KR20220163228A - A method, system and program for constructing digital-twin with respect to cooking object linked to kitchen applicances - Google Patents

Abstract

A method for constructing a digital twin of a cooking object linked to a kitchen appliance according to the present invention comprises: a step of receiving, by a digital twin system, the target cooking condition data and cooking object real-time data from a kitchen appliance; a step of materializing, by the digital twin system, a digital twin simulation based on the received target cooking condition data and cooking object real-time data; a step of provide-receiving, by the digital twin system, the input data necessary for a heat transfer simulation from a machine learning module; and a step of estimating, by the digital twin system, an internal temperature value for real-time monitoring of a cooking process of the cooking object. Therefore, the present invention is capable of improving a degree of precision.

Description

Digital twin construction method, system and program of cooking objects linked with kitchen appliances

The present invention relates to a digital twin construction method, program, and construction system of a cooking object interlocked with kitchen appliances.

A digital twin (hereafter abbreviated as DT) refers to a technology that uses measurable data to visualize a physical system in digital form as if it were a twin. With DT, not only measured data but also values calculated through simulation can be visually checked in real time through digital 2D screens or 3D visualizations. By digitizing the key variables of a physical system, it is possible to analyze the current state of the system, predict future behavior, and even prevent potential hazards such as explosions in chemical processes. As such, DT mainly plays a role in effectively monitoring, managing, and controlling the system, and is also used in plant design, construction, and optimization.

Digital visualization, which is an advantage of DT, has several advantages, especially in terms of monitoring, measurement, and control, which are obtained through simulation of values that cannot be easily measured in physical systems (for example, temperature change over time inside food). One of them is that it can be observed with This is especially useful when a value that cannot be easily measured is an important factor in determining performance such as system efficiency. In addition to monitoring the system, DT helps manage and control while observing changes in work processes. Due to these advantages, the importance of DT as a convergence task between the Digital New Deal and the Green New Deal of the “Korean New Deal” announced by the Korean government on July 14, 2020 is further highlighted. Currently, this technology is widely used in urban planning, chemical engineering, etc.

However, the current application field of the digital twin is mainly used in systems that handle macroscopic and continuous processes such as smart factories, and the system that can visually check information in people's real life is limited. From this point of view, with the advent of the 4th industrial revolution, development to introduce the digital twin into real life is required.

Japanese Patent Laid-open Publication JP 2019-045356 A (2019.03.22)

In order to solve the above problems, the present invention is to provide a method and system for building a digital twin of a cooking object interlocked with kitchen appliances.

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

A digital twin construction method of a cooking object interlocked with kitchen appliances according to the present invention includes the steps of receiving target cooking condition data and real-time cooking object data from a digital twin system from kitchen appliances; The digital twin system materializes a digital twin simulation based on the received target cooking condition data and real-time cooking target data; The digital twin system receives input data necessary for heat transfer simulation from the machine learning module; and estimating, by the digital twin system, an internal temperature value for monitoring the cooking process of the cooking object in real time.

The target cooking condition may include at least one temperature data of the surface of the cooking object, a volume of the cooking object, and a physical measurement value of the cooking object, such as an average radius.

The step of embodying the digital twin simulation may include determining whether real-time data to be cooked is collected by a plurality of sensors; If collected by a plurality of sensors, reflecting a plurality of real-time data to build a digital twin simulation; and converting the data collected by a single sensor into a plurality of data based on real-time simulation data for the cooking utensil, and reflecting the plurality of real-time data to build a digital twin simulation.

The step of converting into a plurality of data based on the real-time data simulation,

The method may further include generating a simulation by acquiring characteristic data of the cooking utensil from the outside.

The step of reflecting the plurality of real-time data to build the digital twin simulation may include determining outer information of the cooking object and determining internal information of the cooking object.

The determining of the outer shape information of the cooking object may include determining a mass, a volume, a 3D shape, and a surface temperature distribution of the cooking object.

Determining the internal information of the cooking object,

Internal information may be determined based on the identified basic information of the cooking object, or may be determined based on basic information of the cooking object inputted through a customer terminal or a kitchen appliance.

The determining of the internal information of the cooking object may include determining a composition, density, and layer structure of the inside of the cooking object.

The receiving of input data necessary for the heat transfer simulation may be a step of receiving variable values required for the heat conduction function.

Variable values required for the heat conduction function may be obtained from a first database obtained through an inverse estimation module.

The method includes receiving unsuitable review data based on a deviation between a digital twin simulation and an actual cooking result from a customer terminal or kitchen appliance; and

Feedback updating of variable values required for the heat conduction function of the first database may be further included.

Variable values required for the heat conduction function may be obtained from a second database obtained through a customer personalization module.

receiving taste review data based on a customer's intended cooking state through a customer terminal; and

The method may further include feedback-updating variable values required for the heat conduction function of the second database.

