Electronic device for estimating weight of laundry and method for operating same

Abstract

A method for operating an electronic device is disclosed. The method for operating an electronic device according to an embodiment of the present disclosure comprises the operations of: measuring the electric current of a motor of the electronic device with regard to a predetermined time interval in response to identifying that a washing or drying cycle regarding laundry is initiated; determining that the weight of the laundry is at a first level or a second level on the basis of the motor's electric current value regarding the predetermined time interval and the degree of change in the electric current; determining the weight of the laundry by using the motor's electric current value as an input value to a machine learning model which is trained to predict the weight of laundry on the basis of the current profile information in response to determining that the weight of the laundry is at the first level; determining the weight of the laundry by using at least two from among the maximum electric current value measured in the predetermined time interval, the cumulative differential value of the electric current measured in the predetermined time interval, the motor's reference electric current value, information regarding the amount of change in the motor's electric current, and the motor's electric current value as input values to the machine learning model in response to determining that the weight of the laundry is at the second level; determining the time required for the washing or drying cycle to be completed on the basis of the determined weight of the laundry; and displaying the determined time.

Description

Electronic device and operating method for estimating the weight of laundry

[0001] Embodiments disclosed in this document relate to an electronic device and an operating method for estimating the weight of laundry.

[0002] Advances in home appliance technology have enabled electronic devices for washing or drying to offer a variety of functions, such as remote control via a user device, optimization of washing cycles and methods, diagnostics and maintenance, and automatic notifications. In this regard, electronic devices for washing or drying may provide functions that inform users of the progress and estimated completion time of a washing or drying cycle. Washing or drying devices may estimate the end time of a cycle based on the weight of the laundry loaded. To measure the weight of the laundry, a weight sensor may be used, or the load applied to the device's motor may be measured to determine the weight.

[0003] The above information is provided solely as background information to aid in understanding the present disclosure. No determination or assertion is made regarding whether any of the above information constitutes prior art in connection with the present disclosure.

[0004] Washing or drying devices can measure the weight of laundry by measuring the current to a weight sensor or motor. Using a weight sensor can be difficult to accurately measure laundry weight, as the drum's weight is relatively large compared to the amount of laundry. Furthermore, measuring laundry weight by measuring motor current can result in reduced accuracy when heavy laundry is loaded or when the drum's weight is significantly skewed due to humidity.

[0005] Aspects of the present disclosure are intended to address at least the problems and shortcomings mentioned above and provide the advantages described below. Accordingly, one aspect of the present disclosure provides a device and method for measuring the weight of laundry based on the current profile of a motor.

[0006] Additional aspects will be disclosed in part in the following description, and in part will be obvious from the description or may be learned by practice of the embodiments presented.

[0007] According to one aspect of the present disclosure, a method for performing washing or drying in an electronic device is provided. The method may include, in response to the electronic device identifying that a washing or drying cycle for laundry has been initiated, measuring a current of a motor of the electronic device for a predetermined time period. The method of operating the electronic device may include, based on a current value of the motor and a degree of change in the current for the predetermined time period, determining a weight of the laundry as either a first level or a second level. The method of operating the electronic device may include, in response to determining the weight of the laundry as the first level, inputting the current value of the motor as an input value to a machine learning model trained to predict the weight of the laundry based on the current profile information, thereby determining the weight of the laundry. The method of operating the electronic device may include, in response to determining the weight of the laundry as the second level, inputting at least two of a maximum current value measured during the predetermined time period, a differential cumulative value of the current measured during the predetermined time period, a reference current value of the motor, information regarding a change amount of the current of the motor, or a current value of the motor as input values ​​to the machine learning model to determine the weight of the laundry. The method of operating the electronic device may include, based on the determined weight of the laundry, an operation of determining a time required for the washing or drying cycle to be completed. The method of operating the electronic device may include an operation of displaying the determined time.

[0008] According to one aspect of the present disclosure, an electronic device is provided. An electronic device includes a memory storing one or more computer programs and one or more processors communicatively connected to the memory, wherein the one or more computer programs include computer-executable instructions, which instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: in response to identifying that a wash or dry cycle for laundry is initiated, measure a current of a motor of the electronic device for a predetermined time period, and determine a weight of the laundry as either a first level or a second level based on the current value of the motor and the degree of change in the current for the predetermined time period, and in response to determining the weight of the laundry as the first level, input the current value of the motor as an input value to a machine learning model that is trained to predict the weight of the laundry based on the current profile information, and in response to determining the weight of the laundry as the second level, input to the machine learning model a maximum current value measured for the predetermined time period, a differential cumulative value of the current measured for the predetermined time period, a reference current value of the motor, and a degree of change in the current of the motor. The method may cause the weight of the laundry to be determined by inputting at least two of the information or the current values ​​of the motor, and, based on the determined weight of the laundry, the time required for the washing or drying cycle to be completed to be determined and the determined time to be displayed.

[0009] In accordance with one aspect of the present disclosure, one or more non-transitory computer-readable storage media are provided storing one or more computer programs comprising computer-executable instructions that, when executed individually or collectively by one or more processors of an electronic device, cause the electronic device to perform operations. The above operations include, in response to identifying that a washing or drying cycle for laundry is initiated, measuring, by the electronic device, a current of a motor of the electronic device for a predetermined time period; determining, by the electronic device, a weight of the laundry as either a first level or a second level based on a current value of the motor and a degree of change in the current for the predetermined time period; determining, by the electronic device, a weight of the laundry as the first level, using the current value of the motor as an input value to a machine learning model trained to predict the weight of the laundry based on the current profile information; determining, by the electronic device, a weight of the laundry as an input value to the machine learning model, using at least two of a maximum current value measured for a predetermined time period, a differential cumulative value of the current measured for the predetermined time period, a reference current value of the motor, information regarding an amount of change in the current of the motor, or a current value of the motor as input values ​​to the machine learning model; and determining, by the electronic device, a weight of the laundry based on the determined weight of the laundry. The operation includes determining a time required for the washing or drying cycle to be completed, and displaying the determined time by an electronic device.

[0010] Embodiments of the present disclosure provide the effect of accurately measuring the weight of laundry or dry goods.

[0011] Embodiments of the present disclosure provide an effect that can improve user convenience by accurately predicting and providing the time required for washing or drying to the user.

[0012] Other aspects, advantages and salient features of the present disclosure will become apparent to those skilled in the art from the following detailed description of various embodiments of the present disclosure taken in conjunction with the accompanying drawings.

[0013] Figure 1 is a perspective view of the exterior of a washing machine according to one embodiment.

[0014] Figure 2 is a side cross-sectional view of a washing machine according to one embodiment.

[0015] FIG. 3 is a functional block diagram schematically illustrating the configuration of a washing machine according to one embodiment from the perspective of function and control.

[0016] Figure 4 illustrates an operational flow of a device for performing washing or drying according to one embodiment.

[0017] FIG. 5 illustrates an operational flow of an electronic device according to one embodiment.

[0018] FIG. 6 illustrates an example of current measured according to a washing or drying cycle according to one embodiment.

[0019] Figure 7 illustrates the weight measurement results of an electronic device according to one embodiment.

[0020] FIG. 8 illustrates an example of an IoT environment including an electronic device according to one embodiment.

[0021] In connection with the description of the drawings, the same reference numerals will be used for identical components.

[0022] The following description, with reference to the accompanying drawings, is provided to facilitate a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. While it includes numerous specific details to facilitate this understanding, these are to be considered merely exemplary. Accordingly, those skilled in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. Furthermore, descriptions of well-known functions and structures may be omitted for clarity and conciseness.

[0023] The terms and words used in the following description and claims are not limited to their bibliographic meanings, but are used by the inventors solely to facilitate a clear and consistent understanding of the present disclosure. Therefore, it will be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustrative purposes only and is not intended to limit the present disclosure as defined by the appended claims and their equivalents.

[0024] The singular forms "a," "an," and "the" should be understood to include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more of those surfaces.

[0025] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" each may include any one of the items listed with the phrase, or all possible combinations thereof. The term "and / or" as used herein should be understood to encompass any and all possible combinations of one or more of the items listed with the term. The terms "first", "second", "first", or "second" as used herein may be used merely to distinguish the corresponding element from other elements and do not limit the corresponding elements in any other respect (e.g., importance or order).

[0026] When a component (e.g., a first component) is referred to as being "coupled," "connected," "connected," "joined," "supported," "connected," or "in contact with" another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it includes instances where the component is directly coupled, connected, joined, supported, or in contact with the other component, as well as instances where the component is indirectly coupled, connected, joined, supported, or in contact with the other component through a third component.

