Equipment control method and device, cleaning equipment and storage medium

Abstract

The invention relates to an equipment control method and device, cleaning equipment and a storage medium. The equipment control method comprises the steps that a first cleaning parameter corresponding to a cleaned object is acquired; in the process of cleaning the cleaned object according to the first cleaning parameter, first friction data between the cleaning main body and the cleaned object is obtained; adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter; and cleaning the cleaned object according to the second cleaning parameter. According to the embodiment of the invention, the first cleaning parameter can be automatically adjusted according to the first friction data, the cleaned object is cleaned according to the second cleaning parameter obtained through adjustment, and the second cleaning parameter matched with the first friction data is used for cleaning the cleaned object, so that high system energy consumption caused by use of too large cleaning parameters is avoided; or the problem that the cleaning effect is poor due to the fact that too small cleaning parameters are used is solved, and balance between system energy consumption and the cleaning effect is achieved.

Description

Technical Field

[0001] The present application relates to the field of smart home appliances, and in particular to a device control method, device, cleaning device and storage medium. Background Art

[0002] Window cleaning robot, also known as automatic window cleaning machine, glass cleaning robot, smart window cleaning device, smart window cleaner, etc., is a kind of smart home appliance. It can firmly adhere to the glass with the vacuum pump or fan device at the bottom of itself, and then automatically detect the corner distance of the window and plan the window cleaning path with the help of certain artificial intelligence. Window cleaning robot generally uses the force of its own adsorption on the glass to drive the rag at the bottom of the body to wipe off the dirt on the glass.

[0003] However, current window cleaning robots still have problems such as high power consumption and poor cleaning effect. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides an equipment control method, device, cleaning equipment and storage medium.

[0005] In a first aspect, the present application provides a device control method, the method comprising:

[0006] Acquiring a first cleaning parameter corresponding to the cleaned object;

[0007] In the process of cleaning the cleaned object according to the first cleaning parameter, obtaining first friction data between the cleaning body and the cleaned object;

[0008] adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter;

[0009] The cleaned object is cleaned according to the second cleaning parameter.

[0010] Optionally, adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter includes:

[0011] determining whether the first friction data is within a preset value range;

[0012] If the first friction data is outside the preset friction range, the first friction data is input into a preset adaptive control algorithm to obtain a second cleaning parameter.

[0013] Optionally, the cleaned object includes a plurality of cleaning areas, and obtaining a first cleaning parameter corresponding to the cleaned object includes:

[0014] Acquire a regional image of a current cleaning region of the cleaning object and second friction data between the cleaning body and the cleaned object;

[0015] determining a first cleaning difficulty level according to the area image and the second friction data;

[0016] If the first cleaning difficulty level is greater than a preset first level threshold, a regional cleaning parameter is determined according to the regional image and / or the second friction data to serve as the first cleaning parameter.

[0017] Optionally, obtaining a first cleaning parameter corresponding to the cleaned object further includes:

[0018] If the first cleaning difficulty level is less than or equal to a preset first difficulty threshold, a third cleaning parameter corresponding to the cleaned object is acquired as the first cleaning parameter.

[0019] Optionally, before acquiring the first cleaning parameter corresponding to the cleaned object, the method further includes:

[0020] Acquiring a surface image of the object to be cleaned;

[0021] determining a dirty area and a second cleaning difficulty level of the dirty area according to the surface image;

[0022] If the second cleaning difficulty level of any dirty area is greater than a preset second level threshold, the third cleaning parameter is determined according to the second cleaning difficulty level.

[0023] Optionally, the method further comprises:

[0024] If the second cleaning difficulty level of any dirty area is less than or equal to the preset second level threshold, an initial cleaning parameter is obtained to serve as the third cleaning parameter.

[0025] Optionally, before acquiring the surface image of the cleaned object, the method further includes:

[0026] Performing surface smoothness detection on the cleaned object to obtain smoothness information;

[0027] Performing material property detection on the cleaned object to obtain material property information;

[0028] Performing coating type detection on the cleaned object to obtain coating type information;

[0029] The initial cleaning parameters are determined according to the smoothness information, the material property information, and the coating type information.