Variable values required for the heat conduction function may be obtained from a third database obtained through a cookware individualization module.

receiving review data for each cookware based on the variation of each cookware from kitchen appliances; and feedback-updating variable values required for the heat conduction function of the third database.

The step of receiving input data necessary for the heat transfer simulation,

The variable values required for the heat conduction function are the first database obtained through the inverse estimation module, the variable values required for the heat conduction function are the second database obtained through the customer personalization module, and the variable values required for the heat conduction function are the cookware individualization module. It may be provided from at least one database among the third databases obtained through, or input data values of the first to third databases may be converted in a predetermined manner and provided.

The conversion in the predetermined method is based on a first weight applied to variable values of the first database, a second weight applied to variable values of the second database, and a third weight applied to variable values of the third data. can be converted

Following the step of estimating the internal temperature value,

When the internal temperature of the digital twin achieves a target cooking condition, requesting a cooking completion process to the kitchen appliance; may further include.

A digital twin system according to an embodiment of the present invention includes a transmitting and receiving unit capable of transmitting and receiving information with a customer terminal, kitchen appliances, and a machine learning module through a network; a memory unit for storing an application providing a function of receiving target condition cooking data from kitchen appliances and receiving input data from a machine learning module to form a digital twin for an object to be cooked; a processor for reading and controlling an application from the memory unit; an input unit that receives a command from a user through the customer terminal; and an output unit outputting a resultant value under the control of the processor.

In the above application, the digital twin system receives target cooking condition data and real-time cooking target data from kitchen appliances, and the digital twin system materializes digital twin simulation based on the received target cooking condition data and cooking target real-time data. The twin system receives input data necessary for heat transfer simulation from the machine learning module, and the digital twin system can estimate the internal temperature value to monitor the cooking process of the cooking object in real time.

The digital twin construction program according to an embodiment of the present invention may be combined with hardware, which is a computer, and stored in a medium to execute the above method.

The digital twin construction method and construction system of a cooking object interlocked with kitchen appliances according to the present invention can estimate the internal temperature value for monitoring the cooking process of the cooking object in real time based on target cooking condition data and real-time cooking object data. have.

Specifically, the estimation of the internal temperature value can be converted into a plurality of data based on the real-time data simulation for the cooking utensil, and the precision can be improved by reflecting the plurality of real-time data to the digital twin simulation construction.

In the construction of the digital twin, the variable values required for the heat conduction function are obtained from the first database obtained through the inverse estimation module, the variable values required for the heat conduction function are obtained from the second database obtained through the customer personalization module, or the Variable values required for the heat conduction function may be obtained from a third database acquired through the cookware individualization module.

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

1 is a block diagram specifically showing the relationship between a digital twin system according to the present invention and a customer terminal, kitchen appliances, and a machine learning module.
2 is a flowchart illustrating a method for constructing a digital twin of a cooking object according to the present invention.
Figure 3 is a flow chart showing the reflection of a plurality of real-time data in the embodiment of the digital twin simulation.
4 is a diagram illustrating sensing of a surface temperature distribution of a cooking object based on a plurality of sensors.
5 is a diagram showing estimating a surface temperature distribution of a cooking object based on a simulation of one sensor and a cooking appliance.
6 is a diagram showing information exchanged between the digital twin system according to the present invention and customer terminals, kitchen appliances, and machine learning modules in order.
7 is a diagram showing an example of thermal conductivity analysis of a cooking object cooked in an induction oven.
8 is a diagram showing an example of thermal conductivity analysis of a cooking object cooked in a microwave oven.

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Advantages and features of the present disclosure, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical idea of the present disclosure is not limited to the following embodiments and can be implemented in various different forms, and only the following embodiments complete the technical idea of the present disclosure, and in the technical field to which the present disclosure belongs. It is provided to completely inform those skilled in the art of the scope of the present disclosure, and the technical spirit of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to components of each drawing, it should be noted that the same components have the same numerals as much as possible even if they are displayed on different drawings. In addition, in describing the present disclosure, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present disclosure, the detailed description will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined. Terminology used herein is for describing the embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase.

Also, terms such as first, second, A, B, (a), and (b) may be used in describing the components of the present disclosure. These terms are only used to distinguish the component from other components, and the nature, order, or order of the corresponding component is not limited by the term. When an element is described as being “connected,” “coupled to,” or “connected” to another element, that element is directly connected or connectable to the other element, but there is another element between the elements. It will be understood that elements may be “connected”, “coupled” or “connected”.

As used in this disclosure, "comprises" and/or "comprising" means that a stated component, step, operation, and/or element is one or more other components, steps, operations, and/or elements. Existence or additions are not excluded.