[0027] The terms "include" or "have" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described herein, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof. When it is said that a component is located "on" another component, this includes not only cases where the component is in contact with the other component, but also cases where another component is present between the two components.

[0028] The expression "configured to" as used herein can be used interchangeably with, for example, "suitable for," "capable of," "designed to," "modified to," "made to," or "capable of." The term "configured to" does not necessarily mean something that is "specially designed" in terms of hardware. Instead, in some contexts, the expression "a device configured to" can mean that the device is "capable of" doing something together with other devices or components. For example, the phrase "a device configured (or set) to perform A, B, and C" can mean a dedicated device for performing the actions in question, or a general-purpose device that can perform various actions including the actions in question.

[0029] The terms “upper side,” “lower side,” and “front-rear direction” used in this document are defined based on the drawings, and the shape and position of each component are not limited by these terms.

[0030] While the description herein focuses on specific embodiments, it should be understood that this document is not limited to such specific embodiments, but rather encompasses various modifications, equivalents, and / or alternatives of the various embodiments described herein. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components.

[0031] Washing machines according to various examples in this document may be examples of clothing treatment devices. Washing machines according to various examples may include top-loading washing machines, in which the loading port for loading or unloading laundry is provided facing upward, or front-loading washing machines, in which the loading port for loading or unloading laundry is provided facing forward. Top-loading washing machines can wash laundry using a water current generated by a rotating body, such as a pulsator. Front-loading washing machines can wash laundry by rotating a drum to repeatedly raise and lower laundry. Front-loading washing machines may include a lifter for lifting laundry. Washing machines according to various examples may include washing machines with various loading and washing methods in addition to the top-loading and front-loading washing machines described above. While this document focuses on front-loading washing machines, this document is not limited thereto.

[0032] Below, the washing machine is described in detail with reference to the drawings.

[0033] It should be recognized that the blocks and combinations of flowcharts within each flowchart can be executed by one or more computer programs containing instructions. One or more computer programs may be stored entirely in a single memory device, or one or more computer programs may be divided into different parts stored in multiple different memory devices.

[0034] Any function or operation described herein may be performed by a single processor or a combination of processors. A processor or a combination of processors is a circuit that performs processing, and includes circuits such as an application processor (AP), e.g., a central processing unit (CPU), a communication processor (CP), e.g., a modem), a graphics processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a Wi-Fi chip, a Bluetooth® chip, a global positioning system (GPS) chip, a near field communication (NFC) chip, a connectivity chip, a sensor controller, a touch controller, a fingerprint sensor controller, a display driver integrated circuit (IC), an audio codec chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, a microprocessor unit (MPU), a system on a chip (SoC), an integrated circuit (IC), and the like.

[0035] Fig. 1 is a perspective view of an exterior of a washing machine according to one embodiment. Fig. 2 is a side cross-sectional view of a washing machine according to one embodiment.

[0036] Referring to FIGS. 1 and 2, the washing machine (1) may include a housing (10) that accommodates various components therein. The housing (10) may have an overall hexahedral shape. The housing (10) may include an opening formed on one surface. Two or more surfaces of the housing (10) may be formed as a single body. Each surface of the housing (10) may be manufactured separately and then assembled. The housing (10) may be formed, for example, by press molding from a sheet metal material or by injection molding from a resin material.

[0037] In one example, a door (20) that opens and closes the opening may be provided in a portion corresponding to the opening of the housing (10). The door (20) may be rotatably coupled to a hinge fixed to one surface of the housing (10). For example, at least a portion of the door (20) may be provided to be transparent or translucent so that the inside may be visible. A user may open and close the door (20) to load laundry into the drum (40) located inside the housing (10) or to take laundry out of the drum (40). The door (20) may be locked, for example, by a locking device (not shown) so as not to be opened while the washing machine (1) is in operation. In one example, the door (20) may include a door frame (21) and a glass member (22). The glass member (22) may be formed of, for example, a transparent tempered glass material so that the inside of the housing (10) may be visible, but the present document is not limited thereto.

[0038] In one example, a washing machine (1) may include a tub (30) fixedly arranged inside a housing (10). The tub (30) may have a roughly cylindrical shape with one side open. A tub opening (31) may be provided at a position corresponding to the opening of the housing (10) on the front of the tub (30). The tub (30) may store washing water. A drain (32) for draining washing water may be provided at the bottom of the tub (30). The drain (32) may be connected to, for example, a drainage device (80).

[0039] In one example, the washing machine (1) may include a damper (12). The damper (12) may be provided to connect the housing (10) and the tub (30). One side of the damper (12) may be fixed to the inner surface of the housing (10) and the other side may be fixed to the tub (30). The damper (12) may be provided to absorb vibration energy transmitted to the tub (30) and / or the housing (10) when the drum (40) rotates, thereby attenuating the vibration.

[0040] In one example, a washing machine (1) may include a drum (40) provided inside a tub (30). The drum (40) may have a roughly cylindrical shape with one side open. A front plate (43) and a rear plate (44) may be arranged on the front and rear sides of the drum (40), respectively. A drum opening may be provided in the front plate (43) at a position corresponding to the opening of the housing (10) and the tub opening (31) of the tub (30). The drum (40) may accommodate laundry. The drum (40) may receive rotational power from a driving device (60) and rotate inside the tub (30). The drum (40) may perform washing, rinsing, and / or dehydration while rotating inside the tub (30).

[0041] In one example, the drum (40) may include a lifter (41) and / or a plurality of holes (42). The lifter (41) may, for example, lift laundry while the drum (40) rotates, thereby causing the laundry to repeatedly rise and fall, thereby evenly washing multiple surfaces of the laundry. The holes (42) may, for example, be passages formed so that washing water contained in the tub (30) may flow into the interior of the drum (40), or washing water inside the drum (40) may be discharged to the outside. In one example, the lifter (41) or the holes (42) may be omitted.

[0042] In one example, the washing machine (1) may include a control panel (50) that supports interaction between a user and the washing machine (1). In one example, the control panel (50) may be positioned on the upper front side of the housing (10) as illustrated in FIG. 1, but the present document is not limited thereto. In one example, the control panel (50) may include an input unit (51) and a display unit (52).

[0043] The input unit (51) may include, for example, any type of user input means for obtaining user input for controlling the washing machine (1). The user may input power on / off of the washing machine (1), washing setting information (e.g., operation start / stop, course selection, time selection, etc.), etc., through the input unit (51). For example, the input unit (51) may be a tact switch, a push switch, a slide switch, a toggle switch, a micro switch, or a touch switch, but the present document is not limited thereto. For example, the input unit (51) may be in the form of a jog shuttle that a user can grasp and rotate. In one example, the input unit (51) may include an infrared sensor. The user may input setting information remotely through a remote control, and the input setting information may be received by the input unit (51) as an infrared signal. In one example, the input unit (51) may include a microphone. Setting information by the user's voice may be obtained through the microphone.

[0044] The display unit (52) can display various washing setting information input by the user and / or operating status information of the washing machine (1). The display unit (52) can include various types of display panels such as, for example, an LCD (Liquid Crystal Display), an LED (Light Emitting Diode), an OLED (Organic LED), a QLED (Quantum dot LED), and a Micro LED. For example, the display unit (52) can be implemented as a touch screen with a touch pad provided on the front, and the present document is not limited to a specific type of display means. In one example, the display unit (52) can include any type of audio means including a speaker, and can express each of the above-described information as an auditory signal through such audio means. In one example, the display unit (52) can operate to audibly provide the user with information for guiding the user's input and / or information related to the currently ongoing cycle.

[0045] In one example, the washing machine (1) may include a drive device (60) for rotating the drum (40). The drive device (60) may include a motor (61) and a drive shaft (62) for transmitting the driving force generated by the motor (61) to the drum (40). The motor (61) may be configured with a fixed stator (611) and a rotor (612) that rotates by electromagnetic interaction with the stator (611), thereby converting electrical power into mechanical rotational power. The rotational power generated by the motor (61) may be transmitted to the drum (40) through the drive shaft (62). The drive shaft (62) may be, for example, provided to be press-fitted into the rotor (612) of the motor (61) and to rotate together with the rotor (612). The drive shaft (62) may, for example, have a portion that penetrates the rear wall of the tub (30) to connect the drum (40) and the motor (61). The driving device (60) can rotate the drum (40) forward or backward to perform washing, rinsing, and / or dehydration operations.

