Method for recommending drying course according to fabric type and dryer in accordance with same

Abstract

Provided are a dryer for recommending a drying course according to fabric type, and a method for controlling the dryer. The dryer comprises: a conductivity sensor; a temperature sensor; a current sensor; a drying module; at least one memory storing one or more instructions; and at least one processor. The at least one processor, by executing the one or more instructions stored in the memory, can: obtain a conductivity value from the conductivity sensor, the temperature inside the dryer from the temperature sensor, and a current value, applied to a drum motor, from the current sensor; determine the fabric type of to-be-dried materials inside the dryer or whether the to-be-dried materials include multiple to-be-dried materials of different fabric types on the basis of the obtained conductivity value, temperature, and current value applied to the drum motor; and control the drying module to perform drying by a drying method corresponding to the determined fabric type or a drying method corresponding to the multiple to-be-dried materials of different fabric types.

Description

How to recommend a drying course according to the quality of the fabric and the dryer accordingly

[0001] The present disclosure relates to a dryer that recommends a drying course and a control method thereof. The present disclosure relates to a dryer that recommends a drying course based on a foam quality, a control method for the dryer, and a computer-readable recording medium storing a computer program that performs the control method for the dryer.

[0002] A dryer dries an object by evaporating the moisture in the object using heat. A dryer uses a heater to heat the air inside the dryer, and a hot air fan to rotate to circulate the heated air inside the dryer, thereby drying the object. A dryer can also use a heat pump to circulate a refrigerant, removing moisture from the object.

[0003] One aspect of one embodiment of the present disclosure may provide a dryer that recommends a drying course based on a foam quality. The dryer may include a conductivity sensor, a temperature sensor, a current sensor, a drying module, at least one memory storing one or more instructions, and at least one processor. The dryer may obtain a conductivity value from the conductivity sensor, a temperature within the dryer from the temperature sensor, and a current value applied to a drum motor from the current sensor by executing one or more instructions stored in the memory. Based on the obtained conductivity value, temperature, and current value applied to the drum motor, the dryer may determine a foam quality of a drying material within the dryer or whether the drying material includes multiple drying materials of different foam quality. The dryer may control the drying module to perform drying using a drying method corresponding to the determined foam quality or a drying method corresponding to multiple drying materials of different foam quality.

[0004] One aspect of one embodiment of the present disclosure may provide a method for controlling a dryer that recommends a drying course according to a foam quality. The method for controlling a dryer may include a step of acquiring a conductivity value from a conductivity sensor, a temperature within the dryer from a temperature sensor, and a current value applied to a drum motor from a current sensor. The method for controlling a dryer may include a step of determining a foam quality of a drying material within the dryer or whether the drying material includes a plurality of drying materials of different foam qualities based on the acquired conductivity value, temperature, and current value applied to the drum motor. The method for controlling a dryer may include a step of performing drying using a drying method corresponding to the determined foam quality or a drying method corresponding to a plurality of drying materials of different foam qualities.

[0005] One aspect of one embodiment of the present disclosure may provide a computer-readable recording medium having recorded thereon a program for performing a method of controlling a dryer that recommends a drying course according to a quality of foam.

[0006] FIG. 1 illustrates a method for performing drying suitable for the quality of a drying material by a dryer according to one embodiment of the present disclosure.

[0007] FIG. 2 illustrates a block diagram of a dryer according to one embodiment of the present disclosure.

[0008] FIG. 3 illustrates a flow chart of a method for performing drying suitable for the quality of a drying material by a dryer according to one embodiment of the present disclosure.

[0009] FIG. 4 illustrates a flowchart of a method for a dryer to determine the quality of a dried material according to one embodiment of the present disclosure.

[0010] FIG. 5 illustrates a method for a dryer to classify foam using an artificial intelligence model, according to one embodiment of the present disclosure.

[0011] FIG. 6 illustrates a method for learning an artificial intelligence model according to one embodiment of the present disclosure.

[0012] FIG. 7 illustrates a method for a dryer to calculate a quality score for a dried product, according to one embodiment of the present disclosure.

[0013] FIG. 8 illustrates a drying method corresponding to the quality of a dried product determined by a dryer according to one embodiment of the present disclosure.

[0014] FIG. 9 illustrates a method for a dryer to display a drying method corresponding to a quality of a drying material, according to one embodiment of the present disclosure.

[0015] FIG. 10 illustrates a method for a dryer to change a foam sensitivity based on a user input selecting a drying method, according to one embodiment of the present disclosure.

[0016] FIG. 11 illustrates a flowchart of a method for a dryer to adjust foam sensitivity based on user input, according to one embodiment of the present disclosure.

[0017] FIG. 12 illustrates a method for a user device to display a user interface for adjusting the foam sensitivity of a dryer, according to one embodiment of the present disclosure.

[0018] FIG. 13 illustrates a block diagram of a dryer according to one embodiment of the present disclosure.

[0019] In this disclosure, the expression “at least one of a, b or c” may refer to “a”, “b”, “c”, “a and b”, “a and c”, “b and c”, “all of a, b and c”, or variations thereof.

[0020] Below, with reference to the attached drawings, embodiments of the present disclosure are described in detail so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. Furthermore, in the drawings, parts irrelevant to the description are omitted for clarity of description of the present disclosure, and similar parts are designated with similar reference numerals throughout the specification.

[0021] The terms used in this disclosure are described as currently common terms, taking into account the functions mentioned herein. However, these terms may mean various other terms depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Therefore, the terms used in this disclosure should not be interpreted solely based on their names, but rather based on the meanings of the terms and the overall content of this disclosure.

[0022] Additionally, while terms such as first, second, etc. may be used to describe various components, the components should not be limited by these terms. These terms are used to distinguish one component from another.

[0023] In addition, the terminology used in this disclosure is only used to describe specific embodiments and is not intended to limit the present disclosure. The singular expression includes the plural meaning unless the context clearly indicates the singular. In addition, throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected" but also the case where it is "electrically connected" with another element in between. In addition, when a part is said to "include" a certain component, this does not mean that other components are excluded, but that other components can be further included, unless specifically stated otherwise.

[0024] The phrases “in some embodiments” or “in an embodiment” appearing in various places throughout this specification are not necessarily all referring to the same embodiment.

[0025] One embodiment of the present disclosure is to provide a dryer and a method of controlling the dryer for determining the quality of a dried product.

[0026] One embodiment of the present disclosure is to provide a dryer and a control method of the dryer that guide a drying method according to the quality of the dried material.

[0027] One embodiment of the present disclosure is to provide a dryer and a control method of the dryer that guide a drying method according to the importance of the quality of the dried material.

[0028] FIG. 1 illustrates a method for performing drying suitable for the quality of a dried product by a dryer (1000) according to one embodiment of the present disclosure.

[0029] Referring to FIG. 1, the dryer (1000) can determine the quality of the material being dried and perform drying using a drying method corresponding to the determined quality.

[0030] The dryer (1000) can perform drying using a drying method corresponding to a mixed foam quality based on determining that multiple dry materials of different foam qualities are being dried. The mixed foam quality can refer to the foam quality of all of the multiple dry materials when the multiple dry materials have two or more foam qualities.

[0031] The dryer (1000) can display information about the quality of the dried material and information about the drying method on the control panel (1510).

[0032] As the dryer (1000) begins drying, it can acquire a conductivity value at a predetermined time interval through a conductivity sensor. In addition, the dryer (1000) can acquire a temperature value through a temperature sensor and a current value of the drum motor through a current sensor.

[0033] The dryer (1000) can determine that the quality of the drying material being dried is one of a plurality of quality levels, or that the drying material being dried includes a plurality of drying materials having different quality levels, based on the conductivity value over time, the temperature inside the dryer (1000), and the current value of the drum motor.

