cannot be added, execute step S201; S119: selecting 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 distributing the to-be-processed tasks in the to-be-processed task list of any of the drones to the selected at least one other drone.

[0012] As an improvement, when at least one other drone is screened out, the recommendation index of each other drone is calculated: P=K1*D i，j +K2*T i，j Among them, D i，j is the distance between the other UAV and any UAV, T i，j The waiting time for the other drones to execute the newly added task; K1 and K2 are empirical constants; Among the tasks to be processed, the tasks that are ranked higher in the queue are preferentially assigned to other drones with smaller recommendation indexes.

[0013] As an improvement, before executing step S113, the following steps are further included: S201 determines whether the number of tasks to be processed in the to-be-processed task list of any UAV is greater than or equal to a preset to-be-processed number threshold, if it is greater than or equal to the preset to-be-processed threshold, executes step S203; if it is less than the preset to-be-processed threshold, executes step S113; S203 dispatches a new drone to the area.

[0014] As an improvement, step S115 specifically includes the following steps: S114 determines whether the current task is completed. If it is completed, control any one of the drones to return to the starting point; if it is not completed, assign the current task to the nearest other drone.

[0015] As an improvement, the steps are also included: S401 calculates the number of secondary cleaning targets in each area, and determines whether the number of secondary cleaning targets is greater than or equal to the amount of tasks that can be added to be processed by all drones that can work again in the area. If so, execute step S403; otherwise, execute step S405; S403 deploys idle and reusable drones in adjacent areas; S405 evenly distributes the secondary cleaning targets in the area to all drones in the area that can work again.

[0016] The present invention is beneficial in that: In view of the scattered distribution of photovoltaic panels in mountain photovoltaic base stations, the present invention integrates adjacent or indirectly adjacent photovoltaic panels with the same cleaning mode into one area through a method similar to "clustering" and coordinates the deployment of drones for cleaning.

[0017] For scattered photovoltaic panels between regions, the present invention handles them separately according to three situations: Scattered photovoltaic panels that have reached a certain level and have relatively concentrated cleaning patterns will be integrated into a single area. For scattered photovoltaic panels that have reached a certain level but have relatively scattered cleaning patterns, they will be absorbed by the surrounding areas according to the cleaning pattern. For scattered photovoltaic panels with a small number, they will be directly included in a designated area.

[0018] For some complex areas (i.e., areas including multiple cleaning modes), the present invention also provides corresponding drone configuration strategies, which can greatly improve the cleaning efficiency and reduce the loss of drones on the road.

[0019] In addition, the present invention can also adjust the drones according to their power. When the remaining power of a drone reaches a power threshold, the drone is allowed to return, thereby avoiding the problem that the drone cannot return due to low remaining power during work due to the large distance between photovoltaic base stations.

[0020] At the same time, the present invention also searches for a successor for the returning drone to take over its unfinished task and ensure the execution of the cleaning task. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale. Obviously, the drawings described below are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without paying creative labor.

[0022] Figure 1 This is a flow chart of Embodiment 1 of the present invention; Figure 2 This is a flow chart of Embodiment 2 of the present invention; Figure 3 This is a flow chart of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] Herein, suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings by themselves. Therefore, "module", "component" or "unit" can be used mixedly.

[0025] In this document, the terms "upper", "lower", "inner", "outer", "front", "back", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0026] In this document, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" 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, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Herein "and / or" includes any and all combinations of one or more of the associated listed items.

[0028] Herein, "plurality" means two or more than two, ie, it includes two, three, four, five, etc.

[0029] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for cleaning photovoltaic panels in mountain photovoltaic base stations based on drone swarms, which is aimed at cleaning photovoltaic panels that are more dispersed, such as photovoltaic base stations installed on relatively rugged terrain such as mountains. Compared with plain areas such as deserts, mountainous areas have more complex terrain, so photovoltaic panels are not suitable for all terrains, making the installation of photovoltaic panels more scattered, and it is not suitable to directly divide the areas by the same area.

