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

Abstract

The present invention relates to the technical field of photovoltaic power generation, and specifically discloses a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, including: S101 taking a specified 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, then dividing the first photovoltaic panel and the first photovoltaic panels adjacent to it and having the same cleaning mode into one area, thereby obtaining several areas; S103 calculating the density of the cleaning modes corresponding to a plurality of scattered second photovoltaic panels between any specified area and its adjacent areas, and dividing the second photovoltaic panels according to the density and quantity; S105 configuring drones with corresponding cleaning modes for each area; S107 after completing the cleaning, performing area scanning to obtain targets to be secondarily cleaned, and performing secondary cleaning. In view of the relatively scattered distribution of photovoltaic panels in the mountain photovoltaic base station, the present invention integrates adjacent photovoltaic panels with the same cleaning mode into one area through a method similar to "clustering" and overall configures drones for cleaning.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm. Background Art

[0002] A photovoltaic panel is an array of solar panels composed of a series of solar cells connected in series and parallel. Due to the obstruction of dust and other stains, the power generation efficiency of the photovoltaic panel 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 building-integrated photovoltaics and large-scale photovoltaic power stations in the northwest desert areas. The solar panels of these photovoltaic power stations are vulnerable to stains such as dust, bird droppings, water scale, and oil stains, resulting in a significant decline in power generation efficiency and economic losses. Therefore, the cleaning of photovoltaic panels is essential.

[0003] The prior art Chinese patent application CN202210411513.6 discloses a photovoltaic cleaning method combined with drones, including the following steps: Step 1, dividing the photovoltaic power station into several areas, and each area is allocated a set of cleaning devices; Step 2, using drones to collect images of the photovoltaic panels, using a cloud platform to identify stains and set cleaning trajectories; Step 3, using drones to drop photovoltaic cleaning robots, and the photovoltaic cleaning robots clean along the planned path; Step 4, the drones finally transport the photovoltaic cleaning robots to the energy storage charging bin.

[0004] The above prior art proposes a specific method for cleaning photovoltaic panels by drones. However, its cleaning method is relatively fixed and has certain limitations. Due to the installation angle and environment of solar photovoltaic panels, dust is not evenly distributed. The fixed cleaning method not only requires more cleaning costs but also cannot guarantee the cleaning efficiency of solar photovoltaic panels.

[0005] Another prior art Chinese patent application CN202310330164.X discloses a monitoring and regulation system for solar photovoltaic panel cleaning operations. The monitoring and regulation system for solar photovoltaic panel cleaning operations includes a photovoltaic panel basic information acquisition module, a photovoltaic panel dust monitoring module, a photovoltaic panel pollution monitoring module, a photovoltaic panel cleaning mode analysis module, a photovoltaic panel cleaning rule analysis module, a photovoltaic panel cleaning regulation plan generation module, and a cleaning operation information database.

[0006] The above prior art monitors the dust concentration and pollution information of each solar photovoltaic panel laid in a specified area, analyzes the cleaning requirements, cleaning modes, and cleaning rules, and then conducts cleaning regulation. However, the above prior art does not conduct corresponding research on drone photovoltaic panel cleaning, making this technical field still relatively weak. Summary of the Invention

[0007] The object of the present invention is to provide a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, which partially solves or alleviates the above deficiencies in the prior art, and can configure corresponding types of drones for cleaning according to the pollution types in the photovoltaic panel area.

[0008] In order to solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions:

[0009] In the first aspect of the present invention, there is provided a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, including the steps of:

[0010] S101 Using a specified first photovoltaic panel as the initial point, if the number of first photovoltaic panels adjacent to it and having the same cleaning mode reaches the first preset threshold N1, then divide the first photovoltaic panel and the first photovoltaic panels adjacent to it and having the same cleaning mode into one area, so as to obtain several areas, and execute step S103; the cleaning mode is one of a normal cleaning mode, a strong cleaning mode, or a mixed cleaning mode;

[0011] S103 Calculate the density Pi of the cleaning modes corresponding to a plurality of scattered second photovoltaic panels between any specified area and its adjacent areas; the density Pi = the number ni of second photovoltaic panels in any cleaning mode / the sum N of the numbers of second photovoltaic panels in three cleaning modes, where i = 1, 2, 3;