[0046] In one example, the washing machine (1) may include a water supply device (70) for supplying washing water to the drum (40) and / or the tub (30). The water supply device (70) may include at least one water supply pipe (71) and at least one water supply valve (72). At least one water supply pipe (71) may be provided to supply washing water into the interior of the tub (30) using an external water source. One of the at least one water supply pipe (71) may be connected to a detergent supply device (13) provided in the housing (10). Here, the detergent supply device (13) may have an interior divided into a plurality of spaces, and detergent or rinsing agent may be provided in each space. Washing water passing through the detergent supply device (13) may be supplied to the tub (30) together with detergent (or rinsing agent) through the detergent supply pipe (131). At least one of the water supply pipes (71) may be directly connected to the tub (30). For example, washing water supplied through the water supply pipe (71) directly connected to the tub (30) may be supplied directly to the tub (30) without passing through an intermediate component such as a detergent supply device (13).

[0047] In one example, the washing machine (1) may include a drainage device (80) for draining the washing water contained in the drum (40) and / or the tub (30). The drainage device (80) may include a drain valve (81), a first drainage pipe (82), a second drainage pipe (83), or a pump room (84). The drainage device (80) may be arranged, for example, at the bottom of the tub (30) to drain the washing water discharged from the tub (30) to the outside of the washing machine (1).

[0048] In one example, the drain valve (81) may be configured to open and close the drain port (32). When the drain valve (81) is opened, the washing water contained in the tub (30) may flow through the drain port (32) to the drain device (80).

[0049] In one example, the first drain pipe (82) and the second drain pipe (83) may form a path that guides the washing water to be discharged to the outside. For convenience of explanation, the upstream side with respect to the pump room (84) is referred to as the first drain pipe (82) and the downstream side is referred to as the second drain pipe (83). The first drain pipe (82) and the second drain pipe (83) may be formed integrally. For example, one end of the first drain pipe (82) may be connected to the drain port (32) and the other end may be connected to the pump room (84). The washing water may move into the pump room (84) along the first drain pipe (82). The second drain pipe (83) may, for example, one end may be connected to the pump room (84) and the other end may be connected to the outside of the washing machine (1). Therefore, the washing water passing through the pump room (84) may be discharged to the outside of the washing machine (1) along the second drain pipe (83).

[0050] In one example, a pump room (84) may be provided at the bottom of the tub (30) to store the washing water drained from the tub (30). Inside the pump room (84), for example, a drain pump (841) may be provided to discharge the stored washing water to the outside. The washing water pumped by the drain pump (841) may be guided to the outside of the housing (10) through the second drain pipe (83).

[0051] According to one example, the washing machine (1) may include a balancer (150). The balancer (150) may include, for example, a balancer housing (151) forming an annular channel (151a) and a plurality of masses (153) arranged in the annular channel (151a) to perform a balancing function of the drum (40) while moving along the annular channel (151a). The plurality of masses (153) may have, for example, a ball shape (spherical shape). The plurality of masses (153) of the drum (40) may move in a direction opposite to the direction of eccentricity caused in the drum (40) by laundry when the drum (40) rotates, thereby compensating for the eccentricity caused by the laundry.

[0052] According to an example, the balancer (150) can be mounted on at least one of the front plate (43) and the rear plate (44) of the drum (40). Since the balancers (150) mounted on the front plate (43) and the rear plate (44) are the same overall, the following description will focus on the balancer (150) mounted on the front plate (43) of the drum (40).

[0053] According to one example, the balancer (150) may be configured to be accommodated in an annular recess (48) formed with a front end open in the front plate (43) of the drum (40). For example, the balancer housing (151) may be accommodated in the annular recess (48) of the drum (40).

[0054] In one example, the balancer housing (151) may be manufactured through injection molding using a plastic material such as polypropylene or ABS resin (Acrylonitrile Butadiene Styrene). In one example, the balancer housing (151) may be manufactured through a method of joining using a heat fusion method.

[0055] According to one example, the washing machine (1) may include a vibration sensor (106). The vibration sensor (106) may be disposed on the outer surface of the drum (40) to detect vibration of the drum (40). For example, the vibration sensor (106) may be disposed at the front and / or rear of the drum (40). Here, the front of the drum (40) may refer to the direction toward the front plate (43), and the rear of the drum (40) may refer to the direction toward the rear plate (44). The vibration sensor (106) may detect vibration while the drum (40) rotates, and the control unit (120) may calculate an eccentricity value of the drum (40) based on the vibration value measured by the vibration sensor (106).

[0056] According to one example, the washing machine (1) can measure the eccentricity value in front of the drum (40) and the eccentricity value in the rear of the drum (40) using one vibration sensor (106). For example, the vibration sensor (106) may be an IMU (Inertial Measurement Unit) sensor (or inertial measurement device). The IMU sensor may be configured to measure acceleration corresponding to linear motion and angular velocity corresponding to rotational motion for each of the x-axis, y-axis, and z-axis. The washing machine (1) can measure vibration values ​​and / or eccentricity values ​​at multiple locations on the drum (40) using one IMU sensor. For example, in the washing machine (1), when the IMU sensor is arranged on the front side of the drum (40), the vibration value and / or eccentricity value in the rear of the drum (40) as well as the front of the drum (40) can be measured / obtained. For example, when an IMU sensor is placed on the rear side of the drum (40), the washing machine (1) can measure / obtain vibration values ​​and / or eccentricity values ​​not only at the rear of the drum (40) but also at the front of the drum (40). For example, when an IMU is placed on the center side of the drum (40), the washing machine (1) can measure vibration values ​​and / or eccentricity values ​​at the front and rear of the drum (40), respectively.

[0057] FIG. 3 is a functional block diagram schematically illustrating the configuration of a washing machine according to one embodiment from the perspective of function and control.

[0058] Referring to FIG. 3, the washing machine (1) may include an input unit (51), as described above with respect to FIG. 1. As described above, the input unit (51) may include any type of user input means for obtaining setting information from a user for controlling the operation of the washing machine (1). Various user inputs obtained through the input unit (51) may be transmitted to the control unit (120) described below. In one example, various user inputs obtained through the input unit (51) may be transmitted to the outside through the communication unit (90) described below, and the present document is not limited thereto.

[0059] In one example, the washing machine (1) may include a communication unit (90) that supports signal transmission and reception with the outside. In one example, the communication unit (90) may receive and / or transmit wired / wireless signals between an external wired / wireless communication system, an external server, and / or other devices according to a predetermined wired / wireless communication protocol. In one example, the communication unit (90) may include one or more modules that connect the washing machine (1) to one or more networks. In one example, the communication unit (90) may include at least one of a mobile communication module, a wired / wireless Internet module, a short-range communication module, and / or a location information module.

[0060] In one example, the mobile communication module may transmit and receive wireless signals with at least one of an external base station, an external terminal, and an external server through a mobile communication network according to any of various communication protocols for mobile communication. The wireless signals may include various types of data signals. In one example, the wireless signals may include voice call signals, video call call signals, and text / multimedia message signals, but this document is not limited thereto.

[0061] In one example, the wired / wireless Internet module may support, but is not limited to, wireless LAN (WLAN), wireless-fidelity (Wi-Fi), Wi-Fi Direct, digital living network alliance (DLNA), wireless broadband (WiBro), world interoperability for microwave access (WiMAX), high speed downlink packet access (HSDPA), high speed uplink packet access (HSUPA), long term evolution (LTE), or long term evolution-advanced (LTE-A). In one example, the wired / wireless Internet module of the communication unit (90) may transmit and receive data according to at least one wired / wireless Internet technology among the Internet technologies not listed above.

[0062] The short-range communication module is for short-range communication, and can support short-range communication using at least one of Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wide Band), ZigBee, NFC (Near Field Communication), Wi-Fi, Wi-Fi Direct, and Wireless USB (Universal Serial Bus) technologies, for example. The short-range communication module can support wireless communication between a washing machine (1) and a wireless communication system, between the washing machine (1) and another device, or between the washing machine (1) and a network in which another device is located, for example, through a short-range wireless communication network.

[0063] The location information module is, for example, a module for obtaining the location of a washing machine (1), and may be a GPS (Global Positioning System) module or a Wi-Fi module. When the washing machine (1) utilizes a GPS module, information regarding the location of the washing machine (1) can be received using signals transmitted from GPS satellites. When the washing machine (1) utilizes a Wi-Fi module, information regarding the location of the washing machine (1) can be received based on information from a wireless AP (Wireless Access Point) that transmits and receives wireless signals with the Wi-Fi module.

[0064] In one example, the communication unit (90) may receive a setting data signal input by a user from the user's mobile terminal in the form of a wireless signal according to a predetermined wireless communication protocol. In one example, the communication unit (90) may receive information and / or commands for controlling the operation of the washing machine (1) from an external server in the form of signals according to a predetermined wired / wireless communication protocol. The communication unit (90) may transmit various received signals to the control unit (120) described below. In one example, the communication unit (90) may transmit various data generated or acquired on the washing machine (1) in the form of wired / wireless signals according to a predetermined wired / wireless communication protocol, for example, to the user's mobile terminal or an external server.

