Clothes processing equipment control method and device and clothes processing equipment

Abstract

The embodiment of the invention provides a control method and device of clothes processing equipment and the clothes processing equipment. The control method of the clothes processing equipment comprises the steps that it is determined that drying operation on a load needing to be dried is started, and a predicted drying progress curve is generated; and controlling to display the predicted drying progress curve in a drying progress display graph. By generating the predicted drying progress curve and displaying the predicted drying progress curve in the drying progress display graph, a user can visually see the drying degree state of the load needing to be dried at the future time point or time period according to the predicted drying progress curve. According to the method provided by the invention, the user can know the drying state of the load needing to be dried more clearly, so that the degree of understanding details by the user is improved, and the user experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of household appliances, and particularly to a control method, device and clothing treatment device for a clothing treatment device. Background Art

[0002] During the process of drying clothes, clothing treatment devices such as dryers and washing and drying integrated machines determine the estimated drying duration according to the temperature change characteristics detected by temperature sensors or the load amount estimated by operation learning detection methods. Due to the different materials, water contents and looseness of the loads, the estimated drying duration determined based on the aforementioned temperature change characteristics or load amount is not accurate, and it is impossible for users to intuitively and clearly understand the drying state of the loads, resulting in poor user experience. Summary of the Invention

[0003] To solve the above technical problems, embodiments of the present disclosure provide a control method, device and clothing treatment device for a clothing treatment device.

[0004] In a first aspect, an embodiment of the present disclosure provides a control method for a clothing treatment device, including:

[0005] Determine to start the drying operation for the load to be dried, and generate a predicted drying progress curve;

[0006] Control to display the predicted drying progress curve in a drying progress display graph.

[0007] Optionally, it further includes: obtaining monitored state characteristic data and corresponding time points, where the state characteristic data represents the drying degree of the load to be dried;

[0008] While controlling to display the predicted drying progress curve in the drying progress display graph, the method further includes: according to the time point, controlling to display the corresponding state characteristic data in the drying progress display graph.

[0009] Optionally, the method further includes: determining a target point in the predicted drying progress curve, where the target point is the point when the drying degree reaches a preset drying degree;

[0010] While controlling to display the predicted drying progress curve in the drying progress display graph, the method further includes: controlling to prominently display the target point in the drying progress display graph.

[0011] Optionally, before generating the predicted drying progress curve, the method further includes:

[0012] Recording the state characteristic data obtained at multiple monitoring times and the time points of the monitoring times, where the state characteristic data represents the drying degree of the load to be dried;

[0013] Generating the predicted drying progress curve includes: generating the predicted drying progress curve based on the state characteristic data and the corresponding time points.

[0014] Optionally, recording the state characteristic data obtained at multiple monitoring time points includes:

[0015] Recording one of the following types of data obtained at multiple monitoring time points: humidity data, conductivity data, impedance data, capacitance reactance data, and duty cycle data.

[0016] Optionally, generating the predicted drying progress curve based on the state characteristic data and the corresponding time points includes:

[0017] Generating a drying progress fitting function based on the state characteristic data and the corresponding time points;

[0018] Generating the predicted drying progress curve based on the drying progress fitting function.

[0019] Optionally, the method further includes: determining the remaining drying duration based on the drying progress fitting function, where the remaining drying duration is the remaining drying duration required for the load to be dried to reach the target drying degree.

[0020] Optionally, determining the remaining drying duration based on the drying progress fitting function includes:

[0021] Obtaining the target drying degree corresponding to when the load to be dried reaches the target drying degree;

[0022] Inputting the target drying degree into the drying progress fitting function to obtain the target drying duration;

[0023] Subtracting the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0024] Optionally, determining the remaining drying duration based on the drying progress fitting function includes:

[0025] Calculating the state change rate at future time points based on the drying progress fitting function, where the state change rate is the change rate of the state characteristic data;

[0026] Determining the target time point at which the state change rate is less than the target change rate, and using the calculation duration corresponding to the target time point as the target drying duration;

[0027] Subtracting the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0028] Optionally, calculating the state change rate at a future time point based on the drying progress fitting function includes:

[0029] Determining a plurality of the future time points at a set time interval and calculating the state change rate at each of the future time points;

[0030] Determining the target time point at which the state change rate is less than the target change rate includes:

[0031] When the state change rates corresponding to N consecutive future time points are all less than the target change rate, taking the last time point among the N consecutive future time points as the target time point, where N≥2.

[0032] Optionally, after obtaining the remaining drying duration, the method further includes:

[0033] Displaying a drying countdown starting from the remaining drying duration.

[0034] Optionally, the method further includes: continuing to record the state feature data obtained at subsequent monitoring time points and the time points of the subsequent monitoring time points;

[0035] In response to obtaining a function update instruction, generating an updated predicted drying progress curve based on the existing state feature data and the corresponding time points;

[0036] Displaying the updated predicted drying progress curve in the drying progress display graph.

[0037] In a second aspect, an embodiment of the present disclosure provides a control device for a clothing treatment device, including:

[0038] A curve generation unit, configured to generate a predicted drying progress curve after determining to start a drying operation on a load to be dried;

[0039] A display control unit, configured to control the predicted drying progress curve to be displayed in a drying progress display graph.

[0040] In a third aspect, an embodiment of the present disclosure further provides a clothing treatment device, including a controller and a display device; the controller is configured to control the display device to display the drying progress curve according to the control method of the clothing treatment device as described above.