[0012] If N is greater than or equal to the second preset threshold N2, and the density Pi of any one cleaning mode is greater than or equal to the partition preset density threshold Pd, then divide each of the scattered second photovoltaic panels between the specified area and its adjacent areas into one area, and execute step S105;

[0013] If N is greater than or equal to the second preset threshold N2, and the densities of all cleaning modes are less than the partition preset density threshold Pd, then divide each of the second photovoltaic panels into the area closest to it and having the same cleaning mode according to its cleaning mode, and execute step S105;

[0014] If N is less than the second preset threshold N2, divide all the second photovoltaic panels into the specified area, and execute step S105;

[0015] S105 Configure drones with corresponding cleaning modes for each area;

[0016] S107 After the cleaning is completed, perform area scanning to obtain the targets to be secondarily cleaned, and perform secondary cleaning.

[0017] As an improvement, when multiple second photovoltaic panels are divided into one area, the step of configuring drones with corresponding cleaning modes for each area in step S105 includes:

[0018] 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;

[0019] 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 other 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;

[0020] S10513 If the difference between the densities of the three cleaning modes in pairs is less than the preset difference threshold, respectively configure multiple drones with the normal cleaning mode, multiple drones with the strong cleaning mode, and multiple drones with the mixed cleaning mode, and execute step S107.

[0021] As a further improvement, in step S10511, when the first cleaning mode is the normal cleaning mode or the strong cleaning mode, configure drones with the corresponding cleaning mode to clean, and the remaining part is to be cleaned twice;

[0022] When the first cleaning mode is the mixed cleaning mode, configure drones with the mixed cleaning mode to clean the entire area.

[0023] As a further improvement, in step S10512, when both the first cleaning mode and the second cleaning mode are one of the normal cleaning mode or the strong cleaning mode, configure drones with the corresponding cleaning mode to clean; and the remaining part is cleaned by drones with the normal cleaning mode and drones with the strong cleaning mode respectively;

[0024] When the first cleaning mode or the second cleaning mode is the mixed cleaning mode, configure drones with the corresponding cleaning mode to clean; and the remaining part is to be cleaned twice.

[0025] As an improvement, when multiple second photovoltaic panels are divided into the designated area, the step S105 of configuring a drone with a corresponding cleaning mode for each area includes:

[0026] In the case where the cleaning mode of the designated area is the hybrid cleaning mode, configure a drone with the hybrid cleaning mode to clean the entire designated area;

[0027] In the case where the cleaning mode of the designated area is the normal cleaning mode or the strong cleaning mode, configure a drone with the corresponding cleaning mode to clean, and the remaining part is to be cleaned twice.

[0028] As an improvement, in S111, when the remaining power of any drone reaches the preset power threshold, obtain the list of tasks to be processed of the any drone, and determine whether the list of tasks to be processed 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 drone to return from the current position to the starting point;

[0029] S113 Obtain the remaining power and the list of tasks to be processed of multiple other drones near the any drone, and execute step S117; among them, the other drones within the preset radius area with the current position of the any drone as the center are the other drones adjacent to the any drone;

[0030] S115 Control the any drone to return from the current position to the starting point;

[0031] S117 Determine whether the other drones can add new cleaning tasks according to the remaining power of the nearby other drones and the number of tasks to be processed in the list of tasks to be processed; if they can add, execute step S119; if they cannot add, execute step S201;

[0032] S119 Select at least one other drone from all other drones whose number of cleaning tasks that can be added is greater than the preset threshold, and evenly distribute the tasks to be processed in the list of tasks to be processed of the any drone to the selected at least one other drone.

[0033] As an improvement, when at least one other drone is selected, calculate the recommendation index of each other drone: P = K1 * D i，j + K2 * T i，j ; where D i，j is the distance between the other drone and the any drone, and T i，j is the waiting duration required for the other drone to execute the newly added task; K1 and K2 are empirical constants;

[0034] Among the tasks to be processed, the tasks to be processed that are ranked higher are preferentially assigned to other drones with a smaller recommendation index.

[0035] As an improvement, before performing step S113, the following steps are further included:

[0036] S201 determines whether the number of tasks to be processed in the task list to be processed of any one of the drones is greater than or equal to a preset task quantity threshold to be processed. If it is greater than or equal to the preset task threshold to be processed, step S203 is executed; if it is less than the preset task threshold to be processed, step S113 is executed;

[0037] S203 reassigns a new drone to the area.