[0030] In a second aspect, the present application provides a device control apparatus, the method comprising:

[0031] A first acquisition module, used to acquire a first cleaning parameter corresponding to the cleaned object;

[0032] A second acquisition module, configured to acquire first friction data between a cleaning body and the cleaned object during the process of cleaning the cleaned object according to the first cleaning parameter;

[0033] an adjustment module, configured to adjust the first cleaning parameter based on the first friction data to obtain a second cleaning parameter;

[0034] A cleaning module is used to clean the cleaned object according to the second cleaning parameter.

[0035] Optionally, the adjustment module includes:

[0036] A first determining unit, used to determine whether the first friction data is within a preset value range;

[0037] The input unit is used to input the first friction data into a preset adaptive control algorithm to obtain a second cleaning parameter if the first friction data is outside a preset friction range.

[0038] Optionally, the cleaned object includes a plurality of cleaning areas, and the first acquisition module includes:

[0039] A first acquisition unit, configured to acquire a regional image of a current cleaning region of the cleaning object and second friction data between the cleaning body and the cleaned object;

[0040] a second determining unit, configured to determine a first cleaning difficulty level according to the area image and the second friction data;

[0041] The third determination unit is configured to determine a regional cleaning parameter according to the regional image and / or the second friction data as the first cleaning parameter if the first cleaning difficulty level is greater than a preset first level threshold.

[0042] Optionally, the first acquisition module further includes:

[0043] The second acquiring unit is configured to acquire, if the first cleaning difficulty level is less than or equal to a preset first difficulty threshold, a third cleaning parameter corresponding to the cleaned object as the first cleaning parameter.

[0044] Optionally, before the first acquisition module, the device further includes:

[0045] A third acquisition module is used to acquire a surface image of the cleaned object;

[0046] A first determination module, configured to determine a dirty area and a second cleaning difficulty level of the dirty area according to the surface image;

[0047] The second determination module is configured to determine the third cleaning parameter according to the second cleaning difficulty level if the second cleaning difficulty level of any dirty area is greater than a preset second difficulty level threshold.

[0048] Optionally, the device further comprises:

[0049] The fourth acquisition module is used to acquire the initial cleaning parameters as the third cleaning parameters if the second cleaning difficulty level of any dirty area is less than or equal to a preset second level threshold.

[0050] Optionally, before the third acquisition module, the device further includes:

[0051] A first detection module is used to detect the surface smoothness of the cleaned object to obtain smoothness information;

[0052] A second detection module is used to detect the material characteristics of the cleaned object to obtain material characteristic information;

[0053] A third detection module is used to detect the coating type of the cleaned object to obtain coating type information;

[0054] The third determination module is used to determine the initial cleaning parameters according to the smoothness information, the material characteristic information and the coating type information.

[0055] In a third aspect, the present application provides a cleaning device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0056] Memory, used to store computer programs;

[0057] The processor is used to implement any device control method described in the first aspect when executing the program stored in the memory.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium, on which a program of a device control method is stored. When the program of the device control method is executed by a processor, the steps of the device control method described in any one of the first aspects are implemented.

[0059] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0060] The embodiment of the present application can automatically adjust the first cleaning parameter according to the first friction data, and clean the cleaned object according to the adjusted second cleaning parameter. By using the second cleaning parameter that matches the first friction data to clean the cleaned object, it is possible to avoid the problem of high system energy consumption caused by using too large a cleaning parameter, or poor cleaning effect caused by using too small a cleaning parameter, thereby achieving a balance between system energy consumption and cleaning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0063] Figure 1 A flow chart of a device control method provided in an embodiment of the present application;

[0064] Figure 2 A structural diagram of a device control device provided in an embodiment of the present application;

[0065] Figure 3 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0067] Since current window cleaning robots still have problems such as high power consumption and poor cleaning effect, the embodiments of the present application provide an equipment control method, device, cleaning equipment and storage medium.