[0030] This embodiment aims to provide a method for pre-setting area division for photovoltaic base stations under construction in mountainous areas, so as to improve the cleaning efficiency of drones. The specific steps include: S101 takes the designated first photovoltaic panel as the starting point. If the number of first photovoltaic panels adjacent to it and having the same cleaning mode reaches a first preset threshold value N1, the first photovoltaic panel and the first photovoltaic panels adjacent to it and having the same cleaning mode are divided into an area, thereby obtaining several areas, and executing step S103; the cleaning mode is one of the normal cleaning mode, the strong cleaning mode or the mixed cleaning mode.

[0031] The dirt on photovoltaic panels mainly includes dust, scale, oil, bird droppings, etc. Among them, the cleaning of dust and scale is relatively simple, so in the present invention, the cleaning mode of the drone for cleaning dust and scale is summarized as the normal mode. The cleaning of oil and bird droppings is more complicated, so in the present invention, the cleaning mode of the drone for cleaning oil and bird droppings is summarized as the strong mode. The cleaning agents and cleaning tools carried by the drone in the two modes are different, resulting in that both cleaning modes can only target their own types of dirt and cannot cover other dirt. In practice, the dirt on some photovoltaic panels is not just one type, so the cleaning mode of the drone in the present invention also includes a mixed cleaning mode, which can perform both ordinary cleaning and strong cleaning. Of course, it is foreseeable that the drone carries more types of cleaning agents and cleaning tools in the mixed cleaning mode, which will greatly reduce the drone's flight time, so it cannot be used as a regular mode.

[0032] In this step, the cleaning mode of each photovoltaic panel in the entire area to be cleaned can be obtained in advance, that is, the corresponding cleaning mode is matched by the type of dirt on each photovoltaic panel. The specific method of obtaining the dirt type can refer to the prior art CN202310330164.X in the background technology, which will not be repeated here.

[0033] The purpose of this step is to divide the preset cleaning areas in order to better coordinate the arrangement of drones for cleaning. In view of the scattered distribution of photovoltaic panels in mountainous areas (of course, generally speaking, the cleaning modes of photovoltaic panels in adjacent areas are roughly the same), it is first necessary to "cluster" photovoltaic panels with the same cleaning type and close distances. Specifically, a certain photovoltaic panel is taken as the center, and the photovoltaic panels of the same type adjacent to it (including indirectly adjacent) are divided into the same preset cleaning area. Of course, as a cleaning area, the number of photovoltaic panels should not be too small. Therefore, a first preset threshold value N1 is set in this embodiment, that is, the number of photovoltaic panels of the same type that reaches the first preset threshold value N1 can be used as a preset area. The purpose is to prevent the preset cleaning areas from being too scattered.

[0034] S103 calculates the density Pi of the cleaning modes corresponding to the scattered multiple second photovoltaic panels between any designated area and its adjacent areas; the density Pi=the number ni of the second photovoltaic panels in any cleaning mode / the sum of the numbers of the second photovoltaic panels in the three cleaning modes N, where i=1,2,3.

[0035] The density of various cleaning modes can be calculated based on the density of photovoltaic panels containing various types of pollution. Simply put, if it is analyzed that there are 80% ordinary pollution, 15% strong pollution and 10% mixed pollution in a preset area, the density of the corresponding cleaning mode is P1=80%, P2=15%, P3=5%, and so on.

[0036] The method in step S101 can integrate the relatively concentrated photovoltaic panels of the same type into a preset cleaning area. After the integration in this step, there will still be some scattered photovoltaic panels (second photovoltaic panels) that are not included in the preset cleaning area. Therefore, the purpose of this step is to integrate the above-mentioned scattered photovoltaic panels, which includes three situations: 1. Become an independent new cleaning area; 2. Be absorbed by the surrounding cleaning areas respectively; 3. Be completely absorbed by a preset cleaning area.

[0037] First, an area is randomly designated in the divided areas, and the scattered photovoltaic panels between the area and its adjacent areas are divided.

[0038] Assume that the number of the above scattered photovoltaic panels is N.

[0039] 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, each of the second photovoltaic panels scattered between the designated area and its adjacent areas is divided into one area, and step S105 is executed.

[0040] That is to say, if 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%), then these scattered photovoltaic panels can be separated into an area. In other words, the types of scattered photovoltaic panels should be relatively concentrated while meeting certain quantity requirements.

