KR20210001723A - Artificial Intelligence Washing machine and Method for controlling the same - Google Patents

Abstract

The present invention relates to a washing machine and a method for controlling the same. According to one embodiment of the present invention, the washing machine comprises: a water tank; a washing tub storing laundry and provided to be rotatable inside the water tank; a motor rotating the washing tub; and a control unit sensing amounts of the laundry stored in the washing tub at different time and determining a moisture content rate by using the sensed amounts of the laundry. The control unit determines an agglomeration degree of the laundry by using multiple types of data measured during rotation of the washing tub. Moreover, the control unit determines the maximum rotation speed of the washing tub and dehydration time in a dehydration process based on the determined moisture content rate and agglomeration degree.

Description

Artificial Intelligence Washing machine and Method for controlling the same}

The present invention relates to a washing machine to which artificial intelligence is applied and a control method of the washing machine, and more particularly, to a washing machine capable of performing artificial intelligence dehydration based on machine learning and a control method of the washing machine.

In general, a washing machine is a device that separates contaminants from laundry such as clothes and bedding (hereinafter, referred to as'cloth') by using physical actions such as chemical decomposition between water and detergent and friction between water and laundry. Collectively.

Washing machines are largely divided into agitation type, vortex type and drum type washing machine. Among them, the drum type washing machine includes a water storage tank (or tub) containing water, and a washing tank (or drum) rotatably provided in the storage tank to receive laundry.

A plurality of through holes through which water passes are formed in the washing tub (or drum). The washing operation is generally divided into a washing operation, a rinsing operation and a dehydration operation. The progress of this process can be checked through a control panel (or display) provided outside the washing machine.

The washing process removes contaminants from the laundry by the friction of the water stored in the water storage tank and the fabric stored in the washing tank, and the chemical action of the detergent stored in the water.

The rinse cycle is to rinse the cloth by supplying water in which the detergent is not dissolved into the storage tank, and in particular, the detergent absorbed (or smeared) in the cloth during the washing cycle is removed. During the rinse cycle, a fabric softener may be supplied with water.

The spin-drying process is to spin the laundry tank at high speed after the rinsing cycle is completed to dehydrate the fabric. Typically, by completing the spin-drying cycle, all operations of the washing machine are terminated. On the other hand, in the case of a washing machine for both drying, a drying cycle may be added after the spin-drying cycle.

In general, the washing operation is set to operate under different conditions depending on the amount of laundry injected into the washing tank (hereinafter, also referred to as'bag amount'). For example, settings such as water supply level, washing intensity, draining time, and spinning time may vary depending on the amount of cloth.

On the other hand, the laundry performance varies depending on the type of laundry as well as the amount of laundry, and there is a problem that sufficient laundry performance cannot be expected when only the amount of laundry is considered when setting the washing operation.

Prior literature (Registration Patent Publication No. 1994-0008628 (1994.09.24.)) discloses a configuration for detecting the amount / quality of the washing machine. Specifically, the prior literature discloses a technique of detecting the average value of the current value as the cloth amount and the deviation of the current value with cloth.

However, the prior literature is only for the purpose of reducing the error of the conventional fabric weight and fabric detection.

That is, the prior literature does not disclose a technique for detecting the degree of agglomeration of the fabric, and does not disclose a configuration in which the maximum rotational speed and the spinning time of the washing tub in the dewatering process are varied according to the degree of agglomeration of the fabric.

Accordingly, the prior literature does not disclose a configuration for artificially intelligently setting the maximum rotational speed and the spin-drying time in the spin-drying process in consideration of not only the moisture content but also the degree of foam aggregation.

Recently, technology development to improve dehydration performance by discharging the moisture contained in the fabric as much as possible in the dehydration process has been actively progressed.

Meanwhile, in recent years, interest in machine learning such as artificial intelligence and deep learning has been greatly increased.

Conventional machine learning centered around classification, regression, and cluster models based on statistics. In particular, in the supervised learning of classification and regression models, humans have previously defined the characteristics of the learning data and a learning model that distinguishes new data based on these characteristics. In contrast, deep learning is where computers find and discriminate features themselves.

One of the factors that accelerated the development of deep learning is the deep learning framework provided as open source. For example, deep learning frameworks include Theano from the University of Montreal in Canada, Torch from New York University in the US, Caffe from the University of California, Berkeley, and TensorFlow from Google.

With the release of deep learning frameworks, for effective learning and recognition, in addition to deep learning algorithms, extraction and selection of learning processes, learning methods, and data used for learning are becoming more important.

In addition, research to use artificial intelligence and machine learning in various products and services is increasing.

In addition, by applying artificial intelligence and machine learning to the dehydration administration, the development to perform the optimized dehydration administration is actively progressing.

An object of the present invention is to provide a washing machine capable of improving dehydration performance and a control method of the washing machine in order to solve the above problems.

An object of the present invention is to provide a washing machine and a control method of a washing machine capable of performing an optimized dehydration process to shorten the drying time as much as possible, taking into account the moisture content of the washed cloth and the degree of agglomeration of the cloth. .

An embodiment of the present invention is a washing machine and a control method of a washing machine capable of minimizing the probability of occurrence of a short circuit during the spinning process by differently setting the maximum rotation speed and spinning time of the washing tub according to the degree of fabric aggregation, even if the laundry has the same moisture content. There is a purpose to provide.

The problems of the present invention are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, a washing machine according to an embodiment of the present invention includes a storage tank, a laundry tank that accommodates a cloth, and is rotatably provided in the storage tank, a motor that rotates the washing tank, and the amount of cloth contained in the laundry tank. It detects at different times and includes a control unit that determines the moisture content by using the detected amount of cloth, and the control unit determines the degree of fabric aggregation using a plurality of types of data measured while rotating the washing tub, and It is characterized in that the maximum speed and spin time at which the washing tub rotates in the spin-drying process are determined based on the moisture content and the degree of agglomeration.

In an embodiment, the control unit measures a first amount of cloth accommodated in the washing tub before water is supplied, measures a second amount of cloth stored in the washing tub after discharging the water, and measures the amount of the first cloth. And determining the moisture content of the fabric accommodated in the washing tub by using the second fabric amount.

In an embodiment, the controller may measure the plurality of types of data while the washing tub is rotated at the specific rotational speed so that the cloth rises by a predetermined height and then falls in a rinsing process performed before the spinning process. .

In an embodiment, the control unit is characterized in that by inputting the measured plurality of types of data as input values of a pre-learned artificial neural network, and outputting the degree of foaming as a result value.

In an embodiment, the control unit is characterized in that to determine any one of the degree of foaming classified into a plurality of pieces through the previously learned artificial neural network.

In an embodiment, the control unit is characterized in that even if the same moisture content is determined, the maximum speed and the spin-drying time are differently set according to the determined degree of foam aggregation.

