KR20220040225A - Cooking device and operating method thereof - Google Patents

Abstract

A cooking appliance according to one embodiment of the present invention is configured such that a food ingredient is identified from an image captured by a camera, it is determined whether or not the identified food ingredient belongs to a food group generating oil mist, and when the identified food ingredient belonging to a food group generating oil mist, cooking state information indicating the cooked state of the food ingredient is obtained. Furthermore, the cooking appliance may determine the contamination level of a cooking chamber on the basis of the obtained cooking state information, display cleaning guide information through a display unit according to the determined contamination level, or control a heating unit so as to enable a cleaning mode of the cooking chamber to operate.

Description

Cooking device and its operation method {COOKING DEVICE AND OPERATING METHOD THEREOF}

The present disclosure relates to a cooking appliance capable of guiding cleaning of a cooking compartment by measuring a contamination level of a cooking compartment through image recognition.

BACKGROUND ART A cooking appliance using heat, such as an oven, is an essential home appliance in daily life.

The cooking device heats the food inside the cooking chamber to cook it. The inside of the cooking compartment may be contaminated depending on the cooking of the food, and it is sometimes necessary to clean the cooking compartment.

Korean Patent Laid-Open Patent KR2012-0046756A provides an oven that can guide cleaning by self-diagnosis, measures ammonia gas generated from a contaminant in the oven cabinet, and when it is above the standard value, notifies about cleaning of the oven and operates the cleaning mode. is starting

However, in the prior art, it is determined whether the oven is cleaned based on the amount of ammonia generated due to decay of the pollutant. In this case, since it can be determined at the level at which harmful gas is generated due to the contaminants, the contamination may have already progressed, so there is a problem in that it is difficult to preemptively guide the sanitation/cleaning of the oven.

In addition, there is a problem in that it is difficult to generalize the standard for determining the hygiene level in the oven only to the amount of ammonia gas measured.

An object of the present disclosure is to provide a cooking appliance capable of preemptively guiding cleaning of a cooking chamber suitable for a level of pollution by recognizing a degree of pollution generated during a cooking process.

An object of the present disclosure is to provide a cooking appliance capable of preemptively and automatically cleaning a cooking chamber suitable for a level of pollution by recognizing a degree of pollution generated during a cooking process.

An object of the present disclosure is to provide a cooking appliance capable of providing cleaning management for each stage according to the degree of contamination of the cooking chamber.

The cooking appliance according to an embodiment of the present disclosure determines the contamination level of the cooking chamber based on cooking state information indicating the cooking state of the food material when the food material identified from the image captured by the camera belongs to a food group generating oil vapor. It may be determined, and cleaning guide information may be displayed through the display unit according to the determined degree of contamination.

The cooking appliance according to an embodiment of the present disclosure may control the heating unit to automatically perform cleaning according to the determined contamination level.

The cooking appliance according to an embodiment of the present disclosure displays, through the display unit, first cleaning guide information for allowing a user to perform simple cleaning when the pollution level is the first pollution level, and the pollution level is higher than the first pollution level. In the case of a large second pollution level, the user causes the user to spray water into the cooking chamber, and displays second cleaning guide information indicating automatic operation in the first cleaning mode through the display unit, and the degree of pollution is When the third pollution level is greater than the second pollution level, third cleaning guide information indicating automatic operation in the second cleaning mode may be displayed through the display unit.

According to an embodiment of the present disclosure, information for cleaning the cooking compartment is guided before the level of contamination of the cooking compartment becomes serious, so that the user can efficiently take measures suitable for the level of contamination.

According to an embodiment of the present disclosure, before the pollution degree of the cooking compartment becomes serious, cleaning of the cooking compartment may be automatically performed to maintain the cleanliness of the cooking compartment.

According to an embodiment of the present disclosure, by providing the user with a sanitation/cleaning step corresponding to the estimated pollution degree of the cooking room to be selectively set, cleaning management according to the user's preference can be performed.