[0068] An embodiment of the present application provides an equipment control method, which can be applied to cleaning equipment. By way of example, the cleaning equipment may refer to a window cleaning robot, an automatic window cleaning machine, a glass cleaning robot, an intelligent window cleaning device, an intelligent window cleaner, etc. The cleaning equipment may include: an image acquisition device (such as a camera, a visual sensor), a friction sensor, a cleaning body (i.e., a window cleaning cloth used to clean the object to be cleaned, etc.), a laser transmitter, etc.

[0069] like Figure 1 As shown, the device control method includes the following steps:

[0070] Step S101, obtaining a first cleaning parameter corresponding to the cleaned object;

[0071] In the embodiment of the present application, the object to be cleaned may refer to glass and materials with surfaces similar to glass, such as marble, ceramic tiles, etc.; the first cleaning parameter includes one or more of cleaning intensity, cleaning frequency, cleaning speed and amount of detergent used.

[0072] The first cleaning parameter can be predetermined based on the dirtiness of the surface of the object to be cleaned and the characteristics of the object to be cleaned itself (such as material characteristics, smoothness characteristics, etc.). The dirtiness of the surface of the object to be cleaned is obtained by performing image processing on the image collected from the surface of the object to be cleaned. The characteristics of the object to be cleaned itself are obtained by irradiating the object to be cleaned with a laser emitter and then using an image acquisition device to collect the reflection and refraction of light.

[0073] In this step, after the system is started, the cleaning device can detect whether a cleaning task start instruction is received, and when the cleaning task start instruction is received, the first cleaning parameter corresponding to the cleaned object can be obtained.

[0074] Step S102, in the process of cleaning the cleaned object according to the first cleaning parameter, obtaining first friction data between the cleaning body and the cleaned object;

[0075] In the embodiment of the present application, the cleaning body may refer to an item such as a window cloth used to clean the object to be cleaned; since the first friction data is collected during the cleaning process of the object to be cleaned, the first friction data may refer to dynamic friction force data.

[0076] After the first cleaning parameter is acquired, the object to be cleaned may be cleaned according to the first cleaning parameter, and the friction sensor may be used to collect first friction data between the cleaning body and the object to be cleaned at each preset time period.

[0077] Step S103, adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter;

[0078] In this step, each parameter in the first cleaning parameters may be adjusted based on the first friction data, or any parameter in the first cleaning parameters may be adjusted based on the first friction data to obtain the second cleaning parameters.

[0079] Step S104: cleaning the cleaned object according to the second cleaning parameters.

[0080] In the embodiment of the present application, the object to be cleaned may be divided into a plurality of cleaning areas according to a preset rule. In this step, each cleaning area of ​​the object to be cleaned may be cleaned according to the adjusted second cleaning parameter.

[0081] After cleaning any cleaning area, the system can determine whether the cleaning task is completed based on the final friction and visual feedback data of the cleaning area. Specifically, when the friction and image processing results of a cleaning area indicate that the surface is smooth and there are no residual stains, the system stops cleaning the area and moves to the next area to be cleaned. If the cleaning of all areas meets the standards, the system will enter sleep mode and wait for new task instructions.

[0082] The embodiment of the present application can automatically adjust the first cleaning parameter according to the first friction data, and clean the cleaned object according to the adjusted second cleaning parameter. By using the second cleaning parameter that matches the first friction data to clean the cleaned object, it is possible to avoid the problem of high system energy consumption caused by using too large a cleaning parameter, or poor cleaning effect caused by using too small a cleaning parameter, thereby achieving a balance between system energy consumption and cleaning effect.

[0083] In another embodiment of the present application, step S103 adjusts the first cleaning parameter based on the first friction data to obtain a second cleaning parameter, including:

[0084] Step S201, determining whether the first friction data is within a preset value range;

[0085] During the system development phase, the ideal friction force for a specific glass type can be calibrated through a large number of experiments: the corresponding relationship between friction force and cleaning effect during cleaning under different surface characteristics (such as material, coating) can be recorded. The optimal value range of friction force can be fitted through experimental results. For example: ordinary glass (smooth surface): experiments show that the cleaning effect is best when the friction force is 2.5~3.0N; frosted glass (rough surface): the friction force is 3.5~4.0N.

