Window cleaning machine control methods, window cleaning machines, electronic equipment, storage media and products

Abstract

This application proposes a window cleaning machine control method, a window cleaning machine, electronic equipment, a storage medium, and a product. The method acquires the adsorption state data of the window cleaning machine, as well as obstacle data and working environment data of the area to be cleaned. Based on the adsorption state data, and at least one of the obstacle data and working environment data, it determines the adsorption adjustment data of the window cleaning machine, and adjusts the adsorption components of the window cleaning machine based on the adsorption adjustment data. Based on the obstacle data, it determines the travel strategy of the window cleaning machine and controls the movement of the window cleaning machine according to the travel strategy. Using the technical solution of this application, the adsorption adjustment strategy of the window cleaning machine can be determined in real time, and the adsorption components of the window cleaning machine can be adjusted to ensure the normal adsorption state of the window cleaning machine, improving the safety of the window cleaning machine. Furthermore, by directly determining the travel strategy of the window cleaning machine based on obstacle data, it avoids the impact of mechanical collision-type working methods on the adsorption state of the window cleaning machine, further improving the safety of the window cleaning machine.

Description

Technical Field

[0001] This application relates to the field of machine control technology, and in particular to a window cleaning machine control method, window cleaning machine, electronic equipment, storage medium and product. Background Technology

[0002] Window cleaning machines are permanently suspended access devices specifically designed for cleaning and maintenance of exterior facades, skylights, and windows of high-rise buildings. When performing automatic cleaning in high-altitude operations, window cleaning machines need to identify the boundaries of the area to be cleaned and obstacles in the path of travel to ensure complete coverage of the area to be cleaned.

[0003] In existing technologies, mechanical collision-based edge finding and obstacle avoidance are commonly used. However, during repeated collisions, the physical impact may affect the adhesion state of the window cleaning machine, thereby affecting its safety. Summary of the Invention

[0004] Based on the above requirements, this application proposes a window cleaning machine control method, a window cleaning machine, electronic equipment, storage medium, and product, which can ensure the adsorption state of the window cleaning machine and improve the safety of the window cleaning machine.

[0005] To achieve the above objectives, this application proposes the following technical solution: According to a first aspect of the embodiments of this application, a window cleaning machine control method is provided, comprising: Acquire data on the adsorption status of the window cleaning machine, as well as data on obstacles and the working environment in the area to be cleaned; Based on the adsorption state data, and at least one of the obstacle data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, and the adsorption components of the window cleaning machine are adjusted based on the adsorption adjustment data. Based on the obstacle data, the travel strategy of the window cleaning machine is determined, and the travel strategy is used to control the movement of the window cleaning machine.

[0006] Optionally, the obstacle data includes: obstacle image data; After acquiring the window cleaning machine's adsorption status data, as well as obstacle data and working environment data for the area to be cleaned, the following is also included: Based on the obstacle image data, the type of target obstacle corresponding to the obstacle image data is determined.

[0007] Optionally, based on the adsorption state data and the obstacle data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the adsorption state data and the pre-set standard adsorption state data, the adsorption adjustment data of the window cleaning machine outside the preset adjustment range of the obstacle is determined; If the target obstacle type corresponding to the obstacle image data belongs to a pre-set cleanable type, the target adsorption state data corresponding to the target obstacle type is determined based on the pre-set correspondence between the obstacle type and the adsorption state data. Based on the target adsorption state data corresponding to the target obstacle type and the adsorption state data, the adsorption adjustment data of the window cleaning machine within the preset adjustment range of the obstacle is determined.

[0008] Optionally, the working environment data includes: dirt data of the area to be cleaned; Based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the dirt data of the area to be cleaned, the target degree of dirt in the area to be cleaned is determined; Based on the pre-set correspondence between the degree of dirtiness and the adsorption state data, the target adsorption state data corresponding to the target degree of dirtiness is determined; Based on the target adsorption state data and the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0009] Optionally, the working environment data includes at least one of the following: current ambient wind speed and current ambient temperature; Based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: When the working environment data includes the current ambient wind speed, the first target adsorption state data corresponding to the current ambient wind speed is determined based on the pre-set correspondence between the ambient wind speed and the adsorption state data. And / or, When the working environment data includes the current ambient temperature, the second target adsorption state data corresponding to the current ambient temperature is determined based on the pre-set correspondence between ambient temperature and adsorption state data. Based on the adsorption state data, as well as the first target adsorption state data and / or the second target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0010] Optionally, based on the adsorption state data, the obstacle data, and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the pre-set correspondence between obstacle types and adsorption state data and the obstacle data, the third target adsorption state data corresponding to the obstacle data is determined; and based on the pre-set correspondence between working environment data and adsorption state data and the working environment data, the fourth target adsorption state data corresponding to the working environment data of the window cleaning machine is determined. Based on the maximum value of the third target adsorption state data and the fourth target adsorption state data, as well as the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0011] Optionally, before determining the travel strategy of the window cleaning machine based on the obstacle data, the method further includes: Obtain updated adsorption state data after adsorption adjustment; Correspondingly, based on the obstacle data, the travel strategy of the window cleaning machine is determined, including: Based on the obstacle data and the updated adsorption state data, the travel strategy of the window cleaning machine is determined.

[0012] Optionally, based on the obstacle data and the updated adsorption state data, the travel strategy of the window cleaning machine is determined, including: If the target obstacle type corresponding to the obstacle image data in the obstacle data belongs to a pre-set cleanable type, it is determined whether the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type; the adsorption standard range corresponding to the target obstacle type represents the range of adsorption state data required to ensure the adsorption state of the window cleaning machine when the window cleaning machine cleans the obstacle of the target obstacle type. If the updated adsorption state data meets the adsorption data standard range corresponding to the target obstacle type, then the target obstacle corresponding to the obstacle data is taken as the objective, and the travel strategy of the window cleaning machine is determined according to the obstacle data. If the updated adsorption state data does not meet the adsorption data standard range corresponding to the target obstacle type, or if the target obstacle type corresponding to the obstacle image data in the obstacle data does not belong to the pre-set cleanable type, then the goal is to avoid the target obstacle corresponding to the obstacle data, and the travel strategy of the window cleaning machine is determined based on the obstacle data.