[0041] The technical solution provided by the embodiment of the present disclosure has the following advantages compared with the prior art:

[0042] By adopting the solution of the laundry treatment device provided in the embodiments of the present disclosure, after starting the drying operation on the load to be dried, by generating a predicted drying progress curve and displaying the predicted drying progress curve in the drying progress display graph, the user can intuitively see the drying degree state of the load to be dried at future time points or time periods according to the predicted drying progress curve. That is, the method of this proposal enables the user to more clearly understand the drying state of the load to be dried, thereby improving the user's understanding of details and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0044] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings, where:

[0045] Figure 1 is a flowchart of the control method of the laundry treatment device provided in the embodiments of the present disclosure;

[0046] Figure 2 is a drying progress display graph provided in some embodiments of the present disclosure;

[0047] Figure 3 is a flowchart of the method for determining the remaining drying duration based on the drying progress fitting function provided in some embodiments;

[0048] Figure 4 is a flowchart of the method for determining the remaining drying duration based on the drying progress fitting function provided in some other embodiments;

[0049] Figure 5 is a flowchart of the control method of the laundry treatment device provided in still some other embodiments of the present disclosure;

[0050] Figure 6 is a structural schematic of the control device of the laundry treatment device provided in the embodiments of the present disclosure;

[0051] Figure 7 is a structural schematic diagram of the laundry treatment device provided in the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0053] As used herein, the term "comprising" and its variations are open-ended, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of the processes executed by these devices, modules or units or their interdependent relationships.

[0054] It should be noted that the modifications of "one" and "plural" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0055] In order to solve the problem that the existing drying equipment only feeds back the drying progress to the user by showing the remaining drying duration, and the user cannot intuitively understand the actual drying state of the load to be dried, the embodiments of the present disclosure provide a new control method for a clothing treatment device.

[0056] Before analyzing the control method for the clothing treatment device provided by the embodiments of the present disclosure, a brief introduction to the clothing treatment device provided by the embodiments of the present disclosure will be given first. The clothing treatment device provided by the embodiments of the present disclosure is a treatment device that can perform a heating operation on the load to be dried, and thus realize the drying of the load to be dried. The clothing treatment device can be a dryer or a washing and drying integrated machine. When the clothing treatment device is a dryer, it may be a direct-exhaust dryer or a heat pump dryer.

[0057] Figure 1 is a flowchart of the control method for the clothing treatment device provided by the embodiments of the present disclosure. As Figure 1 shown, the control method for the clothing treatment device provided by the embodiments of the present disclosure includes S110 - S120.

[0058] S110: Determine to start the drying operation on the load to be dried and generate a predicted drying progress curve.

[0059] In the embodiments of the present disclosure, after the laundry treatment device starts the drying operation on the load to be dried, a predicted drying progress curve can be generated in a specific manner.

[0060] In some embodiments, after the laundry treatment device starts the drying operation on the load to be dried, a corresponding sensor can be triggered to detect the load state characteristics of the load to be dried, and state characteristic data can be obtained. After obtaining the state characteristic data, a predicted drying progress curve representing the drying progress can be generated using the state characteristic data. The aforementioned state characteristic data can be various types of state characteristic data, which will be analyzed later. Specifically, how to generate a predicted drying progress curve using the state characteristic data will also be elaborated and analyzed later.

[0061] For another example, if the user's laundry washing has an obvious periodicity (such as washing clothes once a week), and the quantity and type of clothes do not change particularly, the laundry treatment device can, after starting the drying operation on the load to be dried, generate a predicted drying progress curve from the drying progress curve formed in the previous drying operation or the state characteristic data collected during the previous drying operation.

[0062] S120: Control to display the predicted drying progress curve in the drying progress display diagram.

[0063] After obtaining the predicted drying progress curve, the laundry treatment device can control the display device to display the drying progress display diagram and display the predicted drying progress curve in the drying progress display diagram.

[0064] The drying progress display diagram can be a two-dimensional display diagram, one coordinate axis of which represents the drying time, and the other coordinate axis represents a parameter related to the drying progress of the load to be dried. The aforementioned parameter related to the drying progress of the load to be dried can be humidity or dryness.

[0065] Figure 2 is the drying progress display diagram provided by some embodiments of the present disclosure. As Figure 2 shown, the abscissa of the drying progress display diagram is time, and the ordinate is the humidity of the load to be dried. According to Figure 2 the predicted drying progress curve shown in, it can be seen that as the drying duration increases, the humidity of the load to be dried gradually decreases. After a sufficient drying duration, the humidity of the load to be dried basically no longer changes, indicating that the drying operation can be ended.

[0066] In practical applications, the display device described in the embodiments of the present disclosure may be a display device located on a laundry treatment device or a display device independent of the laundry treatment device. For example, in some embodiments, the display device may be a display device in a user terminal. After the laundry treatment device starts a drying operation and generates a predicted drying progress curve, the predicted drying progress curve may be sent to the user terminal so that an application program in the user terminal drives the display device to display a drying progress display graph and display the predicted drying progress curve in the drying progress display graph.

[0067] By adopting the control method of the laundry treatment device provided in the embodiments of the present disclosure, after starting the drying operation on the load to be dried, by generating a predicted drying progress curve and displaying the predicted drying progress curve in the drying progress display graph, the user can intuitively see the drying degree state of the load to be dried at a future time point or time period. That is, the method of this proposal can enable the user to more clearly understand the drying state of the load to be dried, thereby improving the user's understanding of details and the user experience.