In an embodiment, the control unit may set the maximum speed to be higher and the spin-drying time to be longer as the degree of lumping of the fabric decreases.

In an embodiment, the control unit sets the maximum speed higher than in the third step in which the degree of fabric agglomeration is higher than in the second step, and lengthens the spin-drying time when the degree of fabric agglomeration is determined as the second step. Set, and when the degree of fabric agglomeration is determined to be a first step with a degree of fabric aggregation smaller than that of the second step, the maximum speed is set higher than that in the second step, and the dehydration time is lengthened. It is characterized by setting.

In an embodiment, the control unit may set the maximum speed and the spin-drying time differently according to the moisture content, even if the same degree of agglomeration is detected.

In an embodiment, the control unit is characterized in that as the moisture content decreases, the maximum speed is set higher and the spin-drying time is set longer.

In an embodiment, the control unit is characterized in that when the difference between the second cloth amount and the first cloth amount is smaller than a preset value, the control unit maintains the maximum speed and the spin-drying time at a previously set value.

In an embodiment, the control unit, when the difference between the second cloth amount and the first cloth amount is greater than a preset value, detects a moisture content, detects a degree of agglomeration, and determines the moisture content and the degree of agglomeration. Based on it, it is characterized in that the maximum speed and the spin-drying time are set.

A method of controlling a washing machine according to an embodiment of the present invention includes the steps of detecting the amount of cloth accommodated in the washing tub at different times and determining the moisture content using the sensed cloth amount values, and a plurality of values measured while rotating the washing tub. And determining a degree of agglomeration using data of the type, and determining a maximum speed and a spinning time at which the washing tub rotates during the spinning process, based on the determined moisture content and the degree of fabric agglomeration.

In an embodiment, in the determining of the maximum speed and the spin time, even if the same moisture content is determined, the maximum speed and the spin time are set differently according to a determined degree of agglomeration of the fabric.

In an embodiment, in the determining of the maximum speed and the spin time, the maximum speed and the spin time are differently set according to a moisture content even when the same degree of agglomeration is detected.

Details of other embodiments are included in the detailed description and drawings.

According to an embodiment of the present invention, one or more of the following effects are provided.

First, the present invention provides a new dewatering control method capable of exhibiting maximum dehydration performance for each unbalanced state by determining the maximum rotational speed and dehydration time of the washing tub in the dehydration process by considering not only the moisture content but also the degree of fabric aggregation. I can.

Second, in the present invention, the lower the moisture content is, the less the probability of occurrence of unbalance (or the degree to which unbalance occurs) due to high rotational dehydration of the washing tub in the dehydration process, so the maximum rotational speed is increased and the dehydration time is set longer, so that the dehydration performance It can shorten the drying time later by increasing it.

Third, in the present invention, even with the same moisture content, the smaller the degree of agglomeration of the fabric, the less the probability of occurrence of unbalance (or the degree of occurrence of unbalance) due to high rotational dehydration of the washing tub in the dehydration process. By setting it long, the dehydration performance can be improved, and thus the drying time can be significantly reduced later.

The effects of the present invention are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a side cross-sectional view of a washing machine according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a control relationship between main components of the washing machine of FIG. 1.
3 is a flowchart illustrating a typical control method of the present invention.
4, 5, 6, and 7 are conceptual diagrams for explaining the control method described in FIG. 3.

Hereinafter, exemplary embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but identical or similar elements are denoted by the same reference numerals regardless of reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used interchangeably in consideration of only the ease of preparation of the specification, and do not have meanings or roles that are distinguished from each other by themselves. In addition, in describing the embodiments disclosed in the present specification, when it is determined that a detailed description of related known technologies may obscure the subject matter of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed in the present specification is not limited by the accompanying drawings, and all modifications included in the spirit and scope of the present invention It should be understood to include equivalents or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various elements, but the elements are not limited by the terms. These terms are used only for the purpose of distinguishing one component from another component.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

Singular expressions include plural expressions unless the context clearly indicates otherwise.

In the present application, terms such as "comprises" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

1 is a cross-sectional side view of a washing machine according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a control relationship between main components of the washing machine of FIG. 1.

Referring to FIG. 1, a washing machine according to an embodiment of the present invention includes a casing 1 forming an exterior, a storage tank 3 (or a tub) disposed in the casing 1 and storing washing water, It is installed in the water storage tank 3 so as to be rotatable and includes a washing tub 4 into which laundry is injected, and a motor 9 rotating the washing tub 4.

The washing tub 4 includes a front cover 41 having an opening for entering/exiting laundry, a cylindrical drum 42 that is disposed approximately horizontally and has a front end coupled to the front cover 41, and a rear end of the drum 42. It includes a rear cover 43 to be combined. The rotating shaft of the motor 9 may pass through the rear wall of the water storage tank 3 and be connected to the rear cover 43. A plurality of through holes may be formed in the drum 42 so that water can be exchanged between the washing tank 4 and the water storage tank 3.

A lifter 20 may be provided on the inner circumferential surface of the drum 42. The lifter 20 has a shape protruding from the inner circumferential surface of the drum 42 and extends long in the longitudinal direction (front and rear direction) of the drum 42, and a plurality of lifters may be spaced apart in the circumferential direction. When the washing tub 4 is rotated, the fabric may be scooped up by the lifter 20.

Although not necessarily limited thereto, the height of the lifter 20 protruding from the drum 42 may be preferably 30 mm (or 6.0% of the drum diameter) or less, and more preferably 10 to 20 mm. In particular, when the height of the lifter 20 is 20 mm or less, even if the washing tub 4 is continuously rotated in one direction at approximately 80 rpm, the fabric may flow without sticking to the washing tub 4. That is, when the washing tub 4 is rotated in one direction more than one rotation, the cloth located at the bottom of the washing tub 4 rises to a predetermined height by the rotation of the washing tub 4 and then separates from the washing tub 4 and falls. Can be.

The washing tub 4 is rotated about a horizontal axis. Here, "horizontal" does not mean a geometric horizontal in a strict sense, and it is a case closer to horizontal than vertical even when inclined at a predetermined angle with respect to the horizontal as shown in FIG. 1. It is assumed that it is rotated around a horizontal axis.

A laundry inlet is formed in the front of the casing 1, and a door 2 for opening and closing the laundry inlet is rotatably provided in the casing 1. A water supply valve 5, a water supply pipe 6, and a water supply hose 8 may be installed inside the casing 1. When the water supply valve 5 is opened to supply water, the washing water that has passed through the water supply pipe 6 is mixed with the detergent in the dispenser 7 and then can be supplied to the water storage tank 3 through the water supply hose 8.