Components included in one embodiment and components including common functions may be described using the same names in other embodiments. Unless stated to the contrary, descriptions described in one embodiment may be applied to other embodiments, and detailed descriptions will be omitted to the extent of overlapping or to the extent that those skilled in the art can clearly understand. can

Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a block diagram specifically showing the relationship between a digital twin system 100, a customer terminal 400, kitchen appliances 200, and a machine learning module 300 according to the present invention.

Referring to FIG. 1, the digital twin system 100 is read by a transceiver 101, an input unit 102, an output unit 103, a processor 104, and a memory unit 105, and is controlled by the processor 104. includes applications that

The transceiver 101 may include a transmitter, a receiver, or a transceiver. The transceiver 101 may transmit and receive information to and from the customer terminal 200 , kitchen appliances 400 , and machine learning module 300 through the network 500 .

The input unit 102 and the output unit 103 may be simultaneously configured as input/output units in the form of a touch display in a smartphone. The input unit 102 may include a physical keyboard device, a touch display, an image input sensor constituting a camera, a sensor that receives a fingerprint, a sensor that recognizes an iris, and the like. The output unit 103 may be composed of a monitor, a touch display, and the like. However, it is not limited thereto, and may include a keyboard, a mouse, and a touch screen used as an input unit in a personal computer (PC), and a monitor, speaker, and the like used as an output unit. The input unit 102 receives a command from a user through the customer terminal 200 . The output unit 103 outputs a resultant value under the control of the processor 104 .

The processor 104 executes overall control functions of the terminal using programs and data stored in the memory unit 105 configured in the terminal. The processor 104 may include random access memory (RAM), read only memory (ROM), central processing unit (CPU), graphic processing unit (GPU), and a bus. can be connected to each other through The processor 104 may access the storage unit, perform booting using an operating system (O/S) stored in the memory unit 105, and operate as an application unit using an application stored in the memory unit 105. It can be configured to perform various operations described in the present invention. The processor 104 controls components within the device of the node, that is, the memory unit 105, the input unit 102, the output unit 103, the transmission/reception unit 101, and a camera (not shown), which are disclosed in the present invention. It can be configured to perform various embodiments.

The processor 104 executes an application using a program stored in the memory unit 105 . The application receives target cooking condition data and cooking target real-time data from kitchen appliances, materializes digital twin simulation based on the received target cooking condition data and cooking target real-time data, and input data required for heat transfer simulation from the machine learning module. may be provided, and may be controlled to estimate an internal temperature value for monitoring the cooking process of the cooking object in real time.

The memory unit 105 may be composed of a database (DB) or may be composed of various storage means such as a physical hard disk, a solid state drive (SSD), and a web hard.

The memory unit 105 receives target condition cooking data from the kitchen appliance 200 and receives input data from the machine learning module 300 to store an application providing a function of forming a digital twin for an object to be cooked.

In addition, this digital twin system 100 is a smart phone (smart phone), mobile phone, PDA (personal digital assistant), PMP (portable multimedia player), tablet PC, such as all kinds of types that can be connected to an external server through a wireless communication network It may include a handheld-based wireless communication device, and may also include a communication device that can be connected to an external server through a network, such as a desktop PC, tablet PC, laptop PC, and IPTV including a set-top box. can

In addition, the digital twin system 100 may be implemented as a server operated by a service provider or an external company.

The kitchen appliance 200 includes all kitchen appliances used in the kitchen to cook a cooking object by applying heat. For example, the kitchen appliances 200 include a lightwave oven, a convection oven, a conventional oven, a cooktop, a microwave oven, an air fryer, an induction cooker, a griller, a slow cooker, a sous vide machine, a pressure cooker, a multicooker, a toaster, and the like. Can include kitchen utensils.

The kitchen appliance 200 may include a controller 201 that controls cooking conditions and a sensor unit 202 that collects real-time data of an object to be cooked.

The machine learning module 300 is a module that provides parameters necessary for constructing a digital twin in the digital twin system 100. The machine learning module 300 includes a reverse estimation module 310, a first database 320, a customer personalization module 330, a second database 340, an instrument customization module 350, and a third database 360. can do.

The inverse estimation module 310 may estimate heat transfer related variables based on test data of kitchen appliances. In addition, the inverse estimation module 310 may derive an estimate of a heat transfer related variable based on an appropriate recipe for an object to be cooked.

[Equation 1]

Q = m * _Cp * ΔT

[Equation 2]

Q = k * A *ΔT

Here, Q is Heat Transferred, k is Thermal Conductivity, m is Mass, C _p is Specific Heat Capacity, A is Heat Transfer Area, and ΔT is Temperature Difference (Difference in Temperature).

The inverse estimation module 310 may estimate the remaining variables based on Q and ΔT given in the test data of the kitchen appliance.