[0068] For example, in practical applications, based on the predicted drying progress curve, the user can more clearly determine when the load to be dried can reach the desired drying degree, without having to focus on the idea of "what degree of drying has been achieved now". In practical applications, after seeing the predicted drying progress curve and understanding the content of the predicted drying progress curve, the user can reasonably plan the itinerary according to the drying progress of the load to be dried and then pay attention to the drying progress of the load to be dried after the drying progress generally reaches the expected progress.

[0069] It should be noted that the aforementioned predicted drying progress curve is only a predicted curve. In practical applications, there may be a large difference between the aforementioned predicted drying progress curve and the actual drying progress of the load to be dried. To actually control the drying progress, the laundry treatment device also needs to obtain the state characteristic data monitored by the sensor and control the drying operation progress based on the state characteristic data.

[0070] The obtained monitored state characteristic data reflects the drying degree of the load to be dried, and the drying progress of the load to be dried can be more directly reflected through multiple state characteristic data. To make full use of the obtained state characteristic data, the following S130 may also be executed in the embodiments of the present disclosure.

[0071] S130: Obtain the monitored state characteristic data and the corresponding time points.

[0072] As analyzed above, the state characteristic data in the embodiments of the present disclosure is data representing the drying degree of the load to be dried.

[0073] When performing the foregoing S130, while performing S120, the control method of the laundry treatment device may further perform the following S140.

[0074] S140: Display corresponding state characteristic data in the drying progress display graph.

[0075] Specifically, displaying corresponding state characteristic data in the drying progress display graph is to determine the corresponding time point coordinates according to the time point, and determine the corresponding drying progress coordinates according to the state characteristic data, and then make a mark in the drying progress graph according to the time point package and the drying progress coordinates to display the state characteristic data.

[0076] In specific implementation, the state characteristic data can be displayed in the form of point coordinates, or can be displayed in the form of connecting coordinate points with a broken line.

[0077] It should be noted that the size of the foregoing state characteristic data can directly or indirectly characterize the drying degree of the load to be dried. According to the foregoing explanation of the state characteristic data, it can be determined that data such as temperature data, which has no direct relationship with the drying degree of the load to be dried, cannot be used as state characteristic data. In practical applications, if the state characteristic data is the type of data displayed on the drying progress display axis of the drying progress display graph, the state characteristic data can be directly used as the point coordinates of the corresponding mark. If the state characteristic data is not the type of data displayed on the drying progress display axis in the drying progress display graph, it is also necessary to convert the state characteristic data before it can be displayed in the drying progress display graph.

[0078] By using the foregoing method to display the monitored state characteristic data while displaying the predicted drying progress curve, the user can understand the actual drying progress of the load to be dried, and then determine the reliability of the predicted drying progress curve, and then make an active correction for the predicted drying progress of the load to be dried.

[0079] In addition, in some instances, especially when the predicted drying progress curve is determined based on the state characteristic data, displaying the state characteristic data can also make the user more convinced of the rationality and accuracy of the predicted drying progress curve.

[0080] In practical applications, after the user starts the laundry treatment device, the user may set a preset drying degree in the laundry treatment device. The target drying degree is the drying degree that the user wants to reach when the drying operation ends. In order to enable the user to intuitively understand the time required to reach the preset drying degree or the intermediate drying progress of the load to be dried, the laundry treatment device may also perform the following S150.

[0081] S150: Determine the target point in the predicted drying progress curve.

[0082] The target point is the point when the drying degree reaches the preset drying degree. In specific implementation, a straight line parallel to the time axis can be drawn through the preset drying degree, and the intersection of the straight line and the predicted drying progress curve is taken as the target point.

[0083] In the case of executing the aforementioned S150, while executing the aforementioned S120, the following S160 can also be executed.

[0084] S160: Control to prominently display the target point in the drying progress display graph.

[0085] As analyzed above, by displaying the target point, the user can intuitively understand the time required to reach the preset drying degree or the intermediate drying progress of the load to be dried.

[0086] It was previously mentioned that a predicted drying progress curve can be generated based on the state characteristic data. The method of generating a predicted drying progress curve based on the state characteristic data is analyzed below.

[0087] In some embodiments, the state characteristic data can be data directly representing the drying degree of the load to be dried. In specific implementation, the state characteristic data can be any one of the following: humidity data, conductivity data, impedance data, capacitance reactance data, duty cycle data.

[0088] Humidity data is data directly representing the humidity characteristics of the load to be dried. In specific implementation, the humidity data can be measured by a humidity sensor directly for humidity detection, or can be obtained by converting the data detected by other types of sensors.

[0089] It should be noted that the humidity data obtained by converting the data detected by other types of sensors may not be accurate humidity data, and it is only a relatively accurate data. However, in some cases, the aforementioned humidity data obtained by conversion does not affect the implementation of the subsequent solution and the accuracy of the determined remaining drying duration.

[0090] Conductivity data is data determined by detecting the conductivity of the load to be dried. The conductivity data is detected by a conductivity sensor. When the laundry treatment device performs the drying operation on the load, the load to be dried in a wet state will randomly contact the contact piece in the conductivity sensor to achieve circuit conduction, and then the conductivity of the load to be dried is measured. In specific implementation, the conductivity of the load to be dried decreases as the water content of the load to be dried decreases. That is to say, the conductivity is positively correlated with the humidity of the load to be dried and negatively correlated with the drying degree of the load to be dried.

[0091] The impedance data is the data determined by detecting the environmental impedance of the environment where the load to be dried is located. The impedance data characterizes the impedance characteristics of the load to be dried itself, and is obtained by detecting with an impedance sensor. In practical applications, the impedance of the load to be dried increases as the water content in it decreases. That is to say, the impedance of the load to be dried is inversely correlated with the humidity of the load to be dried and is positively correlated with the drying degree of the load to be dried.