The input port of the pump 11 is connected to the water storage tank 3 by a discharge hose 10, and the discharge port of the pump 11 is connected to the drain pipe 12. Water discharged from the water storage tank 3 through the discharge hose 10 is pumped by the pump 11 and flows along the drain pipe 12, and then discharged to the outside of the washing machine.

2, a washing machine according to an embodiment of the present invention includes a control unit 60 for controlling the overall operation of the washing machine, a motor driving unit 71, an output unit 72, and a communication unit controlled by the control unit 60. (73), a speed sensing unit 74, a current sensing unit 75, a vibration sensing unit 76, a UB sensing unit 77, and a memory 78.

The controller 60 may control a series of washing processes such as washing, rinsing, spinning, and drying. The control unit 60 may perform washing, rinsing, spinning and drying processes according to a preset algorithm, and the control unit 60 may control the motor driving unit 71 according to the algorithm.

The motor driving unit 71 may control driving of the motor 9 in response to a control signal applied from the control unit 60. The control signal may be a signal that controls a target speed, an acceleration slope (or acceleration), a driving time, and the like of the motor 9.

The motor driving unit 71 is for driving the motor 9 and may include an inverter (not shown) and an inverter control unit (not shown). In addition, the motor driving unit 71 may be a concept that further includes a converter or the like that supplies DC power input to the inverter.

For example, when the inverter control unit (not shown) outputs a pulse width modulation (PWM) type switching control signal to the inverter (not shown), the inverter (not shown) performs a high-speed switching operation to supply AC power with a predetermined frequency. It can be supplied to the motor 9.

In this specification, explaining that the control unit 60 controls the motor 9 in a specific manner means that the control unit 60 applies a control signal to the motor driving unit 71 so that the motor 9 is controlled in a specific manner. , It may mean that the motor driver 71 controls the motor 9 in the specific manner based on the control signal. Here, the specific method may include various embodiments described in the present specification.

The speed sensing unit 74 detects the rotational speed of the washing tub 4. The speed detection unit 74 may detect the rotational speed of the rotor of the motor 9. When a planetary gear train for rotating the washing tub 4 by converting the rotation ratio of the motor 9 is provided, the rotational speed of the washing tub 4 is the rotational speed of the rotor detected by the speed sensing unit 74. It may be a value converted by considering the deceleration or speed increase ratio of the planetary gear train.

The control unit 60 may control the motor driving unit 71 so that the motor 9 follows a preset target speed by using the rotational speed of the washing tub transmitted from the speed sensing unit 74 as feedback. In other words, the control unit 60 may control the motor 9 so that the rotational speed of the washing tub reaches the target speed.

The current sensing unit 75 may sense a current applied to the motor 9 (or an output current flowing through the motor 9) and transmit it to the controller 60. The control unit 60 may detect the amount of fabric and the degree of foaming by using the received current.

In this case, the current values include values obtained during a process in which the washing tub 4 is accelerated toward a target speed (or a process in which the motor 9 is accelerated toward a preset target speed).

When the rotation of the motor 9 is controlled by vector control based on torque current and magnetic flux current, the current may be a component of the torque axis (q axis) of the current flowing through the motor circuit, that is, the torque current Iq. have.

The vibration detection unit 76 serves to detect vibration generated in the water storage tank 3 (or washing machine) by the rotation of the washing tank 4 containing the cloth.

The washing machine according to an embodiment of the present invention may include a vibration sensor (or vibration measuring sensor). The vibration sensor may be provided at a point of the washing machine, for example, may be provided at a point of the water storage tank 3. For example, the vibration sensor may be included in the vibration detection unit 76.

The vibration detection unit 76 may receive a vibration value (or vibration signal) measured by a vibration sensor and transmit the vibration value (or vibration signal) to the control unit 60. In addition, the vibration detection unit 76 may calculate a vibration value (or vibration magnitude) of the water storage tank 3 (or washing machine) by using a vibration signal measured by a vibration sensor.

Meanwhile, the present invention may further include a UB detection unit 77. The UB sensing unit 77 may detect an eccentric amount (shake amount) of the washing tub 4, that is, an unbalance (UB) of the washing tub 4. The UB sensing unit 77 may calculate an UB value numerically representing the shaking of the washing tub 4.

A description of the UB detection unit 77 will be described later in more detail.

The speed sensing unit 74, the current sensing unit 75, the vibration sensing unit 76, and the UB sensing unit 77 provided in the washing machine according to an embodiment of the present invention may be referred to as a sensing unit, and the sensing It can be understood as a concept included in wealth.

In addition, in the sensing unit, the rotational speed value (or speed value) of the washing tub 4 measured by the speed sensing unit 74, the current value applied to the motor 9 measured by the current sensing unit 75, and vibration A plurality of types of data (signal, information) including at least one of the vibration value of the storage tank 3 measured by the sensing unit 76 and the shaking value (UB value) of the washing tub 4 measured by the UB sensing unit 77 ) Can be measured (calculated).

The plurality of types of data may mean data related to UB (unbalance) of the washing tub 4, data for measuring UB of the washing tub 4, data generated by rotation of the washing tub 4, and the like. The plurality of types of data may be used to control the washing tub 4 in the spin-drying cycle.

For example, the plurality of types of data may be input as an input value of an artificial neural network learned through machine learning, and may be used to calculate the degree of foaming as an output value.

Meanwhile, in the drawings, the speed sensing unit 74, the current sensing unit 75, the vibration sensing unit 76, and the UB sensing unit 77 are provided separately from the control unit 60, but are not limited thereto.

At least one of the speed sensing unit 74, the current sensing unit 75, the vibration sensing unit 76, and the UB sensing unit 77 may be provided in the control unit 60. In this case, the function/operation/control method performed by the speed detection unit 74, the current detection unit 75, the vibration detection unit 76, and the UB detection unit 77 are performed by the control unit 60. I can.

When the vibration detection unit 76 is included in the control unit 60 or is performed by the control unit 60, the vibration sensor is not included in the vibration detection unit 76, but is provided separately at a point of the washing machine. Can be understood.

The control unit 60 may include a cloth/packing degree learning module (not shown) and a cloth/packing degree recognition module (not shown). The packing quantity/packing degree learning module uses a plurality of types of data detected by at least one of the speed detection unit 74, the current detection unit 75, the vibration detection unit 76, and the UB detection unit 77. I can run. Through such machine learning, the fabrication/packaging degree learning module may update the previously learned artificial neural network stored in the memory 78.

As a learning method of the fabrication/packaging degree learning module (not shown), at least one of unsupervised learning and supervised learning may be included.

The fabrication/foaming degree recognition module (not shown) may determine a level according to the fabrication and a level according to the degree of foaming based on data (or artificial neural network) learned by the fabrication/foaming degree learning module.

The determination of the amount of cloth may be an operation of classifying the cloth put in the washing tub 4 into a plurality of preset levels of cloth according to the weight (load).