The inverse estimation module 310 can derive ΔT given the values of k, C _p , and A.

The first database 320 may be a database that stores heat transfer related variable values derived by the inverse estimation module 310 . The first database 320 may provide heat transfer related variable values to the digital twin system 100 .

The customer personalization module 330 may process variables of the first database 320 based on taste review data on whether the cooking conditions are met with the customer's desired cooking state, and update the second database 340 .

The utensil customization module 350 may update the third database 360 by processing variables of the first database 320 based on review data for each cookware according to differences in characteristics of each kitchen appliance.

The customer terminal 400 may be an electronic device used by a user who wants to discharge waste. For example, the customer terminal 400 is a smart phone (smart phone), mobile phone, PDA (personal digital assistant), PMP (portable multimedia player), tablet PC, such as all kinds of types that can be connected to an external server through a wireless communication network It may include a handheld-based wireless communication device, and may also include a communication device that can be connected to an external server through a network, such as a desktop PC, tablet PC, laptop PC, and IPTV including a set-top box. can

The network 500 may include wired and wireless communication networks such as a local area network (LAN), a wide area network (WAN), a virtual network, and remote communication.

)를 실시간으로 측정하고 동시에 대상체의 두께(

)를 추정 또는 입력받는다. 디지털 트윈 시스템(100)은 센서부(202)를 통해 또는 직접 입력을 통해 조리 대상체를 식별하고, 객체에 따라 열전달 계산에 필요한 열전도도(

), 밀도(

) 및 열용량(

) 등의 모든 내장 입력 데이터값을 미리 정의된 데이터베이스(320, 340, 360)으로부터 제공받는다. 여기서 온도, 시간, 위치에 따라 밀도와 같은 열역학적 특성이 달라지기 때문에 시뮬레이션의 정확성을 위해서 머신러닝 학습모듈(200)을 통해 미리 정의된 데이터베이스의 주기적으로 업데이트 하는 작업이 함께 수행된다. 디지털 트윈 시스템(100)은 시뮬레이션을 통해 요리가 조리되는 과정을 모니터링하고, 조리 온도 및 시간을 사용자에 의해 실시간으로 컨트롤 할 수 있도록 하는 도구를 제공한다. 조리 대상체는 계란, 스테이크, 쌀밥 등 사용자가 조리 과정에서 조리 대상체를 물리적으로 변형시키지 않는 형태의 모든 조리 음식에 적용 가능하다. 예시에 사용된 식은 구체에서의 열전도식이며 이 식은 다음의 식(1)과 같다. 또한, 온도의 초기값이 함께 적용되어야 한다.The operation of the digital twin system 100 will be described as an example. The digital twin system 100 automatically calculates the amount of heat required to cook the cooking object to a specified temperature. In the digital twin system 100, the surface temperature of the object required for heat transfer from the sensor unit 202 (eg, an infrared camera) installed in the kitchen appliance 200 (

) in real time and at the same time the thickness of the object (

) is estimated or input. The digital twin system 100 identifies the cooking object through the sensor unit 202 or through direct input, and the thermal conductivity required for heat transfer calculation according to the object (

), density(

) and heat capacity (

) are provided from predefined databases 320, 340, and 360. Here, since thermodynamic characteristics such as density vary according to temperature, time, and location, a task of periodically updating a predefined database through the machine learning learning module 200 is performed together for simulation accuracy. The digital twin system 100 monitors the cooking process through simulation and provides a tool that allows the user to control the cooking temperature and time in real time. The cooking object may be applied to all types of cooked food in which a user does not physically deform the cooking object during a cooking process, such as eggs, steak, and rice. The equation used in the example is the heat conduction equation in the sphere, and this equation is the following equation (1). In addition, the initial value of the temperature must be applied together.

2 is a flowchart illustrating a method for constructing a digital twin of a cooking object according to the present invention.

Referring to FIG. 2, the digital twin system receives target cooking condition data and cooking target real-time data from kitchen appliances (S101), and the digital twin system uses the digital twin based on the received target cooking condition data and cooking target real-time data. The step of materializing the simulation (S102), the digital twin system receiving input data necessary for the heat transfer simulation from the machine learning module (S103), and the digital twin system detecting the internal temperature value to monitor the cooking process of the cooking object in real time The step of estimating (S104) and the digital twin system include a step of visualizing the degree of cooking of the cooking object based on the estimated internal temperature value (S105).

Figure 3 is a flow chart showing the reflection of a plurality of real-time data in the embodiment of the digital twin simulation. 4 is a diagram illustrating sensing of a surface temperature distribution of a cooking object based on a plurality of sensors. 5 is a diagram showing estimating a surface temperature distribution of a cooking object based on a simulation of one sensor and a cooking appliance.