[0092] The capacitive reactance data is the data determined by detecting the dielectric constant of the environment where the load to be dried is located. The capacitive reactance data is detected by a capacitive reactance sensor, and the insulating medium in the capacitor of the capacitive reactance sensor is the air in the laundry treatment device. In practical applications, as the drying progresses, the water vapor saturation of the environment where the load to be dried is located changes accordingly (the general trend is gradually decreasing), corresponding to an increase in the dielectric constant of the insulating medium in the aforementioned capacitor, and then changing the capacitive reactance characteristics. That is to say, the magnitude of the capacitive reactance data is inversely correlated with the humidity of the load to be dried and is positively correlated with the drying degree of the load to be dried.

[0093] The duty cycle data is the data obtained after normalizing the detection data obtained by detecting the capacitive reactance, impedance, or conductivity of the load to be dried. The duty cycle data may be positively correlated or inversely correlated with the humidity of the load to be dried.

[0094] In a specific embodiment, in the case where the state characteristic data is the aforementioned type of data, before performing the aforementioned S110, the laundry treatment device needs to perform the following S170.

[0095] S170: Record the state characteristic data obtained at multiple monitoring times and the time points of the monitoring times, where the state characteristic data characterizes the drying degree of the load to be dried.

[0096] The monitoring time can be the time when the sensor detects the state characteristic data, or the time when the processor of the laundry treatment device receives the state characteristic data. The embodiments of the present disclosure do not make a limitation.

[0097] After obtaining the state characteristic data, the laundry treatment device can immediately record the aforementioned state characteristic data and the time points of the corresponding monitoring times as a data pair for subsequent use. In practical applications, the sensor samples according to a set sampling period to obtain the state characteristic data. Correspondingly, the state characteristic data at multiple monitoring times and the time points of the detection times recorded can be represented by an array.

[0098] For the convenience hereinafter, taking the state characteristic data as humidity data, the specific implementation process of the present solution is analyzed. In the case where the state characteristic data is humidity data, the state characteristic data and the time points of the detection times recorded can be represented by {{H humidity[t 0 ,H humidity [t 1 ,H humidity [t 2 ,H humidity [t 3 ,…,H humidity [t n}} indicates, where t i represents the time point (i = 0, 1,..., n), and H humidity [t 0 represents the humidity data corresponding to the time point.

[0099] In practical applications, within a period of time after the laundry treatment device has just started the drying operation on the load to be dried, since the moisture in the load to be dried has not fully evaporated, the state characteristic data detected by the sensor during this period cannot accurately reflect the drying degree of the load to be dried. To avoid this problem, the aforementioned S170 may include the following S171 or S172.

[0100] S171: After the drying operation has been executed for the first duration, record the state characteristic data obtained at multiple monitoring times and the time points of the monitoring times.

[0101] The first duration is determined in advance through experience or experiments, and is the operating duration required to ensure that the obtained state characteristic data can accurately represent the drying degree of the load to be dried. In some embodiments, after the drying operation has been executed for the first duration, that is, after the drying operation has been carried out for a period of time, the laundry treatment device begins to record the state characteristic data obtained at multiple detection times. Specifically, during the operation duration before reaching the first duration, the laundry treatment device can still control the sensor to power on and detect to obtain the state characteristic data. However, the state characteristic data before the operation duration reaches the first duration will not be used for subsequent function fitting.

[0102] S172: When the latest obtained state characteristic data reaches the first characteristic data, record the state characteristic data obtained at multiple monitoring times and the time points of the monitoring times.

[0103] In some embodiments, after the laundry treatment device starts the drying operation on the load to be dried, the laundry treatment device immediately controls the sensor to power on and detect to obtain the state characteristic data. After obtaining the state characteristic data, the laundry treatment device then judges the state characteristic data to determine whether it reaches the first characteristic data.

[0104] When the foregoing state characteristic data reaches the first characteristic data, it needs to be understood adaptively according to the type of the state characteristic data. For example, if the state characteristic data is data such as humidity data that gradually decreases as drying progresses, the state characteristic data reaching the first characteristic data means that the state characteristic data is less than or equal to the first characteristic data. If the state characteristic data is data such as impedance that gradually increases as drying progresses, the state characteristic data reaching the first characteristic data means that the state characteristic data is greater than or equal to the first characteristic data.

[0105] In the case of the state characteristic data and the corresponding time points obtained by executing the foregoing S170, the generation of the predicted drying progress curve in the foregoing S110 may be as follows S111.

[0106] S111: Generate a predicted drying progress curve based on the state characteristic data and the corresponding time points.

[0107] Generating a predicted drying progress curve based on the state characteristic data and the corresponding time points may be to perform curve fitting based on the state characteristic data and the pre-set change trend of the drying degree of the load to be dried to generate a predicted drying progress curve.

[0108] In practical applications, the foregoing S111 may include S111A - S111B.

[0109] S111A: Generate a drying progress fitting function based on the state characteristic data and the corresponding time points.

[0110] In a specific implementation, generating a drying progress fitting function based on the state characteristic data and the corresponding time points is to adjust the parameters of the initial fitting function based on the state characteristic data and the corresponding time points to obtain a drying progress fitting function.

[0111] After obtaining the state characteristic data corresponding to multiple monitoring time points and the time points of the monitoring time, the parameters of the initial fitting function can then be adjusted based on the foregoing data.

