Dryer, and method for controlling dryer

Abstract

A dryer according to the present disclosure comprises: a drum for accommodating laundry to be dried; a heating element for heating air; a fan for blowing the heated air into the drum in order to dry the laundry; a first temperature sensor for generating first temperature data corresponding to the temperature of the heated air; a second temperature sensor for generating second temperature data corresponding to the inner temperature of the drum; a dryness sensor which is provided inside the drum and which generates dryness data corresponding to the dryness of the laundry accommodated inside the drum; and at least one control unit, which extracts a temperature feature point on the basis of the first temperature data generated by the first temperature sensor and the second temperature data generated by the second temperature sensor, extracts a dryness feature point on the basis of the dryness data generated by the dryness sensor, identifies the fabric type of the laundry by means of an artificial intelligence model on the basis of the temperature feature point and the dryness feature point, and changes a dry setting of the dryer on the basis of the identified fabric type.

Description

Dryer and method of controlling the dryer

[0001] The present disclosure relates to a dryer capable of detecting the quality of laundry and a method for controlling the dryer.

[0002] Typically, a dryer is a device that forces heated air into a drum to dry wet laundry. These clothes dryers are similar in appearance to drum-type washing machines and dry laundry by forcibly circulating heated air through a heater and blower fan into the drum.

[0003] In order to dry laundry efficiently without damage, it is necessary to change the temperature and strength of the hot air and the rotation speed of the drum depending on the material of the laundry.

[0004] The present disclosure provides a dryer and a method for controlling the dryer that can accurately identify the quality of laundry.

[0005] The present disclosure provides a dryer and a method for controlling the dryer that can identify the quality of laundry without expensive sensors.

[0006] The present disclosure provides a dryer and a method for controlling the dryer, wherein the ability to identify the quality of laundry is gradually improved.

[0007] The present disclosure provides a dryer and a control method for the dryer that perform an efficient drying process according to the quality of laundry.

[0008] The present disclosure provides a dryer and a control method for the dryer that performs a drying process by changing the drying setting according to the quality of laundry.

[0009] The technical problems to be achieved in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0010] According to one embodiment of the present disclosure, a dryer includes a drum that accommodates laundry to be dried; a heating element that heats air; a fan that blows the heated air into the drum to dry the laundry; a first temperature sensor that generates first temperature data corresponding to a temperature of the heated air; a second temperature sensor that generates second temperature data corresponding to a temperature inside the drum; a dryness sensor that is provided inside the drum and generates dryness data corresponding to a dryness of the laundry accommodated inside the drum; and at least one processor that extracts a temperature feature point based on the first temperature data generated by the first temperature sensor and the second temperature data generated by the second temperature sensor, extracts a dryness feature point based on the dryness data generated by the dryness sensor, identifies a texture of the laundry by an artificial intelligence model based on the temperature feature point and the dryness feature point, and changes a drying setting of the dryer based on the identified texture.

[0011] According to one embodiment of the present disclosure, a control method of a dryer includes a drum for accommodating laundry to be dried, a heating element for heating air, a fan for blowing the heated air into the drum, a first temperature sensor for generating first temperature data corresponding to a temperature of the heated air, a second temperature sensor for generating second temperature data corresponding to a temperature inside the drum, a dryness sensor provided inside the drum and generating dryness data corresponding to a dryness of the laundry accommodated inside the drum, and at least one processor, wherein the control method includes: extracting a temperature feature point based on the first temperature data generated by the first temperature sensor and the second temperature data generated by the second temperature sensor; extracting a dryness feature point based on the dryness data generated by the dryness sensor; identifying a texture of the laundry by the artificial intelligence model based on the temperature feature point and the dryness feature point; and changing a drying setting of the dryer based on the identified texture.

[0012] Figure 1 illustrates an example of the appearance of a dryer according to one embodiment.

[0013] Figure 2 illustrates an example of a cross-section of a dryer according to one embodiment.

[0014] Figure 3 illustrates another example of a cross-section of a dryer according to one embodiment.

[0015] FIG. 4 illustrates an example of a block diagram showing the configuration of a dryer according to one embodiment.

[0016] Fig. 5 illustrates an example of a flowchart of a method for controlling a dryer according to one embodiment.

[0017] Figure 6 conceptually illustrates how multiple feature points are extracted according to one embodiment.

[0018] FIG. 7 conceptually illustrates a laundry quality clustering corresponding to a plurality of feature points according to one embodiment.

[0019] Figure 8 conceptually illustrates a process in which multiple feature points are input into an artificial intelligence model according to one embodiment and the quality of laundry is identified.

[0020] FIG. 9 illustrates an example of drying settings corresponding to the quality of laundry according to one embodiment.

[0021] Figure 10 is a flowchart illustrating an example of a process in which an artificial intelligence model is learned according to one embodiment.

[0022] FIG. 11 illustrates an example of an interface provided through a user interface device of a dryer according to one embodiment.

[0023] FIG. 12 illustrates an example of an interface provided through an external device that receives foam information from a dryer according to one embodiment.

[0024] Figure 13 conceptually illustrates how an artificial intelligence model is trained according to one embodiment.

[0025] The embodiments described in this specification and the configurations illustrated in the drawings are merely preferred examples of the disclosed invention, and there may be various modified examples that can replace the embodiments and drawings of this specification at the time of filing of this application.

[0026] The terminology used herein is for the purpose of describing embodiments only and is not intended to limit and / or restrict the disclosed invention.

[0027] In this disclosure, expressions such as “A or B,” “at least one of A and / or B,” or “one or more of A or / and B” can include all possible combinations of the listed items. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” can all refer to cases where (1) only A is included, (2) only B is included, or (3) both A and B are included.

[0028] For example, in this specification, a singular expression may include a plural expression unless the context clearly indicates otherwise.

[0029] Additionally, terms such as “include” or “have” are intended to express the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but do not exclude the possibility of the additional presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0030] When a component is said to be “connected,” “coupled,” “supported,” or “in contact with” another component, this includes not only cases where the components are directly connected, coupled, supported, or in contact, but also cases where the components are indirectly connected, coupled, supported, or in contact through a third component.

[0031] When we say that a component is "on" another component, this includes not only cases where the component is in contact with the other component, but also cases where there is another component between the two components.

[0032] When it is said that a component (e.g., a first component) is “operatively or communicatively coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that the component may be directly coupled to the other component, or may be connected through another component (e.g., a third component).

[0033] The expression "configured to" as used in the present disclosure may be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" may not necessarily mean only "specifically designed to" in terms of hardware.

[0034] In some contexts, the phrase "a device configured to" may mean that the device, in conjunction with other devices or components, is "capable of" performing A, B, and C. For example, the phrase "a processor configured (or set) to perform A, B, and C" may refer to a dedicated processor (e.g., an embedded processor) for performing the operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform the operations by executing at least one software program stored in a memory device.

[0035] Additionally, terms that include ordinal numbers, such as “first,” “second,” etc., are used to distinguish one component from another, and do not limit one component.

[0036] Additionally, terms such as "~part", "~device", "~block", "~absence", and "~module" may refer to a unit that processes at least one function or operation. For example, the terms may refer to at least one piece of hardware such as an FPGA (field-programmable gate array) / ASIC (application specific integrated circuit), at least one piece of software stored in memory, or at least one process processed by a processor.

[0037] The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0038] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0039] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0040] Hereinafter, an embodiment of the disclosed invention will be described in detail with reference to the attached drawings. The same reference numbers or symbols used in the attached drawings may represent parts or components that perform substantially the same functions.

[0041] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0042] Fig. 1 illustrates an example of an exterior view of a dryer according to one embodiment. Fig. 2 illustrates an example of a cross-section of a dryer according to one embodiment. Fig. 3 illustrates another example of a cross-section of a dryer according to one embodiment.

[0043] The dryer shown in Fig. 2 is a dryer that can only perform a drying cycle for drying clothes.

[0044] The dryer illustrated in Fig. 3 is a dryer that can perform both a washing cycle for washing clothes and a drying cycle for drying clothes.

[0045] Referring to FIGS. 1, 2, and 3, a dryer according to one embodiment may include a main body (110) forming an exterior, a drum (120) rotatably installed within the main body (110) and containing laundry, a door (130) for opening and closing the drum (120), a driving device (60) for rotating the drum (120), and a heat pump device (75) for generating hot air for drying laundry (or items to be dried) within the drum (120).

[0046] Laundry can be accommodated in the chamber (30a) formed by the drum (120).

[0047] The main body (110) may include a front cover (12). An opening (12a) is provided in the front cover (12), and a door (130) for opening and closing the opening (12a) may be rotatably installed in the front cover (12).

[0048] A user interface device (40) including an input interface for receiving a user's control command and an output interface for displaying various information about the operation of the dryer (1) or displaying a screen for guiding the user's input may be placed on the top of the front cover (12).

[0049] The drum (120) can be formed into a cylindrical shape with open front and rear sides.

[0050] The drum (120) can rotate clockwise or counterclockwise within the main body (110) by the driving force of the driving device (60).

[0051] A plurality of lifters (121) for tumbling laundry may be provided on the inner surface of the drum (120). The plurality of lifters (121) may be formed to protrude toward the center from the inner surface of the drum (120).

[0052] A front support plate and a rear support plate are respectively provided on the front and rear sides of the drum (120). The front side of the drum (120) may be covered by a front support plate fixedly installed on the front side of the main body (110), and the rear side of the drum (120) may be covered by a rear support plate fixedly installed on the rear side of the main body (110).

[0053] Here, the front support plate and the rear support plate can rotatably support the drum (120).

[0054] To this end, a sliding pad to reduce frictional resistance may be provided at the portion where the front support plate and the drum (120) come into contact, and at the portion where the rear support plate and the drum (120) come into contact, respectively, and a roller to rotatably support the drum (120) may be provided at the lower portions of the front support plate and the rear support plate, respectively. Accordingly, the drum (120) can rotate smoothly.

[0055] During the drying process, the drum (30) can be driven to rotate by a driving device (60).

[0056] The driving device (60) may include a driving motor that generates power to rotate the drum (120) and a driving circuit for driving the driving motor.