Referring to FIG. 3 , receiving real-time data to be cooked (S201), determining whether the real-time data to be cooked is collected by a plurality of sensors (S202), and reflecting the plurality of real-time data to the digital twin ( S203), determining whether there is real-time data simulation for the cookware (S204), converting the data for a single sensor into a plurality of data through simulation (S205), and converting characteristic data for the cookware from the outside. Acquiring and generating a simulation (S206).

In the receiving of real-time data of the cooking object ( S201 ), real-time data of the cooking object (at least one temperature data of the surface of the cooking object, a volume of the object to be cooked, and an average radius) may be transmitted from the kitchen appliance 200 . Referring to FIGS. 4 and 5 , real-time data of cooking objects may be measured by the sensor unit 202 of the kitchen appliance 200 . For example, referring to FIG. 4 , the plurality of sensors 212 , 213 , and 214 measuring the surface temperature of the cooking object may measure the plurality of temperatures T1 , T2 , and T3 . Temperature data of the surface of the cooking object may be collected in real time by the kitchen appliance 200 and transmitted to the digital twin system 100 . Since the completeness of the digital twin increases as the temperature distribution of the surface of the cooking object is checked as a whole, it is preferable to have a plurality of sensors. However, since the number of sensors may be limited depending on the kitchen appliance 200, the limit of the number of sensors may be exceeded by estimating the overall surface temperature distribution based on one surface temperature distribution.

For example, referring to FIG. 5 , since there is a single sensor 214 for sensing the surface temperature of the kitchen appliance 200, the corresponding sensor can sense only the temperature T1 of the first point on the surface of the cooking object. . However, when real-time data simulation of the kitchen appliance 200 exists, the temperature T2 and the temperature T3 of the third point are determined based on the temperature T1 of the first point based on the simulation. By estimating, the precision of the digital twin can be improved. This may satisfy the precision of the digital twin, which is much more improved than simply assuming that the temperature T1 of the first point is the temperature distribution of the entire cooking object.

Accordingly, in step S202, it is determined whether the kitchen appliance 200 collects a plurality of sensors, and when a plurality of sensors are provided, the surface temperature distribution is determined based on the plurality of sensing contents (S203), or a single sensor When is provided, it is possible to move to steps (S204, S205, S206) to improve the precision of the digital twin through simulation.

It checks the existence of real-time data simulation of kitchen appliances (S204), and if it exists, converts the data by a single sensor into a plurality of data through simulation (S205), and if there is not, the characteristic data for the kitchen appliance is exported to the outside. It proceeds to the step (S206) of generating a simulation by acquiring from.

In the generating of the simulation ( S206 ), the physical characteristics of the target kitchen appliance and real-time test data of an internal cooking target under the same cooking condition may be externally acquired to generate the simulation. For example, the simulation may be a program that generates a temperature distribution gradient capable of estimating the remaining temperature points when one temperature point T1 is determined as shown in FIG. 5 .

In the step of converting data from a single sensor into a plurality of data through simulation (S205), when one temperature point T1 is determined, the cooking object is subjected to simulation to generate a temperature distribution gradient capable of estimating the other temperature points. This step is to obtain the surface temperature distribution of

Reflecting a plurality of real-time data on the digital twin ( S203 ) may include determining external information of the cooking object and determining internal information of the cooking object.

The determining of the outer shape information of the cooking object may include determining a mass, a volume, a 3D shape, and a surface temperature distribution of the cooking object.

The determining of the internal information of the cooking object may include determining the internal information based on the identified basic information of the cooking object or based on basic information of the cooking object inputted through a customer terminal or a kitchen appliance.

The determining of the internal information of the cooking object may include determining a composition, density, and layer structure of the inside of the cooking object.

6 is a diagram showing information exchanged between the digital twin system 100 and the customer terminal 400, kitchen appliances 200, and machine learning module 300 in order according to the present invention.

Referring to FIG. 6 , target cooking conditions may be set in the kitchen appliance 200 (S301). The kitchen appliance 200 has its own input unit and control unit so that the user can directly set target cooking conditions for the cooking object through the kitchen appliance 200, but is not limited thereto. The user may transfer target cooking conditions to the kitchen appliance 200 wired/wireless through a separate customer terminal 400 .

The kitchen appliance 200 may calculate the amount of heat required for the cooking condition of the cooking object based on the set target cooking condition (S302). Calculation of the required amount of heat (S302) is performed in the controller 201 of the kitchen appliance 200, but is not limited thereto, and may also be performed in the processor 104 of the digital twin system 100, for example.

The kitchen appliance 200 may transmit real-time data (surface temperature, weight, volume, radius) of the cooking object to the digital twin system 100 (S303).