[0112] The initial fitting function is a function determined through preliminary tests and characterizing the change trend of the drying degree of the load to be dried. The function type and function parameters in the initial fitting function have been determined, but the specific values in the function parameters have not been determined. Adjusting the parameters of the initial fitting function is to adjust the values of the function parameters so that the obtained drying progress fitting function can characterize the overall distribution characteristics of the recorded state characteristic data and the corresponding time points.

[0113] In some embodiments of the present disclosure, when the state characteristic data is humidity data, the initial fitting function may be ψ humidity (t)=(A + B×cos(Ct))×10 -DtA function for characterization, where A, B, C, and D are function parameters to be adjusted. In some other examples of the present disclosure, the initial fitting function can be a function characterized by H humidity (t) = a × e bt + c, where a, b, c are function parameters to be adjusted.

[0114] Based on the state feature data and the corresponding time points, the parameters of the initial fitting function are adjusted by using the least squares method for data fitting to adjust each function parameter so that the sum of squared errors obtained by the least squares method is minimized.

[0115] Taking the initial fitting function ψ humidity (t) = (A + B × cos(Ct)) × 10 -Dt as an example, the sum of squared errors obtained by using the least squares method is. In specific implementation, to adjust each function parameter, the gradient descent algorithm can be used to adjust each function parameter until the accuracy of the obtained drying progress fitting function reaches the optimal or meets the set requirements.

[0116] Specifically, when using the gradient descent algorithm to adjust each function parameter, it can be the foregoing

[0117]

[0118] where dx is a very small value (for example, it can be 0.00001).

[0119] In specific implementation, due to the different materials, types, and load amounts of the load to be dried, the function types of the corresponding initial fitting functions are not the same. To make the obtained drying progress fitting function more accurately fit the characteristics of the load to be dried, before performing the foregoing S111A, the corresponding initial fitting function can also be determined according to the material, type, and load amount of the load to be dried. The material and type of the foregoing load to be dried can be selected and set by the user, and the load amount of the foregoing load to be dried can be determined by the laundry treatment device through weight measurement.

[0120] According to relevant knowledge such as function fitting methods, to achieve a more accurate fitting of the function, a sufficient amount of data is required. That is, there is a need for a sufficient amount of state feature data and time points of the corresponding monitoring times. To meet the foregoing objectives, in the embodiments of the present disclosure, the following method can be used to determine whether S120 can be executed.

[0121] The first method: When the latest obtained state feature data reaches the second feature data, the parameters of the initial fitting function are adjusted based on the state feature data and the corresponding time points. The aforementioned second feature data is data that has a large difference compared to the state feature data recorded at the time points corresponding to the first time period, or data that has a large difference compared to the first feature data. Since the second feature data has a large difference from the previously recorded state feature data at an earlier time or the first feature data, it takes a long time to change from the previous data or the first feature data to the second feature data, and a large amount of state feature data is collected during this long time, which can meet the function fitting requirements.

[0122] The second method: When the recorded state feature data reaches a set number, the parameters of the initial fitting function are adjusted based on the state feature data and the corresponding time points. The second method is to count the recorded state feature data and determine whether the number of state feature data meets the requirements according to the counting result.

[0123] After obtaining the drying progress fitting function, S111B can be executed subsequently.

[0124] S111B: Generate a predicted drying progress curve based on the drying progress fitting function.

[0125] The process of generating a predicted drying progress curve based on the drying progress fitting function can be to substitute each drying time point into the drying progress curve to obtain the corresponding coordinate points, and then smoothly connect the coordinate points in sequence to obtain the predicted drying progress curve.

[0126] In the foregoing embodiments, it is mentioned that the state feature data is data that directly characterizes the drying degree of the load to be pre-dried. In some embodiments, the state feature data can also be data that is not directly related to the drying of the load to be dried, such as the weight data of the load to be dried or data related to the weight. In this case, the drying progress of the load to be dried can still be determined based on the state feature data, and a predicted drying progress curve can be generated. In this case, multiple state feature data detected during the drying process can be input into a pre-constructed function to obtain a drying progress fitting function, and a predicted drying progress curve can be generated according to the drying progress fitting function.

[0127] As mentioned above, in practical applications, a drying progress fitting function can be generated based on the state feature data. In practical applications, when the drying progress fitting function can be obtained, the laundry treatment device can also perform the following operation of S210.

[0128] S210: Determine the remaining drying duration based on the drying progress fitting function.

[0129] After determining the drying progress fitting function, data calculation can then be performed based on the drying progress fitting function to determine the remaining drying duration.

[0130] In the embodiments of the present disclosure, the remaining drying duration refers to the remaining drying duration required for the load to be dried to reach the target drying degree. The aforementioned target drying degree can be the drying degree corresponding to the load to be dried being completely dry, or it can be a drying degree specified by the user (for example, the humidity reaches 10%).

[0131] Determining the remaining drying duration based on the drying progress fitting function is to determine the target drying duration required for the load to be dried to reach the target drying degree based on the drying progress fitting function, and then subtract the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0132] In practical applications, after determining the remaining drying duration based on the drying progress fitting function, the remaining drying duration can then be output so that the user can know the remaining drying duration. In some embodiments, the laundry treatment device can control the output device to start a drying countdown display with the remaining drying duration so that the user can know the remaining drying duration in real time.

[0133] Based on the foregoing control method, a drying progress fitting function is constructed using state characteristic data related to the drying degree during the drying process of the load to be dried, and various parameters that affect the prediction of the drying duration (such as the material, water content, looseness, and load amount of the load to be dried) are directly considered. Using the drying progress fitting function to predict the overall state characteristic changes during the drying process of the load to be dried can efficiently and accurately predict the remaining drying duration, thereby improving the user experience.