[0057] According to various embodiments, the drive motor of the drive device (60) may be connected only to the drum (120), or may be connected to the drum (120) and the blower fan (151). In one embodiment, a pulley connected to the drum (120) may be coupled to one side of the shaft of the drive motor of the drive device (60), and a blower fan (151) may be coupled to the other side.

[0058] For convenience of explanation, below, a motor for rotating a drum (120) is defined as a drum motor, and a motor for rotating a blower fan (151) is defined as a fan motor.

[0059] However, the drum motor and the fan motor may be the same motor or different motors.

[0060] A dryness sensor (160) may be provided in the drum (120). As laundry accommodated in the drum (120) rotates, it may come into contact with the dryness sensor (160), and the electric signal measured by the dryness sensor (160) may vary depending on the dryness of the laundry. That is, the dryness sensor (160) outputs an electric signal corresponding to the dryness of the laundry accommodated in the drum (120). Here, the dryness may refer to the degree of dryness of the laundry.

[0061] The dryness sensor (160) may include an electrode sensor. The electrode sensor may detect contact with a portion of laundry containing moisture during the rotation of the drum (120). The electrode sensor may also be referred to as a touch sensor, as it detects contact with a portion of laundry.

[0062] The number of contacts with wet laundry can be counted by the electrode sensor. In one embodiment, the number of contacts with wet laundry per unit time (e.g., 1 minute) can be used to measure the dryness of the laundry. The number of contacts with wet laundry per unit time (e.g., 1 minute) can be referred to as the number of touches per unit time (e.g., 1 minute).

[0063] The dryness sensor (160) can measure the dryness of laundry by detecting the flow of electricity through moisture in the laundry when moisture remains in the laundry. However, if laundry tumbled on the outside of the drum (120) is completely dry, while laundry tumbled on the inside of the drum (120) is somewhat wet, the dryness of the laundry detected by the dryness sensor (160) may be somewhat inaccurate.

[0064] In one embodiment, the dryer (1) may include a heating element (200). The heating element may dehumidify and / or heat air supplied to the chamber (30a).

[0065] In one embodiment, the heating element (200) may include a heat pump device (75).

[0066] The heat pump device (75) may include a heat exchanger (70), a compressor (73), and an expansion valve (not shown).

[0067] The heat exchanger (70) may include an evaporator (71) and a condenser (72).

[0068] The heat pump device (75) has a refrigerant circulation path that connects from a compressor (73) to a condenser (72), an expansion valve, and an evaporator (71) and then back to the compressor, and the condenser (72) and the evaporator (71) function as a heat exchanger (70).

[0069] The evaporator (71) may be located upstream of the condenser (72) based on the air flow.

[0070] The heat exchanger (70) can heat the air supplied to the chamber (30a).

[0071] Air that passes through the chamber (30a) formed by the drum (120) is dried while passing through the evaporator (71), heated while passing through the condenser (72), and can then be introduced into the chamber (30a) again.

[0072] In one embodiment, the heating element (200) may include a heater (170). The heater (170) is intended to heat air supplied to the chamber (30a) and may be operated for a predetermined period of time at the beginning of the drying cycle.

[0073] In one embodiment, the heater (170) may be provided downstream of the heat exchanger (70), but the location of the heater (170) is not limited thereto, and the heater (170) may be provided upstream of the heat exchanger (70).

[0074] The blower fan (151) can cause air that has passed through the chamber (30a) formed by the drum (120) to pass through the evaporator (71) and the condenser (72) and then flow back into the chamber (30a).

[0075] That is, the blower fan (151) can create an air flow that circulates through the interior (chamber) (30a) of the drum (120) and the heating element (200). To this end, the blower fan (151) can be provided within a duct (180) through which air discharged from the drum (120) flows.

[0076] In one embodiment, the blower fan (151) may be provided downstream of the condenser (72), but the location of the blower fan (151) is not limited thereto, and the blower fan (151) may be installed at any location without limitation as long as it can create air flow within the duct (180).

[0077] A dryer (1) according to one embodiment may include a lint removal device (90). In addition to a door (130) for opening and closing an opening (12a), a front cover (12) may further include an auxiliary door for opening and closing a space in which the lint removal device (90) is accommodated.

[0078] The user can withdraw or insert the lint removal device (90) through the auxiliary door.

[0079] The lint removal device (90) can collect and remove lint (fluff) contained in the air discharged from the chamber (30a). For this purpose, the lint removal device (90) may include a filter.

[0080] The lint removal device (90) may be placed within a duct (180) through which air discharged from the drum (120) flows. The lint removal device (90) may be placed upstream of the heat exchanger (70), and the lint removal device (90) may prevent lint from accumulating in the heat exchanger (70) by removing lint in the air passing through the drum (120).

[0081] The dryer (1) illustrated in Fig. 2 and the dryer (1) illustrated in Fig. 3 differ in the possibility of a washing cycle.

[0082] The dryer (1) illustrated in Fig. 2 can perform a drying process by rotating the drum (120) and supplying hot air to the chamber (30a).

[0083] The dryer (1) illustrated in Fig. 3 can perform not only a drying cycle in which hot air is supplied to a chamber (30a) by rotating a drum (120), but also a washing cycle for washing laundry accommodated in the chamber (30a). The washing cycle may include a water supply cycle, a rinsing cycle, a washing cycle, and / or a spin-drying cycle.

[0084] Referring to FIG. 2, in a dryer (1) according to one embodiment, a heat exchanger (70), a blower fan (151), and a lint removal device (90) may be placed at the lower portion of the main body (110). For example, the heat exchanger (70), the blower fan (151), and the lint removal device (90) may be placed within a duct (180).

[0085] The first duct (181) is positioned at the lower side of the drum (120) and can guide air so that air discharged from the drum (120) is dehumidified and heated and then flows back into the drum (120). A heat exchanger (70) can be accommodated within the first duct (181). The first duct (181) can be referred to as a lower frame. A first circulation path (191) can be provided within the first duct (181).

[0086] The second duct (182) may be positioned at the rear of the drum (120) and may guide air toward the drum (120). Air passing through the heat exchanger (70) may be supplied to the drum (120) through the second duct (182). The second duct (182) may form a part of a circulation path (190). A blower fan (151) may be accommodated within the second duct (182). A second circulation path (192) may be provided within the second duct (182). In one embodiment, a heater (170) may be provided in the second duct (182).

[0087] The third duct (183) can be arranged in front of the drum (120) to guide air inside the drum (30a) toward the heat exchanger (70). The air inside the drum (30a) can flow to the heat exchanger (70) through the third duct (183). The third duct (183) can form a part of the circulation path (190). A third circulation path (193) can be provided within the third duct (183).

[0088] According to various embodiments, the lint removal device (90) may be provided within the first duct (181), the second duct (182), or the third duct (183).

[0089] In one embodiment, a lint removal device (90) may be provided in the third duct (183).

[0090] The second duct (182) and the third duct (183) can allow the air inside the drum (30a) to circulate through the circulation path (190) inside the main body (110).

[0091] The dryer (1) may further include a circulation path (190). The circulation path (190) may include a first circulation path (191), a second circulation path (192), and a third circulation path (193). The first circulation path (191) may be formed by a first duct (181), the second circulation path (192) may be formed by a second duct (182), and the third circulation path (193) may be formed by a third duct (183).

[0092] The blower fan (151) can circulate air within the circulation path (190).

[0093] The blower fan (151) circulates the air within the circulation path (190), thereby allowing the air discharged from the drum (120) to the duct (180) to be heated by the heating element (200) and then flowed back into the drum (120).

[0094] Referring to FIG. 3, the dryer (1) according to one embodiment illustrated in FIG. 3 may further include configurations for performing a washing cycle compared to the dryer (1) according to one embodiment illustrated in FIG. 2.

[0095] In one embodiment, the dryer (1) may include a tub (115) provided inside the main body (110), and a drum (120) provided inside the tub (115) to receive and rotate laundry.

[0096] A water supply device (14) may be provided on top of the tub (115). The water supply device (14) may include a water supply valve (14b) and water supply pipes (14a) for controlling water supply. In addition, a detergent supply device (80) for supplying detergent into the tub (115) during the water supply process may be installed on top of the tub (115). The detergent supply device (80) may be installed on the front cover (12). The detergent supply device (80) may be arranged inside the main body (110). Water flowing into the dryer (1) through the water supply device (14) may flow to the detergent supply device (80).

[0097] According to various embodiments, the detergent supply device (80) may be installed at the bottom of the tub (115).

[0098] The detergent supply device (80) can be connected to the tub (115) through the supply pipe (17). Washing water supplied through the water supply pipe (14a) is mixed with detergent through the detergent supply device (80), and the mixed water containing the washing water and detergent can be supplied into the interior of the tub (115).

[0099] Water supplied into the dryer (1) through the water supply device (14) can flow into the detergent supply device (80). Water passing through the water supply pipe (14a) can flow into the detergent box. For example, the water supply pipe (14a) can be arranged above the detergent box and supply water to the detergent box arranged below. Detergent can be accommodated inside the detergent box, and water supplied into the detergent box from the water supply pipe (14a) can be mixed with the detergent. The water inside the detergent box mixed with the detergent can flow into the tub (115). For example, the supply pipe (17) can be connected to the detergent box and the tub (115) from below the detergent box and supply the water inside the detergent box mixed with the detergent to the tub (115).

[0100] The detergent box may be provided so as to be withdrawable from the front cover (12). According to various embodiments, the detergent box and the lint removal device (90) may be provided so as to be withdrawable from the front cover (12). Although the drawing shows the lint removal device (90) as being provided on the rear side of the tub (115), the lint removal device (90) may be provided on the side of the heat exchanger (70) on the upper side of the tub (115). The lint removal device (90) may be provided between the upper surface (11e) of the main body (110) and the tub (115). The heat exchanger (70) may be provided between the upper surface (11e) of the main body (110) and the tub (115).

