Urban high-rise glass curtain wall cleaning method based on unmanned aerial vehicle group

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses an urban high-rise glass curtain wall cleaning method based on an unmanned aerial vehicle group, and the method comprises the steps: calculating the respective density of three cleaning modes in each region based on the respective cleaning mode of each glass plate in each preset region; and according to the density proportion of each cleaning mode, providing three unmanned aerial vehicle configuration modes to clean each preset area. And after cleaning is completed, area scanning is carried out, the target to be cleaned for the second time is obtained, and secondary cleaning is carried out. The unmanned aerial vehicle of the corresponding type can be configured for cleaning according to the dirt type of the glass plate. Through the configuration mode of the unmanned aerial vehicle, the cleaning efficiency can be greatly improved, and the loss of the unmanned aerial vehicle on the way is reduced.

Description

[0001] Divisional Application This application is a divisional application of the Chinese invention patent application [Application No.: 2025101143903] [Title: A Method for Cleaning Glass Curtain Walls of Urban High-Rise Buildings Based on a Drone Swarm] filed on January 24, 2025. Technical Field

[0002] The present invention belongs to the technical field of drones, and particularly relates to a method for cleaning glass curtain walls of urban high-rise buildings based on a drone swarm. Background Art

[0003] Currently, when cleaning the exterior walls of tall buildings, most of them require "spidermen" to climb on the exterior walls for manual cleaning. This work not only poses risks, but also the staff needs to constantly lift, supply, and replace cleaning equipment, which is time-consuming and laborious.

[0004] The prior art Chinese patent application CN201610865457.8 discloses a glass exterior wall cleaning drone, which includes a drone unit and a ground unit. A signal line, a power line, and a water pipe are tied together to form a working cable, and the drone unit and the ground unit are connected through the working cable.

[0005] The above prior art has realized the cleaning of glass curtain walls by drones. However, this prior art can only perform point-to-point cleaning, and actually still needs to be manually controlled.

[0006] Another prior art Chinese patent application CN202310330164.X discloses a solar glass panel cleaning operation monitoring and regulation system, which includes a glass panel basic information acquisition module, a glass panel dust monitoring module, a glass panel dirt monitoring module, a glass panel cleaning mode analysis module, a glass panel cleaning rule analysis module, a glass panel cleaning regulation plan generation module, and a cleaning operation information database; The above prior art monitors the dust concentration and dirt information of each solar glass panel laid in a specified area, and thus analyzes the cleaning requirements, cleaning modes, and cleaning rules, and then conducts cleaning regulation. Applying this prior art to the cleaning of glass curtain walls can achieve automatic cleaning by drones.

[0007] However, even by combining the above two prior arts, it is still impossible to make overall planning for drones and achieve efficient cleaning operations for glass curtain walls of high-rise buildings. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for cleaning glass curtain walls of urban high-rise buildings based on a drone swarm, which can partially solve or alleviate the above deficiencies in the prior art, and can configure corresponding types of drones for cleaning according to the dirt types of glass panels.

[0009] To solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions: A method for cleaning the glass curtain wall of urban high-rise buildings based on a swarm of drones, comprising the steps of: S101 Calculate the density Pi of each of the three cleaning modes in each area based on the respective cleaning modes of each glass plate in each preset area, and execute step S103, or S105 or S107; the three cleaning modes include: normal cleaning mode, strong cleaning mode and mixed cleaning mode; the density is the number of glass plates corresponding to each cleaning mode in the current area; i = 1, 2, 3; S103 If the density of the first cleaning mode in any area is greater than the first preset density threshold P0, and the sum of the densities of the remaining two cleaning modes is less than the second preset density threshold P0', configure multiple drones with the first cleaning mode for the any area, and execute step S109; the first cleaning mode is any one of the normal cleaning mode, the strong cleaning mode and the mixed cleaning mode; S105 If the density of the first cleaning mode in any area 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 for the any area; 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 S109; S107 If the difference between the densities of any two of the three cleaning modes in any area is less than the preset difference threshold, configure multiple drones with the normal cleaning mode, multiple drones with the strong cleaning mode and multiple drones with the mixed cleaning mode for the any area respectively, and execute step S109; S109 After completing the cleaning, perform area scanning to obtain the targets to be secondarily cleaned, and perform secondary cleaning.

[0010] As an improvement, in step S103, when the first cleaning mode 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 secondarily cleaned; When the first cleaning mode is the mixed cleaning mode, configure drones with the mixed cleaning mode to clean the entire area.