[0013] Optionally, the window cleaning machine control method also includes: Monitor the main power status of the window cleaning machine; When the main power status indicates that the main power is off, the system switches to backup battery power so that the backup battery can power the adsorption components in the window cleaning machine.

[0014] According to a second aspect of the embodiments of this application, a window cleaning machine is provided, including: an environmental sensing module, a negative pressure sensor, and a controller; The environmental sensing module and the negative pressure sensor are respectively connected to the controller; The environmental sensing module is used to collect obstacle data and working environment data of the area to be cleaned. The negative pressure sensor is used to collect the adsorption status data of the window cleaning machine; The controller is used to implement the above-mentioned window cleaning machine control method.

[0015] According to a third aspect of the embodiments of this application, an electronic device is provided, including: a memory and a processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the above-described window cleaning machine control method by running the program in the memory.

[0016] According to a fourth aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-described window cleaning machine control method is implemented.

[0017] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including computer program instructions, which, when executed by a processor, cause the processor to implement the above-described window cleaning machine control method.

[0018] The window cleaning machine control method proposed in this application acquires the adsorption status data of the window cleaning machine, as well as obstacle data and working environment data of the area to be cleaned; based on the adsorption status data, and at least one of the obstacle data and working environment data, it determines the adsorption adjustment data of the window cleaning machine, and adjusts the adsorption components of the window cleaning machine based on the adsorption adjustment data; based on the obstacle data, it determines the travel strategy of the window cleaning machine, and controls the travel of the window cleaning machine according to the travel strategy. Using the technical solution of this application, the adsorption adjustment strategy of the window cleaning machine can be determined in real time, and the adsorption components of the window cleaning machine can be adjusted to ensure the normal adsorption status of the window cleaning machine, improving the safety of the window cleaning machine. Furthermore, by directly determining the travel strategy of the window cleaning machine based on obstacle data, it avoids the impact of mechanical collision-type working methods on the adsorption status of the window cleaning machine, further improving the safety of the window cleaning machine. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a window cleaning machine control method provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart illustrating another window cleaning machine control method provided in an embodiment of this application.

[0022] Figure 3 This is a flowchart illustrating another window cleaning machine control method provided in an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of a window cleaning machine control device provided in an embodiment of this application.

[0024] Figure 5 This is a schematic diagram of the structure of a window cleaning machine provided in an embodiment of this application.

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solution of this application embodiment is applicable to the application scenario of window cleaning machine control. By adopting the technical solution of this application embodiment, the adsorption state of the window cleaning machine can be guaranteed, thus improving the safety of the window cleaning machine.

[0027] Window cleaning machines are permanent suspended access devices specifically designed for cleaning and maintenance of exterior facades, skylights, and windows of high-rise buildings. During automated high-altitude curtain wall cleaning operations, to achieve complete wall coverage without blind spots, ensure a neat and complete cleaning trajectory, and prevent omissions, repetitions, or boundary violations, window cleaning machines must accurately identify the outlines and boundaries of the areas to be cleaned, wall corners, and window sills. Simultaneously, they must anticipate various protruding obstacles along the path, including structural strips, window frame protrusions, curtain wall dividing strips, exterior decorative components, protruding pipes, and irregularly shaped facades, thereby planning a reasonable route and obstacle avoidance strategy.

[0028] In existing technologies, mechanical collision-based edge finding and obstacle avoidance are commonly used. The basic principle of this method is to install touch sensors at the front or around the window cleaning machine. When the machine encounters a boundary or obstacle during its movement, the sensors immediately detect the collision signal and feed this information back to the control system. The control system then adjusts the machine's direction of travel or stops it to avoid further collisions or damage.

[0029] However, while mechanical collision-based edge-finding and obstacle-avoidance methods can meet basic operational needs to some extent, they also have significant limitations. Firstly, repeated physical collisions not only cause wear and tear on the window cleaning machine's mechanical structure, shortening its lifespan, but also potentially affect the machine's adhesion state due to the impact force. In high-altitude operations, the adhesion stability of the window cleaning machine is a key factor in ensuring operational safety. Once the adhesion state is disturbed, the window cleaning machine may experience shaking, displacement, or even detachment, seriously affecting its safety.

[0030] Based on this, this application proposes a window cleaning machine control method. This technical solution can determine the adsorption adjustment strategy of the window cleaning machine in real time, adjust the adsorption components of the window cleaning machine to ensure the normal adsorption state of the window cleaning machine, and directly determine the travel strategy of the window cleaning machine based on obstacle data, thereby avoiding the impact of mechanical collision-type working mode on the adsorption state of the window cleaning machine and solving the problem of low safety of window cleaning machines in the prior art.

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] Exemplary methods See Figure 1 As shown in the figure, this application proposes a window cleaning machine control method. The method includes: S101. Obtain the adsorption status data of the window cleaning machine, as well as the obstacle data and working environment data of the area to be cleaned.

[0033] In this embodiment, the area that the window cleaning machine needs to clean is taken as the area to be cleaned, and obstacle data and working environment data of the area to be cleaned are obtained, as well as the adsorption status data of the window cleaning machine.

[0034] Specifically, in this embodiment, obstacle data and working environment data can be collected using an environmental perception module. Obstacle data includes obstacle image data, and the environmental perception module can include a camera; the obstacle image data can be collected using the camera installed in the window cleaning machine. Obstacle data can also include distance data between the window cleaning machine and the obstacle; the environmental perception module can also include a non-contact ranging sensor; this distance data can be collected using the non-contact ranging sensor installed in the window cleaning machine. Working environment data can include dirt data of the area to be cleaned; the environmental perception module can include a dirt sensor; the dirt data can be collected using the dirt sensor installed in the window cleaning machine. Working environment data can also include the current ambient wind speed; the environmental perception module can also include a wind speed sensor; the current ambient wind speed can be collected using the wind speed sensor installed in the window cleaning machine. Working environment data can also include the current ambient temperature; the environmental perception module can also include a temperature sensor; the current ambient temperature can be collected using the temperature sensor installed in the window cleaning machine.