[0101] The tub (115) stores a mixture of washing water and detergent, and may be formed in a roughly cylindrical shape. The tub (115) may be fixed to the interior of the main body (110). The opening (12a) of the front cover (12) and the tub (115) may be connected by a diaphragm. The diaphragm may seal the space between the front cover (12) and the tub (115).

[0102] A drainage device (50) including a drain pipe (not shown), a drain valve (not shown), a drain pump, etc. for draining water inside the tub (115) may be installed at the bottom of the tub (115).

[0103] The tub (115) is provided so that it can be elastically supported from the main body (110) by springs (not shown) at the top and dampers at the bottom. That is, the springs and dampers are provided so that when vibration generated when the drum (120) rotates is transmitted to the tub (115) and the main body (110), the vibration energy is absorbed between the tub (115) and the main body (110), thereby reducing the vibration transmitted to the tub (115) and the main body (110).

[0104] In a dryer (1) according to one embodiment, a heat exchanger (70), a blower fan (151), and a lint removal device (90) may be arranged at the upper portion of the main body (110). For example, the heat exchanger (70), the blower fan (151), and the lint removal device (90) may be arranged within a duct (180).

[0105] The first duct (181) is positioned above the drum (120) and the tub (115) and can guide air so that air discharged from the drum (120) is dehumidified and heated and then flows back into the drum (120). A heat exchanger (70) can be accommodated within the first duct (181). The first duct (181) can be referred to as an upper frame. A first circulation path (191) can be provided within the first duct (181).

[0106] The second duct (182) may be positioned in front of the drum (120) and the tub (115) to guide air toward the drum (120). Air passing through the heat exchanger (70) may be supplied to the drum (120) through the second duct (182). The second duct (182) may form a part of a circulation path (190). A blower fan (151) may be accommodated within the second duct (182). A second circulation path (192) may be provided within the second duct (182).

[0107] The third duct (183) may be arranged at the rear of the drum (120) and the tub (115) to guide air inside the drum (30a) toward the heat exchanger (70). The air inside the drum (30a) may flow to the heat exchanger (70) through the third duct (183). The third duct (183) may form a part of a circulation path (190). A third circulation path (193) may be provided within the third duct (183). In one embodiment, a heater (170) may be provided in the third duct (183).

[0108] According to various embodiments, the lint removal device (90) may be provided within the first duct (181), the second duct (182), or the third duct (183).

[0109] In one embodiment, a lint removal device (90) may be provided in the first duct (182).

[0110] The second duct (182) and the third duct (183) can allow air inside the drum (30a) to circulate through the circulation path (190) inside the main body (110). In addition, since the air discharged from the second duct (182) can be introduced into the tub (115) and the drum (120) through the diaphragm, the diaphragm can also allow air to circulate through the circulation path (190) inside the main body (110).

[0111] The dryer (1) may further include a circulation path (190). The circulation path (190) may include a first circulation path (191), a second circulation path (192), and a third circulation path (193). The first circulation path (191) may be formed by a first duct (181), the second circulation path (192) may be formed by a second duct (182), and the third circulation path (193) may be formed by a third duct (183).

[0112] The blower fan (151) can circulate air within the circulation path (190).

[0113] The blower fan (151) circulates the air within the circulation path (190), thereby allowing the air discharged from the drum (120) to the duct (180) to be heated by the heating element (200) and then flowed back into the drum (120).

[0114] The dryer (1) may include a first temperature sensor (251) for measuring the temperature of air flowing into the inside of the drum (30a).

[0115] The first temperature sensor (251) can detect the temperature of air heated by the heating element (200).

[0116] In one embodiment, the first temperature sensor (251) may be provided around the heating element (200).

[0117] The first temperature sensor (251) may be provided in the circulation path (190). According to various embodiments, the first temperature sensor (251) may be provided downstream of the heater (170), but the location of the first temperature sensor (251) is not limited thereto. For example, the first temperature sensor (251) may be provided downstream of the heat pump device (75) or downstream of the heating element (200).

[0118] The dryer (1) may include a second temperature sensor (252) that detects the temperature inside the drum (30a).

[0119] Detecting the temperature inside the drum (30a) may include detecting the temperature of air discharged from the drum (120) to the duct (180).

[0120] Detecting the temperature of air discharged from the drum (120) to the duct (180) may include detecting the temperature of air that has passed through laundry contained in the drum (120) after being heated by the heating element (200) and supplied to the inside of the drum (30a).

[0121] In one embodiment, the second temperature sensor (252) may be provided inside the drum (30a) and / or the third duct (183). According to various embodiments, the second temperature sensor (252) may be provided upstream of the heating element (200), but the location of the second temperature sensor (251) is not limited thereto.

[0122] As will be described later, according to the present disclosure, the dryness of laundry can be accurately identified by utilizing the temperature of air heated by the heating element (200) and the temperature inside the drum (30a).

[0123] FIG. 4 illustrates an example of a block diagram showing the configuration of a dryer according to one embodiment.

[0124] Referring to FIG. 4, a dryer (1) according to one embodiment may include a user interface device (40), a heating element (200), a driving device (60), a communication interface (330), a first temperature sensor (251), a second temperature sensor (252), a dryness sensor (160), and / or a control unit (300).

[0125] According to one embodiment, the dryer (1) may further include a water supply device, a detergent supply device, a drainage device, etc., to perform a washing process in addition to a drying process.

[0126] In one embodiment, the dryer (1) may not include some components (e.g., heater (170)).

[0127] The user interface device (40) may include at least one input interface (41) and at least one output interface (42).

[0128] At least one input interface (41) can convert sensory information received from a user into an electrical signal.

[0129] At least one input interface (41) may include a power button, an operation button, a course selection dial (or a course selection button), and a wash / rinse / spin / dry setting button. The at least one input interface (41) may include, for example, a tact switch, a push switch, a slide switch, a toggle switch, a micro switch, a touch switch, a touch pad, a touch screen, a jog dial, and / or a microphone.

[0130] At least one output interface (42) can transmit various information related to the operation of the dryer (1) to the user by generating sensory information.

[0131] For example, at least one output interface (42) can transmit information related to a drying course and the operation time of the dryer (1), a washing course and the operation time of the dryer (1), and washing settings / rinsing settings / spin settings / drying settings to the user. Information related to the operation of the dryer (1) can be output to a screen, an indicator, a voice, etc. At least one output interface (42) can include, for example, a liquid crystal display (LCD) panel, a light emitting diode (LED) panel, a speaker, etc.

[0132] The driving device (60) may include a drum motor that provides driving force to rotate the drum (120) and a driving circuit that drives the drum motor. The drum motor may operate based on a driving current supplied from the driving circuit. The driving device (60) may operate based on a control signal from the control unit (300).

[0133] In one embodiment, the control unit (300) can control the drive device (60) to rotate the drum (120) during the drying process.

[0134] In one embodiment, the driving device (60) may include a fan motor that provides driving force to rotate the blower fan (151) and a driving circuit that drives the fan motor.

[0135] As explained above, the drum motor and fan motor may be the same motor or different motors.

[0136] The control unit (300) controlling the driving device (60) may include controlling the rotation speed of the drum (120).

[0137] The control unit (300) controlling the driving device (60) may include controlling the rotation speed of the blower fan (151).

[0138] The heating element (200) may include a heat pump device (75) and / or a heater (170).

[0139] A heat pump device (75) may include a compressor (73) for compressing a refrigerant, an expansion valve, and a heat exchanger (70). The compressor (73) may operate based on a control signal from a control unit (300). The heat pump device (75) may heat air supplied to the inside of the drum (30a).

[0140] The heater (170) can heat the air supplied to the inside of the drum (30a). The heater (170) can operate based on a control signal from the control unit (300).

[0141] In one embodiment, the control unit (300) can control the compressor (73) and / or the heater (170) so that the temperature of the air flowing into the inside of the drum (30a) during the drying process is maintained at a predetermined target temperature.

[0142] For example, the control unit (300) can control the compressor (73) and / or the heater (170) so that the temperature detected by the first temperature sensor (251) of the drying process is maintained at a predetermined target temperature.

[0143] The communication interface (330) can communicate with external devices (e.g., servers, user devices, and / or home appliances) via wires and / or wirelessly.

[0144] The communication interface (330) may include at least one of a short-range communication module or a long-range communication module.

[0145] The communication interface (330) can transmit data to or receive data from an external device. For example, the communication interface (330) can establish communication with a server, a user device, and / or other home appliances, and transmit and receive various types of data.

[0146] To this end, the communication interface (330) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between external devices, and the performance of communication through the established communication channel. According to one embodiment, the communication interface (330) may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, a corresponding communication module may communicate with the external device through a first network (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These different types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips).

[0147] The short-range wireless communication module may include, but is not limited to, a Bluetooth communication module, a BLE (Bluetooth Low Energy) communication module, a near field communication module, a WLAN (Wi-Fi) communication module, a Zigbee communication module, an infrared (IrDA, infrared Data Association) communication module, a WFD (Wi-Fi Direct) communication module, an UWB (ultrawideband) communication module, an Ant+ communication module, a microwave (uWave) communication module, etc.

[0148] The long-distance communication module may include a communication module that performs various types of long-distance communication and may include a mobile communication unit. The mobile communication unit transmits and receives wireless signals with at least one of a base station, an external terminal, and a server on a mobile communication network.

[0149] In one embodiment, the communication interface (330) can communicate with external devices such as a server, a user device, and other home appliances via a surrounding access point (AP). The access point (AP) can connect a local area network (LAN) to which the dryer (1), other home appliances, and / or user devices are connected to a wide area network (WAN) to which the server is connected. The dryer (1), other home appliances, and / or user devices can be connected to the server via the wide area network (WAN).

[0150] The first temperature sensor (251) can detect the temperature of air heated by the heating element (200). The first temperature sensor (251) can generate first temperature data corresponding to the temperature of air heated by the heating element (200) (hereinafter, “first temperature”).

[0151] The first temperature data generated by the first temperature sensor (251) can be transmitted to the control unit (300).

[0152] The second temperature sensor (252) can detect the temperature inside the drum (30a). The second temperature sensor (252) can generate second temperature data corresponding to the temperature inside the drum (30a) (hereinafter referred to as “second temperature”).