[0065] According to one example, the washing machine (1) may include a sensor unit (100) for detecting the operating status and / or internal environment of the washing machine (1). In one example, the sensor unit (100) may include a water level sensor (101), a current sensor (102), a door sensor (103), a speed sensor (104), and / or a temperature sensor (105), but this is exemplary and the present document is not limited thereto.

[0066] In one example, the water level sensor (101) is a sensor designed to detect the water level within the tub (30). In one example, the water level sensor (101) may be designed to detect the progress of the dehydration process during the dehydration process. The water level sensor (101) may transmit an electrical signal regarding the water level within the tub (30) to the control unit (120).

[0067] The current sensor (102) may be provided to detect the current flowing to the motor (61) of the driving device (60). An electrical signal regarding the current value of the motor (61) generated by the current sensor (102) may be transmitted to the control unit (120).

[0068] A door sensor (103) may be provided to determine whether the door (20) is closed before the control unit (120) performs a washing operation. An electrical signal generated by the door sensor (103) regarding whether the door (20) is open or closed may be transmitted to the control unit (120).

[0069] The speed sensor (104) may be provided to detect the rotational speed, rotational angle, or rotational direction of the motor (61) or the drum (40). In one example, the speed sensor (104) may utilize a method of detecting the position of the rotor and the on / off signal of an adjacent hall sensor while the motor (61) is driven, for example. In one example, the speed sensor (104) may utilize a method of measuring the magnitude of the current applied to the motor (61) while the drum (40) is rotating. An electrical signal regarding the rotational speed, rotational angle, or rotational direction of the drum (40) generated by the speed sensor (104) may be transmitted to the control unit (120).

[0070] The temperature sensor (105) may be configured to detect the ambient temperature of the washing machine (1) or the temperature of internal components, or to detect the temperature of the wash water in the tub (30). The temperature sensor (105) may be implemented, for example, as a thermistor, a type of resistor that utilizes the property of a material's resistance changing depending on temperature. An electrical signal regarding temperature generated by the temperature sensor (105) may be transmitted to the control unit (120).

[0071] The vibration sensor (106) may be provided to detect the vibration amount of the drum (40) while the drum (40) rotates. The vibration sensor (106) may be, for example, an IMU sensor. The vibration sensor (106) may detect the vibration amount of the drum (40) during a spin-drying operation. The vibration-related signal acquired by the vibration sensor (106) may be used to estimate or measure the balance state of the laundry inside the drum (40).

[0072] According to one example, the washing machine (1) may include a control unit (120) that controls the overall operation of the washing machine (1). The control unit (120) may include a memory (122) that stores or memorizes a program and / or data for controlling each component of the washing machine (1), and a processor (121) that generates a control signal for controlling each component of the washing machine (1) according to the program and / or data stored in the memory (122) and information acquired from each of the other components.

[0073] According to one example, the memory (122) can store various data that can be used to control the operation of each component of the washing machine (1). The memory (122) can store, for example, a plurality of application programs used in the washing machine (1), data for controlling the operation of the washing machine (1), and commands. At least some of the application programs stored in the memory (122) can be downloaded from an external server via wireless communication. At least some of the application programs stored in the memory (122) can be stored in the memory (122) from the time of shipment for the basic functions of the washing machine (1). In one example, the memory (122) can store various data that can be used as a reference for the dehydration process to be described later.

[0074] In one example, the processor (121) of the control unit (120) may receive various input / setting information, such as power on / off of the washing machine (1), washing machine operation setting information (e.g., operation start / stop, course selection, time selection, etc.) or other various control information, from the input unit (51) and / or communication unit (90) described above. The processor (121) may obtain various detection information from the sensor unit (100), such as information on the water level in the tub (30) detected by the water level sensor (101), information on the current flowing in the motor (61) detected by the current sensor (102), information indicating whether the door is open or closed detected by the door sensor (103), information on the rotation speed of the motor (61) or drum (40) detected by the speed sensor (104), etc. The processor (121) can estimate or obtain the eccentricity value of the drum (40) by obtaining information indicating the amount of vibration of the drum (40) from, for example, a vibration sensor (106). The processor (121) can estimate or obtain the front eccentricity value of the drum (40) and the rear eccentricity value of the drum (40) by using, for example, one vibration sensor (106).

[0075] In one example, the processor (121) of the control unit (120) may generate an operation control command for each component of the washing machine (1) based on various pieces of information received from the input unit (51), the communication unit (90), and / or the sensor unit (100). In one example, the processor (121) may control each component related to performing at least one of a washing cycle, a rinsing cycle, a dehydration cycle, or a drying cycle. The processor (121) may control the operation of, for example, a driving device (60), a water supply device (70), and / or a drainage device (80) to control the performance of at least one of the washing cycle, the rinsing cycle, the dehydration cycle, or the drying cycle. In one example, the processor (121) may control the operation of the motor (61) of the driving device (60) to rotate the drum (40). For example, the processor (121) can control the opening and closing of the water supply valve (72) of the water supply device (70) to adjust the washing water supplied to the drum (40) and / or the tub (30). For example, the processor (121) can control the drain valve (81) and / or the drain pump (841) of the drain device (80) to drain the washing water in the drum (40) and / or the tub (30). In one example, the processor (121) can continuously obtain information from the input unit (51), the communication unit (90) and / or the sensor unit (100) while performing at least one of the washing cycle, the rinsing cycle, the dehydration cycle or the drying cycle, and can continuously update and control the operation of each component based on the obtained information.

[0076] In one example, the processor (121) of the control unit (120) can generate a command to control whether and how information is displayed through the display unit (52) based on various pieces of information received from the input unit (51), the communication unit (90) and / or the sensor unit (100).

[0077] In this drawing, the control unit (120) is disclosed as a single comprehensive configuration that controls all components included in the washing machine (1), but this document is not limited thereto. In one example, the washing machine (1) may be configured to include a plurality of control unit configurations that individually control some of the components of the washing machine (1). In one example, the washing machine (1) may include a separate control unit having a processor and a memory for controlling the operation of a driving device (60), for example, a motor (61). In one example, the washing machine (1) may include a separate control unit having a processor and a memory for controlling the operation of a user interface according to a user input. The processor (121) of the control unit (120) may include a plurality of processors, and the memory (122) may include a plurality of memory devices. The above description takes a washing machine as an example, but embodiments of the present disclosure may be equally applicable to not only washing machines but also dryers.

[0078] When using a weight sensor to measure the weight of clothing to be washed or dried, it can be difficult to accurately measure the weight of the fabric. To accurately measure the weight of the laundry, the load on the motor of the washing or drying device can be measured to estimate the weight of the laundry. The load on the motor can be measured by measuring the current flowing over a predetermined time period of several seconds. Heavier fabrics generate greater counter-electromotive force, which can increase the average current. However, using only the average current to measure the weight of the laundry can lead to discrepancies with the actual weight of the laundry. This is particularly true in high-load areas, such as when the laundry is relatively heavy or when eccentricity due to the fabric is significant. This can lead to inconsistent current values ​​and significant variations in the average value. Because the system relies on weight sensing technology in washing and drying devices to report the total washing and drying time, significant discrepancies between the actual washing and drying times can cause inconvenience to the user. According to embodiments of the present disclosure, it is possible to provide convenience to users by minimizing errors in cycle time through accurate weight measurement of laundry and dry goods. Embodiments of the present disclosure can accurately measure the weight of laundry and dry goods by additionally calculating not only the average current, but also the standard deviation, maximum current value, and accumulated induction power value (differential accumulated value), and training a machine learning model based on the calculated values. The machine learning model can use the support vector regression (SVR) algorithm, and the SVR algorithm can include an algorithm used to solve regression problems as one of the supervised learning algorithms. In addition, by applying the Gaussian kernel technique, the nonlinear relationship between various current data is converted into a linear regression model with excellent predictive performance, and precise weight detection results can be provided even in the presence of noise.

[0079] Fig. 4 illustrates the operational flow of a device for performing washing or drying according to one embodiment. The device for performing washing or drying may be referred to as an electronic device for convenience of explanation. The electronic device of Fig. 4 may include a device corresponding to the washing machine (1) described in Figs. 1 to 3. In the following description, "laundry" in the following description of Fig. 4 collectively refers to clothing placed in a washing or drying device, and may include both clothing to be washed and clothing to be dried.

[0080] Referring to FIG. 4, at operation 410, in response to identifying that a wash or dry cycle for the laundry has been initiated, the electronic device may measure the current of a motor of the electronic device for a predetermined time period.