[0035] In this embodiment, the non-contact ranging sensor can be, but is not limited to, a Direct Time-of-Flight (dTOF) ranging principle. It incorporates a SPAD (Single-Photon Avalanche Diode) and a unique dTOF acquisition and processing technology, enabling 120 accurate ranging measurements per second. During each measurement, the radar emits an infrared laser, which is reflected back to the single-photon receiving unit upon encountering an object. The time difference between the laser emission time and the time the single-photon receiving unit receives the laser is obtained; this time difference is the time of flight of light, which, combined with the speed of light, allows for the calculation of distance information. The non-contact ranging sensor (TOF sensor) includes two operating modes: a near-range mode and a long-range mode. The near-range mode can be used for edge detection in window cleaning machines. The long-range mode can be used for navigation mapping and path planning in window cleaning machines.

[0036] In this embodiment, the dirt sensor can be a component consisting of a light source (such as an LED) and a light receiver (photoelectric converter). When the sensor is placed on a glass surface, the light emitted by the light source shines onto the glass. If the glass surface is clean or covered by raindrops, most of the light will enter the external space unimpeded, with only a small amount of reflected light reaching the receiver. However, when there is dirt (such as dust, grease, etc.) on the glass surface, the dirt particles will scatter the light, resulting in more reflected light reaching the receiver. By detecting changes in the intensity of the reflected light, the sensor can determine the degree of dirt on the glass surface.

[0037] In this embodiment, the adsorption state data can be collected using a negative pressure sensor, with the pressure value collected by the negative pressure sensor serving as the adsorption state data. Alternatively, based on the pressure value collected by the negative pressure sensor and a pre-set correspondence between pressure value and adhesion, a target adhesion value corresponding to the pressure value can be determined, and this target adhesion value can be used as the adsorption state data. Here, adhesion refers to the degree of contact between the window cleaning machine body and the window glass.

[0038] In this embodiment, the environmental sensing module and the negative pressure sensor can be connected to the controller installed inside the window cleaning machine. The obstacle data and working environment data of the area to be cleaned collected by the environmental sensing module, and the adsorption status data of the window cleaning machine collected by the negative pressure sensor, all need to be transmitted to the controller. The controller acquires the obstacle data and working environment data of the area to be cleaned, as well as the adsorption status data of the window cleaning machine. Both the environmental sensing module and the negative pressure sensor can use I2C communication to transmit data with the controller.

[0039] S102. Based on adsorption state data, obstacle data, and working environment data, determine the adsorption adjustment data of the window cleaning machine, and adjust the adsorption components of the window cleaning machine based on the adsorption adjustment data.

[0040] In this embodiment, the target adsorption state data of the window cleaning machine is determined based on the acquired adsorption state data, as well as at least one of the obstacle data and working environment data of the area to be cleaned. Then, based on the target adsorption state data of the window cleaning machine and the currently acquired adsorption state data of the window cleaning machine, the adsorption adjustment data of the window cleaning machine is determined. The adsorption adjustment data is the data required to adjust the current adsorption state data to the target adsorption state data, and is preferably set as the difference between the target adsorption state data and the current adsorption state data.

[0041] In this embodiment, after determining the adsorption adjustment data of the window cleaning machine, the adsorption components of the window cleaning machine are adjusted based on the adsorption adjustment data. In this embodiment, the adsorption state data, target adsorption state data, and adsorption adjustment data are preferably the pressure values ​​between the window cleaning machine and the window glass. Adjusting the adsorption components of the window cleaning machine based on the adsorption adjustment data means compensating for negative pressure on the adsorption components of the window cleaning machine according to the adsorption adjustment data, thereby adjusting the current adsorption state data of the window cleaning machine to the target adsorption state data.

[0042] Specifically, in this embodiment, a pre-defined correspondence is established between obstacle types and adsorption state data, as well as a correspondence between working environment data and adsorption state data.

[0043] When determining the adsorption adjustment data of a window cleaning machine based on adsorption state data and obstacle data, the first step is to determine the target obstacle type corresponding to the obstacle image data in the obstacle data. Then, according to the pre-set correspondence between obstacle types and adsorption state data, the adsorption state data corresponding to the target obstacle type is determined as the target adsorption state data. Based on the target adsorption state data of the window cleaning machine and the currently acquired adsorption state data of the window cleaning machine, the adsorption adjustment data of the window cleaning machine is determined. Specifically, determining the target obstacle type corresponding to the obstacle image data can be achieved using a pre-established obstacle recognition model. The obstacle image data is input into the model, and the model outputs the target obstacle type. The obstacle recognition model is trained by inputting pre-collected obstacle image samples carrying obstacle type labels, with the goal of the predicted obstacle type output by the obstacle recognition model matching the obstacle type label. Alternatively, a rule engine can be used to determine the target obstacle type corresponding to the obstacle image data. For example, feature extraction can be performed on the obstacle image data to obtain obstacle image features. These features can then be compared with pre-set geometric feature conditions corresponding to various obstacle types to determine the target obstacle type. In this embodiment, obstacle types include: glass boundaries, handles, latches, window trim, raised stains, and adhesive substances.

[0044] When determining the adsorption adjustment data of the window cleaning machine based on adsorption state data and working environment data, the adsorption state data corresponding to the current working environment data is determined as the target adsorption state data according to the pre-set correspondence between working environment data and adsorption state data. Based on the target adsorption state data of the window cleaning machine and the currently acquired adsorption state data of the window cleaning machine, the adsorption adjustment data of the window cleaning machine is determined.

[0045] In one specific implementation, the adsorption adjustment data of the window cleaning machine is determined based on adsorption state data and obstacle data, specifically including the following steps: First, based on the adsorption state data and the pre-set standard adsorption state data, determine the adsorption adjustment data of the window cleaning machine outside the preset adjustment range of the obstacle.