[0153] The second temperature data generated by the second temperature sensor (252) can be transmitted to the control unit (300).

[0154] The dryness sensor (160) can detect the dryness of laundry inside the drum (30a). For example, the dryness sensor (160) can generate dryness data corresponding to the dryness of laundry.

[0155] Dryness data corresponding to the dryness of the laundry may include data related to the number of times of contact with laundry containing moisture.

[0156] Dryness data generated by the dryness sensor (160) can be transmitted to the control unit (300).

[0157] The control unit (300) can control various components of the dryer (1) (e.g., a driving device (60), a heat pump device (75), a heater (170), a user interface device (40), and a communication interface (330)). The control unit (300) can control various components of the dryer (1) to perform at least one operation including water supply, washing, rinsing, dehydration, and / or drying according to a user input. For example, the control unit (300) can control the driving device (60) to adjust the rotation speed of the drum (120) and / or the rotation speed of the blower fan (151), or control the heating element (200) to maintain the temperature of the air flowing into the inside of the drum (30a) at a predetermined target temperature.

[0158] The target temperature of the air supplied to the inside of the drum (30a) may also be referred to as the heating temperature of the heating element (200).

[0159] The control unit (300) can process sensor data generated from various sensors (e.g., the first temperature sensor (251), the second temperature sensor (252), and / or the dryness sensor (160)), and can perform various operations based on the processed sensor data generated from the various sensors.

[0160] The control unit (300) may include hardware such as a CPU, Micom, or memory, and software such as a control program. For example, the control unit (300) may include at least one memory (302) that stores data in the form of a program and an algorithm for controlling the operation of components within the dryer (1), and at least one processor (301) that performs the operations described above and the operations to be described below using the data stored in the at least one memory (302). The memory (302) and the processor (301) may each be implemented as separate chips. The processor (301) may include one or more processor chips or one or more processing cores. The memory (302) may include one or more memory chips or one or more memory blocks. In addition, the memory (302) and the processor (301) may be implemented as a single chip.

[0161] At least one memory (302) can store data required for various embodiments. The memory (302) may be implemented in the form of a memory embedded in the dryer (1) or in the form of a memory that can be attached or detached from the dryer (1), depending on the purpose of data storage. For example, data for operating the dryer (1) may be stored in a memory embedded in the dryer (1), and data for expanding the functions of the dryer (1) may be stored in a memory that can be attached or detached from the dryer (1). Meanwhile, the memory embedded in the dryer (1) may be implemented as at least one of volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)), non-volatile memory (e.g., one time programmable ROM (OTPROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), hard drive, or solid state drive (SSD)). In addition, the memory that can be detachably attached to the dryer (1) may be implemented in the form of a memory card (e.g., compact flash (CF), secure digital (SD), micro secure digital (Micro-SD), mini secure digital (Mini-SD), extreme digital (xD), multi-media card (MMC), etc.), external memory that can be connected to a USB port (e.g., USB memory), etc.

[0162] In one embodiment, at least one memory (302) can store a cycle profile corresponding to a drying course and an operating time of the dryer (1), a washing course and an operating time of the dryer (1), a washing setting / rinsing setting / spin setting / drying setting. The cycle profile can include a rotation speed of the drum (120) in the drying cycle, a rotation speed of the blower fan (151), a target temperature of air supplied to the inside of the drum (30a), etc.

[0163] In one embodiment, at least one memory (302) may store instructions, algorithms, and / or programs for processing sensor data generated from various sensors of the dryer (1) (e.g., the first temperature sensor (251), the second temperature sensor (252), and / or the dryness sensor (160)) to extract feature points.

[0164] In one embodiment, at least one memory (302) may store an artificial intelligence model. As will be described later, the artificial intelligence model may be learned to identify the quality of laundry by using at least one factor obtained by processing sensor data generated from various sensors of the dryer (1) (e.g., the first temperature sensor (251), the second temperature sensor (252), and / or the dryness sensor (160)) as input data.

[0165] Depending on the various embodiments, the artificial intelligence model may be stored on the server or only on the server.

[0166] At least one processor (301) controls the overall operation of the dryer (1). Specifically, at least one processor (301) is connected to each component of the dryer (1) and can control the overall operation of the dryer (1). For example, at least one processor (301) is electrically connected to a memory (302) and can control the overall operation of the dryer (1). The processor (301) may be composed of one or more processors.

[0167] At least one processor (301) can perform operations of the dryer (1) according to various embodiments by executing at least one instruction stored in the memory (302).

[0168] At least one processor (301) may include one or more of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), a MIC (Many Integrated Core), a DSP (Digital Signal Processor), an NPU (Neural Processing Unit), a hardware accelerator, or a machine learning accelerator. At least one processor (301) may control one or any combination of other components of the dryer (1), and may perform operations related to communication or data processing. At least one processor (301) may execute at least one program or instruction stored in the memory (302). For example, at least one processor (301) may perform a method according to at least one embodiment of the present disclosure by executing at least one instruction stored in the memory (302).

[0169] When a method according to at least one embodiment of the present disclosure includes multiple operations, the multiple operations may be performed by one processor or by multiple processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to at least one embodiment, the first operation, the second operation, and the third operation may all be performed by the first processor, or the first operation and the second operation may be performed by the first processor (e.g., a general-purpose processor) and the third operation may be performed by the second processor (e.g., an artificial intelligence-specific processor).

[0170] At least one processor (301) may be implemented as a single core processor including one core, or may be implemented as at least one multicore processor including multiple cores (e.g., homogeneous multicore or heterogeneous multicore). When at least one processor (301) is implemented as a multicore processor, each of the multiple cores included in the multicore processor may include an internal processor memory, such as a cache memory or an on-chip memory, and a common cache shared by the multiple cores may be included in the multicore processor. In addition, each of the multiple cores (or some of the multiple cores) included in the multicore processor may independently read and execute a program instruction for implementing a method according to at least one embodiment of the present disclosure, or all (or some) of the multiple cores may be linked to read and execute a program instruction for implementing a method according to at least one embodiment of the present disclosure.

[0171] When a method according to at least one embodiment of the present disclosure includes a plurality of operations, the plurality of operations may be performed by one core among the plurality of cores included in a multi-core processor, or may be performed by the plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to at least one embodiment, the first operation, the second operation, and the third operation may all be performed by a first core included in the multi-core processor, or the first operation and the second operation may be performed by a first core included in the multi-core processor, and the third operation may be performed by a second core included in the multi-core processor.

[0172] In embodiments of the present disclosure, a processor may mean a system on a chip (SoC) in which at least one processor and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, a GPU, an APU, a MIC, a DSP, an NPU, a hardware accelerator, or a machine learning accelerator, but embodiments of the present disclosure are not limited thereto. Hereinafter, for convenience of description, at least one processor (301) will be referred to as a processor (301).

[0173] The control unit (300) may be mounted on a printed circuit board provided on the rear of a control panel, which is an example of a user interface device (40).

[0174] The control unit (300) may be electrically connected to a user interface device (40), a driving device (60), a heating element (200), a communication interface (330), a first temperature sensor (251), a second temperature sensor (252), and / or a dryness sensor (160).

[0175] Fig. 5 illustrates an example of a flowchart of a method for controlling a dryer according to one embodiment.

[0176] Referring to FIG. 5, a dryer (1) according to one embodiment can start a drying process based on receiving a drying process start command through a user interface device (40) (1000).

[0177] In one embodiment, the dryer (1) can provide multiple drying courses.

[0178] The user can select one of the multiple drying courses through the user interface device (40) and then start the selected drying course.

[0179] Laundry fabrics can be categorized into blends, towels, cotton, denim, blends, comforters, waterproofs, baby clothes, etc.

[0180] The plurality of drying courses may include at least one drying course that specifies the quality of the laundry and a drying course that does not specify the quality of the laundry.

[0181] At least one drying course that specifies the quality of laundry may include courses in which the quality of laundry is included as part of the course name, such as, for example, a 'towel drying course', a 'comforter drying course', a 'mixed fabric drying course', and a 'baby clothes drying course', as well as all drying courses in which the quality of laundry can be specified.

[0182] Drying courses that do not specify the quality of laundry may include a standard drying course, a strong drying course, and / or an artificial intelligence drying course as general courses.

[0183] In one embodiment, at least some of the operations included in the control method of the dryer (1) illustrated in FIG. 5 (e.g., operation 1500) may be performed only when a drying course in which the quality of laundry is not specified is selected among a plurality of drying courses.

[0184] When the processor (301) receives a drying start command from the dryer (1), it can start the drying process according to the default drying settings.

[0185] The drying setting of the dryer (1) may include at least one of the rotation speed of the drum (120), the rotation speed of the blower fan (151), or the heating temperature of the heating element (200).

[0186] The default drying setting is a drying setting preset to correspond to a drying course regardless of the weight and / or quality of the laundry, and data regarding the default drying setting according to a plurality of drying courses may be stored in the memory (302).

[0187] When the processor (301) receives a drying start command from the dryer (1), it can control the driving device (60) and / or the heating element (200) based on the rotation speed of the drum (120), the rotation speed of the blower fan (151), or the heating temperature of the heating element (200) according to the default drying settings.

[0188] In one embodiment, the processor (301) can perform a weight sensing operation based on the start of the drying operation.

[0189] The processor (301) controls the motor (e.g., drum motor) of the driving device (60) to be repeatedly turned on / off to perform a weight sensing operation, and can measure the load (weight of laundry) inside the drum (120) based on the counter electromotive force value generated when the motor is turned off. As another example, the processor (301) can provide the driving device (60) with a target speed command to rotate the drum (120) at a first target speed, and can measure the load (weight of laundry) inside the drum (120) based on the time taken for the drum (120) to reach the first target speed.

[0190] The memory (302) can store data on the weight value of laundry measured through the weight sensing process.

[0191] The processor (301) can extract temperature feature points based on processing the first temperature data generated by the first temperature sensor and the second temperature data generated by the second temperature sensor (1100).

[0192] The processor (301) can extract dryness feature points based on processing dryness data generated by the dryness sensor (1200).

