A method for cleaning glass curtain walls of urban high-rise buildings based on a drone swarm

Abstract

The present invention relates to the technical field of drones, and specifically discloses a method for cleaning the glass curtain walls of urban high-rise buildings based on a drone swarm, including: calculating the density of each of the three cleaning modes in each area based on the respective cleaning modes of each glass plate in each preset area; providing three drone configuration modes according to the density ratio of each cleaning mode to clean each preset area. After the cleaning is completed, area scanning is performed to obtain the targets to be cleaned for the second time, and the second cleaning is carried out. The present invention can configure corresponding types of drones for cleaning according to the types of dirt on the glass plates. Through the above-mentioned drone configuration method, the cleaning efficiency can be greatly improved, and the loss of drones during the journey can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a method for cleaning glass curtain walls of urban high-rise buildings based on a swarm of unmanned aerial vehicles. Background Art

[0002] At present, when cleaning the outer walls of tall buildings, most of them require "spidermen" to climb on the outer walls for manual cleaning. This work not only poses risks, but also the staff needs to continuously lift and supply to replace cleaning equipment, which is time-consuming and laborious.

[0003] The Chinese patent application CN201610865457.8 in the prior art discloses a glass exterior wall cleaning unmanned aerial vehicle, which includes an unmanned aerial vehicle unit and a ground unit. A signal line, a power line and a water pipe are bundled together to form a working cable, and the unmanned aerial vehicle unit and the ground unit are connected through the working cable.

[0004] The above prior art has realized the cleaning of glass curtain walls by unmanned aerial vehicles. However, this prior art can only perform point-to-point cleaning, and actually still needs to be manually controlled.

[0005] Another prior art, the Chinese patent application CN202310330164.X, discloses a solar glass panel cleaning operation monitoring and regulation system. The solar glass panel cleaning operation monitoring and regulation system 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;

[0006] 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 unmanned aerial vehicles.

[0007] However, even by combining the above two prior arts, it is still impossible to make overall plans for unmanned aerial vehicles to 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 swarm of unmanned aerial vehicles, which partially solves or alleviates the above deficiencies in the prior art, and can configure corresponding types of unmanned aerial vehicles for cleaning according to the types of dirt on the glass panels.

[0009] In order to solve the above-mentioned technical problems, the present invention specifically adopts the following technical solutions:

[0010] A method for cleaning glass curtain walls of urban high-rise buildings based on a swarm of unmanned aerial vehicles, comprising the steps:

[0011] S101 calculates 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 executes 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;

[0012] 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;

[0013] 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;

[0014] S107 If the difference between 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 mixed cleaning mode for the any area respectively, and execute step S109;

[0015] S109 After the cleaning is completed, perform area scanning to obtain the targets to be secondarily cleaned, and perform secondary cleaning.

[0016] 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;

[0017] When the first cleaning mode is the mixed cleaning mode, configure drones with the mixed cleaning mode to clean the entire area.

[0018] 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;

[0019] 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 is to be cleaned twice.

[0020] 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.

[0021] As an improvement, before the second cleaning, divide the remaining part after the first cleaning according to the division rule.

[0022] As an improvement, it further includes the steps:

[0023] 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 judge 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;

[0024] 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;

[0025] S115 Control the any drone to return from the current position to the starting point;

[0026] 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;

[0027] 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.

[0028] 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 UAV and any one of the UAVs, and T i，j is the waiting time required for the other UAV to execute the newly added task; K1 and K2 are empirical constants;

[0029] For the tasks to be processed, the tasks to be processed that are ranked higher are preferentially assigned to other UAVs with a smaller recommendation index.

[0030] As an improvement, before executing step S113, the following step is further included:

[0031] S201 determines whether the number of tasks to be processed in the task list to be processed of any one of the UAVs 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;

[0032] S203 re-dispatchs a new UAV for the area.

[0033] As an improvement, any one of the UAVs and multiple nearby other UAVs are in the same area.

[0034] As an improvement, step S115 specifically includes the following steps:

[0035] S114 determines whether the current task is completed. If it is completed, control any one of the UAVs to return to the starting point; if it is not completed, assign the current task to the nearest other UAV.

[0036] As an improvement, the following step is further included:

[0037] 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 UAVs that can work again in the area. If so, step S403 is executed; otherwise, step S405 is executed;

[0038] S403 deploys the idle and reusable UAVs in the adjacent areas;

[0039] S405 evenly distributes each of the secondary cleaning targets in the area to all the UAVs that can work again in the area.

[0040] The beneficial effects of the present invention are as follows:

[0041] 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 ratios of the three cleaning modes, different forms of drone configurations are carried out in three cases.

[0042] In the case where the density of the first cleaning mode is greater than the first preset density threshold P0, and the sum of the densities of the remaining two cleaning modes is less than the second preset density threshold P0', only multiple drones with the first cleaning mode are configured. If the first cleaning mode is a normal cleaning mode or a strong cleaning mode, the corresponding drones are configured for cleaning, and the remaining part is to be cleaned for the second time. If the first cleaning mode is a mixed cleaning mode, drones with the mixed cleaning mode are configured 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 small, it is more economical and efficient to directly configure drones with the mixed cleaning mode for cleaning.