[0038] As an improvement, step S115 specifically includes the following steps:

[0039] S114 determines whether the current task is completed. If it is completed, the any one of the drones is controlled to return to the starting point; if it is not completed, the current task is assigned to the nearest other drone.

[0040] As an improvement, the following steps are further included:

[0041] 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 addable task quantity of all the drones that can work again in the area. If so, step S403 is executed; otherwise, step S405 is executed;

[0042] S403 deploys the idle and reusable drones in the adjacent areas;

[0043] S405 evenly distributes each of the secondary cleaning targets in the area to all the drones that can work again in the area.

[0044] The beneficial effects of the present invention are as follows:

[0045] In view of the characteristics that the photovoltaic panels in the mountain photovoltaic base station are relatively scattered, the present invention integrates the photovoltaic panels with the same cleaning mode and adjacent or indirectly adjacent through a method similar to "clustering", and overall configures drones for cleaning.

[0046] For the scattered photovoltaic panels between areas, the present invention processes them separately according to three situations:

[0047] The scattered photovoltaic panels that reach a certain quantity level and have a relatively concentrated cleaning mode are integrated into a separate area. For the scattered photovoltaic panels that reach a certain quantity level but have a relatively scattered cleaning mode, they are absorbed by the surrounding areas according to the cleaning mode. For the scattered photovoltaic panels with a small quantity, they are directly incorporated into a designated area.

[0048] For some composite 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 during the journey.

[0049] In addition, the present invention can also allocate drones according to the battery power of the drones. When the remaining battery power of a certain drone reaches the battery threshold, the drone is made to return, avoiding the problem that the drone cannot return due to too low remaining battery power during operation due to the vastness of the photovoltaic base stations.

[0050] Meanwhile, the present invention also finds a successor for the returning drone to take over its unfinished tasks and ensure the execution of the cleaning tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale. Obviously, the following described drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative labor.

[0052] Figure 1 It is the flowchart of the first embodiment of the present invention;

[0053] Figure 2 It is the flowchart of the second embodiment of the present invention;

[0054] Figure 3 It is the flowchart of the third embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present invention.

[0056] In this article, suffixes such as "module", "component" or "unit" used to represent elements are only for the convenience of the description of the present invention, and they have no specific meaning themselves. Therefore, "module", "component" or "unit" can be used interchangeably.

[0057] In this text, the orientation or positional relationship indicated by terms such as "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0058] In this text, unless otherwise clearly specified and defined, terms such as "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, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] In this text, "and / or" includes any and all combinations of one or more of the listed related items.

[0060] In this text, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0061] Embodiment 1:

[0062] As Figure 1 shown, this embodiment provides a cleaning method for photovoltaic panels in a mountain photovoltaic base station based on a drone swarm, which is used to clean the relatively scattered photovoltaic panels, such as the photovoltaic base stations on relatively rugged terrains such as mountains. Compared with plain areas such as deserts, the terrain in mountainous areas is more complex. Therefore, not all terrains are suitable for installing photovoltaic panels, resulting in scattered installation of photovoltaic panels, and it is not applicable to directly divide the area with the same area.

[0063] The purpose of this embodiment is to provide a method for presetting area division for a mountain photovoltaic base station under construction, which is used to improve the cleaning efficiency of drones. Its specific steps include:

[0064] S101 Using the specified first photovoltaic panel as the starting point, if the number of the first photovoltaic panels adjacent to it and having the same cleaning mode reaches the first preset threshold N1, then divide the first photovoltaic panel and the first photovoltaic panels adjacent to it and having the same cleaning mode into one area, thereby obtaining several areas, and execute step S103; the cleaning mode is one of the normal cleaning mode, the strong cleaning mode, or the mixed cleaning mode.

[0065] The dirt on the photovoltaic panels mainly includes dust, water scale, oil stain, bird droppings, etc. Among them, the cleaning of dust and water scale is relatively simple. Therefore, in the present invention, the cleaning mode of the drone for cleaning dust and water scale is classified as the normal mode. While the cleaning of oil stain and bird droppings is relatively complex. Therefore, in the present invention, the cleaning mode of the drone for cleaning oil stain and bird droppings is classified 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, however, the dirt on some photovoltaic panels is not of only one type. Therefore, the cleaning mode of the drone in the present invention also includes a mixed cleaning mode, which can perform both normal cleaning and strong cleaning. Of course, it can be foreseen that the types of cleaning agents and cleaning tools carried by the drone in the mixed cleaning mode are more, which will greatly reduce the endurance time of the drone. Therefore, it cannot be used as a conventional mode.