[0041] 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 of the second photovoltaic panels is divided into the area closest to it and with the same cleaning mode according to its cleaning mode, and step S105 is executed.

[0042] That is to say, if the number of scattered photovoltaic panels is greater than or equal to N2 (for example, 500), but the density of all clean types is lower than Pd (70%), then the types of scattered photovoltaic panels are relatively messy and are not suitable for forming a separate area. In this case, these scattered photovoltaic panels can be integrated into the surrounding areas according to their types.

[0043] 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.

[0044] That is to say, if the number of scattered photovoltaic panels is greater than N2 (for example, 500), it means that the scattered photovoltaic panels are small in magnitude and can be directly included in the designated area. Of course, a better method is to include the corresponding area according to the cleaning mode with the highest density.

[0045] S105 configures drones with corresponding cleaning modes for each area.

[0046] The purpose of this step is to configure the drone according to the area divided in step S103. It can be understood that for an area with a single cleaning mode, it is sufficient to directly configure a drone with the corresponding cleaning mode for cleaning. For some composite areas (i.e., areas including multiple cleaning modes), this step also provides corresponding configuration strategies. The rest of the composite type 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.

[0047] Specifically, when the plurality of second photovoltaic panels are divided into one area, the step of configuring a drone with a corresponding cleaning mode for each area in step S105 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 strong cleaning mode and the mixed cleaning mode.

[0048] In the previous step, the density of each cleaning mode in a certain area was calculated. However, if the drones are matched completely according to the density, it is unreasonable in some cases. For example, when the density of a cleaning mode is as high as 90% or more, and the density of other modes combined does not exceed 5% (the remaining 5% does not need to be cleaned), for a drone with a cleaning mode density of only 5%, the round-trip time and the time to jump within the area may be longer than the cleaning time, which is considered uneconomical.

[0049] Therefore, in this step, when the density of a certain cleaning mode (such as the normal cleaning mode) is greater than the first preset density threshold P0 (such as 90%), and the sum of the densities of the other two cleaning modes is less than the second preset density threshold P0' (5%), then only the drone with the normal cleaning mode is configured to go there.

[0050] The mode with a density greater than the preset density threshold P0 may be one of the normal cleaning mode, the strong cleaning mode and the mixed cleaning mode.

[0051] When the first cleaning mode is the normal cleaning mode or the strong cleaning mode, a drone with the normal cleaning mode or the strong cleaning mode is configured to clean 90% of the corresponding area, and the remaining 5% will be cleaned by a drone in the subsequent secondary cleaning.

[0052] When the first cleaning mode is the mixed cleaning mode, a 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. Since the total amount of ordinary + strong dirt in the area is also less than 5%, it is more economical and efficient to directly configure the drone with the mixed cleaning mode for cleaning.

[0053] 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 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 execute step S107.

[0054] Based on the analysis in the previous step, if the density of a certain cleaning mode in a preset area is too small, it is not economical to deploy a drone alone. However, when the density of a certain type of dirt reaches a certain threshold, it is necessary to deploy a drone of the corresponding type for cleaning.

[0055] For example, if the density of the first cleaning mode in a certain area is greater than the third preset density threshold value P0'' (50%), and the density of the second cleaning mode is greater than the fourth preset density threshold value P0''' (for example, 40%), then a drone with the above two cleaning modes should be configured for targeted cleaning.

[0056] In the case where the first cleaning mode and the second cleaning mode are both one of the normal cleaning mode and the strong cleaning mode, the corresponding drone is configured for cleaning. The remaining 10% (of course, it may be less than 10% because some photovoltaic panels do not need to be cleaned) can be cleaned by drones with normal cleaning mode and drones with strong cleaning mode respectively, without the need to configure a drone with mixed cleaning mode for cleaning.

[0057] When the first cleaning mode or the second cleaning mode is a mixed cleaning mode, the corresponding drone is configured for cleaning. The remaining 10% (which may be a normal cleaning mode or a strong cleaning mode) can be cleaned by configuring the corresponding drone in the subsequent secondary cleaning.