[0193] The processor (301) can perform an operation (1100) of extracting temperature feature points and an operation (1200) of extracting dryness feature points until a predetermined time has elapsed after the drying process begins.

[0194] Figure 6 conceptually illustrates how multiple feature points are extracted according to one embodiment.

[0195] Referring to Fig. 6, extracting temperature feature points (Tf1, Tf2, ... Tfn) based on processing the first temperature data and the second temperature data may include extracting temperature feature points (Tf1, Tf2, ... Tfn) based on a difference value between the temperature of air heated by the heating element (200) (first temperature) and the temperature inside the drum (30a). There may be at least one temperature feature point (Tf1, Tf2, ... Tfn).

[0196] That is, the processor (301) can extract temperature feature points (Tf1, Tf2, ... Tfn) based on the difference value between the first temperature value included in the first temperature data and the second temperature value included in the second temperature data.

[0197] According to the present disclosure, much more useful feature points can be extracted than when feature points are extracted based only on the first temperature value or when feature points are extracted based only on the second temperature value.

[0198] According to various embodiments, the processor (301) may extract the difference value between the first temperature and the second temperature per unit time (e.g., per minute) as the first temperature feature point (Tf1).

[0199] According to various embodiments, the processor (301) may extract a value corresponding to the slope of the difference between the first temperature and the second temperature as a second temperature feature point (Tf2).

[0200] The slope of the difference between the first temperature and the second temperature may include the amount of change in the difference between the first temperature and the second temperature per unit time (e.g., per minute).

[0201] According to various embodiments, the temperature feature points (Tf1, Tf2, ... Tfn) may include a value obtained by correcting the difference between the first temperature and the second temperature according to the weight of the laundry. For example, the processor (301) may extract the third temperature feature point (Tfn) by assigning a weight corresponding to the weight of the laundry to the difference between the first temperature and the second temperature.

[0202] Instructions for performing an operation of extracting temperature feature points (Tf1, Tf2, ... Tfn) based on processing the first temperature data and the second temperature data may be stored in the memory (302).

[0203] Since the heat capacity of laundry differs depending on the quality of the laundry, the values ​​corresponding to the temperature characteristic points (Tf1, Tf2, ... Tfn) calculated based on the difference between the first temperature and the second temperature may differ when the quality of the laundry is different.

[0204] The processor (301) can extract temperature feature points (Tf1, Tf2, ... Tfn) by processing the first temperature data and the second temperature data until a predetermined time (pd) has elapsed after the drying process starts.

[0205] According to the present disclosure, in order to utilize the fact that the heat capacity differs depending on the quality of laundry, temperature feature points (Tf1, Tf2, ... Tfn) calculated based on the difference between the first temperature and the second temperature are used as one of the input data for the artificial intelligence model, thereby facilitating accurate quality detection.

[0206] According to the present disclosure, by assigning a weight corresponding to the weight of laundry to the difference between the first temperature and the second temperature, it is possible to extract only temperature feature points (Tf1, Tf2, ... Tfn) corresponding to the change in heat capacity according to the quality of laundry by compensating for the change in heat capacity according to the weight of laundry.

[0207] Extracting dryness feature points (Hf1, Hf2, ... Hfm) based on processing dryness data may include extracting dryness feature points (Hf1, Hf2, ... Hfm) based on the number of touches per unit time. There may be at least one dryness feature point (Hf1, Hf2, ... Hfm).

[0208] That is, the processor (301) can extract dryness feature points (Hf1, Hf2, ... Hfm) based on the number of touches (or contacts) per unit time included in the dryness data. The slope of the number of touches per unit time can include the slope of a graph corresponding to the number of touches detected by the dryness sensor (160).

[0209] According to various embodiments, the processor (301) may extract the number of touches per unit time (e.g., per minute) itself as the first dryness feature point (Hf1).

[0210] According to various embodiments, the processor (301) may extract a value corresponding to the slope of the number of touches per unit time as a second dryness feature point (Hf2).

[0211] The slope of the number of touches per unit time may include the change in the number of touches per unit time (e.g. per minute).

[0212] According to various embodiments, the dryness feature points (Hf1, Hf2, ... Hfm) may include a slope that is a slope of the number of touches per unit time that is corrected according to the weight of the laundry. For example, the processor (301) may extract the third dryness feature point (Hfm) by assigning a weight corresponding to the weight of the laundry to the number of touches per unit time.

[0213] Instructions for performing an operation of extracting dryness feature points (Hf1, Hf2, ... Hfm) based on processing dryness data may be stored in the memory (302).

[0214] Since the moisture content of laundry differs depending on the quality of the laundry, the values ​​corresponding to the dryness feature points (Hf1, Hf2, ... Hfm) calculated based on the dryness data may differ when the quality of the laundry is different.

[0215] The processor (301) can process dryness data until a predetermined time (pd) has elapsed after the drying process begins, thereby extracting dryness feature points (Hf1, Hf2, ... Hfm). Here, information regarding the predetermined time (pd) may be stored in advance in the memory (302) during the manufacturing process of the dryer (1), may be received from an external device via a communication interface (330), or may be set by a user. For example, the predetermined time (pd) may be set to about 20 minutes. According to various embodiments, the predetermined time (pd) may vary depending on the time at which the first temperature reaches the predetermined temperature.

[0216] According to the present disclosure, in order to utilize the fact that the moisture content differs depending on the quality of laundry, accurate quality detection can be achieved by using dryness feature points (Hf1, Hf2, ... Hfm) calculated based on dryness data as one of the input data for an artificial intelligence model.

[0217] According to the present disclosure, by assigning a weight corresponding to the weight of laundry to the number of touches per unit time, it is possible to extract only dryness feature points (Hf1, Hf2, ... Hfm) corresponding to changes in the moisture content of laundry according to the quality of laundry by compensating for changes in the moisture content of laundry according to the weight of laundry.

[0218] The processor (301) can perform an operation (1400) of identifying the quality of laundry based on the fact that the execution time of the drying cycle has reached a predetermined time (pd) (example of 1300).

[0219] The execution time of the drying process refers to the time elapsed since the drying process started.

[0220] In one embodiment, the processor (301) can extract temperature feature points based on processing the first temperature data and the second temperature data until the execution time of the drying process reaches a predetermined time (pd), and can extract dryness feature points based on processing the dryness data.

[0221] In one embodiment, the processor (301) may extract temperature feature points by processing the first temperature data and the second temperature data generated by the first temperature sensor (251) and the second temperature sensor (252) until the execution time of the drying process reaches a predetermined time (pd), and may extract dryness feature points by processing the dryness data generated by the dryness sensor (160) until the execution time of the drying process reaches a predetermined time (pd).

[0222] The processor (301) can identify the quality of laundry by inputting temperature feature points and dryness feature points into the artificial intelligence model in response to the drying cycle execution time reaching a predetermined time (pd) (example of 1300) (1400).

[0223] Here, the artificial intelligence model may be trained to output data on the quality of laundry using temperature and dryness features as input data.

[0224] In one embodiment, the artificial intelligence model may be stored in memory (302) and / or an external device (e.g., a server).

[0225] When the artificial intelligence model is stored in the memory (302), the processor (301) can identify the quality of the laundry by inputting temperature features and dryness features into the artificial intelligence model stored in the memory (302).

[0226] When the artificial intelligence model is stored only in the external device, the processor (301) transmits information on temperature feature points and dryness feature points to the external device through a communication interface, and the external device obtains information on the quality of the laundry by inputting the temperature feature points and dryness feature points received from the dryer (1) into the artificial intelligence model, and the external device transmits information on the quality of the laundry to the dryer (1), and as a result, the processor (301) can identify the quality of the laundry.

[0227] That is, the operation (1400) of identifying the quality of laundry by inputting the temperature feature point and the dryness feature point into the artificial intelligence model by the processor (301) may include the operation of identifying the quality of laundry by inputting the temperature feature point and the dryness feature point into the artificial intelligence model stored in the memory (302) by the processor (301) and / or the operation of identifying the quality of laundry by transmitting information on the temperature feature point and the dryness feature point to an external device in which the artificial intelligence model is stored through the communication interface (330) and receiving information on the quality of laundry from the external device.

[0228] According to the present disclosure, the processor (301) extracts feature points to be input into an artificial intelligence model until a predetermined period of time has elapsed after the drying process begins, and then inputs the feature points into the artificial intelligence model after a certain level of reliability has been secured as time has elapsed, thereby enabling more accurate identification of the quality of laundry.

[0229] In one embodiment, the artificial intelligence model is trained to identify the quality of laundry based on feature points, and can be configured to obtain a value corresponding to the quality of laundry through calculation after assigning a weight to each feature point.

[0230] FIG. 7 conceptually illustrates a laundry quality clustering corresponding to a plurality of feature points according to one embodiment.

[0231] Referring to Fig. 7, the quality of laundry can be clustered according to multiple feature points (a, b, c).

[0232] At this time, although the number of feature points (a, b, c) is shown as three, the number of feature points is not limited to this.

[0233] The plurality of feature points (a, b, c) may include the temperature feature points (Tf1, Tf2, ... Tfn) and dryness feature points (Hf1, Hf2, ... Hfm) described above.

[0234] The laundry quality can be clustered by classifying it by multiple features (a, b, c).

[0235] For example, the first cluster may correspond to the first cluster (CL1), the second cluster may correspond to the second cluster (CL2), the third cluster may correspond to the third cluster (CL3), and the fourth cluster may correspond to the fourth cluster (CL4).

[0236] The artificial intelligence model is trained to identify the quality of laundry from multiple feature points (a, b, c) based on this clustering tendency.

[0237] Referring to the example of Fig. 7, the first cluster (CL1) and the second cluster (CL2) have no significant difference in feature point 3 (c), a significant but small difference in feature point 2 (b), and a large difference in feature point 1 (a).

[0238] In one embodiment, the artificial intelligence model may be trained to give a greater weight to feature point 1(a) than to feature point 2(b) when feature point 3(c) has values ​​corresponding to the first cluster (CL1) and the second cluster (CL2) in order to distinguish between the first cluster (CL1) and the second cluster (CL2).