[0034] Based on determining that the dry material comprises multiple dry materials of different qualities, the dryer (1000) can display notification information (110) indicating that the dry material being dried comprises multiple dry materials of different qualities.

[0035] In addition, based on determining that the drying material includes a plurality of drying materials of different foam properties, the dryer (1000) can perform drying using a drying method corresponding to the mixed foam properties. For example, during drying using the 'standard drying' course, the dryer (1000) can change the drying method to the 'AI customized drying' course, which is a drying method corresponding to the mixed foam properties. In addition, for example, based on displaying guide information guiding the user to change the drying method to the 'AI customized drying' course and receiving a user input for changing the drying method to the guided 'AI customized drying' course, the dryer (1000) can change the drying method to the 'AI customized drying' course.

[0036] Additionally, the dryer (1000) can display notification information (120) indicating that the drying method has been changed to the 'AI customized drying' course.

[0037] Additionally, the dryer (1000) can display information (130) regarding the 'AI customized drying' course. For example, the dryer (1000) can display information regarding the drying degree and the time remaining until drying is complete.

[0038] According to one embodiment of the present disclosure, the dryer (1000) can receive a user input for inputting a foam sensitivity for at least one foam among a plurality of foam qualities. The foam sensitivity may refer to a degree that is not considered when determining a drying method. For example, when multiple foam qualities are mixed, the dryer (1000) can select a drying method more appropriate for a foam quality with a lower foam sensitivity. The dryer (1000) can determine a drying method based on not only sensor data but also the foam sensitivity for at least one input foam quality.

[0039] According to one embodiment of the present disclosure, the dryer (1000) can receive a user input for changing a recommended drying method based on sensor data to another drying method. Based on the user input for changing the drying method, the dryer (1000) can lower the foam sensitivity of the foam corresponding to the changed drying method, thereby increasing the probability that the foam corresponding to the changed drying method is determined as the foam quality of the dried product.

[0040] According to one embodiment of the present disclosure, the dryer (1000) may change the ongoing drying method to a drying method corresponding to a foam identified based on sensor data based on no user input received to change the default drying method to another drying method before starting drying.

[0041] According to one embodiment of the present disclosure, the dryer (1000) can change the drying method in progress to a drying method corresponding to the identified foam as a predetermined time elapses from the time when drying is started.

[0042] According to one embodiment of the present disclosure, as the quality of the dried material is determined, the dryer (1000) can display identification information of the quality of the dried material.

[0043] According to one embodiment of the present disclosure, the dryer (1000) can display notification information indicating that drying is performed using a drying method corresponding to the identified foam or a drying method corresponding to the mixed foam.

[0044] According to one embodiment of the present disclosure, the dryer (1000) can transmit information about the quality of the identified dry matter, whether the dry matter includes multiple dry matters of different qualities, or the drying method being performed to the user device via the server.

[0045] Accordingly, the dryer (1000) can reduce damage and wrinkles to fabrics by varying the drying method depending on the fabric (textile) and prevent overdrying or underdrying. In addition, the dryer (1000) can identify mixed fabrics even when multiple fabrics are mixed, and can perform drying using a drying method suitable for the mixed fabric.

[0046] FIG. 2 illustrates a block diagram of a dryer (1000) according to one embodiment of the present disclosure.

[0047] The dryer (1000) may include a processor (1100), memory (1400), a conductivity sensor (1710), a temperature sensor (1720), a current sensor (1730), and a drying module (1900).

[0048] The processor (1100) may include at least one processor. At least one processor (1100) may control the overall operation of the dryer (1000). The processor (1100) may control the conductivity sensor (1710), the temperature sensor (1720), the current sensor (1730), and the drying module (1900) by executing programs stored in the memory (1400).

[0049] At least one processor (1100) may include a central processing unit (CPU), an application processor (AP), a graphic processing unit (GPU), and an artificial intelligence (AI) processor. The AI ​​processor may be manufactured in the form of a dedicated hardware chip, or may be manufactured as a part of the central processing unit, the application processor, or the graphic processor and mounted on the dryer (1000). The AI ​​processor may be designed with a hardware structure specialized for processing AI models. The AI ​​processor may create an AI model through learning. For example, the AI ​​processor may create an AI model having predefined operation rules set to perform a desired characteristic (or purpose) by learning using a plurality of learning data by a learning algorithm.

[0050] The memory (1400) stores various information, data, commands, programs, etc. required for the operation of the dryer (1000). For example, the memory (1400) can store conductivity sensor data, temperature sensor data, and motor current sensor data. The memory (1400) can store a software module for calculating feature point data from the conductivity sensor data, temperature sensor data, and motor current sensor data. The memory (1400) can store a software module in which an artificial intelligence model is implemented. The memory (1400) can store a calculation feature vector for calculating a foam classification constant value from the feature point data. The memory (1400) can store a foam sensitivity corresponding to each of a plurality of foams.

[0051] A conductivity sensor (1710) may be a sensor that measures the ability of a solution to conduct current. At least one processor (1100) may determine the moisture content of a surface of a drying object based on the conductivity value of the conductivity sensor (1710). The conductivity sensor (1710) may be installed at a location within the dryer (1000) that may be in contact with a rotating drying object.

[0052] A temperature sensor (1720) is provided inside the drum and can detect the temperature inside the drum.

[0053] The current sensor (1730) can detect the current value applied to the drum motor. At least one processor (1100) can determine the weight of the building based on the current value applied to the drum motor.

[0054] The drying module (1900) may include a drum (1910 of FIG. 13), a drum motor (1920 of FIG. 13), a heating module (1930 of FIG. 13), and a blower module (1940 of FIG. 13).

[0055] A drum motor can rotate a drum. When current is supplied to the drum motor, it can rotate around an axis or shaft. A belt can be connected to the drum motor's axis or shaft, and the belt can be wound around the drum. Accordingly, when the drum motor rotates, the belt moves in one direction, thereby rotating the drum.

[0056] The heating module can heat the air inside the dryer (1000). The heating module can include at least one of a heater and a heat pump. The heat pump can be a module that transfers energy from a low heat source to a high heat source by circulating a refrigerant. The heat pump can remove moisture from the drying material by circulating the refrigerant. The heater can remove moisture from the drying material by heating the air.

[0057] The blower module can cause the air heated by the heating module to flow into the drum. The blower module can include a fan and a fan motor.

[0058] At least one processor (1100) can obtain a conductivity value from a conductivity sensor (1710). At least one processor (1100) can obtain a temperature within the dryer (1000) from a temperature sensor (1720). At least one processor (1100) can obtain a current value applied to the drum motor from a current sensor (1730).

[0059] At least one processor (1100) can determine the quality of the dried material in the dryer (1000) based on the acquired conductivity value, temperature, and current value applied to the drum motor. At least one processor (1100) can control the drying module (1900) to perform drying using a drying method corresponding to the determined quality.

[0060] Additionally, at least one processor (1100) can determine whether the drying material includes multiple drying materials of different foam properties based on the acquired conductivity values, temperature, and current values ​​applied to the drum motor. At least one processor (1100) can control the drying module (1900) to perform drying using a mixed foam drying method corresponding to the multiple drying materials of different foam properties.

[0061] FIG. 3 illustrates a flowchart of a method for performing drying suitable for the quality of a dried product by a dryer (1000) according to one embodiment of the present disclosure.

[0062] In step S310, the dryer (1000) can obtain a conductivity value from a conductivity sensor, a temperature inside the dryer (1000) from a temperature sensor, and a current value applied to the drum motor from a current sensor.

[0063] The dryer (1000) can receive a user input to start drying after placing washed clothes in the dryer (1000). Upon receiving the user input to start drying, the dryer (1000) can rotate the drum and control the heating module and the blower module to dry the clothes.