[0058] S10513 If the difference between the densities of the three cleaning modes is less than the preset difference threshold, multiple drones with ordinary cleaning mode, multiple drones with strong cleaning mode and multiple drones with mixed cleaning mode are configured respectively, and step S107 is executed.

[0059] It is foreseeable that if the density difference between the three modes is too small, it will be uneconomical to use alternative cleaning methods (i.e., using a drone with a normal cleaning mode and a drone with a strong cleaning mode to clean mixed dirt, or using a drone with a compound cleaning mode to clean normal dirt or strong dirt).

[0060] Therefore, in this step, when the difference between the densities of the three cleaning modes is less than a preset difference threshold (for example, 10%), the corresponding drone is directly configured for cleaning.

[0061] In addition, when a plurality of the second photovoltaic panels are divided into the designated areas, the step S105 of configuring a drone with a corresponding cleaning mode for each area includes: When the cleaning mode of the designated area is a mixed cleaning mode, a drone with the mixed cleaning mode is configured to clean the entire designated area.

[0062] After scattered photovoltaic panels (second photovoltaic panels) are divided into designated areas, they may bring photovoltaic panels with different cleaning modes to the designated area. Since the number of scattered photovoltaic panels is small, adding this area itself is a mixed cleaning area, so all photovoltaic panels in the entire area can be cleaned directly by drones with mixed cleaning mode.

[0063] When the cleaning mode of the designated area is the normal cleaning mode or the 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.

[0064] If the original area is not a photovoltaic panel in the mixed cleaning mode, then the corresponding drone can be configured according to the original cleaning mode to clean the photovoltaic panels in the cleaning mode, and the remaining uncleaned photovoltaic panels will wait for secondary cleaning.

[0065] When scattered photovoltaic panels are incorporated into the surrounding area according to their own cleaning mode, they only need to be configured according to the original surrounding cleaning mode.

[0066] After the cleaning is completed, S107 performs a regional scan to obtain the target to be cleaned a second time, and performs a second cleaning.

[0067] After the previous step of cleaning, there will still be some scattered photovoltaic panels that have not been cleaned, so it is necessary to perform an area scan to obtain the location of the uncleaned photovoltaic panels and organize drones for secondary cleaning.

[0068] Embodiment 2: Based on the first embodiment, this embodiment allocates the drone according to the power of the drone, and the specific steps include: S111 When the remaining power of any UAV reaches the preset power threshold, obtain the pending task list of any UAV, and determine whether the pending task list is empty. If it is not empty, execute step S113; if it is empty, execute step S115; the preset power threshold can only support the UAV to return from the current position to the starting point.

[0069] Generally speaking, photovoltaic base stations are relatively large, and cleaning work consumes a lot of power from drones, so it is necessary to always pay attention to the power of the drone to avoid being unable to return.

[0070] In this step, the remaining power of the drone is monitored in real time. Once the remaining power reaches the power threshold, the drone should be returned.

[0071] When the drone reaches the preset power level, it may still be performing a mission or have another mission to perform. In the above case, it is necessary to find a successor for the mission.

[0072] S113 obtains the remaining power and pending task lists of multiple other drones near any one of the drones, and executes step S117; wherein, with the current location of any one of the drones as the center of the circle, other drones within a preset radius area are other drones adjacent to any one of the drones.

[0073] When a drone that is still performing a task or has tasks to perform reaches the preset power level, it needs to find a successor. Therefore, it searches for neighboring drones with the current location of the drone as the center and a preset radius, and obtains the remaining power and pending task list of the neighboring drones.

[0074] S115 controls any one of the drones to return from the current position to the starting point.

[0075] Of course, it is foreseeable that in this step, it is necessary to determine whether the current task is completed before returning. If it is completed, the drone is controlled to return to the starting point; if it is not completed, the current task is assigned to the nearest other drone.

[0076] S117 determines whether the other drones nearby can add new cleaning tasks based on the remaining power of the other drones and the number of pending tasks in the pending task list; if they can be added, execute step S119; if they cannot be added, execute step S201.