[0081] In one embodiment, the predetermined time period may include a certain time period after the washing or drying cycle is initiated. For example, referring to FIG. 6, the predetermined time period may include a time period (603) starting 10 seconds after the washing or drying cycle is initiated (601) and ending 40 seconds after the washing or drying cycle is initiated.

[0082] In one embodiment, the electronic device can measure the current of the motor based on the torque change and back electromotive force of the motor measured by the rotation of the laundry by rotating the motor of the electronic device in reverse.

[0083] In one embodiment, the measured motor current may have a form like the graph shown in FIG. 6, and the current data used to measure the weight of the laundry may include only the current data measured at a predetermined time interval.

[0084] In one embodiment, the electronic device may measure the current of a motor of the electronic device to obtain motor current data. The motor current data may be referred to as a motor current profile.

[0085] According to one embodiment, in operation 420, the electronic device may determine the degree of weight of the laundry as either a first level or a second level based on a current value of the motor and a degree of change in the current for a predetermined time period.

[0086] In one embodiment, the electronic device can identify a current value of the motor and a degree of change in the current based on the motor current data. The current value of the motor can include a value relating to the magnitude of the current measured in the motor. The degree of change in the current of the motor can include a value relating to a degree of change in the current of the motor. For example, the current value of the motor can include an average current value of the current measured in the motor over a predetermined time period. For example, the degree of change in the current of the motor can include a standard deviation value of the current of the motor.

[0087] In one embodiment, the average current value of the motor can be determined based on the following mathematical expression 1.

[0088]

[0089] In the above mathematical expression 1, I avg (t1:t2) may refer to the average current value measured during the period from t1 to t5. t1 may refer to a point in time within a predetermined time period (e.g., 10 seconds in FIG. 6), and t2 may refer to the end point of the predetermined time period (e.g., 40 seconds in FIG. 6). In addition, I(t) may refer to the current value (mA) of the motor measured at time t. N may refer to the number of current values ​​measured during the predetermined time period.

[0090] In one embodiment, the standard deviation value of the motor current can be determined based on the following mathematical expression 2.

[0091]

[0092] In the above mathematical expression 2, I stdev(t1:t2) may refer to the standard deviation value of the current measured during the interval from t1 to t2. t1 may refer to a point in time (e.g., 10 seconds in Fig. 6) of a predetermined time interval, and t2 may refer to the end point of the predetermined time interval (e.g., 40 seconds in Fig. 6). In addition, I(t) may refer to the current value (mA) of the motor measured at time t. I avg (t1:t2) may refer to the average current value measured during the period from t1 to t5. N may refer to the number of current values ​​measured during a predetermined time period.

[0093] In one embodiment, the electronic device may determine the degree of weight of the laundry as the first level when the current value of the motor is less than or equal to a first threshold value and the degree of change in the current of the motor is less than or equal to a second threshold value. For example, the electronic device may determine the weight of the laundry as the first level when the average current value is less than or equal to the first threshold value and the standard deviation value of the current is less than or equal to the second threshold value. The first level may mean that the weight of the laundry is low (low load) and the eccentricity value is small.

[0094] In one embodiment, the electronic device may determine the weight level of the laundry as the second level if the current value of the motor is greater than the first threshold value or if the degree of change in the current of the motor is greater than the second threshold value. In other words, the electronic device may determine the weight level of the laundry loaded into the electronic device as the second level if the weight level is not the first level.

[0095] According to one embodiment, at operation 430, the electronic device may determine the weight of the laundry using a machine learning model trained to predict the weight of the laundry based on data regarding the measured current.

[0096] In one embodiment, a machine learning model may be trained to predict the weight of a load based on data about measured current (e.g., current profile data as described in connection with operation 410).

[0097] In one embodiment, the machine learning model may be trained on an electronic device or a server connected to the electronic device.

[0098] In one embodiment, the machine learning model can be trained using a support vector regression (SVR) algorithm, taking data about measured current as input values.

[0099] In one embodiment, the electronic device can measure a reference average current value. The reference average current value may refer to a current value measured when no laundry is loaded into the electronic device. For example, the electronic device may identify the reference average current value based on a current value measured during a wash or dry cycle (e.g., a cycle for sterilizing the internal space of the electronic device) performed without loading laundry.

[0100] In one embodiment, the data regarding the measured current may include an average current value measured over a predetermined time period, a standard deviation value of the current measured over the predetermined time period. In addition, the data regarding the measured current may include a maximum current value measured over a predetermined time period, a differential cumulative value of the current measured over the predetermined time period, a difference value between the average current value and a reference current value, and a value regarding a ratio of the average current value and the reference current value. Rather than using the average current value as is, the difference can be corrected by using a value obtained by excluding a portion occupied by the reference current value from the average current value as a feature point.

[0101] In one embodiment, the electronic device may determine a differential accumulation value of current based on motor current data. The differential accumulation value may be determined based on the following mathematical expression 3.

[0102]

[0103] t1 can refer to a point in time within a predetermined time interval, and t2 can refer to the end point of the predetermined time interval. △ can refer to a sampling time. I(t) can refer to the current value (mA) of the motor measured at time t. I(t+△) can refer to the current value of the motor measured at time t+△.

[0104] In one embodiment, the electronic device may determine a maximum current value based on motor current data. The maximum current value may refer to the largest current value among current values ​​measured over a predetermined time period.

[0105] In one embodiment, the electronic device can identify a difference value and a ratio value between the average current value and the existing average current value based on the motor current data and the reference average current value.

[0106] In one embodiment, a machine learning model may be trained based on multiple values, i.e., feature points, contained in the data regarding the measured current described above. For example, the machine learning model may be trained based on a single feature point or based on multiple feature points.

[0107] In one embodiment, the machine learning model can be trained using Equations 4 and 5 below. In other words, the machine learning model can estimate a weight based on data regarding the measured current, and the weight can be determined based on Equations 4 and 5 below.

[0108]

[0109]

[0110] In the above mathematical expression 4, W may represent the weight of the laundry. Additionally, x may represent a predictor variable, and x' may represent support vectors. K may represent a Gaussian kernel, and a may represent a constant value determined through learning.

[0111] In the above mathematical expression 5, x may represent a predictor variable, and x' may represent support vectors. K may represent a Gaussian kernel. F may represent a feature point value.

[0112] The embodiments of the present disclosure illustrate a machine learning model trained using the SVR algorithm, but decision tree-based random forests and gradient boosting algorithms can also be used. In this regard, variable tuning may be involved to prevent overfitting.

[0113] In one embodiment, when determining the level of weight of laundry as a first level, the electronic device can determine the weight of the laundry using the average current value as an input value of a machine learning model. In other words, the electronic device can estimate the weight using the SVR algorithm, using the average current value as a single feature point.

[0114] In one embodiment, when determining the weight of laundry as a second level, the electronic device can determine the weight of the laundry using the average current value and the standard deviation of the current as input values ​​for a machine learning model. In other words, the electronic device can estimate the weight using the SVR algorithm, using the average current value and the standard deviation of the current as feature points.

[0115] In one embodiment, when determining the weight of laundry as a second level, the electronic device may determine the weight of the laundry by using the average current value, the standard deviation of the current, and at least one of the characteristic point values ​​constituting the data regarding the current described above as input values ​​of a machine learning model. In other words, the electronic device may estimate the weight using an SVR algorithm based on multiple characteristic points.

[0116] In one embodiment, when determining the weight level of laundry as a second level, the electronic device may select at least two feature points from the measured current data to be used as input values ​​for a machine learning model. The at least two feature points may include an average current value and a standard deviation of the current.

[0117] In one embodiment, if the average current value of the current is less than or equal to the first threshold value but the standard deviation value of the current is greater than the second threshold value, the electronic device can select the average current value, the standard deviation value of the current, and the differential accumulation value from among the data regarding the measured current.

[0118] In one embodiment, when the average current value of the current is greater than the second threshold value and the standard deviation value of the current is greater than the first threshold value, the electronic device can select the average current value, the standard deviation value of the current, and the reference current value from among the data regarding the measured current.

[0119] According to one embodiment, at operation 440, the electronic device may determine a time required to complete a wash or dry cycle based on the determined weight of the laundry.

[0120] In one embodiment, the electronic device may transmit data regarding the current of the electronic device's motor measured in operation 420, information regarding the acquired feature points, and information regarding the weight determined in operation 430 to the server. The server may update the machine learning model based on the data received from the electronic device.

[0121] In one embodiment, the electronic device may request updates to the machine learning model from the server at predetermined intervals.

[0122] In one embodiment, when the electronic device determines that a predetermined cycle has arrived, the electronic device may display a user interface indicating that a machine learning model update is required. The user interface may include information related to the update. For example, it may include an object identifying the update cycle, update details, information about the training data underlying the update, and user input for performing the update.