[0239] Referring to another example of Fig. 7, the first cluster (CL1) and the fourth cluster (CL4), or the second cluster (CL2) and the third cluster (CL3) have no significant difference in feature point 1 (a) and feature point 2 (b), respectively, but have a large difference in feature point 3 (c).

[0240] In one embodiment, the artificial intelligence model may be trained to give a greater weight to feature point 3(c) than to feature point 1(a) or feature point 2(b) in order to distinguish between the first cluster (CL1) and the fourth cluster (CL4), or in order to distinguish between the second cluster (CL2) and the third cluster (CL3).

[0241] Figure 8 conceptually illustrates a process in which multiple feature points are input into an artificial intelligence model according to one embodiment and the quality of laundry is identified.

[0242] The artificial intelligence model (m1) is characterized by being created through learning. Here, being created through learning means that a basic artificial intelligence model is learned using a learning algorithm using a plurality of learning data, thereby creating a predefined set of operation rules or an artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0243] The artificial intelligence model (m1) may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model (m1). For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model (m1) is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0244] Referring to FIG. 8, the artificial intelligence model (m1) may include an input layer including multiple input terminals (e.g., a first input terminal (x1), a second input terminal (x2), and / or a third input terminal (x3)).

[0245] Feature points extracted by the processor (301) can be input to each of the multiple input terminals (x1, x2, x3).

[0246] For example, feature point 1 (a) may be input to the first input terminal (x1), feature point 2 (b) may be input to the second input terminal (x2), and feature point 3 (c) may be input to the third input terminal (x3).

[0247] According to various embodiments, feature points 1(a) and 2(b) may be at least one dryness feature point (Hf1, Hf2, ... Hfm), and feature point 3(c) may be at least one temperature feature point (Tf1, Tf2, ... Tfn).

[0248] The artificial intelligence model (m1) can output a value related to the quality through the output terminal (y1) based on feature points input to multiple input terminals (e.g., the first input terminal (x1), the second input terminal (x2), and / or the third input terminal (x3)).

[0249] In one embodiment, the artificial intelligence model (m1) can calculate a value related to the quality by assigning different weights (k1, k2, k3) to each of the feature points input to each of a plurality of input terminals (e.g., a first input terminal (x1), a second input terminal (x2), and / or a third input terminal (x3)).

[0250] For example, the artificial intelligence model (m1) can assign a first weight (k1) to feature point 1 (a) input through the first input terminal (x1), assign a second weight (k2) to feature point 2 (b) input through the second input terminal (x2), assign a third weight (k3) to feature point 3 (c) input through the third input terminal (x3), and output the sum of these to the output terminal (y1).

[0251] In one embodiment, the artificial intelligence model (m1) may be configured to assign a first weight (k1) to temperature feature points (Tf1, Tf2, ... Tfn) to produce a first value (ak1), assign a second weight (k2) to dryness feature points (Hf1, Hf2, ... Hfm) to produce a second value (bk2 and / or ck3), and identify the quality of the laundry based on the first value and the second value.

[0252] According to various embodiments, feature points 1(a), 2(b), and 3(c) may be vectors including magnitude and direction. Accordingly, feature points may also be referred to as feature vectors.

[0253] The artificial intelligence model (m1) can identify a cluster corresponding to the value (vector) output through the output terminal (y1) and identify a quality corresponding to the identified cluster.

[0254] The weights (k1, k2, k3) assigned to the feature points can be updated according to the learning of the artificial intelligence model (m1).

[0255] Referring again to FIG. 5, the processor (301) may change the drying settings of the dryer (1) based on the identified foam (1500). Changing the drying settings of the dryer (1) may include changing the drying settings applied when the drying process began.

[0256] By action 1500, the drying setting of the drying cycle can be changed to a drying setting corresponding to the foam until the drying cycle is finished.

[0257] For example, the processor (301) may start a drying process according to the default drying settings of the dryer (1), and in response to the identified foam quality, may change the default drying settings to drying settings corresponding to the identified foam quality.

[0258] That is, the processor (301) can change the drying setting from the default drying setting to the drying setting corresponding to the identified laundry quality in response to the identified laundry quality.

[0259] The processor (301) can perform a drying process based on the changed drying settings, and can end the drying process based on the completion of the drying process according to the changed drying settings (example of 1600) (1700).

[0260] According to the present disclosure, a dryer (1) is provided that can perform a drying process with a drying setting suitable for the quality of laundry by accurately identifying the quality of laundry by an artificial intelligence model.

[0261] According to the present disclosure, the drying settings are changed according to the quality of the laundry, thereby achieving optimal drying efficiency without damaging the quality of the laundry.

[0262] FIG. 9 illustrates an example of drying settings corresponding to the quality of laundry according to one embodiment.

[0263] Referring to FIG. 9, the drying setting may include at least one of the rotation speed of the drum (120), the rotation speed of the blower fan (151), or the heating temperature of the heating element (200). Although not shown in the drawing, the drying setting may also include the operating time of the drying cycle.

[0264] The rotation speed of the drum (120) and / or the rotation speed of the blower fan (151) are shown in RPM in Fig. 9.

[0265] The rotation speed of the drum (120) and / or the rotation speed of the blower fan (151) may include the rotation speed of the motor of the driving device (60).

[0266] Drying settings may vary depending on the foam.

[0267] For example, for the first foam (P1), the heating temperature of the heating element (200) may be set to the first heating temperature (T1), and the rotation speed of the motor of the driving device (60) may be set to the first RPM (R1).

[0268] For the second foam (P2), the heating temperature of the heating element (200) may be set to the second heating temperature (T2), and the rotation speed of the motor of the driving device (60) may be set to the second RPM (R2).

[0269] For the third foam (P3), the heating temperature of the heating element (200) can be set to the third heating temperature (T3), and the rotation speed of the motor of the driving device (60) can be set to the third RPM (R3).

[0270] For the fourth foam (P4), the heating temperature of the heating element (200) may be set to the fourth heating temperature (T4), and the rotation speed of the motor of the driving device (60) may be set to the fourth RPM (R4).

[0271] For the fifth foam (P5), the heating temperature of the heating element (200) may be set to the fifth heating temperature (T5), and the rotation speed of the motor of the driving device (60) may be set to the fifth RPM (R5).

[0272] In this way, for each of the plurality of foams (P1, P2, P3, P4, P5), different heating temperatures (T1, T2, T3, T4, T5) and different rotation speeds (R1, R2, R3, R4, R5) of the motor of the driving device (60) can be applied to minimize damage to the foam and increase drying efficiency.

[0273] As described above, for each of the plurality of foams (P1, P2, P3, P4, P5), different drying cycle operation times can be applied to minimize foam damage and increase drying efficiency.

[0274] The memory (302) can store drying settings corresponding to multiple foams.

[0275] In one embodiment, the default drying setting may be a drying setting corresponding to the most susceptible foam among the plurality of foams.

[0276] According to the present disclosure, damage to the fabric of laundry can be prevented in advance by presetting the default drying setting to a drying setting corresponding to the fabric that is most susceptible to damage among a plurality of fabric types.

[0277] Figure 10 is a flowchart illustrating an example of a process in which an artificial intelligence model is learned according to one embodiment.

[0278] Referring to Fig. 10, the artificial intelligence model can be continuously updated according to the use of the dryer (1).

[0279] In one embodiment, the control method of the dryer (1) as described above may include an operation (1400) of identifying the quality of laundry.

[0280] As the operation (1400) of identifying the quality of laundry has been described with reference to FIG. 5, redundant description will be omitted.

[0281] The control method of the dryer (1) may include an operation (1410) of providing an interface for inquiring whether the identified foam is the same as the foam of actual laundry.

[0282] According to various embodiments, the operation (1410) of providing an interface for inquiring whether the identified foam is the same as the foam of the actual laundry may ultimately be performed by the dryer (1) or may be performed by an external device.

[0283] In one embodiment, the processor (301) can control the user interface device (40) to provide an interface that inquires whether the identified foam is the same as the foam of the actual laundry.

[0284] FIG. 11 illustrates an example of an interface provided through a user interface device of a dryer according to one embodiment.

[0285] Referring to FIG. 11, the processor (301) can control the user interface device (40) to provide an interface (J1, J2, J3) for inquiring whether the identified foam is identical to the foam of the actual laundry in response to the foam being identified.

[0286] In one embodiment, the interface for inquiring whether the identified foam is identical to the foam of the actual laundry may include information about the identified foam. In one embodiment, the interface for inquiring whether the identified foam is identical to the foam of the actual laundry (hereinafter referred to as the "inquiry interface") may include interface elements (e.g., a "Yes" button, a "No" button) for inquiring whether the identified foam is identical to the foam of the actual laundry.

[0287] In one embodiment, an interface (J1) for inquiring whether the identified foam is the same as the foam of the actual laundry may be provided during the drying cycle.

[0288] That is, in one embodiment, the user interface device (40) can output an inquiry interface (J1) during the drying process.

[0289] In one embodiment, the inquiry interface (J2) may be provided in response to completion of the drying process.

[0290] That is, in one embodiment, the user interface device (40) may output an inquiry interface (J2) in response to completion of the drying process.

[0291] The inquiry interface (J1, J2, J3) may be configured to inquire about the actual quality of the laundry in response to receiving a response that the identified quality is different from the actual quality of the laundry.

[0292] In one embodiment, the processor (301) may determine the identified foam as the actual foam of the laundry in response to receiving a positive response via the inquiry interface (J1, J2). In this case, the positive response means a response that the identified foam is identical to the actual foam of the laundry.

[0293] The processor (301) can maintain the drying settings based on receiving a positive response during the drying process (example of 1420).

[0294] Furthermore, the processor (301) can train an artificial intelligence model (1430) based on receiving a positive response during or after the drying process is completed (example of 1420).

[0295] The operation (1430) of training the artificial intelligence model may include training the artificial intelligence model using data related to feature points (a, b, c) extracted during the drying process and data related to foam identified by the artificial intelligence model as training data.