[0077] When looking for a task successor, it is necessary to determine whether the drone has enough power to support subsequent tasks and new tasks, so as to determine whether the drone is suitable for adding new tasks. It is easy to understand that drones with sufficient power and fewer tasks are preferred.

[0078] S119: selecting 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 distributing the to-be-processed tasks in the to-be-processed task list of any of the drones to the selected at least one other drone.

[0079] In addition to determining whether a drone can add a new task, the distance between the two and the waiting time must also be considered. Therefore, in this embodiment, when at least one other drone is screened out, the recommendation index of each other drone is calculated: P = K1*D i，j +K2*T i，j Among them, D i，j is the distance between the other UAV and any UAV, T i，j The waiting time for the other UAVs to execute the newly added task; K1 and K2 are empirical constants.

[0080] Among the tasks to be processed, the tasks that are ranked higher in the queue are preferentially assigned to other drones with smaller recommendation indexes.

[0081] S201 determines whether the number of tasks to be processed in the to-be-processed task list of any UAV is greater than or equal to a preset to-be-processed number threshold, if it is greater than or equal to the preset to-be-processed threshold, executes step S203; if it is less than the preset to-be-processed threshold, executes step S113; The number of pending tasks in a drone's task list is preset to a pending threshold, indicating that the drone's tasks are saturated and no new tasks can be accepted. When all drones are saturated with tasks, new drones need to be added.

[0082] S203 dispatches a new drone to the area.

[0083] Embodiment three: This embodiment is based on the drone deployment method of the first and second embodiments, and selects an appropriate drone for cleaning during secondary cleaning, thereby improving efficiency. The specific steps include: S401 calculates the number of secondary cleaning targets in each area, and determines whether the number of secondary cleaning targets is greater than or equal to the amount of tasks that can be added to be processed for all drones that can work again in the area. If so, execute step S403; otherwise, execute step S405.

[0084] During the second cleaning, the remaining uncleaned areas after the first cleaning are integrated and re-divided. The division rules are still divided according to the type of dirt.

[0085] After the second area division, there may be drones in the area that are still performing the previous cleaning task. Therefore, it is possible to first determine whether the drones in the area can meet the current cleaning requirements.

[0086] S403 deploys drones that are idle and can work again in adjacent areas.

[0087] If the drones in an area are unable to meet the secondary cleaning needs of the area, drones that can still perform tasks will be deployed from adjacent areas for cleaning. It is foreseeable that if the task lists of drones in adjacent areas are saturated, drones will need to be reconfigured.

[0088] The adjacent area may be an area bordering the target area, or an area whose distance from the target area is less than a distance threshold.

[0089] S405 evenly distributes the secondary cleaning targets in the area to all drones in the area that can work again.

[0090] If the drones in the area are already able to meet the demand, the secondary cleaning tasks can be evenly distributed.

[0091] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0092] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a computer terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0093] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, characterized in that: Includes steps: S101 takes the designated first photovoltaic panel as the initial point, and if the number of first photovoltaic panels adjacent to the designated first photovoltaic panel and having the same cleaning mode reaches a first preset threshold value N1, the first photovoltaic panel and the first photovoltaic panels adjacent to the designated first photovoltaic panel and having the same cleaning mode are divided into one area, thereby obtaining a plurality of areas, and executing step S103; the cleaning mode is one of a normal cleaning mode, a strong cleaning mode or a mixed cleaning mode; S103 calculates the density Pi of the cleaning modes corresponding to the plurality of scattered second photovoltaic panels between any designated area and its adjacent areas; the density Pi=the number ni of the second photovoltaic panels in any cleaning mode / the sum of the numbers of the second photovoltaic panels in the three cleaning modes N, wherein i=1, 2, 3; If N is greater than or equal to a second preset threshold value N2, and the density Pi of any cleaning mode is greater than or equal to a partition preset density threshold value Pd, each of the second photovoltaic panels scattered between the designated area and its adjacent areas is 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 the partition preset density threshold Pd, each of the second photovoltaic panels is divided into the closest 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, all the second photovoltaic panels are divided into the designated area, and step S105 is executed; S105 configures drones with corresponding cleaning modes for each area; After the cleaning is completed, S107 performs a regional scan to obtain the target to be cleaned a second time, and performs a second cleaning.