Determination of the degree of foaming may be an operation of classifying the fabric input into the washing tub 4 into a plurality of pre-set foaming degree levels (or stages, classes) according to the degree of aggregation.

The laundry/packing degree recognition module is based on a plurality of types of data obtained from at least one of the speed detection unit 74, the current detection unit 75, the vibration detection unit 76, and the UB detection unit 77, 4) It is possible to determine which of the plurality of batching stages the fabric put in the inside corresponds to, and which classification corresponds to the degree of foaming at this time (that is, the degree of foaming for each batch).

The fabrication/foaming degree learning module and the fabrication/foaming degree recognition module described above may be independent components and may be included in the control unit 60.

In addition, the function/action/control method performed in the fabrication/packaging degree learning module and the fabrication/packaging degree recognition module may be performed by the controller 60. Hereinafter, for convenience of explanation, it will be described that the controller 60 performs a function/operation/control method performed by the fabrication/packaging degree learning module and the fabrication/packaging degree recognition module.

The output unit 72 may output various information related to the washing machine. For example, the output unit 72 outputs an operating state of the washing machine. The output unit 72 may be an image output device such as an LCD or LED that outputs a visual display, or a sound output device such as a speaker buzzer that outputs sound. Under the control of the control unit 60, the output unit 72 may output information on the amount of fabric or the degree of foaming.

The memory 78 includes a programmed artificial neural network, current patterns for each fabric amount and/or foaming degree, a database (DB) built through machine learning-based learning based on the current pattern, machine learning algorithms, and current detection. The current value sensed by the unit 75, the averaged value of the current values, the averaged values processed according to a parsing rule, data transmitted and received through the communication unit 73, etc. can be stored. have.

In addition, in the memory 78, various control data for controlling the overall operation of the washing machine, washing setting data input by the user, washing time calculated according to the washing setting, data on the washing course, etc., determine whether an error has occurred in the washing machine. Data for doing so may be stored.

The communication unit 73 may communicate with a server connected to a network. The communication unit 73 may include one or more communication modules such as an Internet module and a mobile communication module. The communication unit 73 may receive various data such as learning data and algorithm updates from the server.

The controller 60 may update the memory 78 by processing various types of data received through the communication unit 73. For example, when data input through the communication unit 73 is update data for a driving program previously stored in the memory 78, the control unit 60 may update the memory 78 using the update data. . Also, when the input data is a new driving program, the controller 60 may additionally store the new driving program in the memory 78.

Machine learning means that a computer learns through data without instructing the computer to be directly instructed by logic, and through this, the computer can solve problems on its own.

Deep Learning, based on an artificial neural network (ANN) for constructing artificial intelligence, is an artificial intelligence technology that allows computers to learn like humans without humans teaching them. . The artificial neural network (ANN) may be implemented in the form of software or may be implemented in the form of hardware such as a chip.

For example, the washing machine processes current values sensed by the current sensing unit 75 based on machine learning, and the characteristics of the laundry (cloth) injected into the washing tub 4 (hereinafter referred to as fabric characteristics). Can grasp.

These fabric characteristics may include the amount of fabric and the degree of fabrication.

The controller 60 may determine the degree of foaming for each fabric based on machine learning. For example, the control unit 60 may obtain the amount of fabric and determine which of the pre-classified categories according to the degree of fabrication. The degree of foaming refers to the degree to which (or entangled) the laundry (cloth) put into the washing tank (4) and the degree to which one laundry (cloth) is evenly spread out or aggregated, and the preset foaming degree level (step) It can be classified as

For example, the lower the level of foaming, the less likely the fabric is to be aggregated (i.e., less foaming), and the higher the degree of foaming, the better the fabric will be aggregated (ie, more foaming). Indicates that.

The degree of foaming may be variously defined or classified according to various types of laundry (cloth) existing in the washing tub 4, and may vary according to combinations and characteristics of such various types of laundry. In addition, in the case of one type of laundry, the degree of foaming may be determined or changed according to the material, moisture content, volume difference, and type of the laundry.

As such, the degree of foaming is the material of the fabric, the degree of softness (e.g., soft fabric/hard fabric), the ability of the fabric to hold water (i.e., moisture content), and the dry fabric and the compress. It can be understood as a concept that is distinct from foam, which is defined based on various factors such as the volume difference between the liver.

The control unit 60 uses the current current value detected by the current sensing unit 75 until the target speed is reached, and input data of an artificial neural network previously learned by machine learning. ) Can be used to detect the amount of fabric.

In addition, the controller 60 may determine (predict, estimate, calculate) various information related to the unbalance of the washing tub 4 of the present invention using an artificial neural network (ANN) learned through machine learning.

For example, the control unit 60 may distinguish (determine) a degree of foaming by inputting a plurality of types of data described above as an input value of the artificial neural network (ANN).

Hereinafter, the UB sensing unit 77 for measuring the UB of the washing tub 4 of the present invention will be described in more detail.

The UB sensing unit 77 may measure an unbalance (UB) of the washing tub 4 generated when the washing tub 4 containing the cloth rotates. Here, the unbalance of the washing tub 4 may mean the shaking of the washing tub 4 or a shaking value (or shaking degree) of the washing tub 4.

The UB detection unit 77 may measure (calculate) a shaking value (or shaking degree) of the washing tub 4 (or drum). Here, the shaking value of the washing tub 4 may be referred to as an UB value, an UB amount, an unbalanced value, an unbalanced amount, or an eccentric amount.

In the present specification, UB (UnBalance) may mean the amount of eccentricity of the washing tub 4, that is, the unbalance of the washing tub 4 or the shaking of the washing tub 4, and may be generated by uneven arrangement of fabrics.

The UB value is a value for indicating the magnitude (or degree) of shaking of the washing tub 4, and the amount of change in the rotational speed of the washing tub 4 (or the motor 9) or the washing tub 4 (or the motor 9) ) Can be calculated (calculated) based on the amount of change in acceleration.

For example, the UB detection unit 77 receives the rotational speed value of the washing tub 4 (or motor 9) measured by the speed detection unit 74, and determines the amount of change in the received rotational speed value. Can be used to calculate the UB value. Here, the amount of rotational speed change means, for example, a difference between rotational speeds measured every predetermined time, or a difference between rotational speeds measured every time the washing tub 4 is rotated by a predetermined angle, or the maximum rotational speed and It can also mean the difference in the minimum rotational speed.

For example, the UB detection unit 77 may measure the rotational speed of the washing tub 4 measured by the speed detection unit 74 for each predetermined angle, and measure the UB value through a difference between the measured rotational speeds. . Thereafter, the UB detection unit 77 may calculate the UB value by using a value corresponding to a difference obtained by subtracting the minimum rotation speed from the maximum rotation speed among the measured rotation speed values.

