Electronic device for embodying a temperature prediction and control system and control method thereof

Abstract

To provide an electronic apparatus which provides guide information for reducing an input fuel amount while maintaining a proper process temperature to a user, and a method for controlling the same.SOLUTION: An electronic apparatus comprises: a communication interface; a memory stored with a first learnt nerve network model and a second learnt nerve network model; and one or more processors, when process information including the input fuel information of a glass melting device is received via the communication interface, which execute preprocessing of the received process information, input the preprocessed process information in the first learnt nerve network model to acquire first predicted temperature information corresponding to the first position of the glass melting device, input the acquired first predicted temperature information and process information in the second learnt nerve network model to acquire second predicted temperature information corresponding to the second position of the glass melting device, and provide guide information including the acquired first predicted temperature information and the acquired second predicted temperature information.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to an electronic device for implementing a temperature prediction and control system and a control method thereof. More specifically, the present disclosure relates to an electronic device and a control method thereof that predict the temperature of a lower end portion of a glass melting device using a learned neural network model and control the fuel input to the glass melting device based on the predicted temperature.

Background Art

[0002] When glass raw materials are input through a raw material input port (OBC, On Board Charge) into a glass melting furnace (or glass melting device), heat is applied to the glass material in the melting furnace through a burner or an electric booster included in the glass melting device. The glass material in the melting furnace is eluted through an elution section.

[0003] Note that there is an appropriate range for the temperature and the loading height of the glass material in the glass melting furnace. In this case, there is a problem that it is difficult to maintain the temperature and the loading height of the glass material due to uncertainties such as frequent changes in the elution amount and the moisture content contained in the raw materials.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The present disclosure is for solving the above-described problems, and the present disclosure predicts the temperature of a specific position in a glass melting device using a learned neural network model, and provides an electronic device and a control method thereof that provide guide information for reducing the input fuel amount while maintaining an appropriate process temperature for a user based on the predicted temperature information.

Means for Solving the Problems

[0006] An electronic device for implementing a temperature prediction and control system according to an embodiment of the present disclosure, comprising: a communication interface; a memory storing a first learned neural network model and a second learned neural network model; and one or more processors configured to perform preprocessing on the received process information when process information including input fuel information of a glass melting device is received via the communication interface.

[0007] The one or more processors can input the preprocessed process information into the first learned neural network model to obtain first predicted temperature information corresponding to a first position of the glass melting device.

[0008] The one or more processors can input the obtained first predicted temperature information and the process information into the second learned neural network model to obtain second predicted temperature information corresponding to a second position of the glass melting device.

[0009] The one or more processors can provide guidance information including the obtained first predicted temperature information and the obtained second predicted temperature information.

[0010] A control method for an electronic device for implementing a temperature prediction and control system according to an embodiment of the present disclosure may include a step of performing preprocessing on the received process information when process information including input fuel information of a glass melting device is received.

[0011] The control method may include a step of inputting the preprocessed process information into a first learned neural network model to obtain first predicted temperature information corresponding to a first position of the glass melting device.

[0012] The control method may include a step of inputting the obtained first predicted temperature information and the process information into a second learned neural network model to obtain second predicted temperature information corresponding to a second position of the glass melting device.

[0013] The control method may include a step of providing guide information including the acquired first predicted temperature information and the acquired second predicted temperature information.

[0014] A non - transitory computer - readable recording medium according to an embodiment of the present disclosure stores computer instructions that cause an electronic device to operate when executed by a processor of the electronic device for implementing a temperature prediction and control system. The operation may include a step of pre - processing the received process information when process information including input fuel information of a glass melting device is received.

[0015] The operation may include a step of inputting the pre - processed process information into a first learned neural network model and acquiring first predicted temperature information corresponding to a first position of the glass melting device.

[0016] The operation may include a step of inputting the acquired first predicted temperature information and the process information into a second learned neural network model and acquiring second predicted temperature information corresponding to a second position of the glass melting device.

[0017] The operation may include a step of providing guide information including the acquired first predicted temperature information and the acquired second predicted temperature information.

Advantages of the Invention

[0018] According to the present disclosure, the internal temperature of the glass melting device and the temperature of the glass material can be predicted using a learned neural network model. Based on this, the electronic device 100 can provide guide information to the user for minimizing the fuel input amount while maintaining the temperature of the glass material within an appropriate range.