[0064] As drying begins, the dryer (1000) may acquire a conductivity value through a conductivity sensor, acquire a temperature within the dryer (1000) from a temperature sensor, and acquire a current value applied to the drum motor from a current sensor. The dryer (1000) may acquire sensor values ​​at time intervals corresponding to each sensor. For example, the dryer (1000) may generate conductivity data by periodically acquiring conductivity values ​​from the conductivity sensor for about 20 minutes from the time when drying begins.

[0065] In step S320, the dryer (1000) can determine whether the quality of the dried material in the dryer (1000) or whether the dried material includes multiple dried materials of different quality based on the acquired conductivity value, temperature, and current value applied to the drum motor.

[0066] The dryer (1000) can generate characteristic point data of a dry object from the acquired conductivity data, temperature data, and current data applied to the drum motor. The characteristic point data of the dry object can represent characteristics related to the surface moisture content of the dry object, drying speed, temperature difference between the inside and outside of the dryer (1000), and weight of the dry object.

[0067] Since the surface moisture content or drying speed of a dried material is different depending on the quality of the dried material, and the drying speed changes depending on the weight of the dried material and the temperature difference between the inside and outside of the dryer (1000), the dryer (1000) can determine the quality of the dried material based on characteristic point data representing the characteristics of the surface moisture content of the dried material, the drying speed, the temperature difference between the inside and outside of the dryer (1000), and the weight of the dried material.

[0068] The dryer (1000) can generate feature point data based on the average value, variance value or slope of conductivity data, temperature data and current data.

[0069] The dryer (1000) can calculate a quality score indicating the likelihood of the fabric quality of the dry item being a plurality of fabric qualities based on feature data. The plurality of fabric qualities may include, but are not limited to, cotton shirts, denim, towels, synthetic fibers, and blended fibers. For example, the dryer (1000) can calculate a quality score indicating the likelihood of the fabric quality being a cotton shirt, a quality score indicating the likelihood of the fabric quality being denim, and the like.

[0070] In addition, the dryer (1000) can obtain a foam sensitivity corresponding to each of the plurality of foams, which indicates a degree of non-consideration when determining a drying method for the dried product. For example, the dryer (1000) can obtain a foam sensitivity previously stored in the dryer (1000) from a memory. In addition, for example, the dryer (1000) can receive a user input for inputting a foam sensitivity for at least one foam among the plurality of foams.

[0071] The dryer (1000) may determine one of a plurality of foam qualities as the foam quality of the material in the dryer (1000) based on the calculated foam quality score and foam quality sensitivity, or may determine that the material includes a plurality of materials with different foam qualities. For example, the dryer (1000) may determine the foam quality having the highest foam quality score among at least one foam quality having a foam quality score exceeding the foam quality sensitivity as the foam quality of the material. If there is no foam quality having a foam quality score exceeding the foam quality sensitivity, the dryer (1000) may determine that a plurality of materials with different foam qualities are being dried.

[0072] According to one embodiment of the present disclosure, the dryer (1000) can determine the quality of the dried material being dried and display identification information of the determined quality.

[0073] According to one embodiment of the present disclosure, the dryer (1000) can display notification information indicating that the quality of the dry product is a mixed quality based on determining that the dry product includes a plurality of dry products of different qualities.

[0074] In step S330, the dryer (1000) can perform drying using a drying method corresponding to the determined quality or a drying method corresponding to a plurality of drying materials of different quality.

[0075] The dryer (1000) can store multiple drying courses according to the characteristics of the foam as a drying method. The multiple drying courses can have suitable drying temperatures, drying times, and blowing speeds according to the characteristics of the corresponding foam.

[0076] For example, the multiple drying courses may include, but are not limited to, a 'cotton shirt drying' course, a 'denim drying' course, a 'towel drying' course, a 'synthetic fiber drying' course, and a 'blended fiber drying' course.

[0077] According to one embodiment of the present disclosure, the "cotton shirt drying" method may be a drying method in which the drying temperature is set to a medium level and the fan RPM is low. Furthermore, the "denim drying" method may be a drying method in which the drying temperature is set to a low level and the fan RPM is also low. Furthermore, the "towel drying" method may be a drying method in which the drying temperature is set to a high level and the fan RPM is also high. Furthermore, the "synthetic fiber drying" method may be a drying method in which the drying temperature is set to a medium level and the fan RPM is high.

[0078] According to one embodiment of the present disclosure, the dryer (1000) displays guide information guiding drying by a drying method corresponding to a determined foam quality or a drying method corresponding to a mixed foam quality, and can change the drying method to the guided drying method based on receiving a user input for drying by the guided drying method. In this case, the dryer (1000) can display the guide information via a user device, and can change the drying method to the guided drying method based on receiving a user input for drying by the guided drying method from the user device.

[0079] According to one embodiment of the present disclosure, the dryer (1000) can change the drying method to a drying method corresponding to a determined foam quality or a drying method corresponding to a mixed foam quality based on whether a user input for selecting a drying course is received before starting drying. For example, when the dryer (1000) is turned on, a default drying course is set, and when a user input for starting drying is received without a user input for changing the default drying course to another drying course, the dryer (1000) can change the drying method in progress to a drying method corresponding to a foam quality determined during drying.

[0080] According to one embodiment of the present disclosure, the dryer (1000) can change the drying method in progress to a drying method corresponding to a determined foam quality or a drying method corresponding to a mixed foam quality as a predetermined time elapses from the time when drying is started.

[0081] According to one embodiment of the present disclosure, the dryer (1000) can display notification information indicating that drying is performed using a drying method corresponding to a determined foam quality or a drying method corresponding to a mixed foam quality.

[0082] According to one embodiment of the present disclosure, the dryer (1000) can transmit information about the quality of the determined drying material, whether the drying material includes multiple drying materials of different qualities, or the drying method performed to the user device through the server.

[0083] According to one embodiment of the present disclosure, the dryer (1000) can determine the quality of the drying material being dried based on at least one of a conductivity value, a temperature, or a current value applied to the drum motor. For example, the dryer (1000) can determine the quality of the drying material being dried based solely on the conductivity value and the temperature.

[0084] According to one embodiment of the present disclosure, the dryer (1000) can receive a user input for selecting a drying method. The dryer (1000) can learn the user's intention to dry the object being dried using the selected drying method by lowering the foam sensitivity for the foam corresponding to the selected drying method.

[0085] FIG. 4 is a flowchart illustrating a method by which a dryer (1000) determines the quality of a dried material according to an embodiment of the present disclosure. FIG. 5 illustrates a method by which a dryer (1000) classifies the quality of dried material using an artificial intelligence model according to an embodiment of the present disclosure. FIG. 6 illustrates a method by which an artificial intelligence model is trained according to an embodiment of the present disclosure. FIG. 7 illustrates a method by which a dryer (1000) calculates a quality score for dried material according to an embodiment of the present disclosure. FIG. 8 illustrates a quality of dried material determined by a dryer (1000) and a drying method corresponding to the quality of dried material according to an embodiment of the present disclosure.

[0086] In step S410, the dryer (1000) can obtain conductivity sensor data, temperature sensor data, and motor current sensor data.

[0087] The dryer (1000) can dry the object within the dryer (1000) by driving at least one of a heat pump or a heater based on receiving a user input to start drying.

[0088] As drying begins, the dryer (1000) can obtain a conductivity value through a conductivity sensor. The conductivity sensor can be installed at a location where the drying material can come into contact with the drying material, and the dryer (1000) can obtain the conductivity values ​​of the drying material by the drying material inside the dryer (1000) coming into contact with the conductivity sensor as the drum rotates. The dryer (1000) can obtain the moisture content of the drying material surface and the drying speed of the drying material based on the conductivity value.

[0089] Additionally, during drying of the object, the dryer (1000) can detect the temperature inside the drum through a temperature sensor.

[0090] Additionally, during drying of the dry material, the dryer (1000) can detect the current value applied to the motor that rotates the drum. The dryer (1000) can determine the weight of the dry material based on the current value applied to the motor.