The digital twin system 100 may materialize the digital twin simulation based on the real-time data of the cooking object received (S304). As described above with reference to FIG. 3, the embodiment of the digital twin simulation (S304) is the step of determining whether or not the real-time data to be cooked is collected by a plurality of sensors, and if collected by a plurality of sensors, a plurality of real-time data. It includes the step of reflecting in the digital twin simulation construction and, in the case of collecting by a single sensor, converting into multiple data based on the simulation of real-time data for cooking utensils, and reflecting the multiple real-time data in the digital twin simulation construction. can

The digital twin system 100 may request data necessary for heat transfer simulation from the machine learning learning module 300 (S305), and receive the calculated necessary input data from the machine learning learning module (S306).

As described above, the necessary input data is provided from the first database 320 in which the variables of the heat conduction function calculated through the inverse estimation model 310 are stored, or the variables calculated in the customer personalization module 320 are stored in the second database 320 . Variables in which customer feedback is reflected may be provided from the database 330, or variables in which variation for each instrument is reflected may be provided in the third database 350 in which variables calculated in the instrument individualization module 340 are stored.

The digital twin system 100 may build a digital twin based on the provided variables and derive the internal temperature (T) value of a key variable for monitoring the cooking process of the cooking object in real time (S307).

The digital twin system 100 outputs the constructed digital twin from the kitchen appliance 200 and/or the customer terminal 400 so that the user can check the internal situation of the cooking object in real time (S308).

When the target cooking condition is achieved in the digital twin system 100, the digital twin system 100 transmits a cooking completion processing request to the kitchen appliance 200 (S309), and the kitchen appliance 200 is in the cooking state according to the request. The end may be processed and the result may be returned to the digital twin system 100 (S310).

The kitchen appliance 200 may transmit a cooking completion notification to the customer terminal 400 (S311).

The kitchen appliance 200 generates unsuitable review data and transmits it to the machine learning learning module 300 when the internal temperature of the cooking object on the digital twin and the actual cooking object differ by more than a preset value or other cooking abnormalities occur. do. The machine learning learning module 300 may analyze the unsuitable review analysis data (S313) and feedback-update variables of the first database (S314).

In the customer terminal 400, even if there is no problem in terms of actual cooking, the user can provide feedback (S315) the taste review data if a deviation occurs between the simulation on the digital twin and the actual cooking result of the cooking object and wants to update individual tastes. have. The machine learning learning module 300 may analyze the taste review analysis data (S316) and feedback-update variables of the second database (S317).

When a deviation occurs according to the characteristics of each cookware in the kitchen appliance 200 , review data for each cookware is generated and transmitted to the machine learning learning module 300 . The machine learning learning module 300 may analyze review data for each cookware (S319) and feedback-update variables of the third database (S320).

Although not shown in the drawing, the digital twin system 100 may convert and receive input data values of the first to third databases from the machine learning learning module 200 in a predetermined manner.

The conversion in the predetermined method is based on a first weight applied to variable values of the first database, a second weight applied to variable values of the second database, and a third weight applied to variable values of the third data. it may be converting

For example, the conversion method may be configured as follows.

소정의 변환방식으로 변환된 변수이며,

는 제1 가중치,

는 제2 가중치,

는 제3 가중치,

는 제1 데이터베이스 변수값,

는 제2 데이터베이스 변수값,

는 제3 데이터베이스 변수값을 나타낼 수 있다. From here,

It is a variable converted by a predetermined conversion method,

is the first weight,

is the second weight,

Is the third weight,

Is the first database variable value,

Is the second database variable value,

may represent a third database variable value.

7 is a diagram illustrating an example of thermal conductivity analysis of a cooking object cooked in an induction oven.

Referring to FIG. 7 , a detailed description of the construction of a digital twin and the calculation of a heat conduction function when the cooking utensil is a frying pan and the cooking object is a steak will be described below.

는 열전도율을 나타내며 아래 첨자

는 각각 오일, 스테이크, 프라이팬, 인덕션을 의미하고 각각의 열전도율 값은 이미 알고 있는 파라미터 혹은 온도에 따른 식이라고 가정하였다. 또한,

~

는 각 층의 두께를,

,

,

,

는 두 개의 아래 첨자 사이 경계의 온도를 나타낸다.In Figure 7,

denotes the thermal conductivity and is subscripted

It was assumed that each means oil, steak, frying pan, and induction, and each thermal conductivity value is an expression according to a known parameter or temperature. In addition,

~

is the thickness of each layer,

,

,

,

denotes the temperature of the boundary between the two subscripts.