[0043] In the case where 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''', drones corresponding to the cleaning modes are configured 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, the corresponding drones are configured for cleaning. The remaining part 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. If the first cleaning mode or the second cleaning mode is a mixed cleaning mode, the corresponding drones are configured for cleaning, and the remaining part is to be cleaned for the second time.

[0044] In the case where the difference between the densities of the three cleaning modes pairwise is less than the preset difference threshold, it is only necessary to configure drones corresponding to the cleaning modes for cleaning.

[0045] Through the above drone configuration method, the cleaning efficiency can be greatly improved, and the loss of drones during the journey can be reduced.

[0046] 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, 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.

[0047] 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

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in 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 according to these drawings.

[0049] Figure 1 It is the flowchart of the first embodiment of the present invention;

[0050] Figure 2 It is the flowchart of the second embodiment of the present invention;

[0051] Figure 3 It is the flowchart of the third embodiment of the present invention. Detailed implementation manners

[0052] 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 accompanying 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 scope of protection of the present invention.

[0053] 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.

[0054] 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.

[0055] In this text, unless otherwise clearly specified and defined, terms such as "installation", "equipped with", "connection", etc. shall 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 can be the communication inside two components. 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.

[0056] In this text, "and / or" includes any and all combinations of one or more of the listed related items.

[0057] In this text, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0058] Example 1:

[0059] As Figure 1 shown, the present invention provides a method for cleaning the glass curtain walls of urban high-rise buildings based on a drone swarm, 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. Its specific steps include:

[0060] 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.

[0061] 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. Therefore, 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. 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 the dirt of their own types and cannot cover other dirt. And in practice, the dirt on some glass 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 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.

[0062] In this step, the cleaning mode of each glass plate 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 plate. The specific method of obtaining the dirt type can be referred to the prior art CN202310330164.X in the background technology, which will not be repeated here.

[0063] Since the area to be cleaned may be relatively large, which is not conducive to the deployment of drones, in this embodiment, the area to be cleaned may be divided into several preset areas before the drones are coordinated for cleaning.

[0064] The rule for dividing the preset areas can be to directly divide the area to be cleaned into equal areas, or to divide it according to the type of dirt. In this embodiment, the second division method is more preferred.

[0065] In this embodiment, the types of dirt include ordinary type (dust + scale), strong type (oil + bird droppings) and mixed type (dust + scale + oil + bird droppings), which correspond to the cleaning modes of the drone: ordinary cleaning mode, strong cleaning mode and mixed cleaning mode.

[0066] Of course, it is understandable that even if the division is based on the type of filth, for the sake of simplicity, the regional division is also a rough division, and try to have only one type of filth in one area, but in reality there will still be multiple types of filth in some areas.

[0067] After the preset areas are divided, the density of various cleaning modes can be calculated according to the density of the glass plates with various types of dirt. In simple terms, if it is analyzed that there are 80% ordinary dirt, 15% strong dirt and 10% mixed dirt in a preset area, the density of the corresponding cleaning mode is P1=80%, P2=15%, P3=5%, and so on.

[0068] S103: If the density of the first cleaning mode in any area 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', multiple drones with the first cleaning mode are configured for the any area, and step S109 is executed; the first cleaning mode is any one of the normal cleaning mode, the strong cleaning mode and the mixed cleaning mode.

[0069] In the previous step, the density of each cleaning mode within a certain preset area was calculated. However, it is unreasonable to match drones strictly according to the density in some cases. For example, when the density of a certain cleaning mode is over 90%, while the combined density of other modes is no more than 5% (the remaining 5% does not need cleaning), for a drone 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.

[0070] 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%), while the combined density 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.

[0071] The mode with a density greater than the preset density threshold P0 may be one of the normal cleaning mode, the intensive cleaning mode, and the mixed cleaning mode.

[0072] When the first cleaning mode is the normal cleaning mode or the intensive cleaning mode, configure drones with the normal cleaning mode or the intensive cleaning mode to clean the 90% corresponding area, and the remaining 5% will be cleaned by configuring drones in subsequent secondary cleaning.

[0073] 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 + intensive dirt in this area is also less than 5%, it is more economical and efficient to directly configure drones with the mixed cleaning mode for cleaning.

[0074] 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 intensive cleaning mode, and the mixed cleaning mode, and the second cleaning mode is any one of the other two cleaning modes except 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.

[0075] Based on the analysis of the previous step, when the density of a certain cleaning mode in a certain preset area is too small, it is uneconomical to configure drones alone. However, when the density of a certain type of dirt reaches a certain threshold, drones of the corresponding type need to be configured for cleaning.

[0076] 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.

[0077] When 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. For the remaining 10% (it is possible that it is less than 10% because some glass plates do not need to be cleaned), it can be completed by separately cleaning with drones with the normal cleaning mode and drones with the strong cleaning mode, without the need to separately configure drones with a hybrid cleaning mode for cleaning.