[0086] In this step, the first friction data F c (t) and ideal friction data Compare and if there is a deviation between the first friction data and the ideal friction data It can be determined that the first friction data is outside a preset value range.

[0087] Step S202: if the first friction data is outside a preset friction range, the first friction data is input into a preset adaptive control algorithm to obtain a second cleaning parameter.

[0088] In the embodiment of the present application, the adaptive control algorithm is as follows:

[0089]

[0090] Among them, K p , K i (t), K d (t) is the adaptive coefficient, which is dynamically adjusted based on the current state of the system; Represents the accumulation of the most recent n errors, which is used to limit the cumulative effect of integration; It is a discrete differential term that can reduce the influence of other irrelevant parameters.

[0091] Through the adaptive control algorithm, the first cleaning parameter can be adjusted to the second cleaning parameter. For example, the first cleaning force in the first cleaning parameter can be adjusted to obtain the second cleaning parameter. The embodiment of the present application can detect the change in friction between the cleaning body and the cleaned object through a friction sensor, and automatically adjust the cleaning force to ensure the cleaning effect while reducing energy consumption.

[0092] In another embodiment of the present application, the cleaned object includes a plurality of cleaning areas, and step S101 acquires a first cleaning parameter corresponding to the cleaned object, including:

[0093] Step S301, obtaining a regional image of a current cleaning region of the cleaning object and second friction data between the cleaning body and the cleaned object;

[0094] In the embodiment of the present application, the regional image can be collected by an image collection device arranged at a relatively low height. Since the second friction data is collected before the object to be cleaned is cleaned, the second friction data is static friction data.

[0095] Step S302, determining a first cleaning difficulty level according to the area image and the second friction data;

[0096] In this step, the regional image can be processed using an image processing algorithm to obtain the grayscale value, coverage area, shape, and distribution density of the stains on the surface of the cleaned object, and the first cleaning difficulty level can be determined based on the grayscale value, coverage area, shape, and distribution density of the stains on the surface of the cleaned object and the second friction data.

[0097] If the grayscale value of the stain is low (such as dark stains), it usually indicates that the stain is stubborn, such as oil, scale or dried sticky stains. A grayscale threshold can be set in advance. When the average grayscale value of the stain area is less than the threshold, the stain is considered to be difficult to remove.

[0098] If the stain area is large or the shape is complex (such as irregular boundaries and high concentration), the system may determine that higher intensity or multiple cleanings are required to cover all areas. The system calculates the edge gradient change rate of the stain through morphological analysis. A high gradient indicates that the stain has a complex boundary and requires more detailed cleaning.

[0099] If the stains are highly concentrated in a local area with a high density (a large number of stain pixels per unit area), the system will prioritize repeated cleaning of that area.

[0100] During the cleaning process, the system monitors the real-time friction changes through the friction sensor. If the friction increases abnormally but the stain is not completely removed, it means that the stain may be difficult to remove due to strong adhesion or hardening.

[0101] In one embodiment of the present application, multiple thresholds can be set for the grayscale value, coverage area and morphology, distribution density and second friction data of the stains, each threshold corresponds to a corresponding score, and a different weight is assigned to each parameter. The sum of the scores after multiplying multiple parameters by the weights can then be determined as the first cleaning difficulty level.

[0102] In another embodiment of the present application, the grayscale value, coverage area and morphology, distribution density of the stains and the number of parameters exceeding the corresponding threshold in the second friction data can also be counted. If all parameters exceed the corresponding threshold, the first cleaning difficulty level is determined to be high. If no parameter exceeds the corresponding threshold, the first cleaning difficulty level is determined to be low. If 1, 2, or 3 of the parameters exceed the corresponding threshold, the first cleaning difficulty level is determined to be moderate level 1, 2, and 3, respectively.