1 is a block diagram illustrating a cooking appliance according to an embodiment of the present disclosure.
2 is a perspective view of a cooking appliance according to an embodiment of the present disclosure;
3 is a perspective view illustrating a state in which a door is opened in the cooking appliance of FIG. 2 .
4 is a flowchart illustrating a method of operating a cooking appliance according to an embodiment of the present disclosure.
5 and 6 are diagrams for explaining a learning process of an object detection model according to an embodiment of the present disclosure.
7 shows a learning process of an object identification model according to an embodiment of the present disclosure.
8 may be a view showing a food group generating oil vapor.
9 is a diagram illustrating an oil vapor generation estimation table indicating a degree of oil vapor generation according to a food material according to an embodiment of the present disclosure.
10 is a view for explaining a method of determining an estimated oil vapor generation level according to a cooking period of a food material from a time point of detecting oil vapor according to an embodiment of the present disclosure;
11 is a view for explaining a process of determining a contamination degree of a cooking room according to an estimated level of oil vapor generation and the number of times of cooking of ingredients according to an embodiment of the present disclosure.
12 to 14 are views for explaining an example of displaying guide information through a display unit of a cooking appliance according to a determined degree of pollution.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes 'module' and 'part' for the components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present disclosure , should be understood to include equivalents or substitutes.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is referred to as being 'connected' or 'connected' to another component, it may be directly connected or connected to the other component, but it is understood that other components may exist in between. it should be On the other hand, when it is mentioned that a certain element is 'directly connected' or 'directly connected' to another element, it should be understood that the other element does not exist in the middle.

1 is a block diagram illustrating a cooking appliance according to an embodiment of the present disclosure.

Referring to FIG. 1 , a cooking appliance 100 includes a communication unit 110 , an input unit 120 , a running processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 , a processor 180 and A heating unit 190 may be included.

The communication unit 110 may transmit/receive data to and from external devices such as another AI device or an AI server using wired/wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, a control signal, and the like with external devices.

In this case, the communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity) ), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The communication unit 110 may also be referred to as a communication modem or a communication circuit.

The input unit 120 may acquire various types of data.

In this case, the input unit 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, by treating the camera or the microphone as a sensor, a signal obtained from the camera or the microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model training and input data to be used when acquiring an output using the training model. The input unit 120 may acquire raw input data, and in this case, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.

The input unit 120 may include a camera 121 for inputting an image signal, a microphone 122 for receiving an audio signal, and a user input unit 123 for receiving information from a user. there is.

The voice data or image data collected by the input unit 120 may be analyzed and processed as a user's control command.

The input unit 120 is for inputting image information (or signal), audio information (or signal), data, or information input from a user. Cameras 121 may be provided.

The camera 121 processes an image frame such as a still image or a moving image obtained by an image sensor in a video call mode or a shooting mode. The processed image frame may be displayed on the display unit 151 or stored in the memory 170 .

The microphone 122 processes an external sound signal as electrical voice data. The processed voice data may be utilized in various ways according to a function (or a running application program) being performed by the cooking appliance 100 . Meanwhile, various noise removal algorithms for removing noise generated in the process of receiving an external sound signal may be applied to the microphone 122 .

The user input unit 123 is for receiving information from a user, and when information is input through the user input unit 123 , the processor 180 may control the operation of the cooking appliance 100 to correspond to the input information. .

The user input unit 123 is a mechanical input means (or a mechanical key, for example, a button located on the front/rear or side of the cooking appliance 100 , a dome switch, a jog wheel, a jog switch, etc.) and a touch input means. As an example, the touch input means consists of a virtual key, a soft key, or a visual key displayed on the touch screen through software processing, or a part other than the touch screen. It may be made of a touch key (touch key) disposed on the.

The learning processor 130 may train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision to perform a certain operation.

In this case, the learning processor 130 may perform AI processing together with the running processor of the AI server.

In this case, the running processor 130 may include a memory integrated or implemented in the cooking appliance 100 . Alternatively, the running processor 130 may be implemented using the memory 170 , an external memory directly coupled to the cooking appliance 100 , or a memory maintained in an external device.

The sensing unit 140 may acquire at least one of internal information of the cooking appliance 100 , information on the surrounding environment of the cooking appliance 100 , and user information by using various sensors.

At this time, sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and a lidar. , radar, etc.

The output unit 150 may generate an output related to sight, hearing, or touch.

In this case, the output unit 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The output unit 150 includes at least one of a display unit (Display Unit, 151), a sound output unit (Sound Output Unit, 152), a haptic module (Haptic Module, 153), and an optical output unit (Optical Output Unit, 154) can do.