에서 일정 부분(transfer rate)만 면적당의 열 흐름으로써 프라이팬으로 전해지고 (

), 그 면적당 열 흐름 중 또다시 일부(rate)만 오일층으로 전달된다. 그리고 오일층으로 전달된 모든 면적당 열 흐름이 전부 스테이크로 전해진다고 가정하였다. 표 1에서 주황색으로 표시된 영역은 경계에서의 조건식을, 파란색 영역은 물체 내부 온도구배식을, 초록색 영역은 각 경계를 지칭하는 식이며, 빨간색으로 표시된 데이터는 각 식으로부터 구해지는 값이다.Heat generated by induction

Only a certain portion (transfer rate) is transferred to the frying pan as heat flow per area (

), again only a fraction (rate) of the heat flow per area is transferred to the oil layer. And it was assumed that all the heat flow per area delivered to the oil layer was delivered to the stake. In Table 1, the area marked in orange is the conditional expression at the boundary, the blue area is the temperature gradient equation inside the object, the green area is the expression indicating each boundary, and the data marked in red is the value obtained from each equation.

)를 주방가전(200)을 통해 직접 확인되거나 및 고객단말(400)을 통해 확인할 수 있다. 실시간으로 확인하며 조리할 수 있다.The temperature gradient data inside the steak obtained through this is shaped in a frying pan. Through the screen on the handle, the internal temperature of the steak (not the apparent degree of cooking)

) can be checked directly through the kitchen appliance 200 or through the customer terminal 400. You can check and cook in real time.

8 is a diagram showing an example of thermal conductivity analysis of a cooking object cooked in a microwave oven.

Referring to FIG. 8 , a detailed description of the construction of the digital twin and the calculation of the heat conduction function when the cookware is a microwave oven will be described later.

가 측정된 apparent 비열로서 상변화에서의 잠열을 내포하고 있다. 앞서 언급되었던 식 (1)에서 추가로

(열 발생) 과

(증발로 인한 열손실)에 대한 식(Zeng & Faghri, 1994)은 다음과 같이 간단하게 정리될 수 있다. 여기서

는 각각

축으로의 마이크로파 투과 깊이를, F는 대류 열을 나타낸다. The assumptions made in the flow diagram of heat in and out of a microwave oven are as follows. The average loss of moisture content during thawing in a microwave oven is assumed to be negligible as less than 1%. In addition, it is assumed that the target food object is homogeneous and has a constant density during the thawing process. used in the simulation of the thawing process.

is the measured apparent specific heat and contains the latent heat in the phase change. In addition to the above-mentioned equation (1)

(heat generation) and

The equation for (heat loss due to evaporation) (Zeng & Faghri, 1994) can be simplified as follows: here

are respectively

The axial microwave penetration depth, F represents the convective heat.

The simulation of the thawing process also needs to specify the temperature boundary condition and the initial condition, and the conditions can be organized as follows similar to the above-mentioned example.

)과 해동 시간 (

=0일때의 t의 값)은 주방가전(200)을 통해 직접 확인되거나 및 고객단말(400)을 통해 확인할 수 있다. The temperature value inside the food calculated through this simulation process

) and thawing time (

= 0) can be checked directly through the kitchen appliance 200 or through the customer terminal 400 .

references

engel, Afshin J. Ghajar McGraw-Hill, 2015, CHAPTER 2. HEAT CONDUCTION EQUATIONHeat and Mass Transfer: Fundamentals & Applications 5th Edition in SI Units Yunus A.

engel, Afshin J. Ghajar McGraw-Hill, 2015, CHAPTER 2. HEAT CONDUCTION EQUATION

Chamchong, M., & Datta, A.K. (1999). Thawing of foods in a microwave oven: I. Effect of power levels and power cycling. Journal of Microwave Power and Electromagnetic Energy , 34 (1), 9-21. https://doi.org/10.1080/08327823.1999.11688384

Zeng, X., & Faghri, A. (1994). Experimental and numerical study of microwave thawing heat transfer for food materials. Journal of Heat Transfer , 116 (2), 446-455. https://doi.org/10.1115/1.2911417

As above, exemplary embodiments have been disclosed in the drawings and specifications. Embodiments have been described using specific terms in this specification, but they are only used for the purpose of explaining the technical idea of the present disclosure and are not used to limit the scope of the present disclosure described in the meaning or claims. Therefore, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical scope of protection of the present disclosure should be determined by the technical spirit of the appended claims.