[0091] The dryer (1000) can acquire sensor data by collecting sensor values ​​through the sensor for a predetermined period of time. For example, the dryer (1000) can acquire conductivity sensor data by acquiring conductivity values ​​from the conductivity sensor every second for 20 minutes from the time drying begins.

[0092] In step S420, the dryer (1000) can obtain feature point data from the acquired data.

[0093] The dryer (1000) can determine feature point data based on conductivity sensor data, temperature sensor data, and motor current sensor data. The feature point data may be data representing characteristics of the moisture content of the surface of the drying material, drying speed, weight of the drying material, or temperature difference between the inside of the drum and the outside of the dryer (1000) over time.

[0094] For example, the dryer (1000) can divide the conductivity sensor data acquired for 20 minutes into 5 sections and calculate the average value of the conductivity sensor data in each section. In addition, the dryer (1000) can calculate the value obtained by dividing the sum of the average values ​​of each section by the accumulated touch value as a first characteristic point. The accumulated touch value may be a value obtained by accumulating the conductivity sensor data acquired for 20 minutes. The dryer (1000) can calculate the value obtained by dividing the accumulated touch value by the weight of the dried object as a second characteristic point. The dryer (1000) can calculate the value obtained by dividing the average of the conductivity sensor data in a predetermined time section (for example, between 4 and 6 minutes) by the weight of the dried object as a third characteristic point. The dryer (1000) can calculate the value obtained by dividing the average of the temperature difference between the inside and outside of the drum at a predetermined time by the weight of the dried object as a fourth characteristic point.

[0095] According to one embodiment of the present disclosure, the dryer (1000) can produce four feature point data x0, x1, x2, x3 based on conductivity sensor data, temperature sensor data, and motor current sensor data, as shown in Table 1 below. Each row of Table 1 may mean feature point data extracted from one drying cycle (from the start of drying until the end), and Table 1 shows feature point data extracted from each drying cycle when 13 drying cycles are performed with different foams.

[0096] Dry Numberx0x1x2x311.321112.005983.963350.9783921.648312.097694.641651.197033-0.3585-0.3766-0.43840.1877140.28515-0.3967-0.14030.432935-0.4572-0.1863-0.2337-0.99686-0.8971-0.1204-0.4536-1.0287-0.7011.54 1510.98033-0.21018-0.46741.407111.20272-0.109490.624241.234342.203671.00571100.625751.335082.410690.89238110.92884-1.2621-1.16442.8360112-0.642-1.3054-1.25460.61639130.36953-1.2496-1.19130.89008

[0097] In step S430, the dryer (1000) can determine a foam score corresponding to each of a plurality of foams based on the feature point data.

[0098] The dryer (1000) can generate an artificial intelligence model that outputs which of two different foam qualities is closer when feature point data is input as input to the artificial intelligence model.

[0099] For example, the combination of selecting two different types of cells from n types of cells is n Since there are 2 C, n C2 artificial intelligence models (K n ) can be learned in advance. Referring to Fig. 5, when two different types of foam are selected from among n foams, n C2 combinations can be generated and an artificial intelligence model is generated for each combination. n Two AI models can be obtained. If two of the five fabrics (e.g., cotton shirt, denim, towel, synthetic fiber, and blended fiber) are selected, ten AI models can be generated.

[0100] Each model can output which of the two foam types it represents, when feature point data is input, the model is closer to. For example, referring to Fig. 5, the K1 model (model number 1) can output whether the feature point data is closer to foam type 1 or foam type 2 when input. If a positive number is output, it can be determined to be closer to foam type 1, and if a negative number is output, it can be determined to be closer to foam type 2.

[0101] Referring to FIG. 6, the dryer (1000) is based on sensor data and the drying material. n C2 artificial intelligence models can be trained.

[0102] In step S610, the dryer (1000) can obtain identification information of the foam from which conductivity sensor data, temperature sensor data, motor current sensor data, and data of the sensors are obtained.

[0103] In step S620, the dryer (1000) can generate a data frame including feature point data as input data of training data and the quality from which feature point data is extracted as output data of training data.

[0104] The dryer (1000) is based on the generated data frame in step S630. n C2 artificial intelligence models can be trained. In this case, when feature point data is input, the quality of the feature point data extracted is output as 1 or -1 according to the table in Fig. 5. n C2 artificial intelligence models can be trained. 1 or -1 are exemplary output values, and depending on the embodiment, the output value may be a or -a.

[0105] These artificial intelligence models may be referred to as, but are not limited to, Support Vector Machine (SVM) models.

[0106] The dryer (1000) is learned in step S640. n We can obtain feature vectors for the calculation of C2 artificial intelligence models.

[0107] The dryer (1000) corresponds to each artificial intelligence model as shown in Equation 1 below, and when the feature point data is input, the output value K, which is the output of the corresponding artificial intelligence model, is n which can produce n C2 computational feature vectors can be obtained. The computational feature vectors can be obtained from the neural network parameters of the artificial intelligence model generated through learning of the artificial intelligence model.

[0108] (Formula 1)

[0109] K n = w n0 × x0+ w n1 × x1+ w n2 × x2+ w n3 × x3+ b n

[0110] The feature vector for calculation is w, which is the weight of the a-th feature point. na and intercept b n may include.

[0111] Table 2 below shows 10 computational feature vectors learned from the data frame in Fig. 6.

[0112] K0K1K2K3K4K5K6K7K8K9w n0 2.7270972.9186210.4112630.3897545.044882-1.48599-0.76437-1.17333-0.469720.028547w n1 3.750563-0.434721.5749382.701468-4.816941.1946244.9720371.0440842.398779-0.35081w n2 1.2452280.0797261.7046883.670441-3.392292.2618041.3284330.2988780.684766-3.3635w n31.0306850.812466-1.61036-2.646732.854664-2.07573-4.07262-0.62187-1.388891.83317b n 1.3299381.2554130.472304-0.075555.3026690.8262340.615766-0.24932-0.13146-4.32997

[0113] Referring to FIG. 7, the dryer (1000) can calculate a quality score for n quality points based on the acquired calculation feature vector and feature point data.

[0114] The dryer (1000) can obtain conductivity sensor data, temperature sensor data, and motor current sensor data in step S710. The dryer (1000) can extract feature points for deriving a foam classification constant value in step S720. Steps S710 and S720 can be described with reference to steps S410 and S420 of FIG. 4.

[0115] The dryer (1000) calculates the feature vector and feature point data obtained in step S730. n C2 can determine the constant values ​​of the classification of the foam.

[0116] For example, the dryer (1000) calculates the quality classification constant K by calculating four feature point data (x0 to x3) with the calculation feature vector corresponding to the nth artificial intelligence model. n can be calculated. The constant value of the quality classification K n The larger the absolute value of , the more likely it is that the feature point data extracted from the feature belongs to the feature combination corresponding to the nth artificial intelligence model (for example, feature 1 and feature 2 in the case of the first artificial intelligence model in Fig. 5).

[0117] Table 3 below shows the constant K for the classification of foam. n is an example. Table 3 shows the feature point data (x0, x1, x2, x3) of Table 1 and the feature vector for calculation (w) of Table 2. n0, w n1 , w n2 , w n3 , b n ) is the result of the operation.