The display unit 151 displays (outputs) information processed by the cooking appliance 100 . For example, the display unit 151 may display information on an execution screen of an application program driven in the cooking appliance 100 , or user interface (UI) and graphic user interface (GUI) information according to the information on the execution screen.

The display unit 151 may implement a touch screen by forming a layer structure with the touch sensor or being integrally formed. Such a touch screen may function as the user input unit 123 providing an input interface between the cooking appliance 100 and the user, and may provide an output interface between the terminal 100 and the user.

The sound output unit 152 may output audio data received from the communication unit 110 or stored in the memory 170 in a call signal reception, a call mode or a recording mode, a voice recognition mode, a broadcast reception mode, and the like.

The sound output unit 152 may include at least one of a receiver, a speaker, and a buzzer.

The haptic module 153 generates various tactile effects that the user can feel. A representative example of the tactile effect generated by the haptic module 153 may be vibration.

The light output unit 154 outputs a signal for notifying the occurrence of an event by using the light of the light source of the cooking appliance 100 . Examples of the event occurring in the cooking appliance 100 may be message reception, call signal reception, missed call, alarm, schedule notification, email reception, information reception through an application, and the like.

The memory 170 may store data supporting various functions of the cooking appliance 100 . For example, the memory 170 may store input data obtained from the input unit 120 , learning data, a learning model, a learning history, and the like.

The processor 180 may determine at least one executable operation of the cooking appliance 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the cooking appliance 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and perform a predicted operation or an operation determined to be preferable among the at least one executable operation. It is possible to control the components of the cooking appliance 100 to be executed.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input, and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language, Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine may be learned by the learning processor 130, learned by the learning processor of the AI server, or learned by distributed processing thereof.

The processor 180 collects history information including user feedback on the operation contents or operation of the cooking appliance 100 and stores it in the memory 170 or the learning processor 130, or to an external device such as an AI server. can be transmitted The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the cooking appliance 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the cooking appliance 100 in combination with each other.

The heating unit 190 may generate heat by using supplied energy.

The heating unit 190 may generate heat using supplied electricity, and heat the inside of the cooking appliance 100 using the generated heat.

The heating unit 190 may be provided inside the cooking chamber 12 . The heating unit 190 may be disposed at a side end or a lower end of the cooking chamber 12 .

The heating unit 190 may include a circuit that converts electrical energy into thermal energy.

Hereinafter, the cooking appliance 100 may be referred to as an artificial intelligence cooking appliance 100 or an artificial intelligence oven.

Also, when the cooking appliance 100 is provided in a form attached to a wall, it may be called a wall oven.

FIG. 2 is a perspective view of a cooking appliance according to an embodiment of the present disclosure, and FIG. 3 is a perspective view illustrating a state in which the door is opened in the cooking appliance of FIG. 2 .

The cooking appliance 100 may include a body 10 accommodating various parts therein.

The body 10 may include an inner frame 11 forming the cooking chamber 12 and an outer frame 14 surrounding the inner frame 11 from the outside of the inner frame 11 .

A camera 121 may be provided at an upper end of the inner frame 11 . The camera 121 may photograph the cooking chamber 12 . The captured image may be used to recognize the food being cooked.

A body panel 16 may be provided at the front end of the inner frame 11 .

The body panel 16 may be coupled to the front end of the inner frame 11 or may be formed integrally with the front end.

The door 20 may be rotatably connected to the body 10 by a hinge mechanism 450 .

For example, the hinge mechanism 450 may be connected to the lower end of the door 20 .

In order to minimize the temperature rise due to the heat supplied to the cooking chamber 12 , air outside the door 20 may flow into the door 20 .

Accordingly, the door 20 includes a door air outlet 21 through which the air flowing through the door 20 is discharged, and the main body 10 includes a door air outlet 21 through which the air discharged through the door air outlet 21 is introduced. It may include a body air inlet 17 .

The body air inlet 17 may be formed in the body panel 16 .

In addition, the air introduced into the main body 10 through the main body air inlet 17 may be discharged to the outside of the main body 10 through the main body air outlet 18 after flowing through the main body 10 .

A body air outlet 18 may also be formed in the body panel 16 .

The door 20 may further include a control device 300 .

The control device 300 is not limited, but is located on the upper side of the door 20 and is positioned to face a portion located above the cooking chamber 12 among the body panel 16 in a state where the door 20 is closed. can be

The control device 300 may include at least one of a display unit 151 and a user input unit 123 .