[0078] When the first cleaning mode or the second cleaning mode is a hybrid cleaning mode, configure the corresponding drones for cleaning. And for the remaining 10% (it may be the normal cleaning mode or the strong cleaning mode), configure the corresponding drones for cleaning during the subsequent secondary cleaning.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] S109 After completing the cleaning, perform area scanning to obtain the targets for secondary cleaning, and perform secondary cleaning.

[0083] In steps S103 and S105, for the sake of economy, it is possible that some areas are not cleaned. When the entire area to be cleaned has undergone one cleaning, the uncleaned areas are overall planned 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 by referring to the method in Embodiment 3. For details, see Embodiment 3.

[0084] Embodiment 2:

[0085] Based on the first embodiment, this embodiment allocates drones according to the battery power of the drones. The specific steps are as follows:

[0086] S111 When the remaining battery power of any one drone reaches the preset battery threshold, obtain the list of tasks to be processed of the any one 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 threshold can only support the drone to return from the current position to the starting point.

[0087] 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.

[0088] In this step, by real-time monitoring the remaining battery power of the drone, once the remaining battery power reaches the battery threshold, the drone should be allowed to return.

[0089] When the drone reaches the preset battery power, it may still be executing tasks or there are tasks to be executed. In the above cases, it is necessary to find a successor to the task.

[0090] S113 Obtain the remaining battery power and the list of tasks to be processed 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.

[0091] When a drone that is still executing tasks or has tasks to be executed 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 tasks to be processed of the adjacent drones.

[0092] S115 Control the any one drone to return from the current position to the starting point.

[0093] 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.

[0094] S117 Judge whether the other drones can add a new cleaning task according to the remaining battery 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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;

[0100] The number of pending tasks in a drone's task list is set to a threshold, indicating that the drone's tasks are saturated and no new tasks can be accepted. When all drones are saturated, new drones need to be added.

[0101] S203 dispatches a new drone to the area.

[0102] Embodiment three:

[0103] 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:

[0104] S401 calculates the number of secondary cleaning targets in each area, and determines whether the number of secondary cleaning targets is greater than or equal to the amount of tasks that can be added to be processed for all drones that can work again in the area. If so, execute step S403; otherwise, execute step S405.

[0105] During secondary cleaning, the uncleaned areas remaining after the first cleaning are integrated and re-divided. The division rule still follows the type of dirt.

[0106] After the secondary division of the area, there may be drones still performing the tasks of the previous cleaning within the area. Therefore, it is possible to first determine whether the drones within the area can meet the requirements of this cleaning.

[0107] S403 Allocate the idle and reusable drones within adjacent areas.

[0108] When the drones within the area cannot meet the requirements of the secondary cleaning of the area, drones that can still perform tasks are allocated from adjacent areas for cleaning. It can be foreseen that if the task lists of the drones within the adjacent areas are all saturated, it is necessary to reconfigure the drones.

[0109] The adjacent areas mentioned above can be areas bordering the target area, or areas whose distance from the target area is less than the distance threshold.

[0110] S405 Evenly distribute each of the secondary cleaning targets within the area to all the reusable drones within the area.

[0111] When the drones within the area can already meet the requirements, the secondary cleaning tasks can be evenly distributed.

[0112] 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 explicitly listed, or elements inherent to such a process, method, article or device. Without further limitations, 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.

[0113] 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 method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. 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.

[0114] 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 rather than 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 of the present invention and the scope protected by the claims. All of these 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 calculates the density Pi of the three cleaning modes in each preset area based on the cleaning mode of each glass plate in each preset area, and executes step S103, S105 or S107; the three cleaning modes include: normal 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 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', multiple drones with the first cleaning mode are configured for the any area, and step S109 is executed; the first cleaning mode is any one of the ordinary 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 value P0'', and the density of the second cleaning mode is greater than the fourth preset density threshold value P0''', multiple drones with the first cleaning mode and multiple second drones with the second cleaning mode are configured for the any area; 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 is less than the first preset density threshold, and the fourth preset density threshold is greater than the second preset density threshold, and step S109 is executed; S107: If the difference between the densities of the three cleaning modes in any area 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 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: In step S103, 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. A method for cleaning glass curtain walls of urban high-rise buildings based on drone swarms according to claim 1, characterized in that: In step S105, 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 having the corresponding cleaning mode is configured to perform cleaning; And the remaining parts are cleaned by drones with normal cleaning mode and drones with 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. 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 entire area to be cleaned is divided into a number of preset areas according to a division rule; the division rule is to divide according to the type of dirt.

5. A method for cleaning the glass curtain wall of a high-rise building in a city based on a drone swarm according to claim 4, characterized in that: Before the second cleaning, the remaining parts after the first cleaning are divided according to the division rules.

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: 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.

7. 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: 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.

8. 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 completed, any one of the drones is controlled to return to the starting point; if not completed, the current task is assigned to the nearest other drone.

9. 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: 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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