[0134] As analyzed above, the embodiments of the present disclosure determine the remaining drying duration based on the drying progress fitting function. Figure 3 It is a flowchart of a method for determining the remaining drying duration based on the drying progress fitting function provided by some embodiments. As Figure 3 shown, in some embodiments, the method for determining the remaining drying duration includes S211 - S213.

[0135] S211: Obtain the target characteristic data corresponding to the load to be dried when it reaches the target drying degree.

[0136] In the embodiments of the present disclosure, the target characteristic data is data of the same type as the aforementioned state characteristic data. In specific implementation, obtaining the target characteristic data corresponding to the load to be dried when it reaches the target drying degree can be to determine the corresponding target characteristic data according to the preset target drying degree. The aforementioned preset target drying degree can be the default drying degree of the laundry treatment device or the drying degree input by the user.

[0137] In specific implementation, the degree of drying required by the user is mostly represented by humidity. When the state characteristic data is humidity data, the humidity data input by the user can be directly used as the target characteristic data. When the state characteristic data is not humidity data, the humidity data can be converted to obtain the corresponding target characteristic data.

[0138] S212: Input the target characteristic data into the drying progress fitting function to obtain the target drying duration.

[0139] After obtaining the target characteristic data, subsequently input the target characteristic data into the drying progress fitting function for inverse calculation, and the target drying duration can be obtained.

[0140] S213: Subtract the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0141] In specific implementation, when the recorded state characteristic data is relatively accurate, the drying progress fitting function constructed based on the state characteristic data can more accurately fit the change in the drying degree of the load to be dried, and the remaining drying duration determined based on the foregoing method is relatively accurate.

[0142] In some embodiments, the monitored state characteristic data may have systematic errors, resulting in a large systematic error in the drying progress fitting function constructed based on the state characteristic data, and the remaining drying duration cannot be accurately determined by the target characteristic data using the foregoing S212 - S213. Specifically, the target drying duration determined by the foregoing method may have exceeded a reasonable duration (for example, exceeding 4 hours).

[0143] Figure 4 FIG. is a flowchart of a method for determining the remaining drying duration based on a drying progress fitting function provided by another embodiment. To solve the problem mentioned in the previous paragraph, the embodiments of the present disclosure also provide another method for determining the remaining drying duration, including S214 - S216.

[0144] S214: Calculate the state change rate at future time points based on the drying progress fitting function.

[0145] The state change rate is the change rate of the state characteristic data. Calculating the state change rate at future time points based on the drying progress fitting function can be to calculate the derivative function of the drying progress fitting function, and then input the future time points into the derivative function to obtain the state change rate at each future time point.

[0146] S215: Determine the target time points at which the state change rate is less than the target change rate, and use the calculation duration corresponding to the target time points as the target drying duration.

[0147] After obtaining the state change rate at a future time point, the state change rate can be compared with the target change rate to determine the value at which the state change rate is less than the target change rate. Subsequently, by searching backward based on the state change rate, the corresponding future time point can be determined. The aforementioned future time point is the target time point. After obtaining the target time point, the duration from the start time point of drying to the target time point is the target drying duration.

[0148] S216: Subtract the already executed drying duration from the target drying duration to obtain the remaining drying duration.

[0149] In practical applications, due to systematic errors in the state characteristic data, the calculated drying progress fitting function does not match the actual drying characteristics of the load to be dried. However, the overall change trend of the drying progress fitting function can be the same as the actual drying characteristics change trend of the load to be dried. Based on this, the remaining drying duration can be determined based on the state change rate using S135 - S135.

[0150] In some embodiments, the state characteristic data is humidity data. Correspondingly, the state change rate is the change rate of the humidity data, that is, the humidity change rate. In this case, when |Grad H humidity (t)| <= |Grad H target_humidity |, the corresponding t is taken as the target time point. At this time, the target drying duration can also be determined.

[0151] In some embodiments, the drying progress fitting function may have multiple poles, and the target drying duration calculated directly using the method determined by the aforementioned S214 - S216 may not be accurate (directly taking the time point corresponding to the previous pole as the target drying duration). To avoid the aforementioned problem, S214 includes S2141, and S215 can include S2151 - S2156.

[0152] S2141: Determine multiple future time points at a set time interval and calculate the state change rate of each future time point.

[0153] The set time interval is a relatively reasonable time interval determined through experiments beforehand. Determining multiple future time points at a set time interval starts from the current time point (that is, constructing the drying progress fitting function) and determines multiple future time points at the set time interval.

[0154] Calculating the state change rate of each future time point is to substitute each future time point into the derivative function to determine the corresponding state change rate.

[0155] S2151: Determine whether the state change rate of a future time point is less than the target change rate; if so, execute S1352; if not, execute S2156.

[0156] S2152: Set Flag = Flag + 1.

[0157] S2153: Determine whether Flag is greater than N, where N ≥ 2; if so, execute S1354; if not, execute S1355.

[0158] S2154: Use the last time point among N consecutive future time points as the target time point.

[0159] S2155: Continue to execute S1351.

[0160] S2156: Set Flag = 0 and continue to execute S1351.

[0161] That is to say, by adopting the solution provided in the embodiments of the present disclosure, when the state change rates corresponding to N consecutive future time points are all less than the target change rate, the last time point among N consecutive future time points is used as the target time point.