As another example, the UB detection unit 77 uses values of rotational speed measured at each predetermined angle (for example, rotational speed detected every 30 degrees (RPM)), according to Equations 1 to 3 below. You can also calculate (UB(k)).

The v_k to v_(k-11) denote the rotational speed measured at every predetermined angle (30 degrees), and k is an integer.

Accordingly, the greater the shaking (or vibration) of the washing tub when the washing tub is rotated due to the non-uniform arrangement (UB) of the cloth in the washing tub 4, the greater the UB value.

As another example, the UB value may mean a predetermined value proportional to the rotational speed change amount (a value obtained by subtracting the minimum rotational speed from the maximum rotational speed).

The UB value may be calculated based on not only the rotational speed of the washing tub, but also a current value applied to the motor or a vibration value of the storage tank 3 measured by a vibration sensor.

As such, the UB value may be defined as various values measured by the UB of the washing tub 4. In this specification, it is assumed that the UB value is defined based on the shaking of the rotational speed of the washing tub 4 detected when the washing tub 4 is rotated.

For example, in the case where the fabrics are arranged to be lumped together in the washing tub 4 or if the fabrics are lumped together, the balance is deteriorated (that is, the unbalance becomes severe), and accordingly, the shaking of the washing tub due to the rotation of the washing tub is It increases, and the UB value also increases.

When the shaking of the washing tub increases (when the UB value of the washing tub increases), a large current load is applied to the motor 9 for high speed rotation of the washing tub 4 in the spin-drying stroke, resulting in increased energy consumption and noise. Causes problems.

Conversely, when the cloth is evenly arranged in the washing tub 4 or there is little agglomeration of the cloth, the balance is improved, and accordingly, even if the washing tub is rotated at high speed, the shaking of the washing tub becomes small, and the UB value is also reduced. .

On the other hand, the present invention is based on the fact that in the dehydration process (dehydration stroke), the UB (UnBalance) value generated by the rotation of the washing tub is measured (detected) larger than the preset UB allowable value (that is, the UB value is preset If it exceeds the UB allowable value), the rotation of the washing tank 4 can be stopped.

When the UB value exceeds the preset UB allowable value, it may mean that the fabric in the washing tub 4 is unevenly disposed, and thus shaking or vibration of the rotational speed is greater than the reference value when the washing tub 4 rotates. In this case, when the washing tub 4 is rotated at a high speed RPM, there is a problem that noise is generated and a large current load is consumed.

The controller 60 of the present invention may short-circuit the dehydration process when the UB value exceeds a preset UB allowable value.

Here, short-circuiting the dehydration process initializes the dehydration process so that the dehydration process starts from the beginning (or starts the dehydration process again from the process after the dehydration amount is detected), stops the rotation of the washing tub 4, and the washing tub ( It may include the meaning of shorting the rotation of 4), and may be used interchangeably.

The preset UB allowable value may mean a preset reference value serving as a criterion for stopping rotation of the washing tub (initializing the spinning process) during the spinning process.

The preset UB allowable value may be referred to as a limit allowable UB value, a reference UB value, and a set UB value.

These UB allowable values may be divided and set for each dehydration section according to the amount of cloth/packing degree in the preset UB table.

On the other hand, the present invention can provide a washing machine designed to exhibit an optimized dehydration performance and a control method of the washing machine.

Hereinafter, a washing machine and a control method of the washing machine of the present invention formed to exhibit an optimized dehydration performance will be described in more detail with reference to the accompanying drawings.

3 is a flowchart for explaining a typical control method of the present invention, and FIGS. 4, 5, 6, and 7 are conceptual diagrams for explaining the control method described in FIG. 3.

A washing machine according to an embodiment of the present invention includes a storage tank 3, a vibration sensor provided in the storage tank, and a laundry tank 4 that accommodates a cloth (laundry) and is rotatably provided in the storage tank 3, and the washing tank It may include a motor 9 that rotates and a control unit 60 that senses the amount of fabric accommodated in the washing tub at different times and determines the moisture content by using the detected amount values.

Here, referring to FIG. 3, the control unit 60 detects (measures) the first amount of cloth accommodated in the washing tub before water is supplied to detect (measure) the amount of cloth contained in the washing tub at different times. (S310), after discharging the water supplied, the second cloth amount of the cloth accommodated in the washing tank may be sensed (measured) (S320).

Here, the first cloth amount means a cloth amount before the cloth accommodated in the washing tank is wetted with water, and may be referred to as a dry cloth amount, a dry cloth amount, and a laundry cloth amount.

Here, before water is supplied, it may mean before the first water supply after the washing process is started. This is because the first cloth means the amount of cloth before the water touches it. The control unit 60 may measure the amount of cloth that is not wet with water before water is supplied (ie, before the first water is supplied), and determine (define) the measured amount of the cloth as the first cloth amount.

The second cloth amount refers to the amount of cloth (ie, the amount of cloth containing water) after the cloth accommodated in the washing tank is wet with water, and may be referred to as the amount of wet cloth or dewatering cloth.

Here, after discharging the supplied water may mean after the washing process is finished and drained or after the rinsing process is performed and drained.

That is, after all of the water except for the water contained in the cloth is discharged, the weight (quantity) of the cloth containing water may be referred to as the second cloth amount. The control unit 60 may measure the amount of cloth after water is discharged after the washing process is completed after the water supply is supplied, or after the rinsing process is completed after the water supply is supplied, and the measured amount is determined (defined) as the second cloth amount. have.

For example, the control unit 60 may detect the amount of cloth at at least one time before the start of the washing cycle (the first amount of cloth), after the end of the washing cycle, before the start of the rinsing cycle, and after the rinsing cycle and before the spin-drying cycle.

For example, the control unit 60 may detect the first amount of cloth (the amount of washing cloth), and the amount of the washing cloth is a state in which the cloth is not wet before the washing process starts (ie, before the first water supply).

It can mean the amount of (that is, dry).

For example, the control unit 60 may detect a second amount of cloth (dewatering amount), and the amount of dewatering may refer to an amount of cloth in a wet state in a state in which the rinsing cycle is finished and drainage is completed.

For example, the control unit 60 is an artificial neural network previously learned by machine learning by using the current (current current) value sensed by the current sensing unit 75 until the rotational speed of the washing tub reaches the target speed. The first and second fabrics can be detected by using them as input data of the (Artificial Neural Network). Here, the pre-learned artificial neural network is a first artificial neural network, and may be an artificial neural network that has been learned to detect the amount of fabric.

The control unit 60 may determine the moisture content of the cloth accommodated in the washing tank using (based on) the second cloth amount (package amount, dewatering cloth amount) and the first cloth amount (dry cloth amount, washing cloth amount) (S330). ).