[0103] In practical applications, the degree of difficulty may also be determined by other methods, which are not limited in this solution.

[0104] Step S303: if the first cleaning difficulty level is greater than a preset first level threshold, determine a regional cleaning parameter according to the regional image and / or the second friction data to serve as the first cleaning parameter.

[0105] In an embodiment of the present application, the regional cleaning parameters include: one or more of cleaning intensity, cleaning frequency (number of times), cleaning speed and cleaning agent dosage.

[0106] In this step, the regional cleaning parameters can be determined according to the grayscale value, coverage area and shape, distribution density and second friction data of the stains on the surface of the cleaned object, and the regional cleaning parameters can be used as the first cleaning parameters used when cleaning the cleaning area.

[0107] In one embodiment of the present application, the distribution density of stains can be determined based on the regional image, and the cleaning intensity in the regional cleaning parameters can be determined based on the distribution density to ensure that greater intensity is used in heavily soiled areas, thereby reducing the number of cleaning times, while the intensity is reduced in lightly soiled areas to avoid unnecessary energy consumption.

[0108] In another embodiment of the present application, regional cleaning parameters may be set according to the second friction data of each cleaning area, and the regional cleaning parameters include: number of cleaning times, cleaning intensity, cleaning speed, etc.

[0109] In another embodiment of the present application, the first cleaning difficulty level can also be determined based on the regional image and / or the second friction data. When the first cleaning difficulty level indicates that the stain is difficult to remove, the number of cleaning times in the regional cleaning parameters can be set to multiple repeated cleanings.

[0110] Step S304: if the first cleaning difficulty level is less than or equal to a preset first level threshold, a third cleaning parameter corresponding to the cleaned object is obtained as the first cleaning parameter.

[0111] In an embodiment of the present application, one or more parameters in the regional cleaning parameters are greater than or equal to the corresponding one or more parameters in the third cleaning parameters. For example, the cleaning force in the regional cleaning parameters is greater than the cleaning force in the third cleaning parameters. The third cleaning parameters include one or more of the number of cleaning times, cleaning force, cleaning speed, etc.

[0112] In this step, if the first cleaning difficulty level of the current cleaning area is less than or equal to the preset first level threshold, that is, the current cleaning area is easier to clean, then the third cleaning parameter corresponding to the cleaned object can be obtained. The third cleaning parameter is preliminarily determined before obtaining the first cleaning parameter corresponding to the cleaned object.

[0113] The embodiment of the present application can automatically use different cleaning parameters as the first cleaning parameters according to the first cleaning difficulty level. When the first cleaning difficulty level is high, that is, it is difficult to clean, the corresponding regional cleaning parameters can be determined according to the cleaning difficulty level to achieve targeted cleaning; when the first cleaning difficulty level is low, that is, it is easier to clean, the preliminarily set third cleaning parameter can be used as the first cleaning parameter to automatically adjust the number of cleaning times according to the degree of dirtiness, and repeat cleaning for the heavily stained areas, thereby improving the overall cleaning effect. Through multi-sensor fusion, the cleaning effect is monitored in real time to ensure that the cleaning equipment can maintain an efficient cleaning effect throughout the cleaning process.

[0114] In another embodiment of the present application, before acquiring the first cleaning parameter corresponding to the cleaned object in step S101, the method further includes:

[0115] Step S401, obtaining a surface image of the object to be cleaned;

[0116] In the embodiment of the present application, the object to be cleaned can be captured by an image capture device arranged at a higher height.

[0117] Step S402, determining a dirty area and a second cleaning difficulty level of the dirty area according to the surface image;

[0118] In this step, the surface image can be processed using an image processing algorithm to obtain the grayscale value, coverage area, morphology, and distribution density of the stains on the surface of the object to be cleaned, and the second cleaning difficulty level can be determined based on one or more of the grayscale value, coverage area, morphology, and distribution density of the stains on the surface of the object to be cleaned.