[0011] As an improvement, in step S105, when both the first cleaning mode and the second cleaning mode are one of the normal cleaning mode or the strong cleaning mode, configure a drone with the corresponding cleaning mode 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 the mixed cleaning mode, configure a drone with the corresponding cleaning mode to perform cleaning; and the remaining part awaits secondary cleaning.

[0012] As an improvement, divide the entire area to be cleaned into several preset areas according to the division rule; the division rule is to divide according to the type of dirt.

[0013] As an improvement, before secondary cleaning, divide the remaining part after the first cleaning according to the division rule.

[0014] As an improvement, it further includes the steps: 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; 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; S115 Control the any drone to return from the current position to the starting point; S117 Judge 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; 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.

[0015] As an improvement, when at least one other drone is screened out, 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，jThe duration that the other drones need to wait to execute the newly added task; K1 and K2 are empirical constants; 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.

[0016] As an improvement, before executing step S113, the following step is further included: S201 determines whether the number of tasks to be processed in the task list to be processed of any one drone is greater than or equal to a preset threshold of the number of tasks to be processed. If it is greater than or equal to the preset threshold of the number of tasks to be processed, step S203 is executed; if it is less than the preset threshold of the number of tasks to be processed, step S113 is executed; S203 dispatches a new drone for the area again.

[0017] As an improvement, any one drone and multiple other drones nearby are in the same area.

[0018] As an improvement, step S115 specifically includes the following steps: S114 determines whether the current task is completed. If it is completed, the any one drone is controlled to return to the starting point; if it is not completed, the current task is assigned to the nearest other drone.

[0019] As an improvement, the following step is further 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 to be added that can be processed by all the drones that can work again in the area. If so, step S403 is executed; otherwise, step S405 is executed; S403 deploys the idle and reusable drones in adjacent areas; S405 evenly distributes each of the secondary cleaning targets in the area to all the drones that can work again in the area.

[0020] The beneficial effects of the present invention are as follows: The present invention divides the entire area to be cleaned into several preset areas according to established division rules, and calculates the cleaning density of three cleaning modes in each area. After obtaining the density ratio of the three cleaning modes, different forms of drone configurations are carried out in three cases.

[0021] When 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', only configure multiple drones with the first cleaning mode. If the first cleaning mode is the normal cleaning mode or the strong cleaning mode, configure the corresponding drones for cleaning, and the remaining part awaits secondary cleaning. If the first cleaning mode is the mixed cleaning mode, configure the drones with the mixed cleaning mode to clean the entire area. The drones with the mixed cleaning mode can clean all types of dirt. Since the total amount of normal + strong dirt in this area is small, directly configuring the drones with the mixed cleaning mode for cleaning is more economical and efficient.

[0022] When 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 the drones with the corresponding cleaning modes for cleaning. If both the first cleaning mode and the second cleaning mode are one of the normal cleaning mode or the strong cleaning mode, configure the corresponding drones for cleaning. The remaining part can be completed by cleaning with the drones with the normal cleaning mode and the drones with the strong cleaning mode respectively, without the need to separately configure the drones with the mixed cleaning mode for cleaning. If the first cleaning mode or the second cleaning mode is the mixed cleaning mode, configure the corresponding drones for cleaning, and the remaining part awaits secondary cleaning.

[0023] When the difference between the densities of the three cleaning modes pairwise is less than the preset difference threshold, just configure the drones with the corresponding cleaning modes for cleaning.

[0024] Through the above drone configuration method, the cleaning efficiency can be greatly improved, and the loss of drones during the journey can be reduced.

[0025] 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 power threshold, let the drone return to base, avoiding the problem that the drone cannot return to base due to too low remaining battery power during work caused by the vastness of the photovoltaic base stations.

[0026] At the same time, 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. Description of the Drawings

[0027] 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, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of the first embodiment of the present invention; Figure 2 It is a flowchart of the second embodiment of the present invention; Figure 3 It is a flowchart of the third embodiment of the present invention. Detailed implementation manners

[0029] 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 efforts fall within the protection scope of the present invention.

[0030] 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 in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0031] In this article, terms such as "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", etc. indicate the orientation or positional relationship 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. Therefore, it cannot be understood as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0032] In this article, unless otherwise clearly specified and limited, terms such as "installation", "provided with", "connection", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, 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 situations.

[0033] In this text, "and / or" includes any and all combinations of one or more of the listed related items.