Here, the moisture content refers to the degree to which the bag can contain water, and the greater the difference between the second bag and the first bag, the higher the moisture content is, so that the moisture content may increase.

Meanwhile, the control unit 60 may detect (determine) not only the moisture content but also the degree of agglomeration (S340).

Specifically, the controller 60 may determine (classify, measure) the degree of agglomeration of the fabric currently being washed using the artificial neural network described above.

As described above, the controller 60 may measure a plurality of types of data while rotating the washing tub. The plurality of types of data may mean data related to UB (unbalance) of the washing tub 4, data for measuring UB of the washing tub 4, data generated by rotation of the washing tub 4, and the like.

The control unit 60 may determine the degree of foaming using a plurality of types of data measured in the rinsing process (rinsing process) before the spin-drying process.

For example, the control unit 60 may measure a plurality of types of data while rotating the washing tub at a specific rotational speed, first, before entering the spinning process.

Here, before entering the dehydration process, it may be a rinsing process (or rinsing process).

In addition, the specific rotation speed means, for example, a rotation speed at which the tumbling motion of the fore is performed, and for example, may be any rotation speed between 40 and 50 RPM.

In the tumbling motion, the motor 9 rotates the washing tub 4 in one direction, but the laundry on the inner surface of the drum 42 moves from about 90 degrees to 110 degrees in the rotation direction of the washing tub 4 to the lowest point of the drum 42. It is a motion controlled to fall. In the tumbling motion, the motor 9 rotates the washing tub 4 in one direction, and if the laundry is rotated clockwise, the laundry moves from the third quadrant of the drum to a part of the second quadrant, and then deviates from the inner surface of the drum 42. It falls in the direction of the lowest point of, and after rising again, the falling flow is continuously repeated.

The control unit 60 may measure the plurality of types of data while the washing tub is rotated at the specific rotation speed (eg, 46 RPM) so that the cloth rises by a predetermined height and then falls in a rinsing process performed before the spin-drying process. .

That is, in the rinsing process, the controller 60 may measure a plurality of types of data while rotating the washing tub 4 at the specific rotational speed so that the tumbling motion is performed.

The plurality of types of data include at least one of a current rotational speed (Current RPM), a speed UB (or UB value, or virtual vibration amount), a q-axis current Iq and a starting/holding current, a bag level information, and a vibration value. Can include.

The plurality of types of data may be data related to rotation of the washing tub. For example, the plurality of types of data may be measured (calculated) through at least one of the speed sensing unit 74, the current sensing unit 75, the vibration sensing unit 76, and the UB sensing unit 77 described above. I can.

The plurality of types of data include the rotational speed value (or current speed value) of the washing tub 4 measured by the speed sensing unit 74, and a current value applied to the motor 9 measured by the current sensing unit 75 ( q-axis current and starting/holding current), the vibration value of the storage tank 3 measured by the vibration detection unit 76, and the shaking value of the washing tank 4 measured by the UB detection unit 77 (UB value, speed UB) It may include.

As shown in FIG. 4, the current rotation speed may mean a rotation speed (Current_RPM) of the washing tub 4 that is actually measured, not a target rotation speed (Request_RPM).

As shown in Fig. 4, even when the control unit 60 applies a control signal to the motor driving unit 71 to rotate the washing tub 4 at a target rotation speed, the fabric included in the washing tub 4 is unevenly arranged. In this case, UB is generated and the rotational speed of the washing tub 4 may be shaken.

The current rotational speed may mean data obtained by sequentially summing rotational speed values measured every predetermined time interval (eg, 70ms) by a predetermined number (eg, by 10). However, the present invention is not limited thereto, and the current rotation speed may be an average value of rotation speed values measured at predetermined time intervals (ie, an average value of data sequentially summed by the predetermined number).

The speed UB (UB value) may mean data obtained by sequentially summing the speed UB values measured every predetermined time interval (for example, 70 ms) by a predetermined number (for example, by five). Similarly, the speed UB may be an average value of UB values (or summed data) measured at predetermined time intervals.

The current rotation speed and the speed UB may be measured by the speed detection unit 74.

The q-axis current (Iq) refers to a value of a torque current (Iq) corresponding to a component of the torque axis (q-axis) among currents applied to the motor (or the sum data or average value of current values measured at predetermined time intervals). can do.

The starting current refers to a current applied to accelerate the washing tub 4 to a specific rotational speed (for example, 46RPM), and the holding current is maintained after the washing tub 4 reaches a specific rotational speed. It may mean an applied current.

The starting/holding current is generated after a predetermined time elapses after starting, and may be one representative value. The starting/holding current may be measured (calculated) based on the measured q-axis current.

The q-axis current and starting/holding current may be measured through the current sensing unit 75.

In addition, when the washing machine is equipped with a vibration sensor, the controller 60 may measure the vibration value of the water storage tank 3 by using the vibration sensor. Similarly, the vibration value refers to a vibration displacement value of the storage tank, and may refer to sum data (or average value) of vibration values measured at predetermined time intervals.

That is, the controller 60 of the present invention may not only measure data measured at any one moment, but may use the sum data (or average value) measured for a predetermined time as data.

In addition, the control unit 60 may measure a plurality of different types of data, rather than using only one type of data.

The control unit 60 may determine the degree of foaming by using the measured data of a plurality of types.

The controller 60 may input a plurality of measured data as an input value of a previously learned artificial neural network, and output the degree of foaming as a result value. Here, the pre-learned artificial neural network refers to an artificial neural network that has been learned to calculate the degree of encapsulation, and may be referred to as a second artificial neural network.

Specifically, the control unit 60, through a pre-learned artificial neural network, the degree of foaming classified into a plurality (e.g., the degree of foaming in one level (level) (class 1), the degree of agging in two levels (level) It is possible to determine any one of (class 2) and three levels (level) (class 3)).

Referring to FIG. 5, the controller 60 uses machine learning to convert a plurality of types of data (eg, current rotation speed, speed UB, q-axis current, start/hold current) measured during the rinsing process. By using it as the input data (input layer) of a previously learned artificial neural network (ANN), it is possible to determine (detection, determination, and calculation) the degree of formation.

The controller 60 may input a plurality of measured data as an input value of a pre-learned artificial neural network, and output the degree of foaming as a result value.

At this time, the control unit 60, through a pre-learned artificial neural network (ANN), the degree of foaming classified into a plurality of pieces (1st stage of the degree of foaming, 2nd stage of the degree of foaming, 3rd stage of the degree of foaming) You can decide.

Meanwhile, in the present invention, an artificial neural network (ANN) for calculating the degree of foaming as a result value may be included. Information about the artificial neural network (ANN) may be previously stored in the memory 78 or the controller 60. 5 is a schematic diagram showing an example of an artificial neural network.

Deep learning technology, a kind of machine learning, can mean learning by going down to a deep level in multiple stages based on data.