[0296] In one embodiment, the user interface device (40) may output an inquiry interface (J3) for inquiring about the actual quality of the laundry in response to receiving a negative response through the inquiry interfaces (J1, J2).

[0297] In this case, a negative response means that the identified foam is different from the actual foam of the laundry.

[0298] The inquiry interface (J3) provided in response to receiving a negative response may include an interface element for selecting the quality of the laundry.

[0299] In one embodiment, an interface element for selecting a laundry quality may include a visual indicator (e.g., a text) indicating the laundry quality.

[0300] In one embodiment, when the processor (301) performs an operation of identifying the quality of laundry, the quality of laundry is identified according to a probability distribution that the quality of laundry is a specific quality.

[0301] For example, in the example of FIG. 7, it can be determined that the probability that the value regarding the quality of laundry obtained through the artificial intelligence model corresponds to the first cluster (CL) is 80%, the probability that it corresponds to the second cluster (CL2) is 15%, and the probability that it corresponds to the third cluster (CL3) is 5%.

[0302] In this case, the processor (301) identifies the foam corresponding to the first cluster (CL) with the highest probability as the foam of the laundry.

[0303] That is, the processor (301) can identify the foam corresponding to the cluster identified with the highest priority as the foam of the laundry, and temporarily store the foam corresponding to the cluster identified with the lower priority in the memory (302).

[0304] The processor (301) may provide an interface element for selecting a cluster corresponding to a cluster identified in a later order via an inquiry interface (J3) provided in response to receiving a negative response.

[0305] That is, the inquiry interface (J3) may include an interface element for selecting a cluster corresponding to a cluster identified in a later order.

[0306] According to the present disclosure, since there is a high possibility that an interface element for selecting a quality corresponding to the quality of actual laundry will be placed on the inquiry interface (J3), it is possible to induce an easy selection by the user.

[0307] The user interface device (40) can receive information about the actual quality of laundry from the user through an inquiry interface (J3) that inquires about the actual quality of laundry. That is, the processor (301) can receive information about the actual quality of laundry that is different from the identified quality through the inquiry interface (J3).

[0308] The processor (301) may change the drying settings to drying settings corresponding to the actual quality of the laundry based on receiving a negative response (NO of 1420) during the drying process (1440).

[0309] The identification of the foam is completed when a predetermined time (pd) has elapsed since the drying process began. The user usually inputs a drying start command to the dryer (1) and then leaves.

[0310] Accordingly, it is highly likely that the user will not be able to check the inquiry interface (J1) provided through the user interface device (40) during the drying process.

[0311] Accordingly, according to one embodiment, the operation of providing an inquiry interface (J1) via the user interface device (40) during the drying process may not be performed.

[0312] However, the user may accidentally check the user interface device (40) during the drying process and input a positive or negative response.

[0313] In one embodiment, the processor (301) may change the drying setting to a drying setting corresponding to the actual quality of the laundry (1440) based on receiving a negative response (NO of 1420) during the drying cycle.

[0314] That is, when the processor (301) receives information from the user about the actual quality of laundry that is different from the quality identified by the artificial intelligence model, the processor (301) can change the drying settings corresponding to the quality identified by the artificial intelligence model to the drying settings corresponding to the actual quality of laundry.

[0315] In one embodiment, the processor (301) can train the artificial intelligence model (1450) based on receiving a negative response (No of 1420) during the drying process.

[0316] The operation (1450) of training an artificial intelligence model may include training an artificial intelligence model using data related to feature points (a, b, c) extracted during a drying cycle, data related to the quality of laundry identified by the artificial intelligence model, and / or data related to the actual quality of laundry received from a user as training data.

[0317] According to the present disclosure, even if the quality of laundry is incorrectly identified by an artificial intelligence model, this can be corrected through user intervention.

[0318] In addition, according to the present disclosure, when the quality of laundry is incorrectly identified by an artificial intelligence model, the quality identification accuracy of the artificial intelligence model can be improved by utilizing actual quality information as learning data for training the artificial intelligence model.

[0319] In one embodiment, the processor (301) can transmit information about the identified foam to an external device via the communication interface (330) to provide an interface for the external device to inquire whether the identified foam is identical to the actual foam of the laundry.

[0320] FIG. 12 illustrates an example of an interface provided through an external device that receives foam information from a dryer according to one embodiment.

[0321] Referring to FIG. 12, the processor (301) may transmit information about the identified foam to an external device via a communication interface (330) so that the external device provides an interface (J1, J2, J3) for inquiring whether the identified foam is identical to the actual foam of the laundry in response to the foam being identified.

[0322] In Fig. 12, only the inquiry interface (J1) provided during the drying process and the inquiry interface (J3) provided in response to a negative response received through the inquiry interface (J1) are shown as inquiry interfaces (J1, J2, J3) provided by an external device, but it is of course possible for the external device to also provide the inquiry interface (J2) in response to the completion of the drying process.

[0323] As previously explained, it is highly likely that the user will not be able to check the inquiry interface (J1) provided through the user interface device (40) during the drying process.

[0324] In one embodiment, the processor (301) may transmit information about the identified foam to an external device via the communication interface (330) so that, in response to the foam being identified, the external device may provide an interface (J1) for inquiring whether the identified foam is identical to the actual foam of the laundry.

[0325] Here, the external device may include a user device (2). The processor (301) transmitting information about the identified foam to the external device may include the processor (301) transmitting information about the identified foam to the server, thereby causing the server to transmit information about the identified foam to the user device (2). The processor (301) transmitting information about the identified foam to the external device may include the processor (301) directly transmitting information about the identified foam to the user device (2).

[0326] The user device (2) may be carried by the user or placed in the user's home or office, etc. The user device may include, but is not limited to, a personal computer, a terminal, a mobile phone, a smart phone, a handheld device, a wearable device, a display device, etc.

[0327] When the user device (2) receives a positive response through the inquiry interface (J1), the positive response can be transmitted to the dryer (1). The processor (301) can determine the identified foam as the actual foam of the laundry in response to receiving the positive response from the user device (2) through the communication interface (330).

[0328] The processor (301) can maintain the drying settings based on receiving a positive response during the drying process (example of 1420).

[0329] Furthermore, the processor (301) can train an artificial intelligence model (1430) based on receiving a positive response during or after the drying process is completed (example of 1420).

[0330] The operation (1430) of training the artificial intelligence model may include training the artificial intelligence model using data related to feature points (a, b, c) extracted during the drying process and data related to foam identified by the artificial intelligence model as training data.

[0331] When the user device (2) receives a negative response through the inquiry interface (J1), it can output an inquiry interface (J3) that inquires about the actual quality of the laundry.

[0332] The user device (2) can receive information about the actual quality of laundry from the user through an inquiry interface (J3) that inquires about the actual quality of laundry. That is, the processor (301) can receive information about the actual quality of laundry that is different from the identified quality through the inquiry interface (J3).

[0333] The user device (2) can transmit information about the actual quality of the laundry received through the inquiry interface (J3) to the dryer (1).

[0334] In one embodiment, the processor (301) may change the drying settings to drying settings corresponding to the actual quality of the laundry based on receiving information about the actual quality of the laundry from the user device (2) during the drying cycle (No of 1420) (1440).

[0335] In one embodiment, the processor (301) can train the artificial intelligence model (1450) based on information received from the user device (2) regarding the actual quality of the laundry during the drying cycle (No of 1420).

[0336] According to the present disclosure, by providing an inquiry interface (J1) through a user device (2) at a time when the laundry quality identification is completed by an artificial intelligence model, the drying cycle setting can be quickly changed to a cycle corresponding to the actual quality even if accurate quality identification fails.

[0337] According to various embodiments, when an artificial intelligence model is stored on a server, the server may perform operations (1430, 1450) for training the artificial intelligence model.

[0338] In one embodiment, the server may receive learning data described above or described below from the dryer (1) and / or the user device (2), and use the same to train an artificial intelligence model.

[0339] Figure 13 conceptually illustrates how an artificial intelligence model is trained according to one embodiment.

[0340] Referring to FIG. 13, the artificial intelligence model (m1) may include an input layer including multiple input terminals (e.g., a first input terminal (x1), a second input terminal (x2), a third input terminal (x3), and / or a fourth input terminal (x4)).

[0341] Since the first input terminal (x1), the second input terminal (x2), and the third input terminal (x3) have been described above, a duplicate description will be omitted.

[0342] Data regarding actual foam can be input into the fourth input terminal (x4).

[0343] Data about actual foam may include data about actual foam selected based on user input.

[0344] For example, if a positive response is received through the inquiry interface (J1, J2), the data regarding the actual foam selected according to the user input may mean data regarding the foam identified by the artificial intelligence model.

[0345] As another example, if an actual foam is selected through the inquiry interface (J3), data about the actual foam selected according to user input may mean data about the foam obtained through the inquiry interface (J3).

[0346] The artificial intelligence model (m1) can be trained based on data about actual foam input into the fourth input terminal (x4).

[0347] According to various embodiments, although not depicted in the drawings, the artificial intelligence model may further include a fifth input terminal into which data regarding the foam identified by the artificial intelligence model is input.

[0348] In one embodiment, the artificial intelligence model (m1) may be trained based on first data related to temperature features extracted by the processor (301), second data related to dryness features extracted by the processor (301), third data related to the quality of laundry identified by the artificial intelligence model based on the temperature features and dryness features, and fourth data related to the actual quality of laundry selected according to user input.

[0349] The artificial intelligence model (m1) can output values ​​related to weights (k1, k2, k3) assigned to each feature point through the output terminal (y2) based on learning data input to multiple input terminals (x1, x2, x3, x4).

[0350] In one embodiment, as a result of the learning, the artificial intelligence model (m1) can update the first weight (k1) assigned to the temperature feature points (Tf1, Tf2, ... Tfn) and update the second weight (k2) assigned to the dryness feature points (Hf1, Hf2, ... Hfm).

[0351] That is, training the artificial intelligence model (m1) may include updating the first weight (k1) assigned to the temperature feature points (Tf1, Tf2, ... Tfn) and the second weight (k2) assigned to the dryness feature points (Hf1, Hf2, ... Hfm).