[0118] Drying number K0K1K2K3K4K5K6K7K8K9118.39965.350149.3554617.8158-8.34658.1927810.85960.8715.4147-16.533220.70626.4969210.438920.1024-8.8158.8966111.07670.649725.64208-18.4373-1.41990.49104-1.3177-3.33837.33216-0.4724-2.3296-0.4699-1.4274-2.389 640.891242.60068-0.9716-2.69710.364-1.2874-3.242-1.3093-1.9144-2.91715-1.934-0.82661.197711.023531.840562.823663.788160.64270.86087-5.3196-3.1924-2.18180.795880.30534-0.03863.12324.286721.18121.1182-4.67276.20399-1.55324.621257.96984 -9.58446.3628210.97392.60634.85864-8.573286.71773-0.71344.722648.24762-8.22556.1491310.01252.195724.43896-9.0828911.44243.533554.810068.92888-2.09844.269885.107360.34022.6484-10.3141011.96533.418575.5047510.2614-3.60195.091486.34368 0.575973.18852-11.253110.602496.72634-7.6853-14.90328.1138-10.582-19.466-4.7685-8.33155.2545512-6.24360.35-4.9789-10.08814.3674-3.8963-9.5609-1.6173-4.67641.4593413-2.91523.50536-4.8079-10.03619.7684-5.7577-11.087-2.8972-5.35461.7575

[0119] The dryer (1000) is at step S740, nn U based on C2 foam classification constant values 포질 The value can be calculated. U x can mean a score indicating the probability that the feature point data corresponding to the feature point is not the xth feature point. Therefore, U x A lower value may mean that the corresponding feature data is the xth feature.

[0120] According to one embodiment of the present disclosure, U1 to U n can be calculated as follows.

[0121] U1= (α-K1, α-K2, α-K3, …, α-K n-1 ) If it is a negative number, replace it with 0 and then take the average of n-1

[0122] U2= (α+K1, α-K n , α-K n+1 , … , α-K 2n-3 ) If it is a negative number, replace it with 0 and then take the average of n-1

[0123] U3= (α+K2, α+K n , α-K 2n-2 , … , α-K 3n-6 ) If it is a negative number, replace it with 0 and then take the average of n-1

[0124] …

[0125] U n = (α+K n-1 , α+K 2n-3 , α+K 3n-6 , ..., α-K nC2 ) If it is a negative number, replace it with 0 and then take the average of n-1

[0126] U1 to U nIn the formulas, α is a threshold value and can be adjusted by the user. α can represent a criterion for how far the quality classification constant value must be from the decision boundary to be a valid value. In the case of mixed quality materials with various quality properties, the quality classification constant values ​​do not fall far from the decision boundary, so the difference between the α value and the quality classification constant value may be small. U 포질 By defining the difference between the foam classification constant and the α value, even in cases where the difference between the α value and the foam classification constant is small, such as in the case of mixed foam, and thus cannot be identified as one of n foams, the foam of the dried product can be identified as a mixed foam.

[0127] U1 to U n The ceremonies are n-shaped and shown in Fig. 5. n This can be explained with reference to a table showing the relationship between two artificial intelligence models. When n is 5, the quality 1 can be identified when the output of model 1, model 2, model 3, model 4, or model 5 is 1. Therefore, U1, which indicates the possibility that the quality of the building is not quality 1, can be the average of the values ​​obtained by subtracting K1 corresponding to model 1, K2 corresponding to model 2, K3 corresponding to model 3, K4 corresponding to model 4, and K5 corresponding to model 5 from the threshold α. In addition, quality 2 can be identified when the output of model 1 is -1, or the output of model 5, model 6, or model 7 is 1. Therefore, U2, which indicates the possibility that the quality of the building is not quality 2, can be the average of the values ​​obtained by adding K1 to the threshold α, or subtracting K5, K6, K7, and K8 from the threshold α.

[0128] According to one embodiment of the present disclosure, U n In the expression representing (α ± K x ) If the value is negative, replace it with 0 and then take the average of n-1 U 포질The calculation can be made easier by calculating it as . The dryer (1000) n n U based on C2 foam classification constant values 포질 can be produced.

[0129] Table 4 below shows the five U values ​​calculated for each type of foam based on the 10 foam classification constants in Table 3. 포질 It represents.

[0130] Drying Number Cotton Shirt Denim Towel Synthetic Fiber Blended Fiber 107.186521.619799.738099.27254207.88031.961810.60559.9553132.394211.20053.430131.11183041.444322.175715.047070.98638051.1901400.877613.495762.1681462.068230.259640.240363.443082.1775770.638 34.447106.540896.700680.428344.235790.071666.537586.42478903.885191.298345.933434.921161004.39181.210656.606295.69841116.246458.4127112.98501.56364126.240233.864296.2527800.61483135.18974.711298.8813800.68937

[0131] According to Equation 2 below, the dryer (1000) is U 포질 Based on the quality score S for each quality 포질 can be produced. The quality score S 포질 S may be a score indicating the likelihood that the dried material is of that type. 포질 Silver U 포질 It can be a value converted to a value between 0 and 100 points.

[0132] (Formula 2)

[0133] S 포질 = (-U 포질 / 2 + α) × 100 (0 if negative)

[0134] Table 5 below is U of Table 4 포질 The quality score S calculated based on 포질 It represents.

[0135] Drying Number Cotton Shirt Denim Towel Synthetic Fiber Blended Fiber 110001900210002003040014410042800151100540100560060878800768010000879096009100035001010003900110001222212000169691300016666

[0136] Again, in step S440 of FIG. 4, the dryer (1000) may determine, for each of the plurality of foams, whether the foam score exceeds the foam sensitivity. The foam sensitivity corresponding to a foam may indicate the degree to which the foam is not considered when determining the drying method for the dried product. The dryer (1000) may have foam sensitivity stored in advance corresponding to each of the plurality of foams.

[0137] Table 6 below is an example of the sensitivity of multiple types of foam.

[0138] Cotton shirt denim towel synthetic fiber blended fiber quality sensitivity 90959010090

[0139] The dryer (1000) can determine, for each of a plurality of foams, whether the foam score exceeds the foam sensitivity corresponding to the foam. The dryer (1000) can determine whether drying is in progress only for foams whose foam score exceeds the foam sensitivity corresponding to the foam, and can not consider whether drying is in progress for foams whose foam score is lower than the foam sensitivity corresponding to the foam. The foam sensitivity can be adjusted by a user. The dryer (1000) can receive a user input for adjusting the foam sensitivity of the plurality of foams. Accordingly, the user can adjust the weight for each foam by adjusting the foam sensitivity of the plurality of foams.

[0140] In step S450, the dryer (1000) can determine the foam having the highest foam score among at least one foam having a foam score exceeding a foam sensitivity as the foam quality of the dried product.

[0141] Figure 8 shows the quality of the dried product determined based on the quality score of Table 5 and the quality sensitivity of Table 6, and the drying method corresponding to the determined quality.

[0142] In step S460, the dryer (1000) can perform drying using a drying method corresponding to the determined foam quality.

[0143] In drying No. 1 of Fig. 8, since only the cotton shirt (quality score 100) exceeded the quality sensitivity corresponding to a cotton shirt of 90, the dryer (1000) can determine that the quality of the object being dried is a cotton shirt. In addition, the dryer (1000) can perform drying using the 'AI customized drying' course, which is a drying method corresponding to a cotton shirt.

[0144] In the third drying, since only the synthetic fiber (fiber quality score 144) exceeded the fiber quality sensitivity corresponding to synthetic fibers, which is 100, the dryer (1000) can determine that the fiber quality of the object being dried is synthetic fiber. In addition, the dryer (1000) can perform drying using the 'blouse drying' course, which is a drying method corresponding to synthetic fibers.

[0145] In the fifth drying, since only denim (quality score 100) exceeded 90, which is the quality sensitivity corresponding to denim, the dryer (1000) can determine that the quality of the material being dried is denim. In addition, the dryer (1000) can perform drying using the 'denim drying' course, which is a drying method corresponding to denim.

[0146] Based on the determination in step S440 that there is no foam whose foam score among the plurality of foams exceeds the foam sensitivity, in step S470, the dryer (1000) determines that a plurality of dry products of the plurality of foams are being dried, and can perform drying using a drying method corresponding to the mixed foam.