2. According to the method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 1, it is characterized in that: When the plurality of second photovoltaic panels are divided into one area, the step of configuring a drone with a corresponding cleaning mode for each area in step S105 includes: S10511 If the density of the first cleaning mode is greater than the first preset density threshold value P0, and the sum of the densities of the other two cleaning modes is less than the second preset density threshold value P0', configure multiple drones with the first cleaning mode and execute step S107; the first cleaning mode is any one of the ordinary cleaning mode, the strong cleaning mode and the mixed cleaning mode; S10512: If the density of the first cleaning mode is greater than the third preset density threshold value P0'', and the density of the second cleaning mode is greater than the fourth preset density threshold value P0''', configure a plurality of drones with the first cleaning mode, and a plurality of second drones with the second cleaning mode; the first cleaning mode is any one of the ordinary 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 value is less than the first preset density threshold value, and the fourth preset density threshold value is greater than the second preset density threshold value, and execute step S107; S10513 If the difference between the densities of the three cleaning modes is less than the preset difference threshold, multiple drones with ordinary cleaning mode, multiple drones with strong cleaning mode and multiple drones with mixed cleaning mode are configured respectively, and step S107 is executed.

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

4. According to claim 2, a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, characterized in that: In step S10512, when the first cleaning mode and the second cleaning mode are both one of the normal cleaning mode and the strong 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 strong cleaning mode respectively; When the first cleaning mode or the second cleaning mode is a mixed cleaning mode, a drone with a corresponding cleaning mode is configured to perform cleaning; and the remaining part is to be cleaned a second time.

5. According to claim 2, a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, characterized in that: When the plurality of second photovoltaic panels are divided into the designated areas, the step S105 of configuring a drone with a corresponding cleaning mode for each area includes: When the cleaning mode of the designated area is a mixed cleaning mode, a drone with the mixed cleaning mode is configured to clean the entire designated area; When the cleaning mode of the designated area is the normal cleaning mode or the 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.

6. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 1, characterized in that: Also includes the steps: S111: When the remaining power of any UAV reaches a preset power threshold, obtain the pending task list of any UAV, and determine whether the pending task list is empty. If not, execute step S113; if empty, execute step S115; The preset power threshold is only capable of supporting the drone to return from the current position to the starting point; S113 obtains the remaining power and pending task lists of multiple other drones near the any drone, and executes step S117; wherein, with the current location of the any drone as the center, the other drones within the preset radius are other drones adjacent to the any drone; S115 controls any one of the drones to return from the current position to the starting point; S117 determines whether the other drones nearby can add new cleaning tasks according to the remaining power of the other drones and the number of pending tasks in the pending task list; if they can be added, execute step S119; if they cannot be added, execute step S201; S119: selecting 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 distributing the to-be-processed tasks in the to-be-processed task list of any of the drones to the selected at least one other drone.

7. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 6, characterized in that: When at least one other drone is screened out, the recommendation index of each other drone is calculated: P=K1*D i，j +K2*T i，j ; Among them, D i，j is the distance between the other UAV and any UAV, T i，j The waiting time for the other drones to perform the newly added tasks; K1 and K2 are empirical constants; Among the tasks to be processed, the tasks that are ranked higher in the queue are preferentially assigned to other drones with smaller recommendation indexes.

8. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 6, characterized in that: Before executing step S113, the method further includes the following steps: S201 determines whether the number of tasks to be processed in the to-be-processed task list of any UAV is greater than or equal to a preset to-be-processed number threshold, if it is greater than or equal to the preset to-be-processed threshold, executes step S203; if it is less than the preset to-be-processed threshold, executes step S113; S203 dispatches a new drone to the area.

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

10. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to any one of claims 1 to 9, characterized in that: Also includes the steps: S401 calculates the number of secondary cleaning targets in each area, and determines whether the number of secondary cleaning targets is greater than or equal to the amount of tasks that can be added to be processed by all drones that can work again in the area. If so, execute step S403; otherwise, execute step S405; S403 deploys idle and reusable drones in adjacent areas; S405 evenly distributes the secondary cleaning targets in the area to all drones in the area that can work again.

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