[0123] According to one embodiment, at operation 450, the electronic device may display the determined time.

[0124] In one embodiment, the electronic device may transmit information regarding the determined time to a server and a user device connected to the electronic device. The user device may display the estimated time information received from the electronic device, and the user may be provided with information regarding the time required to complete a wash or dry cycle.

[0125] In one embodiment, when the electronic device identifies that the time required for a wash or dry cycle has changed based on weight sensing, the electronic device may transmit information regarding the time required for the wash or dry cycle to the user device.

[0126] Fig. 5 illustrates an operational flow of an electronic device according to one embodiment. The electronic device of Fig. 5 may include the washing machine (1) of Figs. 1 to 3 and the washing and drying device of Fig. 4. The operational content of Fig. 5 may include all of the operational content described in Fig. 4.

[0127] Referring to FIG. 5, in operation 510, the electronic device may measure the current of the motor of the electronic device for a predetermined time period. Operation 510 may include all of the operation contents of operation 410 of FIG. 4.

[0128] In one embodiment, the electronic device can identify the motor's rotational speed (revolutions per minute, RPM). For example, the electronic device can identify that the motor's rotational speed is 40 RPM. The electronic device can measure the motor's current based on the identified rotational speed.

[0129] In one embodiment, the electronic device may operate the motor at various rotational speeds. For example, the electronic device may operate the motor at 40 RPM, or may operate the motor at RPMs greater than or less than 40.

[0130] In one embodiment, the electronic device can vary the motor speed within a predetermined time period of a wash or dry cycle. Based on the varied motor speed, the electronic device can obtain new current data and perform the following operations based on the data.

[0131] According to one embodiment, in operation 520, the electronic device may determine whether the average current value is greater than a first threshold value and whether the degree of change in the current is greater than a second threshold value. Operation 520 may include all of the operation contents of operation 420 of FIG. 4.

[0132] In one embodiment, the electronic device can identify a current value of the motor and a degree of change in the current based on the motor current data. The current value of the motor can include a value relating to the magnitude of the current measured in the motor. The degree of change in the current of the motor can include a value relating to a degree of change in the current of the motor. For example, the current value of the motor can include an average current value of the current measured in the motor over a predetermined time period. For example, the degree of change in the current of the motor can include a standard deviation value of the current of the motor.

[0133] In one embodiment, the electronic device may determine the degree of weight of the laundry as the first level when the current value of the motor is less than or equal to a first threshold value and the degree of change in the current of the motor is less than or equal to a second threshold value. For example, the electronic device may determine the weight of the laundry as the first level when the average current value is less than or equal to the first threshold value and the standard deviation value of the current is less than or equal to the second threshold value. The first level may mean that the weight of the laundry is low (low load) and the eccentricity value is small.

[0134] In one embodiment, the electronic device may determine the weight level of the laundry as the second level if the current value of the motor is greater than the first threshold value or if the degree of change in the current of the motor is greater than the second threshold value. In other words, the electronic device may determine the weight level of the laundry loaded into the electronic device as the second level if the weight level is not the first level.

[0135] In one embodiment, if the electronic device determines that the average current value is less than the first threshold value and the degree of change in the current is less than the second threshold value, the electronic device may perform operation 530.

[0136] In one embodiment, if the electronic device determines that the average current value is greater than a first threshold value or that the degree of change in current is greater than a second threshold value, the electronic device may perform operation 550.

[0137] According to one embodiment, in operation 530, the electronic device may determine the weight level of the laundry as a first level. The operation content of operation 530 may include all of the operation content of operation 420 of FIG. 4.

[0138] According to one embodiment, at operation 540, the electronic device may determine the weight of the laundry by inputting the current value as an input value to a machine learning model.

[0139] In one embodiment, at operation 550, the electronic device can determine the degree of weight of the laundry as a second level.

[0140] In operation 560 according to one embodiment, the electronic device may determine the weight of the laundry by inputting at least two of the following into a machine learning model: a maximum current value measured over a predetermined time period, a differential cumulative value of the current measured over a predetermined time period, a reference current value of the motor, information regarding the amount of change in the current of the motor, or a current value of the motor. For example, the electronic device may determine the weight of the laundry by inputting a current value and a degree of change in the current into the machine learning model.

[0141] In one embodiment, the electronic device can determine the weight of the laundry using a machine learning model that is trained to predict the weight of the laundry based on data about the measured current.

[0142] In one embodiment, a machine learning model may be trained to predict the weight of a laundry load based on data about measured current (e.g., current profile data).

[0143] In one embodiment, the machine learning model may be trained on an electronic device or a server connected to the electronic device.

[0144] In one embodiment, the machine learning model can be trained using a support vector regression (SVR) algorithm, taking data about measured current as input values.

[0145] In one embodiment, the electronic device can measure a reference average current value. The reference average current value may refer to a current value measured when no laundry is loaded into the electronic device. For example, the electronic device may identify the reference average current value based on a current value measured during a wash or dry cycle (e.g., a cycle for sterilizing the internal space of the electronic device) performed without loading laundry.

[0146] In one embodiment, the data regarding the measured current may include an average current value measured over a predetermined time period, a standard deviation value of the current measured over the predetermined time period. In addition, the data regarding the measured current may include a maximum current value measured over a predetermined time period, a differential cumulative value of the current measured over the predetermined time period, a difference value between the average current value and a reference current value, and a value regarding a ratio of the average current value and the reference current value. Rather than using the average current value as is, the difference can be corrected by using a value obtained by excluding a portion occupied by the reference current value from the average current value as a feature point.

[0147] In one embodiment, the electronic device can determine a differential accumulated value of current based on motor current data.

[0148] In one embodiment, the electronic device may determine a maximum current value based on motor current data. The maximum current value may refer to the largest current value among current values ​​measured over a predetermined time period.

[0149] In one embodiment, the electronic device can identify a difference value and a ratio value between the average current value and the existing average current value based on the motor current data and the reference average current value.

[0150] In one embodiment, a machine learning model may be trained based on multiple values, i.e., feature points, contained in the data regarding the measured current described above. For example, the machine learning model may be trained based on a single feature point or based on multiple feature points.

[0151] In one embodiment, the machine learning model can be trained using Equations 4 and 5 below. In other words, the machine learning model can estimate weight based on data regarding measured current.

[0152] In one embodiment, when determining the level of weight of laundry as a first level, the electronic device can determine the weight of the laundry using the average current value as an input value of a machine learning model. In other words, the electronic device can estimate the weight using the SVR algorithm, using the average current value as a single feature point.

[0153] In one embodiment, when determining the weight of laundry as a second level, the electronic device can determine the weight of the laundry using the average current value and the standard deviation of the current as input values ​​for a machine learning model. In other words, the electronic device can estimate the weight using the SVR algorithm, using the average current value and the standard deviation of the current as feature points.

[0154] In one embodiment, when determining the weight of laundry as a second level, the electronic device may determine the weight of the laundry by using the average current value, the standard deviation of the current, and at least one of the characteristic point values ​​constituting the data regarding the current described above as input values ​​of a machine learning model. In other words, the electronic device may estimate the weight using an SVR algorithm based on multiple characteristic points.

[0155] In one embodiment, when determining the weight level of laundry as a second level, the electronic device may select at least two feature points from the measured current data to be used as input values ​​for a machine learning model. The at least two feature points may include an average current value and a standard deviation of the current.

[0156] In one embodiment, if the average current value of the current is less than or equal to the first threshold value but the standard deviation value of the current is greater than the second threshold value, the electronic device can select the average current value, the standard deviation value of the current, and the differential accumulation value from among the data regarding the measured current.

[0157] In one embodiment, when the average current value of the current is greater than the second threshold value and the standard deviation value of the current is greater than the first threshold value, the electronic device can select the average current value, the standard deviation value of the current, and the reference current value from among the data regarding the measured current.

[0158] The contents of the above-described operations 520, 530, and 540 may correspond to the operation in which the electronic device determines the degree of weight of the laundry as a first level based on the current value of the motor and the degree of change in the current for a predetermined time period in operation 420 of FIG. 4, and determines the weight by using a single feature point (average current value) as an input value of a machine learning model. In addition, the contents of the operations 520, 550, and 560 may correspond to the operation in which the electronic device determines the degree of weight of the laundry as a second level based on the current value of the motor and the degree of change in the current for a predetermined time period in operation 420 of FIG. 4, and determines the weight by using multiple feature points (average current value, standard deviation value, and values ​​included in data about the measured current (e.g., maximum current value, differential accumulation value, difference value between the average current value and the reference current value)) as input values ​​of a machine learning model.