The display unit 151 may be implemented in the form of a touch screen capable of receiving a touch input.

Operation information of the cooking appliance 100 may be displayed and/or an operation command from a user may be input through the control device 300 .

4 is a flowchart illustrating a method of operating a cooking appliance according to an embodiment of the present disclosure.

In particular, FIG. 4 is a view for explaining a method of preemptively responding to cleaning or hygiene of the cooking chamber 12 of the cooking appliance 100 by recognizing a cooking state.

Referring to FIG. 4 , the processor 180 of the cooking appliance 100 acquires an image of the cooking chamber 12 through the camera 121 when cooking of a food ingredient is started ( S401 ).

When receiving a cooking start command through the user input unit 123 or the display unit 151, the processor 180 photographs the inside of the cooking chamber 12 through the camera 121 mounted on the cooking appliance 100, A photographed image can be acquired.

The processor 180 may receive a cooking start command of a food ingredient, and may turn on the camera 121 from the point in time when the cooking start command is received.

The processor 180 identifies one or more ingredients from the acquired image by using the image recognition model (S403).

The processor 180 may identify a food ingredient included in the image by using the image recognition model.

The image recognition model may be a model based only on artificial neurons trained by a deep learning algorithm or a machine learning algorithm.

The image recognition model may be a model for inferring what an object included in the image data is based on the image data. An object can be a food ingredient.

The image recognition model may be a model learned by the artificial intelligence server, received from the artificial intelligence server, and stored in the memory 170 of the cooking appliance 100 .

The processor 180 may identify a food ingredient included in the image from image data corresponding to the image by using the image recognition model stored in the memory 170 .

As another example, the processor 180 may transmit the captured image to the artificial intelligence server through the communication unit 110 . The artificial intelligence server may extract object identification information from the image by using the image recognition model, and may transmit the extracted object identification information to the cooking appliance 100 .

The image recognition model may include an object detection model and an object identification model.

The object detection model may be a model for detecting one or more objects from image data, and the object identification model may be a model for identifying which one or more detected objects are.

A process of detecting a plurality of objects from an image using the object detection model and identifying the detected object using the object identification model will be described.

5 and 6 are diagrams for explaining a learning process of an object detection model according to an embodiment of the present disclosure.

Referring to FIG. 5 , the object detection model 510 obtains an object boundary box set including a plurality of objects from each training image data by using a training image data set 500 including a plurality of image data. can

The object bounding box set may be a set of bounding boxes including an object.

The object detection model 510 may detect a plurality of objects from image data by using a Single Shot Multibox Detector (SSD) MobilenetV2, Faster R-CNN Inception, and You Only Look Once (YOLO) algorithms.

The You Only Look Once (YOLO) algorithm may consist of a plurality of CNNs.

The You Only Look Once (YOLO) algorithm may include a grid segmentation process, a prediction process, a reliability calculation process, and an object selection process.

The grid division process may be a process of dividing image data into a plurality of grids. Each of the plurality of grids may have the same size.

The prediction process may be a process of predicting the number of bounding boxes designated in a predefined shape with a grid center as a center for each grid.

A bounding box designated as a predefined shape may be generated from data by a K-means algorithm, and may contain prior information about the size and shape of an object.

Each bounding box may be designed to detect objects of different sizes and shapes.

Each bounding box may indicate the shape or boundary of an object.

The reliability calculation process may be a process of calculating the reliability of the bounding box according to whether an object is included in each of the bounding boxes obtained in the prediction process or whether there is only a background alone.

The object determination process may be a process of determining that an object exists in a bounding box having a reliability greater than or equal to a preset value according to a reliability calculation process.

A plurality of bounding boxes 601 to 607 included in the image data 600 may be extracted through the object determination process.

The processor 180 may obtain identification information of each object from a plurality of bounding boxes extracted through the object detection model 510 .

The processor 180 may identify an object existing in the bounding box from image data corresponding to each bounding box by using the object identification model.

The object identification model may be an artificial neural network-based model trained using a deep learning algorithm or a machine learning algorithm.

The object identification model may be a model learned through supervised learning.

The object identification model may be a model for inferring object identification information from image data. The object identification information may be information identifying the object, such as the name of the object and the identifier of the object.