[0046] In this embodiment, standard adsorption state data is preset. This standard adsorption state data is the adsorption state data required to ensure normal cleaning operation on a work surface without obstacles. This embodiment first needs to determine whether the window cleaning machine is within the preset adjustment range of the obstacle. If the window cleaning machine is not within the preset adjustment range, that is, outside the preset adjustment range, the adsorption adjustment data for the window cleaning machine outside the preset adjustment range is determined directly based on the adsorption state data and the preset standard adsorption state data. In other words, the difference between the adsorption state data and the preset standard adsorption state data is used as the adsorption adjustment data for the window cleaning machine outside the preset adjustment range. Based on this adsorption adjustment data, the adsorption state data of the window cleaning machine can be adjusted to the preset standard adsorption state data.

[0047] In this embodiment, the obstacle data includes the distance data between the window cleaning machine and the obstacle. To determine whether the window cleaning machine is within the preset adjustment range of the obstacle, it is necessary to determine whether the distance data between the window cleaning machine and the obstacle is less than the preset adjustment range. If the distance data is less than the preset adjustment range, it means that the window cleaning machine is within the preset adjustment range of the obstacle. If the distance data is not less than the preset adjustment range, it means that the window cleaning machine is outside the preset range of the obstacle.

[0048] In this embodiment, the standard adsorption state data of the window cleaning machine can be set to a fixed value, or different standard adsorption state data can be set according to different working environment data of the window cleaning machine. For example, different ambient wind speeds correspond to different standard adsorption state data. The higher the ambient wind speed, the higher the corresponding standard adsorption state data. Different ambient temperatures correspond to different standard adsorption state data. The higher the ambient temperature, the higher the corresponding standard adsorption state data.

[0049] Second, if the target obstacle type corresponding to the obstacle image data belongs to a pre-set cleanable type, the target adsorption state data corresponding to the target obstacle type is determined based on the pre-set correspondence between the obstacle type and the adsorption state data. Based on the target adsorption state data and the adsorption state data corresponding to the target obstacle type, the adsorption adjustment data of the window cleaning machine within the preset adjustment range of the obstacle is determined.

[0050] In this embodiment, after acquiring obstacle data and working environment data of the area to be cleaned, as well as the adsorption status data of the window cleaning machine, it is also necessary to determine the target obstacle type corresponding to the obstacle image data based on the obstacle image data. The specific steps for determining the target obstacle type corresponding to the obstacle image data have been described above and will not be repeated in this embodiment.

[0051] If the target obstacle type corresponding to the obstacle image data belongs to a pre-set cleanable type, the window cleaning robot may affect the adhesion between the robot and the window glass due to the protrusion of the obstacle (such as causing air leakage). In this case, it is necessary to adjust the adhesion data of the window cleaning robot, for example, by increasing the adhesion force, to avoid the adhesion being affected by the protrusion of the obstacle. Therefore, it is necessary to determine the target adhesion data corresponding to the target obstacle type based on the pre-set correspondence between obstacle types and adhesion data, and then determine the adhesion adjustment data based on the target adhesion data corresponding to the target obstacle type and the current adhesion data. This adhesion adjustment data will be used as the adhesion adjustment data for the window cleaning robot within the preset adjustment range of the obstacle. Among the obstacle types, protruding stains are cleanable, while glass edges, handles, latches, window coverings, and adhesive substances are not. Protruding stains in the obstacle types can also include different types, such as mud spots and bird droppings. Different types of obstacles correspond to different adsorption state data. For example, the adsorption state data of obstacles that are easier to clean (such as mud spots) can be less than that of obstacles that are not easy to clean (such as bird droppings).

[0052] If the type of the target obstacle corresponding to the obstacle image data does not belong to the pre-set cleanable type, it means that the window cleaning machine needs to bypass the obstacle. In this case, it is not necessary to determine the adsorption state data corresponding to the target obstacle type, nor is it necessary to determine the adsorption adjustment data according to the adsorption state data. It is only necessary to determine the adsorption adjustment data of the window cleaning machine within the preset adjustment range of the obstacle based on the adsorption state data and the pre-set standard adsorption state data.

[0053] In this embodiment, after determining the adsorption adjustment data of the window cleaning machine within a preset range of obstacles and the adsorption adjustment data outside the preset range of obstacles, when the window cleaning machine travels outside the preset range of obstacles, the adsorption components of the window cleaning machine are adjusted according to the adsorption adjustment data outside the preset range of obstacles, and when the window cleaning machine travels within the preset range of obstacles, the adsorption components of the window cleaning machine are adjusted according to the adsorption adjustment data within the preset range of obstacles.

[0054] In one specific implementation, the working environment data includes: dirt data of the area to be cleaned; correspondingly, based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, specifically including the following steps: First, based on the dirt data of the area to be cleaned, determine the target level of dirt in the area to be cleaned.

[0055] This embodiment utilizes a dirt sensor installed on the window cleaning machine to collect dirt data of the area to be cleaned. Based on the dirt data range corresponding to each preset dirt level, it determines the dirt level corresponding to the dirt data range to which the dirt data of the area to be cleaned belongs, and uses this dirt level as the target dirt level of the area to be cleaned.

[0056] Second, based on the pre-set correspondence between the degree of dirtiness and the adsorption state data, the target adsorption state data corresponding to the target degree of dirtiness is determined.

[0057] In this embodiment, a pre-defined correspondence between the degree of dirtiness and adsorption state data is established. Different degrees of dirtiness correspond to different adsorption state data. The higher the degree of dirtiness, the greater the adsorption force required to improve the cleaning power of the window cleaning machine. Therefore, the higher the degree of dirtiness, the greater the corresponding adsorption state data. After determining the target degree of dirtiness in the area to be cleaned, this embodiment needs to determine the adsorption state data corresponding to the target degree of dirtiness based on the pre-defined correspondence between the degree of dirtiness and adsorption state data, and use this as the target adsorption state data.

[0058] Third, based on the target adsorption state data and the adsorption state data, determine the adsorption adjustment data for the window cleaning machine.

[0059] In one specific implementation, the working environment data includes at least one of the following: current ambient wind speed and current ambient temperature. Correspondingly, based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, specifically including the following steps: First, given that the working environment data includes the current ambient wind speed, the first target adsorption state data corresponding to the current ambient wind speed is determined based on the pre-set correspondence between the ambient wind speed and the adsorption state data.