[0119] Distribution density refers to the spatial distribution of stains on the glass surface. For example, are the stains evenly distributed across the entire glass surface, or concentrated in certain areas (such as edges, corners, or the center)? Subsequent effects: By analyzing the distribution, the robot can prioritize cleaning areas where stains are concentrated, improve efficiency, and avoid unnecessary repeated cleaning.

[0120] Coverage and morphology: refers to the specific shape and characteristics of the stain. For example, is the stain streaked, dotted, patchy, or diffused in a haze? Follow-up: The shape of the stain affects the cleaning strategy, for example, streaked stains may be left by rain and require a horizontal cleaning action. Patchy stains (such as dried liquid marks) require increased cleaning force and wetting time.

[0121] In the field of image processing, grayscale refers to the numerical representation of pixel brightness, usually ranging from 0 (black) to 255 (white). For stain images, the grayscale reflects the brightness difference of the stains: high grayscale: the stains are lighter, which may be dust or water stains. Low grayscale: the stains are darker, which may be oil stains or long-term dirt.

[0122] In one embodiment of the present application, the second cleaning difficulty level can be determined based on the gray value, coverage area, morphology and distribution density of the stains on the surface of the cleaned object. For example, the severity of the stains, i.e., the second cleaning difficulty level, can be quantified by the gray value. A gray threshold can be set, and when the average gray value of a certain area is lower than the threshold, it is determined to be "heavy stains".

[0123] The second cleaning difficulty level is determined based on multiple factors including the grayscale value, coverage area, shape and distribution density of stains on the surface of the object to be cleaned. The method for determining the second cleaning difficulty level may be similar to the first cleaning difficulty level in the aforementioned embodiment, except that the second friction data is reduced, which will not be repeated here.

[0124] Step S403: if the second cleaning difficulty level of any dirty area is greater than a preset second level threshold, determine the third cleaning parameter according to the second cleaning difficulty level.

[0125] In one embodiment of the present application, areas with higher grayscale values ​​require light force and quick cleaning, while areas with lower grayscale values ​​require increased cleaning force or repeated cleaning.

[0126] After cleaning, scan the surface grayscale value again. When it returns to a grayscale value close to the background value (no stains), the cleaning is considered complete.

[0127] Step S404: if the second cleaning difficulty level of any dirty area is less than or equal to the preset second level threshold, an initial cleaning parameter is obtained to serve as the third cleaning parameter.

[0128] The embodiment of the present application can automatically use different cleaning parameters as the second cleaning parameters according to the second cleaning difficulty level. When the second cleaning difficulty level is high, that is, it is difficult to clean, the third cleaning parameters can be determined based on the cleaning difficulty level to achieve targeted cleaning. When the second cleaning difficulty level is low, that is, it is easier to clean, the initial cleaning parameters that were initially set can be used as the third cleaning parameters to automatically adjust the third cleaning parameters of the cleaning equipment according to the second cleaning difficulty level, thereby ensuring that the cleaning equipment can maintain a high efficiency cleaning effect throughout the cleaning process.

[0129] In another embodiment of the present application, before acquiring the surface image of the cleaned object in step S101, the method further includes:

[0130] Step S501, performing surface smoothness detection on the cleaned object to obtain smoothness information;

[0131] Step S502, performing material property detection on the cleaned object to obtain material property information;

[0132] Step S503, performing coating type detection on the cleaned object to obtain coating type information;

[0133] Step S504: determining the initial cleaning parameters according to the smoothness information, the material property information and the coating type information.

[0134] Material properties, for example: Ordinary glass: smooth surface, moderate hardness; Cleaning force: moderate (avoid excessive pressure that may cause glass breakage); Cleaning speed: fast (small surface resistance, no need for excessive friction); Cleaning fluid dosage: moderate (dust and light stains are easily dissolved);

[0135] Surface smoothness, for example: Highly smooth surfaces (such as polished glass): Cleaning force: low (highly smooth surfaces do not require high friction); Cleaning speed: fast (light force is enough to complete the cleaning); Cleaning cloth material: soft material (to avoid scratching the smooth surface);

[0136] Special coating types, for example: Adjust cleaning parameters for different coated glass; For anti-glare coated glass: Cleaning force: low (to prevent coating wear); Cleaning speed: medium speed (evenly act on the surface); Cleaning liquid dosage: no water or low liquid volume (to reduce liquid erosion on the coating); Sensor monitoring: ensure coating integrity through reflectivity sensor.