[0352] According to the present disclosure, the artificial intelligence model is continuously trained based on data regarding actual foam selected according to user input, so that the accuracy of foam identification of the artificial intelligence model can be improved as the dryer (1) performs more drying cycles.

[0353] Additionally, according to the present disclosure, when many users use the dryer (1), the artificial intelligence model can be rapidly trained using abundant learning data.

[0354] A dryer (1) according to one embodiment of the present disclosure comprises: a drum (120) for accommodating laundry to be dried; a heating element (200) for heating air; a fan (151) for blowing the heated air into the drum (120) to dry the laundry; a first temperature sensor (251) for generating first temperature data corresponding to the temperature of the heated air; a second temperature sensor (252) for generating second temperature data corresponding to the temperature inside the drum (120); a dryness sensor (160) provided inside the drum (120) for generating dryness data corresponding to the dryness of laundry inside the drum (120); And it may include at least one processor (301) that extracts temperature feature points based on the first temperature data generated by the first temperature sensor (251) and the second temperature data generated by the second temperature sensor (252), extracts dryness feature points based on the dryness data generated by the dryness sensor (160), identifies the quality of the laundry by an artificial intelligence model based on the temperature feature points and the dryness feature points, and changes the drying setting of the dryer (1) based on the identified quality of the laundry.

[0355] Additionally, the at least one processor (301) can extract the temperature feature point based on the difference between the temperature of the air heated by the heating element (200) and the temperature inside the drum (120).

[0356] In addition, the at least one processor (301) may perform an operation of starting a drying process according to a default drying setting when a drying start command is received, and identifying the quality of the laundry in response to a predetermined time elapsed after the drying process is started.

[0357] Additionally, the at least one processor (301) may, in response to the laundry quality being identified, change the drying setting from the default drying setting to a drying setting corresponding to the identified laundry quality.

[0358] Additionally, the at least one processor (301) may control the user interface device (40) to provide an interface (J1, J2, J3) for inquiring whether the identified foam quality is identical to the actual foam quality of the laundry, in response to the identified foam quality of the laundry being identified.

[0359] In addition, the at least one processor (301) can transmit information about the identified foam quality to the external device (2) through the communication interface so that, in response to the foam quality of the laundry being identified, the external device (2) provides an interface (J1, J2, J3) for inquiring whether the identified foam quality is identical to the actual foam quality of the laundry.

[0360] Additionally, the interface (J1, J2, J3) may be configured to inquire about the actual quality of the laundry in response to receiving a response that the identified quality is different from the actual quality of the laundry.

[0361] Additionally, the at least one processor (301) may, in response to receiving information about the actual quality of the laundry that is different from the identified quality through the interface (J1, J2, J3), change the drying setting to a drying setting corresponding to the actual quality of the laundry.

[0362] In addition, the artificial intelligence model may be trained based on first data related to the temperature feature point extracted by the at least one processor (301), second data related to the dryness feature point extracted by the at least one processor (301), third data related to the quality of the laundry identified by the artificial intelligence model based on the temperature feature point and the dryness feature point, and fourth data related to the actual quality of the laundry selected according to a user input.

[0363] In addition, the artificial intelligence model is configured to calculate a first value by assigning a first weight to the temperature feature point, calculate a second value by assigning a second weight to the dryness feature point, and identify the quality of the laundry based on the first value and the second value, and learning the artificial intelligence model may include updating the first weight and the second weight.

[0364] Additionally, the at least one processor (301) may change the drying setting based on the identified laundry quality only when performing a drying course in which the laundry quality is not specified among the plurality of drying courses.

[0365] Additionally, the temperature characteristic point may include the slope of the difference between the temperature of the air heated by the heating element (200) and the temperature inside the drum (120).

[0366] In addition, the dryness sensor (160) includes an electrode sensor that detects contact with a portion of the laundry containing moisture during rotation of the drum (120), and the dryness characteristic point may include a slope of the number of contacts detected by the electrode sensor per unit time.

[0367] In addition, the drying setting may include at least one of the rotation speed of the drum (120), the rotation speed of the blower fan (151), the heating temperature of the heating element (200), or the operation time of the drying process.

[0368] A method for controlling a dryer (1) according to one embodiment of the present disclosure may include: extracting a temperature feature point based on processing first temperature data collected by a first temperature sensor (251) for detecting the temperature of air heated by the heating element (200) and second temperature data collected by a second temperature sensor (252) for detecting the temperature inside the drum (120); extracting a dryness feature point based on processing dryness data collected by a dryness sensor (160) for detecting the dryness of laundry inside the drum (120); inputting the temperature feature point and the dryness feature point into an artificial intelligence model to identify a quality of the laundry; and changing a drying setting of the dryer (1) based on the identified quality of the laundry.

[0369] In addition, extracting the temperature feature point may include extracting the temperature feature point based on the difference value between the temperature of the air heated by the heating element (200) and the temperature inside the drum (120).

[0370] In addition, the control method of the dryer (1) may further include: starting a drying process according to a default drying setting when a drying start command is received; and performing an operation of identifying the quality of the laundry in response to a predetermined time elapsed after the drying process is started.

[0371] In addition, the control method of the dryer (1) may further include providing an interface for inquiring whether the identified quality of the laundry is the same as the actual quality of the laundry in response to the identification of the quality of the laundry.

[0372] In addition, the control method of the dryer (1) may further include, in response to receiving information about the actual quality of the laundry that is different from the identified quality through the interface, changing the drying setting to a drying setting corresponding to the actual quality of the laundry.

[0373] In addition, the control method of the dryer (1) may further include changing the drying setting of the dryer (1) based on the identified quality of the laundry only when a drying course in which the quality of the laundry is not specified is performed among a plurality of drying courses.

[0374] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0375] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0376] Additionally, a computer-readable recording medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0377] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable recording medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated on a machine-readable recording medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0378] The disclosed embodiments have been described with reference to the attached drawings as described above. Those skilled in the art will understand that the present invention can be implemented in forms other than the disclosed embodiments without altering the technical spirit or essential features of the present invention. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims

1. A drum that holds the laundry to be dried; A heating element that heats the air; A fan that blows the heated air into the drum to dry the laundry; A first temperature sensor that generates first temperature data corresponding to the temperature of the heated air; A second temperature sensor that generates second temperature data corresponding to the temperature inside the drum; A dryness sensor provided inside the drum and generating dryness data corresponding to the dryness of the laundry accommodated inside the drum; and A dryer comprising at least one processor for extracting temperature feature points based on the first temperature data generated by the first temperature sensor and the second temperature data generated by the second temperature sensor, extracting dryness feature points based on the dryness data generated by the dryness sensor, identifying the quality of the laundry by an artificial intelligence model based on the temperature feature points and the dryness feature points, and changing the drying setting of the dryer based on the identified quality of the laundry.

2. In paragraph 1, At least one processor, A dryer including extracting the temperature feature point based on the difference between the temperature of the heated air and the temperature inside the drum.

3. In paragraph 1, At least one processor, A dryer that starts a drying process according to default drying settings when a drying start command is received, and identifies the quality of the laundry in response to a predetermined time elapsed after the drying process starts.

4. In paragraph 3, At least one processor, A dryer that changes the drying setting from the default drying setting to a drying setting corresponding to the identified laundry quality in response to the identified laundry quality.

5. In paragraph 1, further comprising a user interface device; At least one processor, A dryer that controls the user interface device to provide an interface that in response to the identified quality of the laundry being washed, asks whether the identified quality is identical to the actual quality of the laundry being washed.

6. In paragraph 1, Further comprising a communication interface for communicating with an external device; At least one processor, A dryer that, in response to the identification of the quality of the laundry, transmits information about the identified quality to the external device through the communication interface.

7. In paragraph 5 or 6, A dryer wherein the at least one processor controls the user interface device to inquire about the actual quality of the laundry based on the interface receiving a response in response to the inquiry that the identified quality is different from the actual quality of the laundry.

8. In paragraph 7, At least one processor, A dryer that changes the drying setting to a drying setting corresponding to the received actual laundry quality based on the response received by the interface regarding the actual laundry quality in response to the inquiry regarding the actual laundry quality.

9. In paragraph 1, The above artificial intelligence model is, A dryer that is learned based on first data related to the extracted temperature feature point, second data related to the extracted dryness feature point, third data related to the identified quality of the laundry, and fourth data related to the actual quality of the laundry according to user input.

10. In paragraph 9, The above artificial intelligence model is, A first value is calculated based on a first weight assigned to the extracted temperature feature point, a second value is calculated based on a second weight assigned to the extracted dryness feature point, and the quality of the laundry is identified based on the first value and the second value. The above first weight and the above second weight are updated to train the artificial intelligence model.

11. In paragraph 1, At least one processor, A dryer that changes the drying setting based on the identified laundry quality only when a drying course is performed in which the laundry quality is not specified.

12. In paragraph 1, The above temperature characteristic points are, A dryer comprising a slope corresponding to the difference between the temperature of the heated air and the temperature inside the drum.

13. In paragraph 1, The above dryness sensor includes an electrode sensor that detects contact with a portion of the laundry containing moisture during rotation of the drum, The above dryness characteristics are: A dryer comprising a slope corresponding to the number of contacts detected by the electrode sensor per unit time.

14. In paragraph 1, The above drying settings are: A dryer comprising at least one of the rotation speed of the drum, the rotation speed of the fan, the heating temperature of the heating element, or the operation time of the drying cycle.

15. A method for controlling a dryer including a drum, a heating element, and a blower fan for blowing air heated by the heating element into the inside of the drum, Extracting temperature feature points based on processing first temperature data generated by a first temperature sensor that detects the temperature of air heated by the heating element and second temperature data generated by a second temperature sensor that detects the temperature inside the drum; Extracting dryness feature points based on processing dryness data generated by a dryness sensor that detects the dryness of laundry inside the drum; By inputting the above temperature feature points and the above dryness feature points into an artificial intelligence model, the quality of the laundry is identified; A method for controlling a dryer, comprising: changing the drying settings of the dryer based on the identified foam.

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