[0159] Fig. 6 illustrates an example of current measured according to a washing or drying cycle according to one embodiment. Referring to Fig. 6, a time period from 10 seconds to 40 seconds from a cycle start time (601), which is the time period when a washing or drying cycle begins, may correspond to a predetermined time period (603). Based on the current measured in the time period (603), an average current value, a standard deviation of the current, a maximum current value, a differential accumulation value, and a difference value from a reference current may be calculated. These values, i.e., feature points, may be used as input values ​​for a machine learning model to measure the weight of the laundry, depending on whether the weight of the laundry is level 1 or level 2.

[0160] Figure 7 illustrates the weight measurement results of an electronic device according to one embodiment.

[0161] Referring to Figure 7, when measuring the weight of laundry using only a single feature point, a small error occurs when the actual mass of the laundry is light, but the error increases as the weight increases. This is because the measured weight becomes more and more error-prone as the weight increases, and the eccentricity that occurs with a high weight can also cause an error in the measured weight.

[0162] According to an embodiment of the present disclosure, the weight of laundry can be divided into a first level (low load) and a second level (high load), and it can be seen that the mass can be more accurately predicted by using only a single feature point or by using both a single feature point and multiple feature points depending on the level.

[0163] FIG. 8 illustrates an example of an IoT environment including an electronic device according to one embodiment.

[0164] Referring to FIG. 8, the IoT environment may include a server device (801), a user device (803), an AP (805), and an IoT device (807).

[0165] A server device (801) according to one embodiment may refer to a server, cloud server, or cloud server device that is interconnected with a plurality of electronic devices located remotely to support an IoT platform. An IoT network server may refer to any server or device that supports an Internet of Things platform.

[0166] According to one embodiment, a user device (803) may refer to an electronic device carried by a user. For example, the user device (803) may include a mobile computing device such as a smart phone, a tablet PC, a wearable device (e.g., a smart watch), a personal digital assistant (PDA), a laptop computer, a media player, a micro server, a global positioning system (GPS) device, etc.

[0167] According to one embodiment, the AP (805) may serve as a base station in a wireless LAN, thereby connecting a wired network and a wireless network. For example, the AP (805) may serve as a bridge connecting a wireless LAN to which an IoT device (807) is connected and a server device (801). For example, the AP (805) may serve as a bridge connecting an IoT device (807) and a user device (803).

[0168] An IoT device (807) according to one embodiment may include a washing machine and a dryer device. The IoT device (807) may include a device corresponding to the washing machine (1) of FIGS. 1 to 3, the device for performing washing and drying of FIG. 4, and the electronic device of FIG. 5.

[0169] An IoT device (807) according to one embodiment may be connected to a server device (801) in at least one of a wired communication method and a wireless communication method.

[0170] According to one embodiment, an IoT device (807) can be connected to a server device (801) through an AP (805).

[0171] In one embodiment, in order for the IoT device (807) to be connected to the server device (801), technologies such as Wibro (wireless broadband), WiMax (world interoperability for microwave access), CDMA (code division multiple access), WCDMA (wideband CDMA), 3G (third generation), 4G (fourth generation), LTE (long term evolution), LTE-A, 5G (fifth generation), NR (new radio), near field communication (NFC), Bluetooth, WLAN (wireless local access network), WiFi, etc. may be used.

[0172] In one embodiment, the IoT device (807) may transmit data regarding the current measured in FIGS. 4 and 5 to the server device (801) via the AP (805). For example, the IoT device (807) may transmit information regarding the average current value, the standard deviation value of the current, the maximum current value, the minimum current value, the differential accumulation value, the reference current value, the difference between the average current value and the reference current value, and the ratio between the average current value and the reference current value, for the current measured in a predetermined time period, to the server device (801). For example, the IoT device (807) may transmit information regarding the measured weight and the time calculated based on the measured weight to the server device (801).

[0173] In one embodiment, the server device (801) can update the machine learning model based on data received from the IoT device (807).

[0174] In one embodiment, the server device (801) may transmit information about an updated machine learning model to the IoT device (807).

[0175] In one embodiment, the IoT device (807) may request an update of the machine learning model from the server device (801). For example, if it is determined that a predetermined period for update has elapsed, the IoT device (807) may request an update of the machine learning model for estimating the weight of laundry from the server device (801).

[0176] In one embodiment, the IoT device (807) may transmit information regarding the estimated time of a wash or dry cycle calculated based on the measured weight to the user device (803). For example, information regarding the end time of the calculated wash or dry cycle, and if the end time changes, information regarding the changed end time, may be transmitted to the user device (803).

[0177] In one embodiment, a method of operating an electronic device for performing washing or drying may include, in response to identifying that a washing or drying cycle for laundry is initiated, measuring a current of a motor of the electronic device for a predetermined time period. The method of operating the electronic device may include, based on a current value of the motor and a degree of change in the current for the predetermined time period, determining a weight of the laundry as either a first level or a second level. The method of operating the electronic device may include, in response to determining the weight of the laundry as the first level, inputting a current value of the motor as an input value to a machine learning model trained to predict a weight of the laundry based on the current profile information, thereby determining a weight of the laundry. The method of operating the electronic device may include, in response to determining the weight of the laundry as the second level, inputting at least two of a maximum current value measured during the predetermined time period, a differential cumulative value of the current measured during the predetermined time period, a reference current value of the motor, information regarding a change amount of the current of the motor, or a current value of the motor as input values ​​to the machine learning model to determine the weight of the laundry. The method of operating the electronic device may include, based on the determined weight of the laundry, an operation of determining a time required for the washing or drying cycle to be completed. The method of operating the electronic device may include an operation of displaying the determined time.

[0178] According to one embodiment, the method of operating the electronic device may include an operation of determining the weight of the laundry as the second level when the current value of the motor is greater than or equal to a first threshold value and the degree of change in the current is greater than or equal to a second threshold value.

[0179] According to one embodiment, the machine learning model can be trained based on a support vector regression (SVR) algorithm.

[0180] According to one embodiment, a method of operating an electronic device may include an operation of identifying a revolutions per minute (RPM) of the motor, an operation of measuring a current of the motor based on the revolutions, and an operation of determining an average current value of the motor for the predetermined time period as the current value of the motor.

[0181] According to one embodiment, the current value of the motor may include an average current value of the motor for the predetermined time period, and the degree of change in the current may include a standard deviation value of the current of the motor for the predetermined time period.

[0182] According to one embodiment, a method of operating an electronic device may include measuring a reference current of the motor in response to identifying that a wash or dry cycle is initiated without laundry, and determining a weight of the laundry based on the reference current.

[0183] According to one embodiment, in response to determining the weight of the laundry as the second level, the operation of determining the weight of the laundry may include: selecting at least one value among the maximum current value, the differential accumulation value, or the reference current value, and determining the weight of the laundry using the selected value, information about the amount of change in the current, and the current value of the motor as input values.

[0184] According to one embodiment, a method of operating an electronic device may include transmitting data regarding the measured current of the motor to a server.

[0185] According to one embodiment, the method of operating the electronic device may include transmitting an update request of the machine learning model to a server at predetermined intervals.

[0186] According to one embodiment, a method of operating an electronic device may include receiving an updated machine learning model from a server.

[0187] According to one embodiment, a method of operating an electronic device may include, in response to identifying that the predetermined period has arrived, displaying a user interface including information for updating the machine learning model.

[0188] According to one embodiment, a method of operating an electronic device may include transmitting information regarding a time required for the washing or drying cycle to be completed to a user device.

[0189] According to one embodiment, an electronic device may include a memory storing at least one program; and at least one processor electrically connected to the memory and configured to execute at least one instruction of the program stored in the memory. The at least one processor may measure a current of a motor of the electronic device for a predetermined time period in response to identifying that a washing or drying cycle for laundry is initiated. The at least one processor may determine a weight of the laundry as either a first level or a second level based on a current value of the motor and a degree of change in the current for the predetermined time period. The at least one processor may determine a weight of the laundry by inputting the current value of the motor as an input value to a machine learning model trained to predict a weight of the laundry based on the current profile information in response to determining the weight of the laundry as the first level. The at least one processor may determine the weight of the laundry by inputting at least two of the following into the machine learning model: a maximum current value measured during the predetermined time period, a differential cumulative value of the current measured during the predetermined time period, a reference current value of the motor, information about the amount of change in the current of the motor, or a current value of the motor, in response to determining the weight of the laundry as the second level. The at least one processor may determine a time required for the washing or drying cycle to be completed based on the determined weight of the laundry. The at least one processor may be configured to display the determined time.