[0034] In this text, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0035] Embodiment 1: As Figure 1 shown, the present invention provides a method for cleaning glass curtain walls of urban high-rise buildings based on a swarm of drones, which is used to clean the glass panels on the relatively concentrated glass curtain walls, such as the high-rise building groups in the city. The specific steps include: S101 Calculate the density Pi of each of the three cleaning modes in each area based on the respective cleaning modes of each glass panel in each preset area, and execute step S103, or S105 or S107; the three cleaning modes include: ordinary cleaning mode, strong cleaning mode and mixed cleaning mode; the density is the number of glass panels corresponding to each cleaning mode in the current area; i = 1, 2, 3.

[0036] The dirt on the glass panels of urban high-rise building glass curtain walls mainly includes dust, water scale, oil stain, bird droppings, etc. Among them, the cleaning of dust and water scale is relatively simple, so in the present invention, the cleaning mode of the drone for cleaning dust and water scale is classified as the ordinary mode. While the cleaning of oil stain and bird droppings is relatively complex, so 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. And in practice, the dirt on some glass panels is not of only 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 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, so it cannot be used as a conventional mode.

[0037] In this step, the respective cleaning modes of each glass panel in the entire area to be cleaned (which may include several high-rise buildings) can be obtained in advance, that is, the corresponding cleaning mode is matched according to the dirt type on each glass panel. For the specific method of obtaining the dirt type, reference can be made to the prior art CN202310330164.X in the background art, which will not be elaborated here.

[0038] Since the area to be cleaned may be relatively extensive, which is not conducive to the deployment of drones, in this embodiment, the area to be cleaned can be divided into several preset areas first and then the drones can be overall arranged for cleaning.

[0039] The rule for dividing the preset area can be to directly divide the area to be cleaned into equal - area averages, or to divide it according to the type of dirt. In this embodiment, the second division method is more preferred.

[0040] In this embodiment, the types of dirt include ordinary type (dust + water scale), strong type (oil stain + bird droppings), and mixed type (dust + water scale + oil stain + bird droppings), which correspond to the cleaning modes of the drone: ordinary cleaning mode, strong cleaning mode, and mixed cleaning mode respectively.

[0041] Of course, it can be understood that even when dividing according to the type of dirt, for the simplicity of division, the area division is a rough division, trying to have only one type of dirt in one area, while in fact there will still be multiple types of dirt in some areas.

[0042] After dividing the preset area, the density of each cleaning mode can be calculated according to the density of the glass plates with various types of dirt included. Simply put, if it is analyzed that there is 80% ordinary - type dirt, 15% strong - type dirt, and 10% mixed - type dirt in a certain preset area, the corresponding density of the cleaning modes is P1 = 80%, P2 = 15%, P3 = 5%, and so on.

[0043] S103 If the density of the first cleaning mode in any area 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 for the any area and execute step S109; the first cleaning mode is any one of the ordinary cleaning mode, the strong cleaning mode, and the mixed cleaning mode.

[0044] In the previous step, the density of each cleaning mode in a certain preset area was calculated. But if the drones are completely matched according to the density, it is unreasonable in some cases. For example, when the density of a certain cleaning mode is as high as 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 with a cleaning mode 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.

[0045] Therefore, in this step, when the density of a certain cleaning mode (such as the ordinary 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 the drones with the ordinary cleaning mode to go.

[0046] The mode with a density greater than the preset density threshold P0 may be one of the ordinary cleaning mode, the strong cleaning mode, and the mixed cleaning mode.

[0047] 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 cleaned by drones configured during subsequent secondary cleaning.

[0048] When the first cleaning mode is the mixed cleaning mode, configure drones with the mixed cleaning mode to clean the entire area. Drones with the mixed 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 drones with the mixed cleaning mode for cleaning is more economical and efficient.

[0049] S105 If the density of the first cleaning mode in any area 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 for the any area; 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 S109.

[0050] 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 alone. However, when the density of a certain type of dirt reaches a certain threshold, it is necessary to configure corresponding types of drones for cleaning.

[0051] 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.

[0052] When both the first cleaning mode and the second cleaning mode are one of the normal cleaning mode or the strong cleaning mode, configure corresponding drones for cleaning. The remaining 10% (of course, it may be less than 10% because some glass plates 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 drones with the mixed cleaning mode for cleaning.