Deep learning may represent a set of machine learning algorithms that extract core data from a plurality of data while passing through hidden layers in sequence.

The deep learning structure may include an artificial neural network (ANN), for example, the deep learning structure consists of a deep neural network (DNN) such as a convolutional neural network (CNN), a recurrent neural network (RNN), and a deep belief network (DBN). Can be.

Referring to FIG. 5, an artificial neural network (ANN) may include an input layer, a hidden layer, and an output layer. Having multiple hidden layers is called a deep neural network (DNN). Each layer includes a plurality of nodes, and each layer is associated with the next layer. Nodes can be connected to each other with a weight.

The output from an arbitrary node belonging to the first hidden layer 1 becomes an input to at least one node belonging to the second hidden layer 2. In this case, the input of each node may be a value to which a weight is applied to the output of the node of the previous layer. Weight (weight) may mean the strength of the connection between nodes. The deep learning process can also be viewed as a process of finding an appropriate weight.

An artificial neural network (ANN) applied to a washing machine according to an embodiment of the present invention takes a plurality of types of data (current rotation speed, speed UB, q-axis current, and start/maintenance current) discussed above as input data, and It may mean a supervised learning deep neural network (DNN) using the degree of foaming measured by the result data.

The supervised learning may mean a method of machine learning for inferring a function from training data.

The artificial neural network (ANN) of the present invention experimentally measures the degree of foaming for each of a plurality of types of data, and inputs the degree of foaming measured for each of the plurality of types of data as the result value. Thus, the hidden layer may be a learned deep neural network. Here, learning the hidden layer may mean adjusting (update) the weight of the connection lines between nodes included in the hidden layer.

Using such an artificial neural network (ANN), the control unit 60 of the present invention calculates a plurality of types of data at a certain point in time, and determines the degree of foaming by using the plurality of types of data as input values of the artificial neural network. (Prediction, extraction, calculation, judgment, estimation) can be performed.

The controller 60 may perform learning by using a plurality of types of data corresponding to the current rotation speed, speed UB, q-axis current, and start/hold current as training data.

Further, the present invention is not limited thereto, and the controller 60 may use the vibration value and the amount of cloth as additional training data, and may use the vibration value and the amount of cloth as input values.

In addition, each time the control unit 60 recognizes or determines the degree of foaming, the determination result and a plurality of types of data input at that time are added to the database to provide a deep neural network (DNN) such as weight or bias. The structure can be updated. In addition, the controller 60 may update a deep neural network (DNN) structure such as a weight by performing a supervised learning process with the obtained training data after a predetermined number of training data is secured.

The controller 60 may input the plurality of types of data as an input value of a previously learned artificial neural network, and output (determine, detect) the degree of foaming as an output value.

Thereafter, the control unit 60 can determine (variable) the maximum speed at which the washing tub rotates during the spinning process (maximum rotation speed, maximum RPM (Revolution Per Minute) and spinning time) based on the determined moisture content and the degree of agglomeration. Yes (S350).

Here, the dehydration time may mean the total dehydration time in the dehydration process, or the time during which the washing tub rotates at the maximum speed during the dehydration process.

If the dehydration time is prolonged, dehydration of the water contained in the fabric occurs a lot, and the dehydration performance can be improved.

The improvement in the dehydration performance may include the meaning of shortening the drying time after dehydration.

The lower the moisture content (lower), the less water is drained from the high-speed RPM, so the probability of UB occurrence decreases. That is, as the moisture content decreases, the difference between before and after water is removed from the fabric is smaller, so that the probability of occurrence of UB due to a change in the weight of the fabric at high speed RPM may decrease.

Accordingly, the control unit 60 of the washing machine of the present invention increases the maximum speed (maximum rotational speed, maximum RPM) of the washing tub in the spinning process as the moisture content decreases, increases the spinning time, and improves the spinning performance. It can be dehydrated as much as possible to shorten the drying time later.

For example, as shown in FIG. 6, when the moisture content of the fabric accommodated in the washing tank is the first step (Lv.1) less than the second step (Lv.2), the maximum speed ( Maximum RPM) can be set higher (a1, a2, a3> b1, b2, b3).

As another example, the control unit 60 may set the spinning time to be longer when the moisture content of the fabric accommodated in the washing tub is less than the second step (Lv.2) in the first step (Lv.1) (t1, t2, t3> t1', t2', t3')

On the other hand, the control unit 60 of the washing machine according to the present invention may set the maximum speed and the spin-drying time differently according to the determined degree of agglomeration, even if the same moisture content is determined.

Specifically, the control unit 60 may set the maximum speed (maximum rotation speed, maximum RPM) of the washing tub in the dehydration process to be higher and set the spinning time to be longer as the degree of fabric aggregation decreases.

For example, as shown in Fig. 6, even if the moisture content is the same (for example, Lv. 1), the control unit 60, when the degree of agglomeration is determined as the second step (class 2), the second step The maximum speed can be set higher (a2>a3) than in the third stage (class 3) where the degree of fabric agglomeration is higher, and the spinning time can be set longer (t2>t3).

In addition, when the degree of fabric agglomeration is determined as a first step (class 1) having a lesser degree of fabric aggregation than the second step (class 2), the control unit 60 determines that the degree of fabric agglomeration is the second step (class 2). It is possible to set the maximum speed higher than before (a1>a2), and set the spinning time to be longer (t1>t2).

This means that even with the same moisture content, in the case of a fabric (cloth) with a large amount of fabric (for example, class 3), UB occurs at a higher RPM than a fabric (for example, class 1) with a large amount of fabric aggregation. This is because the probability is high.

Therefore, even with the same moisture content, as the degree of agglomeration (class 1) with a low probability of UB occurrence, the maximum speed (maximum rotational speed, maximum RPM) in the dehydration process is increased, and the spinning time is lengthened, thereby reducing the probability of occurrence of UB. Maximum dehydration performance can be exhibited.

On the other hand, the control unit 60 may set the maximum speed and the dehydration time differently according to the moisture content even if the same degree of agglomeration is detected (determined).

As described above, as the moisture content decreases, the probability of occurrence of UB in the high-speed RPM decreases. Therefore, the control unit 60 may set the maximum speed higher and the spin-drying time longer as the moisture content rate decreases.

For example, the control unit 60, referring to FIG. 6, increases the maximum speed (maximum RPM) as the moisture content decreases (Lv.1 <Lv.2), even if the same foam agglomeration degree (class 2) is detected. It can be set high (a2>b2), and the spinning time can be set long (t2>t2').

On the other hand, the control unit 60 may differently set the dehydration time according to the degree of agglomeration regardless of the moisture content (t1 = t1', t2 = t2', t3 = t3'). This is to prevent the total washing time from becoming longer depending on the moisture content. That is, the control unit 60 may be designed such that the dehydration time is not affected by the moisture content, and only varies depending on the degree of agglomeration.