The object identification model may be a model that outputs identification information of an object using, as input data, a training data set including image data for training and labeling data labeled on the image data for training.

7 shows a learning process of an object identification model according to an embodiment of the present disclosure.

Referring to FIG. 7 , the object identification model 700 may infer object identification information by using a training data set including training image data and labeling data labeled thereto.

The labeling data is correct answer data and may be object identification information.

The object identification model 700 may be trained to minimize a cost function corresponding to the difference between the labeling data and the object identification information.

The cost function of the object identification model 700 may be expressed as a square average of a difference between a label for object identification information corresponding to each image data and object identification information inferred from each image data.

When an input feature vector is extracted from the training image data and input, an object identification result is output as a target feature vector, and the object identification model 700 loses a loss corresponding to the difference between the output target feature vector and the labeled object identification information. It can be learned to minimize a function.

The object identification model 700 may be learned by the learning processor 130 of the cooking appliance 100 or the learning processor of the AI server and mounted on the cooking appliance 100 .

The object identification model 700 may determine first object identification information from the first image data corresponding to the first bounding box 601 illustrated in FIG. 6 . For example, the first object identification information may be a stake.

The object identification model 700 may determine second object identification information from the second image data corresponding to the second bounding box 603 . For example, the second object identification information may be carrots.

In this way, through the object identification model 1700, from the image data, it can be identified what kind of food the object is.

Again, FIG. 4 will be described.

The processor 180 determines whether the identified food material belongs to a food group generating oil vapor (S405).

The processor 180 may include a plurality of food products capable of generating oil vapor in the oil vapor generating food group. Information on the food group generated during the oil phase may be stored in the memory 170 .

8 may be a view showing a food group generating oil vapor.

The oil vapor generating food group 800 may include meat 810 , fish 830 , and bread 850 .

The memory 170 of the cooking appliance 100 may include names of foods belonging to the oil vapor generating food group 800 .

The processor 180 may determine whether the identified food material belongs to the oil vapor generating food group 800 and output the determination result.

Again, FIG. 4 will be described.

When it is determined that the identified food material is a food group generating oil vapor, the processor 180 obtains cooking state information indicating the cooking state of the food material (S407).

The cooking state information may include one or more of a cooking time, a cooking temperature, and an oil vapor generation level of the food material.

The cooking time may be a time from the start time of the cooking of the ingredients to the time at which the cooking ends.

The cooking temperature may be a set temperature when cooking the food.

The degree of oil vapor generation may correspond to an estimated amount of oil vapor generated during the cooking time.

The sensing unit 140 of the cooking appliance 100 may include a gas sensor (not shown). The gas sensor may be provided inside the cooking chamber 12 .

During the cooking time, the processor 180 may detect whether oil vapor is generated through the gas sensor, and when the oil vapor is generated, predict the generation level of the oil vapor.

The processor 180 may obtain the oil vapor generation degree by using the oil vapor generation estimation table indicating the oil vapor generation degree according to the food material.

This will be described with reference to FIG. 9 .

9 is a diagram illustrating an oil vapor generation estimation table indicating a degree of oil vapor generation according to a food material according to an embodiment of the present disclosure.

The oil vapor generation estimation table 900 of FIG. 9 may be stored in the memory 170 .

Referring to FIG. 9 , in the oil vapor generation estimation table 900, the oil vapor generation estimation level corresponding to identification information (category) of the food material obtained through the image recognition basis is matched.

The estimated level of oil vapor generation may be divided into three levels of large/medium/small, but this is only an example. When the oil vapor generation estimator has a range of 0 to 100, the large level may correspond to a value of 70 or more and 100 or less, the medium level may correspond to a value of 30 or more and less than 70, and the small level may correspond to a value of 0 or more and less than 30.

For example, when the identified food material is Beef, Por, Poultry, or fish, the estimated level of oil vapor generation may be a large level.

When the identified ingredient is pizza, the estimated level of oil vapor generation may be a medium level.

When the identified food material is a Dessert (eg, bread, cake, cookie, etc.), the estimated level of oil vapor generation may be a small level.

The oil vapor generation estimation table 900 may be a table to be obtained by learning by the oil steam generation estimation model.

The oil vapor generation estimation model may be an artificial neural network-based learning model for estimating the amount of oil vapor generated according to food ingredients.