[0162] In specific implementation, the aforementioned N can be determined according to the characteristics of the drying progress fitting function (i.e., the number of poles).

[0163] Figure 5 It is a flowchart of the control method of the clothing treatment device provided in some other embodiments of the present disclosure. As Figure 5 shown, in some embodiments, the control method of the clothing treatment device is as S310 - S360.

[0164] S310: Determine to start the drying operation for the load to be dried, record the state characteristic data obtained at multiple monitoring time points and the time points of the monitoring time points, and the magnitude of the state characteristic data represents the drying degree of the load to be dried.

[0165] S320: When the latest obtained state characteristic data reaches the second characteristic data, generate a predicted drying progress curve based on the state characteristic data and the corresponding time points.

[0166] S330: Control to display the predicted drying progress curve in the drying progress display diagram.

[0167] S340: Continue to record the state characteristic data obtained at subsequent monitoring time points and the time points of the subsequent monitoring time points.

[0168] S250: In response to obtaining a curve update instruction, generate an updated predicted drying progress curve based on the existing state characteristic data and the corresponding time points.

[0169] S260: Display the updated predicted drying progress curve in the drying progress display diagram.

[0170] In the embodiments of the present disclosure, the implementation processes of S310 - S330 are the same as the corresponding steps in the previous embodiments, which will not be repeated here. For details, please refer to the previous description.

[0171] Different from the previous description, in the embodiments of the present disclosure, while generating and displaying the predicted drying progress curve, the state characteristic data and the time points of subsequent detection moments are still recorded. In addition, while monitoring and recording the state characteristic data, it is monitored whether a curve update instruction is obtained.

[0172] The curve update instruction is an instruction for updating the drying progress fitting function. In some embodiments, when the display device of the clothing processing device is awakened, the wake-up instruction for awakening the display device is used as the curve update instruction. In other embodiments, when the user clicks a specific button on the clothing processing device, the instruction of clicking the specific button is used as the curve update instruction.

[0173] After obtaining the curve update instruction, the clothing processing device regenerates the predicted drying progress curve based on the recorded state characteristic data and the corresponding time points. The specific method for regenerating the predicted drying progress function is as in the previous embodiments, which will not be repeated here. After obtaining the updated predicted drying progress curve, the method described above is then used to display the predicted drying progress curve in the drying progress display diagram.

[0174] In addition to providing the control method for the clothing processing device described above, the embodiments of the present disclosure also provide a control device for the clothing processing device. Figure 6 It is a schematic structural diagram of the control device for the clothing processing device provided by the embodiments of the present disclosure. As Figure 6 shown, the control device 600 of the clothing processing device includes a curve generation unit 601 and a display control unit 602.

[0175] The curve generation unit 601 is used to generate a predicted drying progress curve after determining to start the drying operation on the load to be dried; the display control unit 602 is used to control the display of the predicted drying progress curve in the drying progress display diagram.

[0176] In some embodiments, the control device 600 of the clothing processing device further includes a monitoring unit. The monitoring unit is used to obtain the monitored state characteristic data and the corresponding time points, and the state characteristic data represents the drying degree of the load to be dried; while controlling the display of the predicted drying progress curve in the drying progress display diagram, the display control unit 602 controls the display of the corresponding state characteristic data in the drying progress display diagram according to the time points.

[0177] In some embodiments, the control device 600 of the laundry treatment device further includes a target point determination unit. The target point determination unit is configured to determine a target point in the predicted drying progress curve, where the target point is the point when the drying degree reaches a preset drying degree;

[0178] The display control unit 602 controls to prominently display the target point in the drying progress display graph while displaying the predicted drying progress curve in the drying progress display graph.

[0179] In some embodiments, the curve generation unit 601 generates a predicted drying progress curve based on the state characteristic data and the corresponding time points.

[0180] In some embodiments, the monitoring unit records one of the following types of data obtained at multiple monitoring times: humidity data, conductivity data, impedance data, capacitance reactance data, and duty cycle data.

[0181] In some embodiments, the curve generation unit generates a drying progress fitting function based on the state characteristic data and the corresponding time points; subsequently, a predicted drying progress curve is generated based on the drying progress fitting function.

[0182] In some embodiments, the control device 600 of the laundry treatment device further includes a remaining duration determination unit. The remaining duration determination unit determines the remaining drying duration based on the drying progress fitting function, where the remaining drying duration is the remaining drying duration required for the load to be dried to reach the target drying degree.

[0183] In some embodiments, the remaining duration determination unit obtains the target drying degree corresponding to when the load to be dried reaches the target drying degree, then inputs the target drying degree into the drying progress fitting function to obtain the target drying duration, and finally subtracts the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0184] In some embodiments, the remaining duration determination unit determines the remaining drying duration based on the drying progress fitting function, including: calculating the state change rate at a future time point based on the drying progress fitting function, where the state change rate is the change rate of the state characteristic data; determining the target time point when the state change rate is less than the target change rate, and taking the calculation duration corresponding to the target time point as the target drying duration; subtracting the executed drying duration from the target drying duration to obtain the remaining drying duration.

[0185] In some embodiments, the remaining duration determination unit calculates the state change rate at future time points based on the drying progress fitting function, including: determining a plurality of future time points at a set time interval and calculating the state change rate at each future time point; determining the target time point at which the state change rate is less than the target change rate, including: when the state change rates corresponding to N consecutive future time points are all less than the target change rate, taking the last time point among the N consecutive future time points as the target time point, where N≥2.