[0053] When the first cleaning mode or the second cleaning mode is a hybrid cleaning mode, configure the corresponding drones for cleaning. For the remaining 10% (which may be either the normal cleaning mode or the strong cleaning mode), configure the corresponding drones for cleaning during subsequent secondary cleaning.

[0054] S107 If the difference between any two of the densities of the three cleaning modes in any area is less than the preset difference threshold, configure multiple drones with the normal cleaning mode, multiple drones with the strong cleaning mode, and multiple drones with the hybrid cleaning mode for the any area respectively, and execute step S109.

[0055] 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 the normal cleaning mode and drones with the strong cleaning mode to cooperate in cleaning the hybrid dirt, or using drones with the composite cleaning mode to clean the normal dirt or strong dirt).

[0056] Therefore, in this step, when the difference between any two of the densities of the three cleaning modes is less than the preset difference threshold (e.g., 10%), just directly configure the corresponding drones for cleaning.

[0057] S109 After completing the cleaning, perform area scanning to obtain the targets for secondary cleaning, and perform secondary cleaning.

[0058] In steps S103 and S105, for the sake of economy, there may be some areas that are not cleaned. After the entire area to be cleaned has undergone one cleaning, the uncleaned areas are coordinated and re-divided, and steps S101 - S109 are repeatedly executed until all glass plates are cleaned. Of course, the secondary cleaning can also be carried out in the manner of Embodiment 3, see Embodiment 3 for details.

[0059] Embodiment 2: Based on Embodiment 1, this embodiment allocates the drones according to the battery power of the drones. The specific steps include: S111 When the remaining battery power of any drone reaches the preset battery 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 battery power threshold can only support the drone to return from the current position to the starting point.

[0060] 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.

[0061] In this step, by real-time monitoring the remaining power of the drone, once the remaining power reaches the power threshold, the drone should return.

[0062] When the drone reaches the preset power, it may still be performing a task or there are tasks to be executed. In the above cases, a successor for the task needs to be found.

[0063] S113 Obtain the remaining power and the list of tasks to be processed of multiple other drones near any one of the drones, and execute step S117; wherein, taking the current position of any one of the drones as the center, the other drones within the preset radius area are the other drones adjacent to any one of the drones.

[0064] When a drone that is still performing a task or has tasks to be executed reaches the preset power, a successor for the task needs to be found. Therefore, search for adjacent drones with the current position of the drone as the center and a preset radius, and obtain the remaining power and the list of tasks to be processed of the adjacent drones.

[0065] S115 Control any one of the drones to return from the current position to the starting point.

[0066] Of course, it can be foreseen that in this step, it is also necessary to judge whether the current task is completed before returning. If it is completed, control the drone to return to the starting point; if not, allocate the current task to the nearest other drone.

[0067] S117 Judge whether the other drones can add a new cleaning task according to the remaining power of the other drones nearby 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.

[0068] When looking for a successor for the task, it is necessary to judge whether the drone has enough power to support the subsequent tasks and the newly added tasks, so as to judge whether the drone is suitable for adding new tasks. It is easy to understand that drones with sufficient power and few tasks are preferred.

[0069] 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 allocate the tasks to be processed in the list of tasks to be processed of any one of the drones to the at least one selected other drone.

[0070] In addition to judging whether the drone can add new tasks, the distance and waiting time between the two also need to be considered. Therefore, in this embodiment, when at least one other drone is screened out, 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 any one of the drones, and T i，j is the waiting time required for the other drone to execute the newly added task; K1 and K2 are empirical constants.

[0071] For 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.

[0072] S201 determines whether the number of tasks to be processed in the task list of any one of the drones is greater than or equal to a preset threshold of the number of tasks to be processed. If it is greater than or equal to the preset threshold of the number of tasks to be processed, step S203 is executed; if it is less than the preset threshold of the number of tasks to be processed, step S113 is executed; If the number of tasks to be processed in the task list of a certain drone is equal to the preset threshold of the number of tasks to be processed, it means that the tasks of this drone are saturated and it cannot accept new tasks. When all drones are task-saturated, new drones need to be added.

[0073] S203 dispatches a new drone for the area again.

[0074] Embodiment III: Based on the drone deployment methods of Embodiment I and Embodiment II, this embodiment selects appropriate drones for cleaning during secondary cleaning, thereby improving efficiency. The specific steps include: S401 calculates the number of secondary cleaning targets for each area and determines whether the number of secondary cleaning targets is greater than or equal to the amount of tasks to be added that can be accepted by all the drones that can work again in the area. If so, step S403 is executed; otherwise, step S405 is executed.