[0066] 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 according to the type of dirt on each photovoltaic panel. 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 elaborated here.

[0067] The purpose of this step is to divide the preset cleaning area to better overall arrange the drones for cleaning. Considering the relatively scattered distribution of mountain photovoltaic panels (of course, generally speaking, the cleaning modes of photovoltaic panels in adjacent areas are still roughly the same), first, the photovoltaic panels with the same cleaning type and close distance need to be "clustered". Specifically, taking a specified photovoltaic panel as the center, the same-type photovoltaic panels 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, in this embodiment, a first preset threshold N1 is set, that is, the same-type photovoltaic panels with a quantity reaching the first preset threshold N1 can be used as a preset area. The purpose is to prevent the preset cleaning area from being too scattered.

[0068] S103 Calculate the density Pi of the cleaning modes corresponding to the scattered multiple second photovoltaic panels between any specified area and its adjacent areas; the density Pi = the number ni of the second photovoltaic panels of any cleaning mode / the sum N of the number of the second photovoltaic panels of the three cleaning modes, where i = 1, 2, 3.

[0069] The density of various cleaning modes can be calculated according to the density of the photovoltaic panels containing various types of dirt. Simply put, it is analyzed that there are 80% normal-type dirt, 15% strong-type dirt, and 5% mixed-type dirt in a certain preset area, and the corresponding cleaning mode densities are P1 = 80%, P2 = 15%, P3 = 5%, and so on.

[0070] The method in step S101 can integrate relatively concentrated photovoltaic panels of the same type into a preset cleaning area. After the integration of this step, there will still be some scattered photovoltaic panels (the second photovoltaic panels) 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:

[0071] One, forming a new independent cleaning area; two, being absorbed by the surrounding cleaning areas respectively; three, all being absorbed by a certain preset cleaning area.

[0072] First, arbitrarily specify an area in the already divided area, and divide the scattered photovoltaic panels between this area and its adjacent areas.

[0073] Let the number of the above-mentioned scattered photovoltaic panels be N.

[0074] If N is greater than or equal to the second preset threshold N2, and the density Pi of any one cleaning mode is greater than or equal to the partition preset density threshold Pd, divide the scattered second photovoltaic panels between the specified area and its adjacent areas into one area, and execute step S105.

[0075] That is to say, the number of scattered photovoltaic panels is greater than or equal to N2 (for example, 500), and the density of any one cleaning mode is greater than Pd (70%), then these scattered photovoltaic panels can form an independent area. That is to say, try to make the scattered photovoltaic panels relatively concentrated in type under the condition of meeting a certain quantity requirement.

[0076] If N is greater than or equal to the second preset threshold N2, and the densities of all cleaning modes are less than the partition preset density threshold Pd, divide each of the second photovoltaic panels into the area closest to it and with the same cleaning mode according to its cleaning mode, and execute step S105.

[0077] That is to say, the number of scattered photovoltaic panels is greater than or equal to N2 (for example, 500), but the densities of all cleaning types are lower than Pd (70%), then it means that the types of scattered photovoltaic panels are relatively disorderly and not suitable for forming an independent area. At this time, these scattered photovoltaic panels can be integrated into the surrounding areas according to their types.

[0078] If N is less than the second preset threshold N2, divide all the second photovoltaic panels into the specified area, and execute step S105.

[0079] That is to say, the number of scattered photovoltaic panels is greater than N2 (for example, 500), indicating that the quantity of scattered photovoltaic panels is relatively small, and they can be directly incorporated into the specified area. Of course, a better method is to incorporate them into the corresponding area according to the cleaning mode with the highest density.

[0080] S105 configures drones with corresponding cleaning modes for each area.

[0081] The purpose of this step is to configure drones according to the areas divided in step S103. It can be understood that for areas with a single cleaning mode, drones with the corresponding cleaning mode can be directly configured 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 mainly refers to the case where multiple second photovoltaic panels are divided into a single area. Of course, this method can also be analogized to other areas that incorporate other cleaning mode photovoltaic panels.