[0060] This embodiment pre-defines the correspondence between ambient wind speed and adsorption state data. This correspondence can be between multiple pre-defined ranges of ambient wind speed and adsorption state data. The higher the ambient wind speed, the more unstable the adsorption state of the window cleaning machine, requiring greater adsorption force to maintain its adsorption state. Therefore, the higher the ambient wind speed, the higher the corresponding adsorption state data.

[0061] When the working environment data includes the current ambient wind speed, the adsorption state data corresponding to the current ambient wind speed is determined based on the correspondence between the ambient wind speed and the adsorption state data, and is used as the first target adsorption state data.

[0062] Second, given that the working environment data includes the current ambient temperature, the second target adsorption state data corresponding to the current ambient temperature is determined based on the pre-set correspondence between the ambient temperature and the adsorption state data.

[0063] This embodiment pre-sets a correspondence between ambient temperature and adsorption state data. This correspondence can be between multiple pre-defined ambient temperature ranges and adsorption state data. The higher the ambient temperature, the more unstable the adsorption state of the window cleaning machine, requiring greater adsorption force to maintain its adsorption state. Therefore, the higher the ambient temperature, the larger the corresponding adsorption state data.

[0064] When the working environment data includes the current ambient temperature, the adsorption state data corresponding to the current ambient temperature is determined based on the correspondence between the ambient temperature and the adsorption state data, and is used as the second target adsorption state data.

[0065] Third, based on the adsorption state data, as well as the first target adsorption state data and / or the second target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0066] When the working environment data only includes the current ambient wind speed, the adsorption adjustment data of the window cleaning machine is determined based on the adsorption state data and the first target adsorption state data. That is, the difference between the adsorption state data and the first target adsorption state data is used as the adsorption adjustment data of the window cleaning machine.

[0067] When the working environment data only includes the current ambient temperature, the adsorption adjustment data of the window cleaning machine is determined based on the adsorption state data and the second target adsorption state data. That is, the difference between the adsorption state data and the second target adsorption state data is used as the adsorption adjustment data of the window cleaning machine.

[0068] When the working environment data includes the current ambient wind speed and the current ambient temperature, the maximum value between the first target adsorption state data and the second target adsorption state data is taken as the final target adsorption state data. Based on the adsorption state data and the final target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined. That is, the difference between the adsorption state data and the final target adsorption state data is taken as the adsorption adjustment data of the window cleaning machine.

[0069] Furthermore, when the working environment data includes dirt data of the area to be cleaned, and at least one of the current ambient wind speed and current ambient temperature, the target dirt level of the area to be cleaned is determined based on the dirt data. Based on a pre-set correspondence between dirt level and adsorption state data, the target adsorption state data corresponding to the target dirt level is determined. Based on a pre-set correspondence between ambient wind speed and adsorption state data and / or ambient temperature and adsorption state data, a first target adsorption state data corresponding to the current ambient wind speed and / or a second target adsorption state data corresponding to the current ambient temperature are determined. The maximum value among the target adsorption state data corresponding to the target dirt level, the first target adsorption state data, and / or the second target adsorption state data is selected as the final target adsorption state data. Finally, based on the adsorption state data and the final target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined; that is, the difference between the adsorption state data and the final target adsorption state data is used as the adsorption adjustment data of the window cleaning machine.

[0070] In one specific implementation, based on adsorption state data, as well as obstacle data and working environment data, the adsorption adjustment data of the window cleaning machine is determined, specifically including the following steps: First, based on the pre-set correspondence between obstacle types and adsorption state data and obstacle data, the third target adsorption state data corresponding to the obstacle data is determined. Second, based on the pre-set correspondence between working environment data and adsorption state data and working environment data, the fourth target adsorption state data corresponding to the working environment data of the window cleaning machine is determined.

[0071] This embodiment determines the target obstacle type based on obstacle image data from obstacle data. If the target obstacle type corresponds to a pre-set cleanable type, the adsorption state data corresponding to the target obstacle type is determined as the third target adsorption state data for the window cleaning machine within the preset adjustment range of the obstacle, based on the correspondence between the pre-set obstacle type and adsorption state data. If the target obstacle type corresponds to a pre-set cleanable type, the pre-set standard adsorption state data is used as the third target adsorption state data for the window cleaning machine outside the preset adjustment range of the obstacle. If the target obstacle type corresponds to a pre-set cleanable type, the pre-set standard adsorption state data is directly used as the third target adsorption state data.

[0072] Furthermore, based on the pre-set correspondence between working environment data and adsorption state data, the adsorption state data corresponding to the working environment data of the window cleaning machine is determined as the fourth target adsorption state data. When the working environment data includes multiple data types, it is necessary to determine the target adsorption state data corresponding to each type of data, and select the maximum value as the fourth target adsorption state data.

[0073] Second, based on the maximum value in the adsorption state data of the third and fourth targets, as well as the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0074] The difference between the maximum value of the adsorption state data of the third target and the adsorption state data of the fourth target and the currently acquired adsorption state data is used as the adsorption adjustment data of the window cleaning machine.

[0075] S103. Based on obstacle data, determine the travel strategy of the window cleaning machine and control the travel of the window cleaning machine according to the travel strategy.

[0076] This embodiment determines the type of target obstacle corresponding to the obstacle image data based on the obstacle image data in the obstacle data, and determines the travel strategy of the window cleaning machine based on the distance data between the window cleaning machine and the obstacle in the obstacle data and the type of target obstacle corresponding to the obstacle image data.

[0077] Specifically, based on the type of target obstacle corresponding to the obstacle image data, the window cleaning robot determines its appropriate movement method for the target obstacle, such as detouring, slowing down, cleaning along the edges, or stopping. For example, if the target obstacle is a latch, the movement method could be to detour around it at a safe distance to ensure cleaning coverage and avoid collisions. If the target obstacle is a raised stain, the movement method could be to reduce the speed and attempt repeated wiping to enhance the cleaning effect.