The processor 180 of the cooking appliance 100 may obtain an estimated oil vapor generation level corresponding to the identified food material by using the oil vapor generation estimation table 900 .

Again, FIG. 4 will be described.

According to another embodiment, the processor 180 may acquire the oil vapor generation estimated level based on a time point at which the oil vapor is sensed and a cooking period from a time point at which the oil vapor is sensed.

This will be described with reference to FIG. 10 .

10 is a view for explaining a method of determining an estimated oil vapor generation level according to a cooking period of a food material from a time point of detecting oil vapor according to an embodiment of the present disclosure;

In the graph of FIG. 10 , the horizontal axis represents time, and the vertical axis represents temperature.

When it is determined that oil vapor is detected through a gas sensor provided in the cooking chamber 12 after cooking is started, the processor 180 may acquire a first time point t1 , which is the generation time of the oil vapor.

When the generation of oil vapor is maintained from the first time point t1 to a period less than or equal to the second time point t2, the processor 180 may determine the estimated oil vapor generation level as a small level.

When the generation of oil vapor is maintained from the first time point t1 to a period equal to or less than the third time point t3, the processor 180 may determine the estimated oil vapor generation level as a medium level.

When the generation of oil vapor is maintained from the first time point t1 to the period exceeding the fourth time point t4 , the processor 180 may determine the estimated oil vapor generation level as a large level.

The processor 180 may increase the value of the oil vapor generation estimation level as the oil vapor generation and maintenance time increases.

As another example, the estimated oil vapor generation level may be determined by further considering the temperature during the oil vapor generation and maintenance period.

The processor 180 maintains the generation of oil vapor from the first time point t1 to a period less than or equal to the second time point t2, and when the heating temperature of the cooking chamber 12 is equal to or higher than a predetermined temperature K, the estimated level of oil vapor generation can be determined as a small level.

The processor 180 maintains the generation of oil vapor from the first time point t1 to a period less than or equal to the third time point t3, and when the heating temperature of the cooking chamber 12 is equal to or higher than a predetermined temperature K, the oil vapor generation estimated level can be determined as an intermediate level.

The processor 180 maintains the generation of oil vapor from the first time point t1 to a period exceeding the fourth time point t4, and when the heating temperature of the cooking chamber 12 is equal to or higher than a predetermined temperature K, the oil vapor generation estimated level can be determined as a large level.

Again, FIG. 4 will be described.

The processor 180 determines the contamination level of the cooking chamber 12 based on the obtained cooking state information after cooking of the ingredients is finished ( S409 ).

The contamination level of the cooking chamber 12 may be divided into a first contamination level, a second contamination level greater than the first contamination level, and a third contamination level greater than the second contamination level.

In an embodiment, the processor 180 may determine the contamination level of the cooking chamber 12 by using the estimated level of oil vapor generation included in the cooking state information.

For example, when the estimated level of oil vapor generation is a large level, the processor 180 may determine the pollution degree as the third pollution level. When the estimated oil vapor generation level is the medium level, the processor 180 may determine the pollution degree as the second pollution level. When the estimated level of oil vapor generation is a small level, the processor 180 may determine the pollution degree as the first pollution level.

In another embodiment, the processor 180 may determine the contamination level of the cooking chamber 12 based on the cooking state information and the cooking history state information.

The cooking history state information may include the number of times the food ingredients are cooked after the last cleaning of the cooking chamber 12 . Here, the food material may be the same material as the material used in the previous cooking, but is not limited thereto.

When the door 20 is opened and closed once after the last cleaning of the cooking compartment 12 , the processor 180 may determine this as the number of times of cooking once.

The number of times of cooking may be expressed as the number of times of use of the cooking chamber 12 .

The processor 180 may determine the pollution degree of the cooking compartment 12 based on the estimated level of oil vapor generation and the number of cooking times.

This will be described with reference to FIG. 11 .

11 is a view for explaining a process of determining a degree of contamination of a cooking room according to an estimated level of oil vapor generation and the number of times of cooking of ingredients according to an embodiment of the present disclosure.

The pollution level determination table 1100 of FIG. 11 may be previously stored in the memory 170 of the cooking appliance 100 .

For example, the processor 180 may determine the pollution degree as the third pollution level when the estimated level of oil vapor generation is a large level or a medium level and the number of times of cooking the food material is 7 or more.