[0082] Specifically, when multiple second photovoltaic panels are divided into one area, the step of configuring drones with corresponding cleaning modes for each area in step S105 includes:

[0083] 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.

[0084] In the previous step, the density of each cleaning mode in a certain area was calculated. However, if drones are completely matched according to density, it is unreasonable in some cases. For example, when the density of a certain cleaning mode is over 90%, and the sum of the densities of other modes does not exceed 5% (the remaining 5% does not need to be cleaned), for the drones of the cleaning mode with a density of only 5%, the round-trip time and the time for jumping within the area may be longer than the cleaning time, and this situation is considered uneconomical.

[0085] 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 configure drones with the normal cleaning mode to go.

[0086] 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.

[0087] When the first cleaning mode is the normal cleaning mode or the strong cleaning mode, configure drones with the normal cleaning mode or the strong cleaning mode to clean 90% of the corresponding area, and the remaining 5% will be configured with drones for cleaning in subsequent secondary cleaning.

[0088] When the first cleaning mode is the hybrid cleaning mode, configure a drone with the hybrid cleaning mode to clean the entire area. The drone with the hybrid cleaning mode can clean all types of dirt. Since the total amount of normal + strong dirt in this area is also less than 5%, directly configuring a drone with the hybrid cleaning mode for cleaning is more economical and efficient.

[0089] 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 hybrid 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.

[0090] Based on the analysis of the previous step, when the density of a certain cleaning mode in a preset area is too small, it is uneconomical to configure drones separately. However, when the density of a certain type of dirt reaches a certain threshold, it is necessary to configure corresponding drones for cleaning.

[0091] For example, if 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''' (e.g., 40%), then drones with the above two cleaning modes should be configured for targeted cleaning.

[0092] When the first cleaning mode and the second cleaning mode are both one of the normal cleaning mode or the strong cleaning mode, configure corresponding drones for cleaning. And the remaining 10% (of course, it may be less than 10% because some photovoltaic panels do not need to be cleaned) can be completed by cleaning with drones with the normal cleaning mode and drones with the strong cleaning mode respectively, without the need to separately configure a drone with the hybrid cleaning mode for cleaning.

[0093] When the first cleaning mode or the second cleaning mode is the hybrid cleaning mode, configure corresponding drones for cleaning. And the remaining 10% (which may be the normal cleaning mode or the strong cleaning mode) can be cleaned by configuring corresponding drones in the subsequent secondary cleaning.

[0094] If the differences between the densities of the three cleaning modes pairwise are less than a preset difference threshold, configure multiple drones with a normal cleaning mode, multiple drones with a strong cleaning mode, and multiple drones with a hybrid cleaning mode respectively, and execute step S107.

[0095] It can be foreseen that if the densities of the three modes differ too little, it is uneconomical to use the alternative cleaning method (i.e., using drones with a normal cleaning mode and drones with a strong cleaning mode to cooperate in cleaning the hybrid fouling, or using drones with a composite cleaning mode to clean the normal fouling or strong fouling).

[0096] Therefore, in this step, when the differences between the densities of the three cleaning modes pairwise are less than a preset difference threshold (e.g., 10%), just directly configure the corresponding drones for cleaning.

[0097] In addition, when multiple said second photovoltaic panels are divided into the specified area, the steps of step S105 for configuring drones with corresponding cleaning modes for each area include:

[0098] When the cleaning mode of the specified area is the hybrid cleaning mode, configure drones with the hybrid cleaning mode to clean the entire specified area.

[0099] After the scattered photovoltaic panels (second photovoltaic panels) are divided into the specified area, it may bring photovoltaic panels with different cleaning modes to the specified area. Since the number of scattered photovoltaic panels is small and the area itself is a hybrid cleaning area, then directly use drones with the hybrid cleaning mode to clean all the photovoltaic panels in the entire area.

[0100] When the cleaning mode of the specified area is the normal cleaning mode or the strong cleaning mode, configure drones with the corresponding cleaning mode for cleaning, and the remaining part is to be cleaned for the second time.

[0101] If the original area is not a photovoltaic panel with the hybrid cleaning mode, just configure the corresponding drones according to the original cleaning mode to clean the photovoltaic panels of this cleaning mode, and the remaining uncleaned photovoltaic panels wait for the second cleaning.