[0147] For example, in drying No. 6 of Table 5, since the quality scores of all the quality scores did not exceed the quality sensitivity of each quality, the dryer (1000) determines that the quality of the dried material being dried is a mixed quality, as in drying No. 6 of FIG. 8, and can perform drying using the 'AI customized drying' course, which is a drying method corresponding to the mixed quality.

[0148] According to one embodiment of the present disclosure, the dryer (1000) can receive a user input for changing the quality sensitivity. For example, if the user wants to dry towels according to different quality of towels even though there are many towels in the drying load, the quality sensitivity of the towels can be changed to be higher than the quality sensitivity of the other quality of towels. Since the quality sensitivity of the towels is set to be higher than the quality sensitivity of the other quality of towels, the dryer (1000) can determine the quality of the drying load as one of the other quality of towels or as a mixed quality, rather than determining the quality of the towels even if the quality score of the towels is higher than the quality scores of the other quality of towels. Accordingly, the dryer (1000) can perform drying according to a drying method other than a drying method corresponding to 'towel drying'.

[0149] Additionally, the user can adjust the likelihood that the dried product will be judged as a mixed-material product by adjusting the quality sensitivity. For example, based on receiving a user input that sets the quality sensitivity corresponding to the quality components to a high level overall, the dryer (1000) can increase the likelihood that the dried product will be judged as a mixed-material product. For example, referring to Table 5, based on receiving a user input that sets the quality sensitivity of the quality components to 120, the dryer (1000) can determine the quality of the dried product as a mixed-material product rather than a cotton shirt even in the first drying cycle.

[0150] FIG. 9 illustrates a method in which a dryer (1000) displays a drying method corresponding to the quality of a dried material according to one embodiment of the present disclosure.

[0151] Referring to FIG. 9, the dryer (1000) can display identification information (930) of the determined quality of the dried material being dried. In addition, the dryer (1000) can display a drying method (940) corresponding to the determined quality.

[0152] For example, upon receiving a user input to start drying, the dryer (1000) may start drying in a 'standard drying' course, which is a default drying method. The dryer (1000) may display a phrase (910) indicating that the drying method is 'standard drying'. In addition, the dryer (1000) may display information (920) regarding 'standard drying'. The information (920) regarding 'standard drying' may include characteristics and a drying degree of standard drying. In addition, the dryer (1000) may display the remaining time until drying is completed when drying in the 'standard drying' course.

[0153] As drying begins, the dryer (1000) can acquire conductivity data, temperature data, and current data for a predetermined period of time. Based on the acquired data, the dryer (1000) can determine the quality of the material being dried as 'denim'.

[0154] When the quality of the dry material is determined to be 'denim', the dryer (1000) can display guide information guiding the user to change the drying method to the 'denim drying' course, which is a drying method corresponding to 'denim'. 'Denim drying' may be, for example, a drying method in which both the drying temperature and fan RPM are set low. Based on the user input for changing the drying method to the 'denim drying' course, the dryer (1000) can change the drying method to the 'denim drying' course.

[0155] As the drying method is changed to the 'denim drying' course, the dryer (1000) can display information (930) indicating that the fabric of the object being dried is denim. In addition, the dryer (1000) can display a phrase (940) indicating that the drying method has been changed to the 'denim drying' course. In addition, the dryer (1000) can display information (950) regarding 'denim drying'. The information (950) regarding 'denim drying' can include a phrase indicating that the drying level is 2 and that it is a method for delicately drying denim garments. In addition, as the drying method is changed to the 'denim drying' course, the dryer (1000) can recalculate and display the remaining time until the end of drying.

[0156] FIG. 10 illustrates a method for a dryer (1000) to change a foam sensitivity based on a user input selecting a drying method, according to one embodiment of the present disclosure.

[0157] Referring to FIG. 10, the dryer (1000) can display a drying method determined based on sensor data through the user device (2000). The dryer (1000) can receive a user input for changing the drying method through the user device (2000). The dryer (1000) can change the foam sensitivity based on the changed drying method.

[0158] For example, when it is determined based on sensor data that the object being dried includes multiple objects having different foam properties and the drying method is changed to an 'AI customized drying' course, the dryer (1000) can transmit information about the determined foam properties and information about the changed drying method to the user device (2000) via the server (3000). The user device (2000) can display information (960) indicating that the object being dried includes multiple objects having different foam properties and 'AI customized drying' (970) as the drying method. In addition, the user device (2000) can display a list (980) of drying methods that the dryer (1000) can provide.

[0159] The user device (2000) may receive a user input for changing the drying method. For example, the user device may receive a user input for selecting 'mixed fiber drying' from the list of drying methods (980). The user device (2000) may transmit a control command to the dryer (1000) via the server (3000) to change the drying method to the 'mixed fiber drying' course.

[0160] The dryer (1000) can change the drying method to a 'blended fiber drying' course and, based on the blistering score of the blended fiber in the drying material, reduce the blistering sensitivity of the blended fiber. For example, the dryer (1000) can determine the blistering score of the blended fiber in the drying material as the blistering sensitivity of the blended fiber.

[0161] By lowering the swell sensitivity of the blended fiber, the dryer (1000) can increase the possibility of drying using the drying method of 'blended fiber drying' even if the swell sensitivity score of the blended fiber in the next drying is the same as that of the previous drying.

[0162] Although not disclosed in FIG. 10, the dryer (1000) may receive user input for selecting a drying method before starting drying, and adjust the foam sensitivity based on the selected drying method.

[0163] For example, the dryer (1000) may receive a user input selecting 'drying a cotton shirt' before starting drying, and then determine that the fabric of the dry object is a towel based on sensor data during drying. In this case, the dryer (1000) may lower the fabric sensitivity of the cotton shirt fabric.

[0164] FIG. 11 illustrates a flowchart of a method for adjusting the foam sensitivity of a dryer (1000) based on user input, according to one embodiment of the present disclosure.

[0165] In step S1110, the dryer (1000) may display a user interface for setting the foam sensitivity.

[0166] The dryer (1000) can display a user interface for setting the foam sensitivity based on receiving user input for setting the foam sensitivity.

[0167] Foam sensitivity can refer to the degree to which a material is not considered when determining the drying method. Accordingly, even if the foam sensitivity of a material is high, the material may not be dried using a drying method that corresponds to the foam quality.

[0168] The user interface may include an input field or drop down button for entering a sensitivity value for each of the plurality of foams.

[0169] In step S1120, the dryer (1000) can receive a user input for inputting a foam sensitivity for at least one foam among a plurality of foams through a user interface.

[0170] In step S1130, the dryer (1000) may determine one of the plurality of foams as the foam quality of the drying material based on the received foam quality sensitivity, or may determine that the drying material includes a plurality of drying materials of different foam quality.

[0171] The dryer (1000) can calculate a foam quality score corresponding to each of a plurality of foam qualities for a drying material being dried. The dryer (1000) can compare the foam quality score for each of the plurality of foam qualities with an input foam quality sensitivity to determine one of the plurality of foam qualities as the foam quality of the drying material, or determine that the drying material includes a plurality of drying materials having different foam qualities.

[0172] FIG. 12 illustrates a method for a user device to display a user interface for adjusting the foam sensitivity of a dryer (1000), according to one embodiment of the present disclosure.

[0173] Referring to FIG. 12, the user device (2000a, 2000b) can display a user interface for setting the sensitivity of multiple foams.

[0174] Referring to the left drawing of FIG. 12, the user device (2000a) may display a user interface (125) for inputting the foam sensitivity of a plurality of foams. The user interface (125) for inputting the foam sensitivity may include an input field for inputting the foam sensitivity as a numeric value. In addition, the user device (2000a) may display a phrase (126) indicating the meaning of the foam sensitivity.