Brief Description of the Drawings

[0019]

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Mode for Carrying Out the Invention

[0020] Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.

[0021] The terms used in this specification will be briefly explained, and the present disclosure will be specifically described.

[0022] In the embodiments of the present disclosure, the terms used are selected as general terms that are currently widely used as much as possible in consideration of the functions in the present disclosure. However, this can change depending on the intentions or precedents of those skilled in the art, the emergence of new technologies, etc. Also, in certain cases, there are terms arbitrarily selected by the applicant, and in such cases, the meaning thereof will be described in detail in the explanatory part of the relevant disclosure. Therefore, the terms used in the present disclosure should be defined based not on the simple name of the terms but on the meaning of the terms and the overall content of the present disclosure.

[0023] In this specification, expressions such as "having", "may have", "including", or "may include" refer to the presence of the feature (e.g., a component such as a numerical value, a function, an operation, or a part), and do not exclude the presence of additional features.

[0024] The expression "at least one of A or / and B" should be understood to indicate any one of "A" or "B" or "A and B".

[0025] Expressions such as "first", "second", "the first", or "the second" used in this specification can modify various components regardless of order and / or importance, and are only used to distinguish one component from another component, and do not limit the component.

[0026] When it is mentioned that any component (e.g., the first component) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., the second component), it should be understood that any component may be directly coupled to the other component or may be coupled through another component (e.g., the third component).

[0027] Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "including" or "comprising" are intended to specify the presence of the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof in advance.

[0028] In the present disclosure, a "module" or a "unit" performs at least one function or operation, and may be implemented by hardware or software, or by a combination of hardware and software. Also, multiple "modules" or multiple "units" may be integrated into at least one module and implemented by at least one processor (not shown), except for the "module" or "unit" that needs to be implemented by specific hardware.

[0029] An electronic device according to an embodiment of the present disclosure may include an artificial intelligence model (or an artificial neural network model or a learning network model) composed of at least one neural network layer. The artificial neural network may include a deep neural network (DNN), and for example, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), BRDNN (Bidirectional Recurrent Deep Neural Network), or Deep Q-Networks, etc., but is not limited to the foregoing examples.

[0030] In addition, in this specification, a "parameter" is a value used in the calculation process of each layer constituting a neural network, and may include, for example, a weight value used when applying an input value to a predetermined arithmetic expression. Also, a parameter can be expressed in matrix form. A parameter is a value set as a result of training, and can be updated via separate training data as needed.

[0031] FIG. 1 is a diagram for schematically explaining a control method of an electronic device according to an embodiment.

[0032] Referring to FIG. 1, according to an embodiment, the glass melting device 1 may include a plurality of fuel input units including a plurality of burners 10 and electric boosters 20. The burner 10 is a heat source corresponding to the relatively upper side of the glass melting device 1, and the electric booster 20 is a heat source corresponding to the relatively lower side of the glass melting device 1. The temperature of the glass material contained in the glass melting device 1 can be maintained by the amount of fuel input to the burner and the electric booster.

[0033] According to an embodiment, the electronic device 100 can acquire predicted temperature information corresponding to a specific position of the glass melting device based on the process information of the glass melting device 1, and provide guide information for guiding the amount of fuel input to the plurality of fuel input units based on the acquired predicted temperature information.

[0034] Hereinafter, various embodiments will be described in which the temperature of a specific position in the glass melting device 1 is predicted using a learned neural network model, and guide information for reducing the amount of fuel input while maintaining an appropriate process temperature for the user is provided based on the predicted temperature information.

[0035] FIG. 2 is a block diagram showing the configuration of an electronic device according to an embodiment.

[0036] Referring to FIG. 2, the electronic device 100 may include a communication interface 110, a memory 120, and one or more processors 130.

[0037] According to one embodiment, the electronic device 100 may be implemented as a device that processes data and communicates with an external device like a server, but is not limited thereto. For example, the electronic device 100 may be implemented as various devices such as a smart TV, a tablet, a monitor, a smartphone, a desktop computer, a laptop computer, etc. The electronic device 100 according to one embodiment of the present disclosure is not limited to the aforementioned devices, and the electronic device 100 may be implemented as an electronic device having two or more functions of the aforementioned devices.