[0137] For low-e coated glass: Cleaning force: medium (the coating is harder, allowing appropriate force); Cleaning speed: slow (ensure scratches are avoided during cleaning).

[0138] When the system is started, the embodiment of the present application can first initialize each sensor through the control module and perform a self-check to ensure that the hardware modules such as the friction sensor, the visual sensor, and the cleaning drive are in normal working condition. The system sets the initial cleaning parameters according to the basic characteristics of the glass (such as material, surface smoothness, and coating type, etc.). At this time, the system is in standby mode, waiting for the start instruction of the cleaning task for subsequent use.

[0139] In another embodiment of the present application, a device control apparatus is also provided, such as Figure 2 As shown, the method includes:

[0140] A first acquisition module 11, used to acquire a first cleaning parameter corresponding to the cleaned object;

[0141] A second acquisition module 12 is used to acquire first friction data between a cleaning body and the cleaned object during the process of cleaning the cleaned object according to the first cleaning parameter;

[0142] An adjustment module 13, configured to adjust the first cleaning parameter based on the first friction data to obtain a second cleaning parameter;

[0143] The cleaning module 14 is used to clean the cleaned object according to the second cleaning parameter.

[0144] Optionally, the adjustment module includes:

[0145] A first determining unit, used to determine whether the first friction data is within a preset value range;

[0146] The input unit is used to input the first friction data into a preset adaptive control algorithm to obtain a second cleaning parameter if the first friction data is outside a preset friction range.

[0147] Optionally, the cleaned object includes a plurality of cleaning areas, and the first acquisition module includes:

[0148] A first acquisition unit, configured to acquire a regional image of a current cleaning region of the cleaning object and second friction data between the cleaning body and the cleaned object;

[0149] a second determining unit, configured to determine a first cleaning difficulty level according to the area image and the second friction data;

[0150] The third determination unit is configured to determine a regional cleaning parameter according to the regional image and / or the second friction data as the first cleaning parameter if the first cleaning difficulty level is greater than a preset first level threshold.

[0151] Optionally, the first acquisition module further includes:

[0152] The second acquiring unit is configured to acquire, if the first cleaning difficulty level is less than or equal to a preset first difficulty threshold, a third cleaning parameter corresponding to the cleaned object as the first cleaning parameter.

[0153] Optionally, before the first acquisition module, the device further includes:

[0154] A third acquisition module is used to acquire a surface image of the cleaned object;

[0155] A first determination module, configured to determine a dirty area and a second cleaning difficulty level of the dirty area according to the surface image;

[0156] The second determination module is configured to determine the third cleaning parameter according to the second cleaning difficulty level if the second cleaning difficulty level of any dirty area is greater than a preset second difficulty level threshold.

[0157] Optionally, the device further comprises:

[0158] The fourth acquisition module is used to acquire the initial cleaning parameters as the third cleaning parameters if the second cleaning difficulty level of any dirty area is less than or equal to a preset second level threshold.

[0159] Optionally, before the third acquisition module, the device further includes:

[0160] A first detection module is used to detect the surface smoothness of the cleaned object to obtain smoothness information;

[0161] A second detection module is used to detect the material characteristics of the cleaned object to obtain material characteristic information;

[0162] A third detection module is used to detect the coating type of the cleaned object to obtain coating type information;

[0163] The third determination module is used to determine the initial cleaning parameters according to the smoothness information, the material characteristic information and the coating type information.

[0164] In another embodiment of the present application, a cleaning device is further provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0165] Memory, used to store computer programs;

[0166] The processor is used to implement the device control method described in any of the above method embodiments when executing the program stored in the memory.