The processor 180 may determine the pollution degree as the second pollution level when the estimated oil vapor generation level is a large level or a medium level and the number of times of cooking of the food material is 1 or more and 6 or less.

The processor 180 may determine the contamination level as the second contamination level when the estimated level of oil vapor generation is a small level and the number of times of cooking the food material is 6 or more.

The processor 180 may determine the contamination level as the first contamination level when the estimated level of oil vapor generation is a small level and the number of times of cooking of the food material is 3 or more and 6 or less.

The processor 180 outputs guide information for guiding cleaning of the cooking compartment 12 or starts automatic cleaning of the cooking compartment 12 based on the determined contamination level of the cooking compartment 12 ( S411 ).

The guide information may be information for guiding a cleaning method of the cooking chamber 12 suitable for the determined contamination level. The guide information may include one or more of a text or an image guiding a cleaning method of the cooking compartment 12 .

The processor 180 may display guide information through the display unit 151 .

The processor 180 may transmit guide information to the user's mobile terminal through the communication unit 110 .

The processor 180 may start automatic cleaning of the cooking compartment 12 if necessary according to the determined contamination level while outputting the guide information.

For example, when the determined contamination level is the second contamination level, the processor 180 may control the heating unit 190 to heat the cooking chamber 12 to the first temperature for a predetermined time.

As another example, when the determined contamination level is the third contamination level, the processor 180 may control the heating unit 190 to heat the cooking chamber 12 at a high temperature above the second temperature for a predetermined period of time.

This will be described with reference to FIGS. 12 to 14 .

12 to 14 are views for explaining an example of displaying guide information through the display unit of the cooking appliance according to the determined pollution level.

12 is a view showing the first cleaning guide information 1200 displayed when the determined pollution level is the first pollution level.

When the pollution degree is determined as the first pollution level, the processor 180 may display the first cleaning guide information 1200 through the display unit 151 .

The first cleaning guide information 1200 may include a first guide text 1210 and a first guide image 1230 .

The first guide text 1210 may include text indicating that the contamination level is the first contamination level and simple cleaning is performed. The simple cleaning may be a user's manual cleaning of the cooking chamber 12 .

The first guide image 1230 may be an image representing a method of performing simple cleaning.

13 is a view showing second cleaning guide information 1300 displayed when the determined pollution level is the second pollution level.

When the pollution degree is determined as the second pollution level, the processor 180 may display the second cleaning guide information 1300 through the display unit 151 .

The second cleaning guide information 1300 may include a second guide text 1310 and a second guide image 1330 .

The second guide text 1310 may include text indicating that the pollution level is the second pollution level, automatically operating the Easy Clean mode, and inducing spraying of water into the cooking chamber 12 .

The Easy Clean mode may be a mode in which the interior of the cooking compartment 12 is cleaned by heating the cooking compartment 12 to the first temperature for a predetermined time.

The second guide image 1330 may be an image representing that water is sprayed into the cooking chamber 12 .

14 is a view showing third cleaning guide information 1400 displayed when the determined pollution level is the third pollution level.

When the pollution degree is determined as the third pollution level, the processor 180 may display the third cleaning guide information 1400 through the display unit 151 .

The third cleaning guide information 1400 may include a third guide text 1410 .

The third guide text 1410 may include text indicating that the pollution level is the third pollution level and automatically operates in the self-clean mode.

The self-clean mode may be a cleaning mode in which the inside of the cooking chamber 12 is heated to a high temperature at a second temperature greater than the first temperature for a predetermined time.

As such, according to an embodiment of the present disclosure, the degree of contamination of the cooking compartment 12 may be automatically measured, and a cleaning guide of the cooking compartment 12 optimized for the level of contamination may be provided to the user.

In addition, there is an effect of preemptively responding to contamination of the cooking compartment 12 by grasping the state of the cooking compartment 12 and operating in the cleaning/sanitation guide and cleaning mode.

Meanwhile, according to another embodiment of the present disclosure, the pollution level may vary according to a user's setting. As the level of contamination changes, the corresponding cleaning guide information may also vary.

That is, the level of contamination may vary according to the user's preference, and thus a personalized cleaning guide may be provided.

According to an embodiment of the present disclosure, the above-described method may be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is this.