[0038] Note that the electronic device 100 can communicate and connect with an external device and an external server in various ways. According to one embodiment, a communication module for communication with an external device and an external server may be implemented identically. For example, the electronic device 100 can communicate with an external device using a Bluetooth module, and the external server can also communicate using a Bluetooth module.

[0039] According to another embodiment, communication modules for communication with an external device and an external server may be implemented separately. For example, the electronic device 100 can communicate with an external device using a Bluetooth module, and can communicate with an external server using an Ethernet (registered trademark) modem or a Wi-Fi module.

[0040] Note that according to one embodiment, an external device (not shown) may be implemented as the glass melting device 1, but is not limited thereto.

[0041] The communication interface 110 can input and output various types of data. For example, the communication interface 110 can transmit and receive various types of data with an external device (e.g., a source device), an external storage medium (e.g., a USB memory), and an external server (e.g., a web hard) via communication methods such as Wi-Fi (Wireless LAN network) of an AP infrastructure, Bluetooth (registered trademark), Zigbee (registered trademark), wired / wireless LAN (Local Area Network), WAN (Wide Area Network), Ethernet, IEEE 1394, HDMI (registered trademark) (High-Definition Multimedia Interface), USB (Universal Serial Bus), MHL (Mobile High-Definition Link), AES / EBU (Audio Engineering Society / European Broadcasting Union), Optical, Coaxial, etc.

[0042] By way of example, the communication interface 110 may include a BLE (Bluetooth Low Energy) module. BLE means a Bluetooth technology capable of transmitting and receiving low-power and low-capacity data in a 2.4 GHz frequency band with a reach radius of about 10 m. However, it is not limited thereto, and the communication interface 110 can also include a Wi-Fi communication module. That is, the communication interface 110 may include at least one of a BLE (Bluetooth Low Energy) module or a Wi-Fi communication module.

[0043] In one example, the communication interface 110 can use different communication modules to communicate with external devices such as remote control devices and external servers. For example, the communication interface 110 can use at least one of an Ethernet module or a Wi-Fi module to communicate with an external server, and can also use a Bluetooth module to communicate with an external device such as a remote control device. However, this is only one embodiment, and when the communication interface 110 communicates with multiple external devices or external servers, it can use at least one communication module among various communication modules.

[0044] The memory 120 can store data required for various embodiments. The memory 120 can be embodied in a memory form embedded in the electronic device 100 according to the data storage purpose, or can also be embodied in a detachable memory form of the electronic device 100. For example, in the case of data for driving the electronic device 100, it is stored in the memory embedded in the electronic device 100, and in the case of data for the extended function of the electronic device 100, it is stored in the detachable memory of the electronic device 100.

[0045] In the case of the memory embedded in the electronic device 100, it may be implemented by at least one of a volatile memory (e.g., DRAM (dynamic RAM), SRAM (static RAM), or SDRAM (synchronous dynamic RAM), etc.), a non-volatile memory (e.g., OTPROM (one time programmable ROM), PROM (programmable ROM), EPROM (erasable and programmable ROM), EEPROM (electrically erasable and programmable ROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash, etc.), hard drive, or solid state drive (SSD)). Also, in the case of the memory detachable from the electronic device 100, it may be implemented in the form of a memory card (e.g., CF (compact flash), SD (secure digital), Micro-SD (micro secure digital), Mini-SD (mini secure digital), xD (extreme digital), MMC (multi-media card), etc.), an external memory connectable to a USB port (e.g., USB memory), etc.

[0046] According to an embodiment, the first learned neural network model and the second learned neural network model may be stored in the memory 120. Note that the first learned neural network model and the second learned neural network model will be described in detail with reference to FIGS. 4 and 5.

[0047] One or more processors 130 (hereinafter referred to as the processor) are electrically connected to the communication interface 110 and the memory 120, and control the overall operation of the electronic device 100. The processor 130 may be composed of one or more processors. Specifically, the processor 130 can perform the operations of the electronic device 100 according to various embodiments of the present disclosure by executing at least one instruction stored in the memory 120.