[0167] In the cleaning device provided by an embodiment of the present invention, the processor can automatically adjust the first cleaning parameter according to the first friction data by executing the program stored in the memory, and clean the cleaned object according to the adjusted second cleaning parameter. By using the second cleaning parameter that matches the first friction data to clean the cleaned object, it is avoided that the use of excessively large cleaning parameters leads to high system energy consumption, or the use of excessively small cleaning parameters leads to poor cleaning effect, thereby achieving a balance between system energy consumption and cleaning effect.

[0168] The communication bus 1140 mentioned in the above cleaning device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0169] The communication interface 1120 is used for communication between the cleaning device and other devices.

[0170] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0171] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0172] In another embodiment of the present application, a computer-readable storage medium is provided, on which a program of a device control method is stored. When the program of the device control method is executed by a processor, the steps of the device control method described in any of the aforementioned method embodiments are implemented.

[0173] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0174] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be 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 the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A device control method, characterized in that: The method comprises: Acquiring a first cleaning parameter corresponding to the object to be cleaned; In the process of cleaning the cleaned object according to the first cleaning parameter, obtaining first friction data between the cleaning body and the cleaned object; adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter; The cleaned object is cleaned according to the second cleaning parameter.

2. The device control method according to claim 1, characterized in that: Adjusting the first cleaning parameter based on the first friction data to obtain a second cleaning parameter includes: determining whether the first friction data is within a preset value range; If the first friction data is outside the preset friction range, the first friction data is input into a preset adaptive control algorithm to obtain a second cleaning parameter.

3. The device control method according to claim 1, characterized in that: The cleaned object includes a plurality of cleaning areas, and obtaining a first cleaning parameter corresponding to the cleaned object includes: Acquire a regional image of a current cleaning region of the cleaning object and second friction data between the cleaning body and the cleaned object; determining a first cleaning difficulty level according to the area image and the second friction data; If the first cleaning difficulty level is greater than a preset first level threshold, a regional cleaning parameter is determined according to the regional image and / or the second friction data to serve as the first cleaning parameter.

4. The device control method according to claim 3, characterized in that: Acquiring a first cleaning parameter corresponding to the cleaned object further includes: If the first cleaning difficulty level is less than or equal to a preset first difficulty threshold, a third cleaning parameter corresponding to the cleaned object is acquired as the first cleaning parameter.

5. The device control method according to claim 4, characterized in that: Before acquiring the first cleaning parameter corresponding to the cleaned object, the method further includes: Acquiring a surface image of the object to be cleaned; determining a dirty area and a second cleaning difficulty level of the dirty area according to the surface image; If the second cleaning difficulty level of any dirty area is greater than a preset second level threshold, the third cleaning parameter is determined according to the second cleaning difficulty level.

6. The device control method according to claim 5, characterized in that: The method further comprises: If the second cleaning difficulty level of any dirty area is less than or equal to the preset second level threshold, an initial cleaning parameter is obtained to serve as the third cleaning parameter.

7. The device control method according to claim 6, characterized in that: Before acquiring the surface image of the cleaned object, the method further includes: Performing surface smoothness detection on the cleaned object to obtain smoothness information; Performing material property detection on the cleaned object to obtain material property information; Performing coating type detection on the cleaned object to obtain coating type information; The initial cleaning parameters are determined according to the smoothness information, the material property information, and the coating type information.

8. A device control device, characterized in that: The device comprises: A first acquisition module, used to acquire a first cleaning parameter corresponding to the cleaned object; A second acquisition module, configured to acquire first friction data between a cleaning body and the cleaned object during the process of cleaning the cleaned object according to the first cleaning parameter; an adjustment module, configured to adjust the first cleaning parameter based on the first friction data to obtain a second cleaning parameter; A cleaning module is used to clean the cleaned object according to the second cleaning parameter.

9. A cleaning device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the device control method described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program of the device control method, and when the program of the device control method is executed by the processor, the steps of the device control method described in any one of claims 1 to 7 are implemented.

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