[0186] In some embodiments, after obtaining the remaining drying duration, the display control unit 602 also controls to display the drying countdown starting from the remaining drying duration.

[0187] In some embodiments, the monitoring unit continues to record the state feature data obtained at subsequent monitoring time points and the time points of the subsequent monitoring time points; the curve generation unit 601 generates an updated predicted drying progress curve based on the existing state feature data and the corresponding time points in response to obtaining the function update instruction; the display control unit 602 controls to display the updated predicted drying progress curve in the drying progress display graph.

[0188] In addition to providing the foregoing control method and control device for a laundry treatment device, embodiments of the present disclosure also provide a laundry treatment device. Figure 7 It is a schematic structural diagram of the laundry treatment device provided by an embodiment of the present disclosure. As Figure 7 shown, the laundry treatment device includes a controller 701 and a display device 702. The controller is configured to control the display device to display the drying progress curve according to the control method of the laundry treatment device as described above.

[0189] In a specific implementation, the controller 701 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the laundry treatment device to perform desired functions.

[0190] Embodiments of the present disclosure also provide a storage medium storing a program or instructions, and the program or instructions cause a computer to execute any control method for a laundry treatment device provided by embodiments of the present disclosure. When the computer-executable instructions are executed by a computer controller, they can also be used to execute the above-mentioned arbitrary control method for a laundry treatment device provided by embodiments of the present disclosure to achieve corresponding beneficial effects.

[0191] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented by means of software and necessary general-purpose hardware. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such an understanding, the technical solution of the embodiments of the present disclosure, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk, or an optical disc of a computer, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present disclosure.

[0192] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a laundry treatment device, characterized in that, comprising: Determine to start the drying operation on the load to be dried, and generate a predicted drying progress curve; Control to display the predicted drying progress curve in the drying progress display chart.

2. The control method according to claim 1, characterized in that, further comprising: Obtain the monitored state characteristic data and the corresponding time points, where the state characteristic data represents the drying degree of the load to be dried; While controlling to display the predicted drying progress curve in the drying progress display chart, the method further comprises: controlling to display the corresponding state characteristic data in the drying progress display chart.

3. The control method according to claim 1, the method further comprising: Determine the target point in the predicted drying progress curve, where the target point is the point when the drying degree reaches the preset drying degree; While controlling to display the predicted drying progress curve in the drying progress display chart, the method further comprises: controlling to highlight the target point in the drying progress display chart.

4. The control method according to any one of claims 1-3, characterized in that, Before generating the predicted drying progress curve, the method further comprises: Record the state characteristic data obtained at multiple monitoring times and the time points of the monitoring times, where the state characteristic data represents the drying degree of the load to be dried; The generating of the predicted drying progress curve includes: generating the predicted drying progress curve based on the state characteristic data and the corresponding time points.

5. The method according to claim 4, characterized in that, The recording of the state characteristic data obtained at multiple monitoring times includes: Recording one of the following types of data obtained at multiple monitoring times: humidity data, conductivity data, impedance data, capacitance reactance data, and duty cycle data.

6. The method according to claim 4, characterized in that, The generating of the predicted drying progress curve based on the state characteristic data and the corresponding time points includes: Generating a drying progress fitting function based on the state characteristic data and the corresponding time points; Generating the predicted drying progress curve based on the drying progress fitting function.

7. The method according to claim 6, characterized in that, The method further comprises: Determine the remaining drying duration based on the drying progress fitting function, where the remaining drying duration is the remaining drying duration required for the load to be dried to reach the target drying degree.

8. The method according to claim 7, characterized in that, The determining of the remaining drying duration based on the drying progress fitting function includes: Obtain the target drying degree corresponding to when the load to be dried reaches the target drying degree; Input the target drying degree into the drying progress fitting function to obtain the target drying duration; Subtract the executed drying duration from the target drying duration to obtain the remaining drying duration.

9. The method according to claim 7, characterized in that, The determining of the remaining drying duration based on the drying progress fitting function includes: Calculate the state change rate at a future time point based on the drying progress fitting function, where the state change rate is the change rate of the state characteristic data; Determine the target time point at which the state change rate is less than the target change rate, and use the calculation duration corresponding to the target time point as the target drying duration; Subtract the executed drying duration from the target drying duration to obtain the remaining drying duration.

10. The method according to claim 9, wherein, The calculating the state change rate at a future time point based on the drying progress fitting function includes: Determine a plurality of the future time points at a set time interval, and calculate the state change rate of each of the future time points; The determining the target time point at which the state change rate is less than the target change rate includes: When the state change rates corresponding to N consecutive future time points are all less than the target change rate, use the last time point among the N consecutive future time points as the target time point, where N≥2.

11. The method according to claim 7, wherein, After obtaining the remaining drying duration, the method further includes: Perform a drying countdown display starting from the remaining drying duration.

12. The method according to claim 4, wherein, The method further includes: Continue to record the state characteristic data obtained at subsequent monitoring time points and the time points of the subsequent monitoring time points; In response to obtaining a function update instruction, generate an updated predicted drying progress curve based on the existing state characteristic data and the corresponding time points; Display the updated predicted drying progress curve in the drying progress display graph.

13. A control device for a laundry treatment device, wherein, It includes: A curve generation unit, configured to generate a predicted drying progress curve after determining to start a drying operation on a load to be dried; A display control unit, configured to control the display of the predicted drying progress curve in a drying progress display graph.

14. A laundry treatment device, wherein, It includes a controller and a display device; the controller is configured to control the display device to display the drying progress curve according to the control method of the laundry treatment device according to any one of claims 1-12.

Images