[0175] The user device (2000a) can transmit identification information of a plurality of foam qualities and foam sensitivities for each of the plurality of foam qualities to the dryer (1000) via the server. The dryer (1000) can compare foam quality scores for each of the plurality of foam qualities and the input foam quality sensitivities with respect to the dry matter being dried. The dryer (1000) can determine the foam quality with the highest foam quality score among the foam qualities exceeding the foam quality sensitivities as the foam quality of the dry matter. If the foam quality scores of all the foam qualities do not exceed the foam quality sensitivities, the dryer (1000) can determine that the dry matter includes a plurality of dry matters having different foam qualities.

[0176] Referring to the right drawing of FIG. 12, the user device (2000b) may display a user interface (128) for selecting a significant particle among a plurality of particles. The user device (2000b) may receive a user input for selecting at least one significant particle through the user interface (128). In addition, the user device (2000b) may display a phrase (129) indicating the meaning of the significant particle.

[0177] The user device (2000b) can transmit identification information of the selected important foam to the dryer (1000) via the server. The dryer (1000) can lower the foam sensitivity of the foam selected as the important foam to a value lower than that of the unselected foams. Accordingly, even if the foam score of the foam selected as the important foam is lower than that of other foams, the dryer (1000) can perform drying using a drying method corresponding to the selected foam if the foam score is higher than the reference value.

[0178] FIG. 13 illustrates a block diagram of a dryer (1000) according to one embodiment of the present disclosure.

[0179] Referring to FIG. 13, the dryer (1000) may include a processor (1100), a microphone (1200), a communication module (1300), a memory (1400), an input interface (1500), an output module (1600), a sensor (1700), and a drying module (1900).

[0180] Not all of the components illustrated are essential components of the dryer (1000). The dryer (1000) may be implemented with more components than those illustrated in FIG. 13, or the dryer (1000) may be implemented with fewer components than those illustrated in FIG. 13.

[0181] The same reference numbers are used for configurations identical to those illustrated in Fig. 2.

[0182] The processor (1100) controls the overall operation of the dryer (1000). The processor (1100) can control components of the dryer (1000) by executing a program stored in the memory (1400).

[0183] The processor (1100) may include various processing circuits and / or multiple processors. For example, the term “processor” as used herein, including in the claims, may include various processing circuits, including at least one processor. One or more processors in at least one processor may be configured to perform various functions described herein, individually and / or collectively, in a distributed fashion. As used herein, “processor,” “at least one processor,” and “one or more processors” may be configured to perform various functions. However, these terms encompass, without limitation, situations where one processor performs some of the functions and other processor(s) perform other parts of the functions, and situations where a single processor may perform all of the functions. Furthermore, at least one processor may include a combination of processors that perform various functions of the disclosed functions in a distributed manner. At least one processor may execute program instructions to achieve or perform various functions.

[0184] The memory (1400) stores various information, data, commands, programs, etc. required for the operation of the dryer (1000). The memory (1400) may include at least one of volatile memory and non-volatile memory, or a combination thereof. The memory (1400) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. In addition, the dryer (1000) may also operate a web storage or cloud server (not shown) that performs a storage function on the Internet.

[0185] At least one processor (1100) and at least one memory (1400) may be included in one control unit. For example, at least one processor (1100) and at least one memory (1400) may be included in one microcontroller unit (MCU).

[0186] The drying module (1900) may include a drum (1910), a drum motor (1920), a heating module (1930), and a blower module (1940).

[0187] A drum motor (1920) can rotate the drum.

[0188] The dryer (1000) may include a control panel (1510 of FIG. 1) disposed on one side of the housing. The control panel (1510) may provide a user interface for interaction between a user and the dryer (1000). The user interface may include at least one input interface (1500) and at least one output module (1600).

[0189] At least one input interface (1500) can convert sensory information received from a user into an electrical signal. The input interface (1500) can receive user input for controlling the dryer (1000).

[0190] The input interface (1500) may include, but is not limited to, a user input device including a touch panel that detects a user's touch, a button that receives a user's push operation, a wheel that receives a user's rotation operation, a keyboard, and a dome switch.

[0191] Additionally, the input interface (1500) may include a motion detection sensor (not shown). For example, the motion detection sensor (not shown) may detect a user's movement and receive the detected movement as a user input.

[0192] The input interface (1500) receives user input and transmits it to the processor (1100).

[0193] At least one input interface (1500) may include a power button, an operation button, a course selection dial (or a course selection button), and a drying setting button. The at least one input interface (1500) may include, for example, a tact switch, a push switch, a slide switch, a toggle switch, a micro switch, a touch switch, a touch pad, a touch screen, a jog dial, and / or a microphone (1200).

[0194] At least one output module (1600) can visually or audibly convey information related to the operation of the dryer (1000) to the user.

[0195] For example, at least one output module (1600) can transmit information related to the operating time and drying settings of the dryer (1000) to the user. Information related to the operation of the dryer (1000) can be output through a screen, indicator, voice, etc. At least one output module (1600) can include a display (1610) and an audio output module (1620).

[0196] The display (1610) can output image data processed by an image processing unit (not shown) through a display panel (not shown) under the control of the processor (1100). The display panel (not shown) can include at least one of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display, and an electrophoretic display.

[0197] The audio output module (1620) can output audio signals to the outside of the dryer (1000). The audio output module (1620) can include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback.

[0198] The microphone (1200) can receive a user's voice command or voice request. Accordingly, the processor (1100) can control an operation corresponding to the voice command or voice request to be performed. In addition, the microphone (1200) can receive sounds surrounding the dryer (1000).

[0199] The communication module (1300) can transmit and receive information according to a protocol with an external device (not shown) or a server (3000) under the control of the processor (1100). The communication module (1300) can include at least one communication module and at least one port for transmitting and receiving data with an external device (not shown) or a server (3000).

[0200] Additionally, the communication module (1300) can communicate with an external device via at least one wired or wireless communication network. The communication module (1300) may include at least one of a short-range communication module or a long-range communication module, or a combination thereof. The communication module (1300) may include at least one antenna for wirelessly communicating with another device.

[0201] The communication module (1300) can transmit data to an external device (e.g., a server, a user device, and / or a home appliance) or receive data from an external device. For example, the communication module (1300) can establish communication with a server (3000), a user device (2000), and / or other home appliances, and transmit and receive various types of data.

[0202] To this end, the communication module (1300) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between external devices, and the performance of communication through the established communication channel. According to one embodiment, the communication module (1300) can include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (e.g., a local area network (LAN) communication module, or a power line communication module). The communication module (1300) can communicate with the external device through a first network (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). Different types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips).

[0203] The short-range wireless communication module may include, but is not limited to, a Bluetooth communication module, a BLE (Bluetooth Low Energy) communication module, a near field communication module, a WLAN (Wi-Fi) communication module, a Zigbee communication module, an infrared (IrDA, infrared Data Association) communication module, a WFD (Wi-Fi Direct) communication module, an UWB (ultrawideband) communication module, an Ant+ communication module, a microwave (uWave) communication module, etc.

[0204] The remote communication module may include a communication module that performs various types of remote communication, and may include a mobile communication module. The mobile communication module transmits and receives wireless signals with at least one of a base station, an external terminal, and a server (3000) on a mobile communication network.

[0205] In one embodiment, the communication module (1300) can communicate with external devices such as the server (3000), the user device (2000), and other home appliances through a surrounding access point (AP). The access point (AP) can connect a local area network (LAN) to which the dryer (1000) or the user device is connected to a wide area network (WAN) to which the server (3000) is connected. The dryer (1000) or the user device (2000) can be connected to the server (3000) through the wide area network (WAN).

[0206] The sensor (1700) may include, but is not limited to, a conductivity sensor (1710), a temperature sensor (1720), a humidity sensor (not shown), and a current sensor (1730).

[0207] At least one processor (1100) can obtain a conductivity value from a conductivity sensor (1710), a temperature within the dryer (1000) from a temperature sensor (1720), and a current value applied to the drum motor from a current sensor (1730).