[0078] Based on the obstacle size displayed in the obstacle image data and the distance data between the window cleaning machine and the obstacle in the obstacle data, the specific execution method of the window cleaning machine's movement response to the target obstacle corresponding to the obstacle data is determined. For example, it first travels to the preset range of the obstacle, and then moves towards the obstacle according to the movement response method of the window cleaning machine for the target obstacle corresponding to the obstacle data and the size of the obstacle.

[0079] In this embodiment, after determining the travel strategy of the window cleaning machine, the controller controls the travel of the window cleaning machine according to the travel strategy, specifically by controlling the travel components set in the window cleaning machine to work according to the travel strategy.

[0080] As described above, the window cleaning machine control method proposed in this application acquires obstacle data and working environment data of the area to be cleaned, as well as the adsorption state data of the window cleaning machine; based on the adsorption state data, and at least one of the obstacle data and working environment data, it determines the adsorption adjustment data of the window cleaning machine, and adjusts the adsorption components of the window cleaning machine based on the adsorption adjustment data; based on the obstacle data, it determines the travel strategy of the window cleaning machine, and controls the travel of the window cleaning machine according to the travel strategy. By adopting the technical solution of this embodiment, the adsorption adjustment strategy of the window cleaning machine can be determined in real time, and the adsorption components of the window cleaning machine can be adjusted to ensure the normal adsorption state of the window cleaning machine, improving the safety of the window cleaning machine. Furthermore, by directly determining the travel strategy of the window cleaning machine based on obstacle data, it avoids the impact of mechanical collision-type working methods on the adsorption state of the window cleaning machine, further improving the safety of the window cleaning machine.

[0081] As an optional implementation, this application also proposes a window cleaning machine control method. See [link to relevant documentation]. Figure 2 As shown, the method includes: S201. Obtain the adsorption status data of the window cleaning machine, as well as the obstacle data and working environment data of the area to be cleaned.

[0082] S202. Based on adsorption state data, and at least one of obstacle data and working environment data, determine the adsorption adjustment data of the window cleaning machine, and adjust the adsorption components of the window cleaning machine based on the adsorption adjustment data.

[0083] S203. Obtain the updated adsorption state data after adsorption adjustment.

[0084] In this embodiment, after adjusting the adsorption components of the window cleaning machine based on adsorption adjustment data, updated adsorption state data is obtained. Specifically, the target adsorption state data used to determine the adsorption adjustment data can be directly obtained as the updated adsorption state data. The method for determining the target adsorption state data has been specifically described in the above embodiments and will not be repeated here. Alternatively, a negative pressure sensor installed on the window cleaning machine can be used to collect adsorption state data (such as pressure values) after adsorption adjustment as the updated adsorption state data.

[0085] S204. Based on obstacle data and updated adsorption status data, determine the travel strategy of the window cleaning machine.

[0086] This embodiment requires determining whether the updated adsorption status data meets the adsorption standard range corresponding to the target obstacle type, based on the obstacle image data in the obstacle data. If the target obstacle type corresponds to a pre-set cleanable type, the embodiment pre-sets adsorption standard ranges for various cleanable obstacle types. This ensures the window cleaning machine's adsorption status is maintained when cleaning obstacles according to the adsorption status data within the adsorption standard range corresponding to the obstacle type.

[0087] If the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type, then the target obstacle corresponding to the covered obstacle data is taken as the target, and the travel strategy of the window cleaning machine is determined according to the obstacle data. If the updated adsorption status data does not meet the adsorption standard range corresponding to the target obstacle type, or if the target obstacle type corresponding to the obstacle image data in the obstacle data does not belong to the pre-set cleanable type, then the goal is to avoid the target obstacle corresponding to the obstacle data, and the travel strategy of the window cleaning machine is determined based on the obstacle data.

[0088] The method for determining the travel strategy of the window cleaning machine based on obstacle data in this embodiment has been specifically described in the above embodiments, and will not be repeated here.

[0089] The specific execution method of steps S201-S202 in this embodiment is the same as that of steps S101-S102 in the above embodiment, and will not be repeated in this embodiment.

[0090] As an optional implementation method, see [link to implementation details]. Figure 3 As shown in another embodiment of this application, the window cleaning machine control method further includes the following steps: S301. Monitor the main power status of the window cleaning machine.

[0091] This embodiment requires real-time monitoring of the main power status of the window cleaning machine to determine whether the main power supply of the window cleaning machine is interrupted.

[0092] S302. When the main power status indicates that the main power is off, switch to backup battery power.

[0093] In this embodiment, the window cleaning machine is equipped with a backup battery. Under normal circumstances, the window cleaning machine uses the main power supply to power the various electrical components in the window cleaning machine. If the main power status of the window cleaning machine indicates that the main power supply is cut off, it means that the main power supply of the window cleaning machine is abnormal or the connection between the main power supply and the main power supply is abnormal. At this time, the controller controls the power supply component to switch to the backup battery, so that the backup battery can supply power to the various electrical components in the window cleaning machine. In this way, in the event of a main power failure, the backup battery can directly take over, ensuring that the window cleaning machine will not fall due to power failure.

[0094] Exemplary device Accordingly, this application also provides a window cleaning machine control device, see [link to relevant documentation]. Figure 4 As shown, the device includes: The data acquisition module 100 is used to acquire the adsorption status data of the window cleaning machine, as well as the obstacle data and working environment data of the area to be cleaned; The adsorption adjustment module 110 is used to determine the adsorption adjustment data of the window cleaning machine based on adsorption state data, obstacle data, and working environment data, and to adjust the adsorption components of the window cleaning machine based on the adsorption adjustment data. The travel module 120 is used to determine the travel strategy of the window cleaning machine based on obstacle data, and control the travel of the window cleaning machine according to the travel strategy.

[0095] As an optional implementation, another embodiment of this application discloses that the obstacle data includes: obstacle image data; the window cleaning machine control device further includes: a type determination module.

[0096] The type determination module is used to determine the type of the target obstacle corresponding to the obstacle image data based on the obstacle image data.