Claims (14)

In the cooking appliance,
display unit;
cuisine;
a heating unit for heating the inside of the cooking chamber;
a camera for photographing the cooking room; and
From the image captured by the camera, the food material is identified, whether the identified food material belongs to a food group generating oil vapor, and when the identified food material belongs to a food group generating oil vapor, the cooking state of the food material is displayed Obtaining cooking state information, determining the contamination level of the cooking compartment based on the obtained cooking state information, displaying cleaning guide information through the display unit according to the determined contamination level, or operating the cleaning mode of the cooking compartment comprising a processor for controlling wealth
cooking appliance. According to claim 1,
the processor is
When the pollution level is the first pollution level, first cleaning guide information for allowing a user to perform simple cleaning is displayed through the display unit,
When the pollution level is a second pollution level greater than the first pollution level, the user causes the user to spray water into the cooking chamber and displays second cleaning guide information indicating automatic operation in the first cleaning mode marked through wealth
cooking appliance. 3. The method of claim 2,
the processor is
When the contamination level is a third contamination level greater than the second contamination level, third cleaning guide information indicating automatic operation in the second cleaning mode is displayed through the display unit,
Under the second cleaning mode, each of the heating temperature and heating time of the heating unit is greater than each of the heating temperature and heating time of the heating unit under the first cleaning mode
cooking appliance. According to claim 1,
The cooking status information is
Including the estimated level of oil vapor generation of the food material,
The cooking appliance further includes a memory for storing an oil vapor generation estimation table in which the oil steam generation estimation level is matched with the identification information of the food material,
the processor is
using the oil vapor generation estimation table to obtain the oil vapor generation estimation level suitable for the identified food material,
Determining the pollution level using the obtained oil vapor generation estimated level
cooking appliance. According to claim 1,
The cooking status information is
Including the estimated level of oil vapor generation of the food material,
the processor is
obtaining a time point of generation of oil vapor during cooking of the food material, and determining the estimated level of oil vapor generation from the time point of generation, based on a generation and maintenance time of the oil vapor;
Determining the pollution level using the determined oil vapor generation estimated level
cooking appliance. 6. The method of claim 5,
the processor is
As the generation and maintenance time of the oil vapor increases, the value of the estimated oil vapor generation level increases.
cooking appliance. 6. The method of claim 5,
It is disposed in the cooking chamber, further comprising a gas sensor for detecting the generation of the oil vapor
cooking appliance. According to claim 1,
the processor is
determining the contamination level based on the cooking state information and the cooking history state information,
The cooking status information is
Including the estimated level of oil vapor generation of the food material,
The cooking history state information includes the number of times of cooking in the cooking chamber.
cooking appliance. According to claim 1,
Further comprising a communication unit for wirelessly transmitting the cleaning guide information to the user's mobile terminal
cooking appliance. According to claim 1,
Further comprising a memory for storing an image recognition model for identifying the food material from the image data,
the processor is
Using the image recognition model to identify the food material from the photographed image
cooking appliance. 11. The method of claim 10,
The image recognition model is
an object detection model for obtaining an object bounding box including the food material from the image; and
From the object bounding box, including an object identification model for obtaining identification information of the food material,
Each of the object detection model and the object identification model is an artificial neural network-based model learned through a deep learning algorithm or a machine learning algorithm.
cooking appliance. According to claim 1,
the processor is
Receives a cooking start command of the food, and turns on the camera from the time the cooking start command is received
cooking appliance. A method of operating a cooking appliance, comprising:
photographing the cooking room through a camera;
identifying the food material from the photographed image;
determining whether the identified food material belongs to a food group generating oil vapor;
when the identified food material belongs to a food group generating oil vapor, obtaining cooking state information indicating a cooking state of the food material;
determining a contamination level of the cooking chamber based on the obtained cooking state information; and
Displaying cleaning guide information through a display unit according to the determined contamination level
How cooking appliances work. In a computer-readable inactive recording medium recording an operation method of a cooking appliance,
The method of operation is
photographing the cooking room through a camera;
identifying the food material from the photographed image;
determining whether the identified food material belongs to a food group generating oil vapor;
when the identified food material belongs to a food group generating oil vapor, obtaining cooking state information indicating a cooking state of the food material;
determining a contamination level of the cooking chamber based on the obtained cooking state information; and
Displaying cleaning guide information through a display unit according to the determined contamination level
Inactive recording media.

Images