[0048] According to one embodiment, the processor 130 may be implemented as a digital signal processor (DSP), a microprocessor, a GPU (Graphics Processing Unit), an AI (Artificial Intelligence) processor, an NPU (Neural Processing Unit), or a TCON (Time controller) that processes digital video signals. However, it is not limited thereto, and may include one or more of a central processing unit (CPU), an MCU (Micro Controller Unit), an MPU (micro processing unit), a controller, an application processor (AP), or a communication processor (CP), or can be defined by the term. In addition, the processor 130 can also be implemented as a SoC (System on Chip) or an LSI (large scale integration) with a built-in processing algorithm, and can also be implemented in the form of an ASIC (application specific integrated circuit) or an FPGA (Field Programmable gate array).

[0049] According to an embodiment, the processor 130 may be implemented by a digital signal processor (DSP), a microprocessor, or a TCON (Time controller). However, it is not limited thereto, and may include one or more of a central processing unit (CPU), a microcontroller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), or a communication processor (CP), or may be defined by the terms. Further, the processor 130 may be implemented by a system on chip (SoC) or a large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of a field programmable gate array (FPGA).

[0050] According to an embodiment, the processor 130 may receive process information including input fuel information of the glass melting apparatus 1 via the communication interface 110. Here, the process information is information regarding various environments of the glass melting apparatus 1 in which the glass melting process is performed. By way of example, the process information may include input fuel information input to the glass melting apparatus 1 to perform the glass melting process or process state information corresponding to the glass melting apparatus 1.

[0051] The input fuel information may, by way of example, include information regarding the input amount of fuel input for performing the glass melting process. By way of example, the input fuel information may include information regarding the fuel input amounts corresponding to each of a plurality of fuel input parts included in the glass melting apparatus 1. Note that the process state information corresponding to the glass melting apparatus 1 may, for example, include process environment information regarding the glass melting apparatus 1, or, by way of example, the process state information may include at least one of input oxygen amount information input to the glass melting apparatus 1, elution amount (or production amount) information eluted from the glass melting apparatus 1, environmental information around the glass melting apparatus 1, and power amount information of an electric booster included in the glass melting apparatus 1. The environmental information around the glass melting apparatus 1 may, for example, include temperature information or humidity information inside the glass melting apparatus 1.

[0052] According to one embodiment, when the process information is received, the processor 130 can perform preprocessing of the received process information. By way of example, the processor 130 can identify outlier data among a plurality of data included in the received process information using a predetermined algorithm, and can obtain process information from which the identified outlier data has been removed. Alternatively, when process information corresponding to a predetermined time interval is not received during the time when the process is being performed, the processor 130 can also perform preprocessing by receiving the process information that has not been received from the communication interface 110. However, it is not limited thereto.

[0053] According to one embodiment, the processor 130 can input the preprocessed process information into a first learned neural network model, and obtain first predicted temperature information corresponding to a first position of the glass melting apparatus 1.

[0054] Here, the first position of the glass melting device 1 may, by way of example, correspond to a position relatively above (or the ceiling) of the glass melting device 1. A temperature sensor may be provided at the first position of the glass melting device 1, and the temperature relatively above the glass melting device 1 can be identified via the sensor corresponding to the first position. In this case, the temperature relatively above may be the temperature of the gas in which the melt to be melted has vaporized. By way of example, the processor 130 can input the preprocessed process information into the first learned neural network model stored in the memory 120 and obtain the first predicted temperature information corresponding to the first position of the glass melting device 1. Here, a plurality of types of temperature information including the first predicted temperature information and the second predicted temperature information may include information regarding temperature values.

[0055] Note that the first learned neural network model will be described in detail with reference to FIG. 4.

[0056] According to one embodiment, the processor 130 can input the acquired first predicted temperature information and process information into the second learned neural network model and obtain the second predicted temperature information corresponding to the second position of the glass melting device 1.

[0057] Here, the second position of the glass melting device 1 may, by way of example, correspond to a position relatively below (or the bottom surface provided at the lower end and in contact with the glass material) of the glass melting device 1. A temperature sensor may be provided at the second position of the glass melting device 1, and the temperature relatively below the glass melting device 1 can be identified via the sensor corresponding to the second position. In this case, the temperature relatively below may be the temperature of the melt (or glass material) to be melted. By way of example, the processor 130 can input the preprocessed process information and the first predicted temperature information into the second learned neural network model stored in the memory 120 and obtain the second predicted temperature information corresponding to the second position of the glass melting device 1. That is, in order to accurately measure the temperature of the glass material, information regarding the temperature of the gas in the glass melting device 1 (or the temperature of the upper end of the glass melting device 1) is necessary.