[0097] As an optional implementation, another embodiment of this application discloses that the adsorption adjustment module 110 is specifically used for: Based on the adsorption state data and the pre-set standard adsorption state data, determine the adsorption adjustment data of the window cleaning machine outside the preset adjustment range of the obstacle; If the target obstacle type corresponding to the obstacle image data belongs to a pre-set cleanable type, the target adsorption state data corresponding to the target obstacle type is determined based on the pre-set correspondence between the obstacle type and the adsorption state data. Based on the target adsorption state data and the adsorption state data corresponding to the target obstacle type, the adsorption adjustment data of the window cleaning machine within the preset adjustment range of the obstacle is determined.

[0098] As an optional implementation, another embodiment of this application discloses that the working environment data includes: dirt data of the area to be cleaned; the adsorption adjustment module 110 is further used for: Based on the dirt data of the area to be cleaned, determine the target level of dirt in the area to be cleaned; Based on the pre-set correspondence between the degree of dirtiness and the adsorption state data, the target adsorption state data corresponding to the target degree of dirtiness is determined; Based on the target adsorption state data and the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0099] As an optional implementation, another embodiment of this application discloses that the working environment data includes at least one of the following: current ambient wind speed and current ambient temperature; the adsorption adjustment module 110 is further used for: Given that the working environment data includes the current ambient wind speed, the first target adsorption state data corresponding to the current ambient wind speed is determined based on the pre-set correspondence between the ambient wind speed and the adsorption state data. And / or, Given that the working environment data includes the current ambient temperature, the second target adsorption state data corresponding to the current ambient temperature is determined based on the pre-set correspondence between the ambient temperature and the adsorption state data. Based on the adsorption state data, as well as the first target adsorption state data and / or the second target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0100] As an optional implementation, another embodiment of this application discloses that the adsorption adjustment module 110 is further used for: Based on the pre-set correspondence between obstacle types and adsorption state data and obstacle data, the third target adsorption state data corresponding to the obstacle data is determined; and based on the pre-set correspondence between working environment data and adsorption state data and working environment data, the fourth target adsorption state data corresponding to the working environment data of the window cleaning machine is determined. Based on the maximum value in the adsorption state data of the third and fourth targets, as well as the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

[0101] As an optional implementation, another embodiment of this application discloses that the data acquisition module 100 is further used to acquire updated adsorption state data after adsorption adjustment; Correspondingly, the travel module 120 is also used to determine the travel strategy of the window cleaning machine based on obstacle data and updated adsorption status data.

[0102] As an optional implementation, another embodiment of this application discloses a travel module 120, specifically used for: If the target obstacle type corresponding to the obstacle image data in the obstacle data belongs to a pre-set cleanable type, determine whether the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type; the adsorption standard range corresponding to the target obstacle type represents the range of adsorption state data required to ensure the adsorption state of the window cleaning machine when cleaning obstacles of the target obstacle type. If the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type, then the target obstacle corresponding to the covered obstacle data is taken as the target, and the travel strategy of the window cleaning machine is determined according to the obstacle data. If the updated adsorption status data does not meet the adsorption standard range corresponding to the target obstacle type, or if the target obstacle type corresponding to the obstacle image data in the obstacle data does not belong to the pre-set cleanable type, then the goal is to avoid the target obstacle corresponding to the obstacle data, and the travel strategy of the window cleaning machine is determined based on the obstacle data.

[0103] As an optional implementation, another embodiment of this application discloses that the window cleaning machine control device further includes a power monitoring module and a power switching module.

[0104] The power monitoring module is used to monitor the main power status of the window cleaning machine; The power switching module is used to switch to backup battery power when the main power status indicates that the main power is off, so that the backup battery can power the adsorption components in the window cleaning machine.

[0105] The window cleaning machine control device provided in this embodiment belongs to the same concept as the window cleaning machine control method provided in the above embodiments of this application. It can execute the window cleaning machine control method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects for executing the window cleaning machine control method. Technical details not described in detail in this embodiment can be found in the specific processing content of the window cleaning machine control method provided in the above embodiments of this application, and will not be repeated here.

[0106] Exemplary device Optionally, embodiments of this application also provide a window cleaning machine, see [link to relevant documentation]. Figure 5As shown, the window cleaning machine includes: an environmental sensing module 200, a negative pressure sensor 210, and a controller 220. The environmental sensing module 200 and the negative pressure sensor 210 are respectively connected to the controller 220. The environmental sensing module 200 is used to collect obstacle data and working environment data of the area to be cleaned; the negative pressure sensor 210 is used to collect the adsorption status data of the window cleaning machine. The controller 220 is used to execute the window cleaning machine control method described in any of the above embodiments.

[0107] Specifically, the environmental perception module 200 may include a non-contact ranging sensor, a camera, a dirt sensor, a wind speed sensor, and a temperature sensor. The non-contact ranging sensor is used to collect distance data between the window cleaning machine and obstacles, the camera is used to collect image data of obstacles, the dirt sensor is used to collect dirt data, the wind speed sensor is used to collect the current ambient wind speed, and the temperature sensor is used to collect the current ambient temperature.

[0108] Exemplary electronic devices Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 6 As shown, the device includes: Memory 300 and processor 310; The memory 300 is connected to the processor 310 and is used to store programs; The processor 310 is used to implement the window cleaning machine control method disclosed in any of the above embodiments by running the program stored in the memory 300.

[0109] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 320, an input device 330, and an output device 340.

[0110] The processor 310, memory 300, communication interface 320, input device 330, and output device 340 are interconnected via a bus. Among them: A bus can include a pathway for transmitting information between various components of a computer system.

[0111] The processor 310 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0112] The processor 310 may include a main processor, as well as a baseband chip, modem, etc.

[0113] The memory 300 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 300 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0114] Input device 330 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0115] Output device 340 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0116] The communication interface 320 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0117] The processor 310 executes the program stored in the memory 300 and calls other devices, which can be used to implement the various steps of any of the window cleaning machine control methods provided in the above embodiments of this application.