[0208] At least one processor (1100) can determine whether the quality of the dried material in the dryer (1000) or whether the dried material includes multiple dried materials of different quality based on the acquired conductivity value, temperature, and current value applied to the drum motor.

[0209] At least one processor (1100) can control the drying module (1900) to perform drying using a drying method corresponding to the determined quality or a drying method corresponding to a plurality of drying materials of different quality.

[0210] At least one processor (1100) may, based on determining that the building includes a plurality of buildings of different qualities, display notification information indicating that the building includes a plurality of buildings of different qualities through the display (1610).

[0211] At least one processor (1100) can calculate a foam score indicating the likelihood that the foam of the dry material is each of a plurality of foams based on the acquired conductivity values, temperature, and current values ​​applied to the drum motor.

[0212] At least one processor (1100) can obtain a quality sensitivity corresponding to each of a plurality of qualities, which indicates the degree to which the qualities are not taken into account when determining a drying method for the dry product.

[0213] At least one processor (1100) may determine one of the plurality of qualities as the quality of the dry matter, or determine that the dry matter comprises a plurality of qualities of different dry matters, based on the generated quality score and quality sensitivity.

[0214] At least one processor (1100) can display a user interface for setting the sensitivity of the foam via the display (1610).

[0215] At least one processor (1100) can receive user input via a user interface to input a sensitivity for at least one of the plurality of foams.

[0216] At least one processor (1100) displays notification information indicating that the drying method corresponding to the determined foam or the drying method corresponding to a plurality of drying materials of different foams is dried,

[0217] At least one processor (1100) can receive a user input for changing a drying method of the dryer (1000) via an input interface (1500). At least one processor (1100) can lower the foam sensitivity for the foam corresponding to the changed drying method.

[0218] At least one processor (1100) can change the drying method to a drying method corresponding to the determined quality or a drying method corresponding to a plurality of drying materials of different quality based on whether a user input for selecting a drying method is received before the dryer (1000) starts drying.

[0219] At least one processor (1100) can change the drying method to a drying method corresponding to a determined quality or a drying method corresponding to a plurality of drying materials of different quality as a predetermined time elapses from the time when the dryer (1000) starts drying.

[0220] At least one processor (1100) can display notification information indicating that the quality of the dried material being dried in the dryer (1000) is the determined quality through the display (1610).

[0221] At least one processor (1100) can display, through the display (1610), notification information indicating that drying is performed using a drying method corresponding to the determined quality or a drying method corresponding to a plurality of drying materials of different quality.

[0222] At least one processor (1100) can transmit information about the quality of the dry material determined through the communication module (1300), whether the dry material includes multiple dry materials of different qualities, or the drying method being performed to the user device through the server.

[0223] At least one processor (1100) can display guide information through the display (1610) to guide changing the drying method to a drying method corresponding to the determined foam.

[0224] At least one processor (1100) can perform drying using a drying method corresponding to the determined foam quality based on receiving a user input for changing the drying method to a guided drying method through an input interface (1500).

[0225] A device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0226] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

Claims

1. In the dryer (1000), Conductivity sensor (1710); Temperature sensor (1720); Current sensor (1730); Drying module (1900); At least one memory (1400) storing one or more instructions; and comprising at least one processor, wherein said at least one processor executes said one or more instructions stored in said memory (1400), Obtain a conductivity value from the conductivity sensor (1710), a temperature inside the dryer (1000) from the temperature sensor (1720), and a current value applied to the drum motor from the current sensor (1730). Based on the obtained conductivity value, temperature and current value applied to the drum motor, it is determined whether the quality of the dried material in the dryer (1000) or whether the dried material includes multiple dried materials of different quality, A dryer (1000) that controls the above drying module (1900) to perform drying using a drying method corresponding to the determined foam quality or a drying method corresponding to a plurality of drying materials of different foam quality.

2. In paragraph 1, At least one processor, A dryer that displays notification information indicating that the dry product includes a plurality of dry products of different foam properties based on determining that the dry product includes a plurality of dry products of different foam properties.

3. In paragraph 1 or 2, At least one processor, Based on the obtained conductivity value, temperature, and current value applied to the drum motor, a foam score indicating the possibility that the foam of the dried material is each of a plurality of foams is calculated, In response to each of the above multiple foams, a foam sensitivity indicating the degree to which the foam is not considered when determining the drying method of the dried product is obtained, A dryer that determines one of the plurality of qualities as the quality of the dried product based on the calculated quality score and quality sensitivity, or determines that the dried product includes a plurality of dried products having different qualities.

4. In paragraph 3, At least one processor, Display a user interface for setting the above sensitivity, A dryer that receives a user input for inputting a foam sensitivity for at least one of the plurality of foams through the user interface.

5. In paragraph 3, At least one processor, Receive user input for selecting a drying method for the above-mentioned dry material, A dryer that lowers the foam sensitivity for foam corresponding to the above-mentioned selected drying method.

6. In any one of paragraphs 1 to 5, At least one processor, A dryer that performs drying using the drying method corresponding to the determined foam quality or the drying method corresponding to a plurality of drying objects of different foam quality, based on whether a user input for selecting a drying method is received before the dryer starts drying.

7. In any one of paragraphs 1 to 6, At least one processor, A dryer that changes the drying method to the drying method corresponding to the determined foam quality or the drying method corresponding to a plurality of drying objects of different foam quality as a predetermined time elapses from the time the dryer starts drying.

8. In any one of paragraphs 1 to 7, At least one processor, A dryer that displays the identification information of the determined foam.

9. In any one of paragraphs 1 to 8, At least one processor, A dryer that displays the drying method corresponding to the determined foam quality or the drying method corresponding to a plurality of dried products of different foam quality.

10. In any one of paragraphs 1 to 9, At least one processor, Display guide information that guides changing the drying method to a drying method corresponding to the determined foam quality, A dryer that performs drying by a drying method corresponding to the determined foam quality based on receiving a user input for changing the drying method by the above-determined guided drying method.

11. In the method of controlling a dryer, A step of obtaining a conductivity value from a conductivity sensor, a temperature inside the dryer from a temperature sensor, and a current value applied to the drum motor from a current sensor; A step of determining whether the quality of the dried material in the dryer or whether the dried material includes a plurality of dried materials of different quality based on the obtained conductivity value, temperature, and current value applied to the drum motor; and A method comprising a step of performing drying by a drying method corresponding to the determined foam quality or a drying method corresponding to a plurality of dried products of different foam quality.

12. In paragraph 11, The above method, A method further comprising the step of displaying notification information indicating that the building comprises a plurality of buildings of the different foams, based on determining that the building comprises a plurality of buildings of the different foams.

13. In paragraph 11 or 12, The step of determining whether the quality of the dried material in the dryer or whether the dried material includes a plurality of dried materials of different quality is as follows: A step of calculating a foam score indicating the possibility that the foam of the dried material is each of a plurality of foams based on the obtained conductivity value, temperature, and current value applied to the drum motor; A step of obtaining a foam sensitivity representing the degree to which each of the plurality of foams is not considered when determining a drying method of the dried product; and A method comprising a step of determining one of the plurality of qualities as the quality of the dried product based on the calculated quality score and the quality sensitivity, or determining that the dried product includes a plurality of dried products having different qualities.

14. In paragraph 13, The step of obtaining a quality sensitivity representing the degree to which each of the above multiple qualities is not considered when determining the drying method of the dried product is as follows: A step of displaying a user interface for setting the above foam sensitivity; and A method comprising the step of receiving a user input inputting a foam sensitivity for at least one foam among the plurality of foams through the user interface.

15. In paragraph 13, The above method, A step of receiving a user input for selecting a drying method for the above-mentioned dry material; and A method further comprising the step of lowering the foam sensitivity for the foam corresponding to the selected drying method.

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