[0058] Regarding the second learned neural network model, it will be described in detail with reference to FIG. 5.

[0059] According to one embodiment, the processor 130 can provide guidance information including the acquired first predicted temperature information and the acquired second predicted temperature information. By way of example, when the acquired second predicted temperature information is identified as exceeding a predetermined range, the processor 130 can acquire guidance information for guiding the second predicted temperature information to fall within the predetermined range based on the information stored in the memory 120. By way of example, the memory 120 may store information regarding a reference temperature range corresponding to each of the first position and the second position.

[0060] FIG. 3 is a flowchart for explaining a control method of an electronic device according to one embodiment.

[0061] Referring to FIG. 3, according to one embodiment, the control method can identify whether process information including the input fuel information of the glass melting device 1 is received (S310).

[0062] Next, according to one embodiment, when the process information is received (S310: Y), the control method can perform preprocessing of the received process information (S320). By way of example, the processor 130 can identify outlier data among a plurality of data included in the received process information using a predetermined algorithm, and can acquire process information in which the identified outlier data has been removed.

[0063] Next, according to one embodiment, the control method can input the preprocessed process information into the first learned neural network model and acquire first predicted temperature information corresponding to the first position of the glass melting device 1 (S330). By way of example, the processor 130 can input process information including the preprocessed input fuel information and process state information into the first learned neural network model stored in the memory 120 and acquire the first predicted temperature information.

[0064] Next, according to one embodiment, the control method can input the acquired first predicted temperature information and process information into a second learned neural network model to acquire second predicted temperature information corresponding to the second position of the glass melting apparatus 1 (S340). According to an example, the processor 130 can input the first predicted temperature information and the process information received via the communication interface 110 into the second learned neural network model stored in the memory 120 to acquire the second predicted temperature information.

[0065] Next, according to one embodiment, the control method can provide guide information including the acquired first predicted temperature information and the acquired second predicted temperature information (S350).

[0066] According to an example, when the acquired second predicted temperature information is identified as exceeding a predetermined range, the processor 130 can acquire guide information for guiding the second predicted temperature information to belong within the predetermined range based on the information stored in the memory 120.

[0067] For example, when the acquired second predicted temperature information is identified as exceeding a first threshold value, the processor 130 can acquire and provide guide information for the second predicted temperature information to be equal to or less than the first threshold value. Or, for example, when the acquired second predicted temperature information is identified as being less than a second threshold value, the processor 130 can acquire and provide guide information for the second predicted temperature information to be equal to or greater than the second threshold value.

[0068] According to the above-described embodiment, the internal temperature of the glass melting apparatus 1 and the temperature of the glass material can be predicted using the learned neural network model. Based on this, the electronic device 100 can provide the user with guide information for minimizing the fuel input while maintaining the temperature of the glass material within an appropriate range.

[0069] FIG. 4 is a diagram for explaining a first learned neural network model according to one embodiment.

[0070] An artificial neural network (or neural network model) including a first learned neural network model and a second learned neural network model according to an embodiment of the present disclosure may include a deep neural network (DNN), or for example, a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), an RBM (Restricted Boltzmann Machine), a DBN (Deep Belief Network), a BRDNN (Bidirectional Recurrent Deep Neural Network), or a Deep Q-Networks, etc., but is not limited to the above-described examples.

[0071] According to an embodiment, a first learned neural network model may be stored in the memory 120. The first learned neural network model can be learned to output first predicted temperature information based on the input fuel amount when process information including fuel amount information input to the glass melting device 1 is input.

[0072] Referring to FIG. 4, according to an embodiment, a dataset including process information 410 including input fuel information and process state information is input as learning data to a first neural network model 400, and the first neural network model can be learned. In this case, the dataset may include first temperature information 420 output based on (or caused by) the input fuel as a label.

[0073] For example, a dataset including process information including input fuel amount information and process state information at the n-th time point is input as learning data to the first neural network model, and the neural network model can be learned. In this case, the dataset may include temperature information corresponding to the first position of the glass melting device 1 at the (n + 1)-th time point of the first preheating chamber measured based on the fuel amount input at the n-th time point as a label and learning can be performed.