[0190] According to one embodiment, the at least one processor may determine the weight of the laundry as the second level when the current value of the motor is greater than or equal to a first threshold value and the degree of change in the current is greater than or equal to a second threshold value.

[0191] According to one embodiment, the machine learning model can be trained based on a support vector regression (SVR) algorithm.

[0192] According to one embodiment, the at least one processor can identify a revolutions per minute (RPM) of the motor, measure a current of the motor based on the revolutions per minute (RPM), and determine an average current value of the motor over the predetermined time period as the current value of the motor.

[0193] According to one embodiment, the current value of the motor may include an average current value of the motor for the predetermined time period, and the degree of change in the current may include a standard deviation value of the current of the motor for the predetermined time period.

[0194] In one embodiment, the at least one processor may measure a reference current of the motor in response to identifying that a wash or dry cycle is initiated without laundry, and determine a weight of the laundry based on the reference current.

[0195] According to one embodiment, in response to determining the weight of the laundry as the second level, determining the weight of the laundry may include selecting at least one of the maximum current value, the differential accumulation value, or the reference current value, and determining the weight of the laundry using the selected value, information about the amount of change in the current, and the current value of the motor as input values.

[0196] According to one embodiment, the at least one processor can transmit data regarding the measured motor current to a server.

[0197] According to one embodiment, the at least one processor can generate a value relating to a maximum current value measured during the predetermined time period, a differential cumulative value of the current measured during the predetermined time period, a difference value between the average current value and the reference current value, and a ratio of the average current value and the reference current value.

[0198] According to one embodiment, the at least one processor can estimate weight using an SVR algorithm based on multiple feature points.

[0199] According to one embodiment, one or more non-transitory computer-readable storage media are provided storing one or more computer programs, one or more computer programs comprising computer-executable instructions that, when executed individually or collectively by one or more processors of an electronic device, cause the electronic device to perform operations. The above operations include, in response to identifying that a washing or drying cycle for laundry is initiated, measuring, by the electronic device, a current of a motor of the electronic device for a predetermined time period; determining, by the electronic device, a weight of the laundry as either a first level or a second level based on a current value of the motor and a degree of change in the current for the predetermined time period; determining, by the electronic device, a weight of the laundry as the first level, using the current value of the motor as an input value to a machine learning model trained to predict the weight of the laundry based on the current profile information; determining, by the electronic device, a weight of the laundry as an input value to the machine learning model, using at least two of a maximum current value measured for a predetermined time period, a differential cumulative value of the current measured for the predetermined time period, a reference current value of the motor, information regarding an amount of change in the current of the motor, or a current value of the motor as input values ​​to the machine learning model; and determining, by the electronic device, a weight of the laundry based on the determined weight of the laundry. The operation includes determining a time required for the washing or drying cycle to be completed, and displaying the determined time by an electronic device.

[0200] According to one embodiment, if the current value of the motor is greater than or equal to a first threshold value and the degree of change in the current of the motor is greater than or equal to a second threshold value, the weight of the laundry can be determined as a second level.

[0201] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, washing machines and dryers.

[0202] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0203] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more commands stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., processor (121)) of a device (e.g., electronic device (101)) can call at least one command among one or more commands stored from a storage medium and execute it. This enables the device to operate to perform at least one function according to the called at least one command. The one or more commands may include code generated by a compiler or code executable by an interpreter. A storage medium readable by the device may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not include a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily in the storage medium.

[0204] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or may be provided through an application store (e.g., Play Store). TM) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0205] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0206] While the present disclosure has been illustrated and described with reference to various embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents.

Claims

1. A method for washing or drying performed by an electronic device, In response to identifying that a wash or dry cycle for the laundry has been initiated, the act of measuring, by the electronic device, current in a motor of said electronic device for a predetermined period of time; An operation of determining the weight of the laundry as either a first level or a second level based on the current value of the motor and the degree of change in the current for the predetermined time period by an electronic device; An operation of determining the weight of the laundry by inputting the current value of the motor as an input value to a machine learning model that is trained to predict the weight of the laundry based on the current profile information, in response to determining the weight of the laundry as the first level by an electronic device; An operation of determining the weight of the laundry by an electronic device, in response to determining the weight of the laundry as the second level, by inputting at least two of the maximum current value measured in the predetermined time period, the differential cumulative value of the current measured in the predetermined time period, the reference current value of the motor, information about the amount of change in the current of the motor, or the current value of the motor as input values ​​to the machine learning model. An operation of determining, by an electronic device, the time required for the washing or drying cycle to be completed based on the determined weight of the laundry, and A method comprising an action of displaying the determined time by an electronic device.

2. In claim 1, A method including an operation of determining the weight of the laundry as the second level when the current value of the motor is greater than or equal to a first threshold value and the degree of change in the current is greater than or equal to a second threshold value.

3. In any one of claims 1 to 2, An action to identify the rotational speed (revolutions per minute, RPM) of the above motor, An operation of measuring the current of the motor based on the rotational speed, and A method comprising an operation of determining an average current value of the motor for the predetermined time period as a current value of the motor.

4. In claim 1, The current value of the above motor includes the average current value of the motor for the above predetermined time period, A method wherein the degree of change in the current includes a standard deviation value of the current of the motor for the predetermined time period.

5. In claim 4, The operation of measuring the reference current of said motor in response to identifying that a wash or dry cycle has been initiated without laundry; and A method comprising an operation of determining the weight of the laundry based on the reference current.

6. In claim 1, In response to determining the weight of the laundry as the second level, the operation of determining the weight of the laundry is: An operation of selecting at least one of the maximum current value, the differential cumulative value, or the reference current value, and A method comprising an operation of determining the weight of the laundry by using the selected value, information about the amount of change in the current, and the current value of the motor as input values.

7. In claim 1, An operation of sending an update request of the machine learning model to the server at predetermined intervals; and A method comprising the action of receiving an updated machine learning model from a server.

8. In claim 1, A method comprising: in response to identifying that the predetermined period has arrived, displaying a user interface including information for updating the machine learning model.

9. In electronic devices, A memory storing one or more computer programs; and comprising one or more processors communicatively connected to said memory; The one or more computer programs comprise computer-executable instructions, which, when executed individually or collectively by the one or more processors, cause the electronic device to: In response to identifying that a wash or dry cycle for the laundry has been initiated, measuring the current of the motor of said electronic device for a predetermined period of time; Based on the current value of the motor and the degree of change in the current for the predetermined time period, the weight of the laundry is determined as either the first level or the second level, In response to determining the weight of the laundry as the first level, the current value of the motor is input as a machine learning model that is trained to predict the weight of the laundry based on the current profile information, thereby determining the weight of the laundry. In response to determining the weight of the laundry as the second level, the machine learning model determines the weight of the laundry by inputting at least two of the maximum current value measured during the predetermined time period, the differential cumulative value of the current measured during the predetermined time period, the reference current value of the motor, information about the amount of change in the current of the motor, or the current value of the motor as input values. Based on the weight of the laundry determined above, the time required to complete the washing or drying cycle is determined, and An electronic device causing the display of the above-determined time.

10. In claim 9, the one or more computer programs further comprise computer-executable instructions, which, when executed individually or collectively by the one or more processors, cause the electronic device to: An electronic device that causes the weight of the laundry to be determined as the second level when the current value of the motor is greater than or equal to a first threshold value and the degree of change in the current is greater than or equal to a second threshold value.

11. In any one of claims 9 to 10, the one or more computer programs further comprise computer-executable instructions, which, when executed individually or collectively by the one or more processors, cause the electronic device to: Identify the rotational speed (revolutions per minute, RPM) of the above motor, Measure the current of the motor based on the above rotational speed, An electronic device that causes an average current value of said motor for said predetermined time period to be determined as a current value of said motor.

12. In claim 9, The current value of the above motor includes the average current value of the motor for the above predetermined time period, An electronic device, wherein the degree of change in the current includes a standard deviation value of the current of the motor for the predetermined time period.

13. In claim 12, the one or more computer programs further comprise computer-executable instructions, which, when executed individually or collectively by the one or more processors, cause the electronic device to: In response to identifying that a wash or dry cycle is initiated without laundry, the reference current of said motor is measured, An electronic device configured to determine the weight of the laundry based on the reference current.

14. In claim 9, In response to determining the weight of said laundry as the second level, determining the weight of said laundry: selecting at least one of the maximum current value, the differential cumulative value, or the reference current value, and An electronic device comprising: determining the weight of the laundry by using the selected value, information about the amount of change in the current, and the current value of the motor as input values.

15. In claim 9, the one or more computer programs further comprise computer-executable instructions, which, when executed individually or collectively by the one or more processors, cause the electronic device to: An electronic device configured to transmit data regarding the measured current of the motor to a server.

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