On the other hand, in the present invention, when the moisture content is detected (measured), when a cloth (laundry, cloth) wet with water is introduced into the washing tank from the beginning, the reliability of measuring the moisture content may be significantly lowered.

That is, referring to FIG. 7, in the step of calculating the moisture content, the control unit 60 includes a second cloth volume (a cloth measured after the water is discharged) and a first cloth amount (measured before water is initially supplied). It can be determined whether the difference between the cloth quantity) is smaller than a preset value (a preset value or a reference value).

For example, when the second cloth and the first cloth are measured to be approximately the same value (or the difference between the second and first cloth is smaller than a preset value), the control unit 60 It can be judged that the wet cloth was put in.

In this case, since the moisture content of the fabric cannot be calculated, it is possible to maintain the previously set value without changing the maximum speed (maximum RPM) and the spinning time during the spinning process. Here, the previously set value may be the maximum speed and spin time associated with the second fabric amount, the maximum speed and spin time associated with the degree of fabric aggregation, or the maximum speed initially set in the washing machine (or set for each course) and It can mean dehydration time.

On the other hand, when the difference between the second and the first fabric is larger than the preset value, the controller 60 enters step S330 of FIG. 3 to detect the moisture content, and then detects the degree of agglomeration of the fabric. The maximum speed and spin time can be set (determined) based on the moisture rate and the degree of lumping.

When the difference between the second and first cloth amounts is smaller than the preset value, it means that the moisture content is less than a certain value, and the constant value here is small enough to be put into the washing tub while the cloth is wet from the beginning. It can be a mean value.

Through this configuration, the present invention maintains the maximum speed and dehydration time at the previously set values, or at a value linked to the degree of agglomeration, even when the moisture content cannot be calculated as the fabric is introduced in a wet state from the beginning. By setting the maximum speed and spin time, it is possible to provide an optimized washing machine control method capable of improving spin-drying performance.

According to an embodiment of the present invention, one or more of the following effects are provided.

First, the present invention provides a new dewatering control method capable of exhibiting maximum dehydration performance for each unbalanced state by determining the maximum rotational speed and dehydration time of the washing tub in the dehydration process by considering not only the moisture content but also the degree of fabric aggregation. I can.

Second, in the present invention, the lower the moisture content is, the less the probability of occurrence of unbalance (or the degree to which unbalance occurs) due to high rotational dehydration of the washing tub in the dehydration process, so the maximum rotational speed is increased and the dehydration time is set longer, so that the dehydration performance It can shorten the drying time later by increasing it.

Third, in the present invention, even with the same moisture content, the smaller the degree of agglomeration of the fabric, the less the probability of occurrence of unbalance (or the degree of occurrence of unbalance) due to high rotational dehydration of the washing tub in the dehydration process. By setting it long, the dehydration performance can be improved, and thus the drying time can be significantly reduced later.

The effects of the present invention are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by those skilled in the art from the description of the claims.

The above-described present invention can be implemented as a computer-readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is this. In addition, the computer may include a processor or a control unit. Therefore, the detailed description above should not be construed as restrictive in all respects and should be considered as illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Claims (15)

Reservoir;
A washing tank that accommodates the fabric and is rotatably provided in the storage tank;
A motor rotating the washing tub; And
And a control unit configured to detect the amount of cloth contained in the washing tank at different times and determine a moisture content rate using the detected amount values,
The control unit,
Using a plurality of types of data measured while rotating the washing tub to determine the degree of agglomeration,
A washing machine, characterized in that, based on the determined moisture content and the degree of agglomeration of the fabric, a maximum speed and a spinning time at which the washing tub is rotated during a spinning process are determined. The method of claim 1,
The control unit,
Before water is supplied, the first cloth amount of the cloth accommodated in the washing tank is measured,
After discharging the water, the second cloth amount of the cloth accommodated in the washing tank is measured,
A washing machine, characterized in that determining the moisture content of the cloth accommodated in the washing tub by using the first and second cloth amounts. The method of claim 1,
The control unit,
In a rinsing process performed before the spin-drying process, the plurality of types of data are measured while the washing tub is rotated at the specific rotational speed so that the cloth rises by a predetermined height and then falls. The method of claim 1,
The control unit,
And inputting the measured data as an input value of a previously learned artificial neural network, and outputting the degree of foaming as a result value. The method of claim 4,
The control unit,
The washing machine, characterized in that to determine any one of the degree of foaming classified into a plurality of pieces through the previously learned artificial neural network. The method of claim 1,
The control unit,
Even if the same moisture content is determined, the maximum speed and the spinning time are set differently according to the determined degree of agglomeration. The method of claim 6,
The control unit,
The washing machine, characterized in that, as the degree of fabric agglomeration decreases, the maximum speed is set higher and the spin-drying time is set longer. The method of claim 7,
The control unit,
When the degree of fabric agglomeration is determined as the second step, the maximum speed is set higher than in the third step in which the degree of fabric agglomeration is higher than that of the second step, and the spinning time is set longer,
When the degree of fabric agglomeration is determined as the first step having a lower degree of fabric aggregation than in the second step, setting the maximum speed higher than when the degree of fabric aggregating is in the second step, and setting the dehydration time longer Washing machine characterized by. The method of claim 1,
The control unit,
The washing machine, characterized in that the maximum speed and the spin-drying time are set differently according to the moisture content even if the same degree of lumping is detected. The method of claim 9,
The control unit,
A washing machine, characterized in that as the moisture content decreases, the maximum speed is set higher and the spin-drying time is set longer. The method of claim 2,
The control unit,
When the difference between the second cloth and the first cloth is smaller than a preset value, the maximum speed and the spinning time are maintained at a previously set value. The method of claim 11,
The control unit,
When the difference between the second and the first fabric amounts is greater than a preset value, the moisture content is sensed, the degree of agglomeration is sensed, and the maximum speed and the dehydration are based on the moisture content and the degree of agglomeration. Washing machine, characterized in that to set the time. Detecting the amount of fabric contained in the washing tank at different times and determining a moisture content rate using the detected amount values;
Determining a degree of agglomeration using a plurality of types of data measured while rotating the washing tub; And
A control method of a washing machine comprising the step of determining a maximum speed and a spin time at which the washing tub rotates during a spin-drying process based on the determined moisture content and a degree of agglomeration. The method of claim 13,
The step of determining the maximum speed and dehydration time,
Even if the same moisture content is determined, the maximum speed and the spinning time are set differently according to the determined degree of agglomeration. The method of claim 13,
The step of determining the maximum speed and dehydration time,
The control method of a washing machine, characterized in that the maximum speed and the spin-drying time are set differently according to the moisture content, even if the same degree of lumping is detected.

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