[0074] FIG. 5 is a diagram for explaining a second learned neural network model according to an embodiment.

[0075] According to an embodiment, a second learned neural network model may be stored in the memory 120. By way of example, the second learned neural network model can be learned to output second predicted temperature information based on the input fuel amount when the first predicted temperature information output via the first learned neural network model and the received process information are input. Here, the second predicted temperature information is predicted temperature information corresponding to the lower end portion (or the glass material) of the glass melting apparatus 1.

[0076] Referring to FIG. 5, according to an embodiment, a dataset including process information 520 and first predicted temperature information 530 is input as learning data to a second neural network model 500, and the second neural network model 500 can be learned. Here, the first predicted temperature information 530 is information output from the first learned neural network model 400. By way of example, the process information 520 may include input fuel information and process state information. In this case, by way of example, the dataset may include, as a label, second temperature information 510 output based on (or resulting from) the input fuel. The second temperature information is temperature information measured from a sensor provided at a second position of the glass melting apparatus 1.

[0077] For example, a dataset including process information including input fuel amount information and process state information at the n-th time point and first predicted temperature information corresponding to the first position output from the first learned neural network model 400 is input as learning data to a second neural network model, and the neural network model can be learned. In this case, the dataset may include, as a label, temperature information corresponding to the second position of the glass melting apparatus 1 at the (n + 1)-th time point of the first preheating chamber measured based on the fuel amount input at the n-th time point, and learning can be performed.

[0078] FIG. 6 is a diagram for explaining a method of providing guidance information according to an embodiment.

[0079] Referring to FIG. 6, according to one embodiment, the control method can identify whether the acquired second predicted temperature information exceeds a predetermined range (S610).

[0080] According to an example, the reference range information corresponding to the second predicted temperature information may be stored in the memory 120. According to an example, when the second predicted temperature information is acquired, the processor 130 can identify whether the second predicted temperature acquired based on the information stored in the memory 120 is within the reference range.

[0081] Next, according to one embodiment, when the second predicted temperature information exceeds a predetermined range (S610: Y), the control method can acquire guide information for guiding the second predicted temperature information to belong within the predetermined range based on the information stored in the memory 120 (S620). According to an example, the memory 120 may further include temperature change information corresponding to the second position according to the unit fuel input amount. That is, when fuel corresponding to a predetermined unit is input into the glass melting apparatus 1, information regarding the magnitude of the temperature change at the second position may be stored in the memory 120. The processor 130 can acquire guide information for guiding the second predicted temperature information to belong within the predetermined range by using the temperature change information stored in the memory 120.

[0082] Note that, according to one embodiment, the processor 130 can also acquire temperature change information by using the learned neural network model. This will be described in detail with reference to FIG. 7.

[0083] Next, according to one embodiment, the control method can provide a UI including the acquired guide information (S630).

[0084] FIG. 7 is a diagram for explaining a method of acquiring temperature change information according to one embodiment.

[0085] Referring to FIG. 7, according to one embodiment, the control method can identify sub-input fuel information in which information regarding the input fuel quantity included in the input fuel information is changed (S710). Here, the sub-input fuel information can be process information in which the input fuel quantity is changed by a predetermined value.

[0086] Next, according to one embodiment, the control method can input sub-process information including the sub-input fuel information into a second learned neural network model to obtain second sub-predicted temperature information (S720). By way of example, when the sub-input fuel information is identified, the processor 130 can obtain sub-process information in which the magnitude of the identified input fuel quantity is updated. The processor 130 can input the sub-process information into a second learned neural network model to obtain second sub-predicted temperature information. Here, the second sub-predicted temperature information means predicted temperature information corresponding to the sub-input fuel information in which the input fuel quantity is changed.

[0087] Next, according to one embodiment, the control method can obtain temperature change information corresponding to a second position per unit fuel input using the second predicted temperature information and the second sub-predicted temperature information (S730). By way of example, the processor 130 can compare the second predicted temperature information and the second sub-predicted temperature information output via the second learned neural network model to obtain second temperature change information corresponding to the second position per unit fuel input.

[0088] However, without being limited thereto, according to one embodiment, the second temperature change information corresponding to the second position per unit fuel input may already be stored in the memory 120. Or, according to one embodiment, the processor 130 can of course also obtain the second temperature change information corresponding to the second position per unit fuel input using the process history information regarding the glass melting apparatus 1 via a predetermined algorithm. Here, the process history information may include history information regarding the input fuel and history information regarding the process state.