[0118] Exemplary computer program products and storage media In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the window cleaning machine control methods according to various embodiments of this application as described in the "Exemplary Methods" section of this specification.

[0119] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0120] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor in the steps of the window cleaning machine control method according to various embodiments of this application described in the "Exemplary Methods" section above.

[0121] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0122] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0123] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.

[0124] The modules and sub-modules in the various embodiments of the present application's devices and terminals can be merged, divided, and deleted according to actual needs.

[0125] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0126] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.

[0127] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.

[0128] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0130] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0131] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a window cleaning machine, characterized in that, include: Acquire the adsorption status data of the window cleaning machine, as well as obstacle data and working environment data of the area to be cleaned; Based on the adsorption state data, and at least one of the obstacle data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, and the adsorption components of the window cleaning machine are adjusted based on the adsorption adjustment data. Based on the obstacle data, the travel strategy of the window cleaning machine is determined, and the travel strategy is used to control the movement of the window cleaning machine.

2. The window cleaning machine control method according to claim 1, characterized in that, The obstacle data includes: obstacle image data; After acquiring the window cleaning machine's adsorption status data, as well as obstacle data and working environment data for the area to be cleaned, the following is also included: Based on the obstacle image data, the type of target obstacle corresponding to the obstacle image data is determined.

3. The window cleaning machine control method according to claim 2, characterized in that, Based on the adsorption state data and the obstacle data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the adsorption state data and the pre-set standard adsorption state data, the adsorption adjustment data of the window cleaning machine outside the preset adjustment range of the obstacle is determined; If the target obstacle type corresponding to the obstacle image data belongs to a pre-set cleanable type, the target adsorption state data corresponding to the target obstacle type is determined based on the pre-set correspondence between the obstacle type and the adsorption state data. Based on the target adsorption state data corresponding to the target obstacle type and the adsorption state data, the adsorption adjustment data of the window cleaning machine within the preset adjustment range of the obstacle is determined.

4. The window cleaning machine control method according to claim 1, characterized in that, The working environment data includes: dirt data of the area to be cleaned; Based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the dirt data of the area to be cleaned, the target degree of dirt in the area to be cleaned is determined; Based on the pre-set correspondence between the degree of dirtiness and the adsorption state data, the target adsorption state data corresponding to the target degree of dirtiness is determined; Based on the target adsorption state data and the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

5. The window cleaning machine control method according to claim 1, characterized in that, The working environment data includes at least one of the following: current ambient wind speed and current ambient temperature; Based on the adsorption state data and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: When the working environment data includes the current ambient wind speed, the first target adsorption state data corresponding to the current ambient wind speed is determined based on the pre-set correspondence between the ambient wind speed and the adsorption state data. And / or, When the working environment data includes the current ambient temperature, the second target adsorption state data corresponding to the current ambient temperature is determined based on the pre-set correspondence between ambient temperature and adsorption state data. Based on the adsorption state data, as well as the first target adsorption state data and / or the second target adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

6. The window cleaning machine control method according to claim 1, characterized in that, Based on the adsorption state data, the obstacle data, and the working environment data, the adsorption adjustment data of the window cleaning machine is determined, including: Based on the pre-set correspondence between obstacle types and adsorption state data and the obstacle data, the third target adsorption state data corresponding to the obstacle data is determined; and based on the pre-set correspondence between working environment data and adsorption state data and the working environment data, the fourth target adsorption state data corresponding to the working environment data of the window cleaning machine is determined. Based on the maximum value of the third target adsorption state data and the fourth target adsorption state data, as well as the adsorption state data, the adsorption adjustment data of the window cleaning machine is determined.

7. The window cleaning machine control method according to claim 1, characterized in that, Before determining the travel strategy of the window cleaning machine based on the obstacle data, the process further includes: Obtain updated adsorption state data after adsorption adjustment; Correspondingly, based on the obstacle data, the travel strategy of the window cleaning machine is determined, including: Based on the obstacle data and the updated adsorption state data, the travel strategy of the window cleaning machine is determined.

8. The window cleaning machine control method according to claim 7, characterized in that, Based on the obstacle data and the updated adsorption state data, the travel strategy of the window cleaning machine is determined, including: If the target obstacle type corresponding to the obstacle image data in the obstacle data belongs to a pre-set cleanable type, it is determined whether the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type; the adsorption standard range corresponding to the target obstacle type represents the range of adsorption state data required to ensure the adsorption state of the window cleaning machine when the window cleaning machine cleans the obstacle of the target obstacle type. If the updated adsorption state data meets the adsorption standard range corresponding to the target obstacle type, then the target obstacle corresponding to the obstacle data is taken as the objective, and the travel strategy of the window cleaning machine is determined according to the obstacle data. If the updated adsorption state data does not meet the adsorption standard range corresponding to the target obstacle type, or if the target obstacle type corresponding to the obstacle image data in the obstacle data does not belong to the pre-set cleanable type, then the goal is to avoid the target obstacle corresponding to the obstacle data, and the travel strategy of the window cleaning machine is determined based on the obstacle data.

9. The window cleaning machine control method according to claim 1, characterized in that, Also includes: Monitor the main power status of the window cleaning machine; When the main power status indicates that the main power is off, the system switches to backup battery power so that the backup battery can power the adsorption components in the window cleaning machine.

10. A window cleaning machine, characterized in that, include: Environmental sensing module, negative pressure sensor and controller; The environmental sensing module and the negative pressure sensor are respectively connected to the controller; The environmental sensing module is used to collect obstacle data and working environment data of the area to be cleaned. The negative pressure sensor is used to collect the adsorption status data of the window cleaning machine; The controller is used to implement the window cleaning machine control method as described in any one of claims 1 to 9.

11. An electronic device, characterized in that, include: Memory and processor; The memory is connected to the processor and is used to store programs; The processor is configured to implement the window cleaning machine control method as described in any one of claims 1 to 9 by running a program in the memory.

12. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the window cleaning machine control method as described in any one of claims 1 to 9.

13. A computer program product, characterized in that, It includes computer program instructions that, when executed by a processor, cause the processor to implement the window cleaning machine control method as described in any one of claims 1 to 9.

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