[0075] During secondary cleaning, the remaining uncleaned areas after the first cleaning are integrated and re-divided. The division rule is still based on the type of dirt.

[0076] After the area is re-divided, there may be drones in the area still executing the tasks of the previous cleaning. Therefore, it can be first determined whether the drones in the area can meet the requirements of this cleaning.

[0077] S403 deploys the idle and reusable drones in the adjacent areas.

[0078] When the drones in the area cannot meet the requirements of the secondary cleaning of the area, drones that can still execute 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, new drones need to be reconfigured.

[0079] The adjacent area described above can be an area adjacent to the target area or an area whose distance from the target area is less than the distance threshold.

[0080] S405 evenly distributes each of the secondary cleaning targets within the area to all the drones that can work again within the area.

[0081] When the drones within the area can already meet the requirements, the secondary cleaning tasks can be evenly distributed.

[0082] 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 additional identical elements in the process, method, article or device including that element.

[0083] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment 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 method. Based on this 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 disc) 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.

[0084] 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 purpose of the present invention and the scope protected by the claims. These all fall within the protection scope of the present invention.

Claims

1. A method for cleaning the glass curtain wall of a city high-rise building based on a drone swarm, characterized in that: Includes steps: S101: Equal area average division: The entire area to be cleaned is divided into several preset areas by equal area average, and based on the cleaning mode of each glass plate in each preset area, the density Pi of the three cleaning modes in each area is calculated, and step S103, or S105 or S107 is executed; the three cleaning modes include: ordinary cleaning mode, strong cleaning mode and mixed cleaning mode; the density is the percentage of glass plates corresponding to each cleaning mode in the current area; i=1, 2, 3; S103: If the density of the first cleaning mode in any area is greater than 90%, and the sum of the densities of the other two cleaning modes is less than 5%, and if the first cleaning mode is the normal cleaning mode or the strong cleaning mode, multiple drones with the normal cleaning mode or the strong cleaning mode are configured for the any area, and step S109 is executed; if the first cleaning mode is the mixed cleaning mode, multiple drones with the mixed cleaning mode are configured for the any area; S105: If the density of the first cleaning mode in any area is greater than 50%, and the density of the second cleaning mode is greater than 40%, and if both the first cleaning mode and the second cleaning mode are ordinary cleaning mode or strong cleaning mode, multiple drones with ordinary cleaning mode and multiple second drones with strong cleaning mode are configured for any area, and the remaining part is cleaned by drones with ordinary cleaning mode and drones with strong cleaning mode respectively; if the first cleaning mode or the second cleaning mode is the mixed cleaning mode, multiple drones with mixed cleaning mode are configured for any area, and step S109 is executed; S107: If the difference between the densities of the three cleaning modes in any area is less than 10%, a plurality of drones with a normal cleaning mode, a plurality of drones with a strong cleaning mode, and a plurality of drones with a mixed cleaning mode are respectively configured for the any area, and step S109 is executed; After the cleaning is completed, S109 performs a regional scan to obtain a second cleaning target and performs a second cleaning; 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 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; 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.

2. A method for cleaning the glass curtain wall of a high-rise building in a city based on a drone swarm according to claim 1, characterized in that: Before the second cleaning, the remaining parts after the first cleaning are divided according to the division rules.

3. A method for cleaning the glass curtain wall of a high-rise building in a city based on a drone swarm according to claim 1, 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.

4. A method for cleaning the glass curtain wall of a city high-rise building based on a drone swarm according to claim 1, 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 number threshold, executes step S203; if it is less than the preset to-be-processed number threshold, executes step S113; S203 dispatches a new drone to the area.

5. The method for cleaning the glass curtain wall of a high-rise building in a city based on a drone swarm according to claim 1, 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.

6. A method for cleaning the glass curtain wall of a high-rise building in a city based on a drone swarm according to claim 1, characterized in that: The steps for performing secondary cleaning specifically include the following steps: S401 integrates the uncleaned areas remaining after the first cleaning, and re-divides the areas according to the types of dirt, then 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; If the task lists of drones in the adjacent area are saturated, the drones are reconfigured; S405 evenly distributes the secondary cleaning targets in the area to all drones in the area that can work again.

7. A method for cleaning the glass curtain wall of a city high-rise building based on a drone swarm according to claim 6, characterized in that: The adjacent area may include an area bordering the target area, or an area whose distance from the target area is less than a preset distance threshold.

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