[0089] FIG. 8 is a diagram for explaining a method of obtaining temperature change information according to one embodiment.

[0090] Referring to FIG. 8, according to one embodiment, the control method can identify the relationship information between the fuel input amount and the temperature at the second position based on the process history information (S810). Here, the process history information may include the history information regarding the input fuel, the history information regarding the process state, and the temperature history information at the second position.

[0091] Note that the relationship information may be a mathematical formula corresponding to the relationship between the input fuel information and the temperature value at the second position obtained using a regression analysis model. By way of example, the processor 130 can obtain the relationship information between the input fuel information and the temperature value at the second position from the process history information including the history information regarding the input fuel, the history information regarding the process state, and the temperature history information at the second position using a predefined algorithm (e.g., a regression analysis model).

[0092] Next, according to one embodiment, the control method can obtain and store the temperature change information corresponding to the second position per unit fuel input amount based on the identified relationship information (S820). By way of example, the processor 130 can obtain the temperature change information corresponding to the second position per unit fuel input amount based on the relationship information between the input fuel information and the temperature value at the second position, and can provide guide information using this.

[0093] FIGS. 9A to 9D are diagrams for explaining a UI providing method according to one embodiment.

[0094] According to one embodiment, the process history information may be stored in the memory 120. By way of example, the process history information may include the temperature history information at the first position and the temperature history information at the second position. Or, by way of example, the process history information can also include the history information regarding the input fuel and the history information regarding the process state.

[0095] Referring to FIG. 9a, according to one embodiment, the processor 130 can provide a UI 900 that includes process history information. By way of example, the processor 130 can obtain temperature history information of a first location (ARCH #3) and temperature history information of a second location (MELTER BT #11) included in the process history information based on the information stored in the memory 120. For example, the processor 130 can provide a UI 900 that includes graph information corresponding to the temperature history of the first location. Or, for example, the processor 130 can also provide a UI 900 that includes graph information corresponding to the temperature history of the second location.

[0096] Note that, according to one embodiment, the electronic device 100 may further include a display (not shown), and by way of example, the processor 130 can display the UI 900 via the display (not shown).

[0097] Referring to FIG. 9b, according to one embodiment, the processor 130 can provide a UI 910 that includes guidance information regarding the amount of fuel input. By way of example, the guidance information may include information 911 regarding the currently input amount of fuel corresponding to each of the plurality of fuel input parts in the glass melting device 1 including the first fuel input part and the second fuel input part. Or, by way of example, the guidance information regarding the amount of fuel input may include guidance information 912 regarding the amount of fuel input to each of the plurality of fuel input parts in the glass melting device 1 including the first fuel input part and the second fuel input part. Here, each item displayed on the UI (for example, "MAIN", "1L", "2L", "3L", …, "4R") corresponds to an item of a plurality of fuel input parts including a plurality of burners 10 included in the glass melting device 1.

[0098] Here, the guidance information 912 regarding the fuel amount to be input means information regarding the recommended input amount corresponding to the fuel input to each of a plurality of positions (or a plurality of fuel input parts) where the fuel amount to be input decreases while the temperature at the second position is maintained within a predetermined range. That is, the guidance information 912 regarding the fuel amount to be input means the recommended input amount at which the fuel amount input to each of the plurality of positions becomes minimum under the condition that the temperature at the second position is maintained within the predetermined range. For example, the processor 130 can obtain the guidance information 912 regarding the fuel amount to be input from the process information, the process history information, and the second predicted temperature information using a predetermined algorithm. Alternatively, the guidance information 912 regarding the fuel amount to be input may already be stored in the memory 120.

[0099] Referring to FIG. 9c, according to one embodiment, the processor 130 can provide a UI 910-1 including guidance information regarding the oxygen amount corresponding to each of a plurality of fuel input parts. For example, the guidance information regarding the oxygen amount corresponding to each of the plurality of fuel input parts may include information 911-1 regarding the current oxygen amount corresponding to each of the plurality of fuel input parts in the glass melting apparatus 1 including the first fuel input part and the second fuel input part. Alternatively, for example, the guidance information regarding the oxygen amount corresponding to each of the plurality of fuel input parts may include guidance information 912-1 regarding the oxygen amount to be input to each of the plurality of fuel input parts in the glass melting apparatus 1 including the first fuel input part and the second fuel input part. Here, each item displayed on the UI (for example, "MAIN", "1L", "2L", "3L",..., "4R") corresponds to a plurality of fuel input parts including a plurality of burners 10 included in the glass melting apparatus 1.