[0102] When the scattered photovoltaic panels are incorporated into the surrounding area according to their own cleaning modes, just configure them according to the original surrounding cleaning mode.

[0103] After completing the cleaning in S107, perform area scanning to obtain the targets to be cleaned for the second time, and perform the second cleaning.

[0104] After the cleaning in the previous step, there will still be some scattered photovoltaic panels that have not been cleaned. Therefore, it is necessary to perform area scanning to obtain the positions of the uncleaned photovoltaic panels and organize the drones for secondary cleaning.

[0105] Embodiment 2:

[0106] Based on Embodiment 1, this embodiment allocates the drones according to the battery power of the drones. The specific steps include:

[0107] S111 When the remaining battery power of any one drone reaches the preset battery power threshold, obtain the list of pending tasks of the any one drone, and determine whether the list of pending tasks is empty. If it is not empty, execute step S113; if it is empty, execute step S115; the preset battery power threshold can only support the drone to return from the current position to the starting point.

[0108] Generally speaking, photovoltaic base stations are relatively vast, and the cleaning work consumes a large amount of battery power of the drones. Therefore, it is necessary to always pay attention to the battery power of the drones to avoid being unable to return.

[0109] In this step, by real-time monitoring the remaining battery power of the drone, once the remaining battery power reaches the battery power threshold, the drone should be made to return.

[0110] When the drone reaches the preset battery power, it may still be performing tasks or there are tasks to be performed. In the above cases, it is necessary to find a successor to the task.

[0111] S113 Obtain the remaining battery power and the list of pending tasks of multiple other drones near the any one drone, and execute step S117; among them, the other drones within the preset radius area with the current position of the any one drone as the center are the other drones adjacent to the any one drone.

[0112] When a drone that is still performing tasks or has tasks to be performed reaches the preset battery power, it is necessary to find a successor to the task. Therefore, search for adjacent drones with the current position of the drone as the center and a preset radius, and obtain the remaining battery power and the list of pending tasks of the adjacent drones.

[0113] S115 Control the any one drone to return from the current position to the starting point.

[0114] Of course, it can be foreseen that in this step, it is also necessary to determine whether the current task is completed before returning. If it is completed, control the drone to return to the starting point; if it is not completed, allocate the current task to the nearest other drone.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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;

[0121] 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.

[0122] S203 dispatches a new drone to the area.

[0123] Embodiment three:

[0124] 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:

[0125] S401 calculates the secondary cleaning target quantity for each area, and determines whether the secondary cleaning target quantity is greater than or equal to the addable pending task quantity of all the drones that can work again within the area. If so, step S403 is executed; otherwise, step S405 is executed.

[0126] During secondary cleaning, the un-cleaned areas remaining after the first cleaning are integrated and re-divided. The division rule is still based on the type of dirt.

[0127] After the area is re-divided, there may still be drones in the area that are performing the tasks of the previous cleaning. Therefore, it is possible to first determine whether the drones in the area can meet the requirements of this cleaning.

[0128] S403 deploys the idle and re-workable drones in the adjacent areas.

[0129] In the case where the drones in the area cannot meet the secondary cleaning requirements of the area, drones that can still perform tasks are deployed from the adjacent areas for cleaning. 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.

[0130] The adjacent area mentioned above can be the area adjacent to the target area, or the area whose distance from the target area is less than the distance threshold.

[0131] S405 evenly distributes each of the secondary cleaning targets in the area to all the drones that can work again in the area.

[0132] In the case where the drones in the area can already meet the requirements, the secondary cleaning tasks can be evenly distributed.

[0133] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. 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. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0135] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. All of these are within the protection scope 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 again, and performs a secondary cleaning; Wherein, 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 a normal cleaning mode, multiple drones with a strong cleaning mode, and multiple drones with a mixed cleaning mode are configured respectively, and step S107 is executed; Wherein, 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.

2. According to claim 1, 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.

3. According to claim 1, 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.

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: 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 it is not empty, execute step S201; if it is 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; 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; 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.

5. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 4, 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.

6. The method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to claim 4, 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.

7. A method for cleaning photovoltaic panels in a mountain photovoltaic base station based on a drone swarm according to any one of claims 4 to 6, 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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