Claims (20)

In the digital twin construction method of a cooking object linked with kitchen appliances,
The digital twin system receiving real-time cooking target data from kitchen appliances;
The digital twin system materializes a digital twin simulation based on the real-time data of the cooking target;
The digital twin system receives input data necessary for heat transfer simulation from the machine learning module; and
The digital twin system is a method of constructing a digital twin of a cooking object interlocked with kitchen appliances, including the step of estimating an internal temperature value for monitoring the cooking process of the cooking object in real time. According to claim 1,
The cooking object real-time data includes at least one temperature data of the surface of the cooking object, a volume of the cooking object, and an average radius. According to claim 1,
The step of materializing the digital twin simulation is,
determining whether real-time cooking data is collected by a plurality of sensors;
If collected by a plurality of sensors, reflecting a plurality of real-time data to build a digital twin simulation; and
When collected by a single sensor, converting into a plurality of data based on the real-time data simulation for the cooking utensil, and reflecting the plurality of real-time data to build the digital twin simulation; A method of building a digital twin of a cooking object, including. According to claim 3,
The step of converting into a plurality of data based on real-time data simulation,
A method for constructing a digital twin of a cooking object, further comprising generating a simulation by acquiring characteristic data of the cooking utensil from the outside. According to claim 3,
The step of reflecting the plurality of real-time data to the digital twin simulation construction,
determining external information of the object to be cooked; and
A method for constructing a digital twin of a cooking object comprising: determining internal information of the cooking object. According to claim 5,
The step of determining the outer shape information of the cooking object,
A digital twin construction method of the cooking object, which is the step of determining the mass, volume, three-dimensional appearance, and surface temperature distribution of the cooking object. According to claim 5,
Determining the internal information of the cooking object,
A method for constructing a digital twin of a cooking object, wherein internal information is determined based on the basic information of the identified cooking object or based on basic information of the cooking object inputted through a customer terminal or kitchen appliance. According to claim 5,
Determining the internal information of the cooking object,
A digital twin construction method of the cooking object, which is the step of determining the composition, density, and layer structure of the inside of the cooking object. According to claim 1,
The step of receiving input data necessary for the heat transfer simulation,
A digital twin construction method of a cooking object, which is a step in which variable values required for a heat conduction function are provided. According to claim 9,
The method of constructing a digital twin of a cooking object, wherein the variable values required for the heat conduction function are obtained from a first database obtained through an inverse estimation module. According to claim 10,
Receiving nonconformity review data based on the deviation between digital twin simulation and actual cooking results from a customer terminal or kitchen appliance; and
Feedback-updating a variable value required for the heat conduction function of the first database; Method for constructing a digital twin of a cooking object further comprising. According to claim 9,
The method of constructing a digital twin of a cooking object, wherein the variable values required for the heat conduction function are obtained from a second database obtained through a customer personalization module. According to claim 12,
receiving taste review data based on a customer's intended cooking state through a customer terminal; and
The digital twin construction method of the cooking object further comprising the step of feedback updating the variable values required for the heat conduction function of the second database. According to claim 9,
The method of constructing a digital twin of a cooking object, wherein the variable values required for the heat conduction function are obtained from a third database obtained through a cookware individualization module. According to claim 14,
receiving review data for each cookware based on the variation of each cookware from kitchen appliances; and
The digital twin construction method of the cooking object further comprising the step of feedback updating the variable values required for the heat conduction function of the third database. According to claim 1,
The step of receiving input data necessary for the heat transfer simulation,
The variable values required for the heat conduction function are the first database obtained through the inverse estimation module, the variable values required for the heat conduction function are the second database obtained through the customer personalization module, and the variable values required for the heat conduction function are the cookware individualization module. The method of constructing a digital twin of a cooking object that is provided from at least one of the third databases obtained through, or converted in a predetermined manner to input data values of the first to third databases. According to claim 16,
The conversion in the predetermined method is based on a first weight applied to variable values of the first database, a second weight applied to variable values of the second database, and a third weight applied to variable values of the third data. How to build a digital twin of a cooking object that is to transform. According to claim 1,
Following the step of estimating the internal temperature value,
A method of constructing a digital twin of a cooking object, further comprising requesting a cooking completion process from a kitchen appliance when the internal temperature of the digital twin achieves a target cooking condition. In the digital twin system,
The digital twin system,
A transmitting and receiving unit capable of transmitting and receiving information to and from customer terminals, kitchen appliances, and machine learning modules through a network;
a memory unit for storing an application providing a function of receiving target condition cooking data from kitchen appliances and receiving input data from a machine learning module to form a digital twin for an object to be cooked;
a processor for reading and controlling an application from the memory unit;
an input unit that receives a command from a user through the customer terminal; and
An output unit outputting a result value under the control of the processor; includes,
The application,
The digital twin system receives target cooking condition data and cooking target real-time data from kitchen appliances,
The digital twin system materializes the digital twin simulation based on the target cooking condition data and real-time cooking target data received,
The digital twin system receives input data necessary for heat transfer simulation from the machine learning module,
The digital twin system is characterized by estimating the internal temperature value for monitoring the cooking process of the cooking object in real time. A digital twin building program stored in a medium to execute the method of any one of claims 1 to 18, combined with hardware that is a computer.

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