[0100] Here, the guidance information 912-1 regarding the amount of oxygen corresponding to each of the plurality of fuel input units means information regarding the recommended oxygen amount corresponding to each of the plurality of fuel input units where the amount of fuel input decreases while the temperature at the second position maintains a predetermined range. That is, the guidance information 912-1 regarding the amount of oxygen corresponding to each of the plurality of fuel input units means the recommended input amount at which the recommended oxygen amount corresponding to each of the plurality of fuel input units becomes minimum under the condition that the temperature at the second position maintains a predetermined range. For example, the processor 130 can obtain the guidance information 912-1 regarding the amount of oxygen corresponding to each of the plurality of fuel input units from the process information, the process history information, and the second predicted temperature information using a predetermined algorithm. Alternatively, the guidance information 912-1 regarding the amount of oxygen corresponding to each of the plurality of fuel input units may already be stored in the memory 120.

[0101] Note that according to one embodiment, the processor 130 can provide both the UI 910 including the guidance information regarding the amount of fuel input shown in FIG. 9b and the UI 910-1 including the guidance information regarding the amount of oxygen corresponding to each of the plurality of fuel input units, or can provide the UI 910 including the guidance information regarding the amount of fuel input and the UI 910-1 including the guidance information regarding the amount of oxygen corresponding to each of the plurality of fuel input units respectively, of course.

[0102] Referring to FIG. 9d, according to one embodiment, the processor 130 can provide a UI 920 including predicted temperature information. By way of example, the processor 130 can obtain a UI 920 including first predicted temperature information corresponding to the acquired first position (Arch #3) and second predicted temperature information corresponding to the second position (Melter BT #11). In this case, the processor 130 can identify the current temperature information corresponding to the first position and the second position based on the received process information, and can also provide a UI 920 including the identified current temperature information corresponding to the first position and the second position. In this case, the processor 930 can obtain an expected value (effect) when fuel corresponding to the guidance information 912 is input using the first learned neural network model and the second learned neural network model. By way of example, the expected value may include temperature information of the first position corresponding to the guidance information 912 and temperature information of the second position corresponding to the guidance information 912.

[0103] FIG. 10 is a diagram for explaining a method of identifying control information according to an embodiment and transmitting it to a control engine.

[0104] Referring to FIG. 10, according to one embodiment, when a user input corresponding to the acquired guide information is received, the control method can identify control information corresponding to the received user input (S1010). Here, the control information means a control signal for controlling the control engine, and the control engine means an engine for controlling the glass melting apparatus 1. By way of example, the electronic device 100 may further include a user interface (not shown), and the processor 130 can receive a user input corresponding to the guide information acquired via the user interface (not shown). Next, the processor 130 can identify control information corresponding to the received user input based on the information stored in the memory 120.

[0105] Next, according to one embodiment, the control method can transmit the identified control information to a control engine (S1020). By way of example, the processor 130 can transmit the identified control information to the control engine via the communication interface 110. Here, the control engine is an engine for controlling the glass melting apparatus 1.

[0106] FIG. 11 is a block diagram showing a detailed configuration of an electronic device according to an embodiment.

[0107] Referring to FIG. 11, the electronic device 100' may include a communication interface 110, a memory 120, one or more processors 130, a microphone 140, a speaker 150, a display 160, a user interface 170, and at least one sensor 180. For configurations that overlap with those shown in FIG. 2 among the configurations shown in FIG. 11, detailed descriptions will be omitted.

[0108] The microphone 140 refers to a module that acquires sound and converts it into an electrical signal, and can be a condenser microphone, a ribbon microphone, a moving coil microphone, a piezoelectric element microphone, a carbon microphone, or a MEMS (Micro Electro Mechanical System) microphone. Also, it may be implemented in an omnidirectional, bidirectional, unidirectional, sub - cardioid, super - cardioid, or hyper - cardioid mode.

[0109] There can be various embodiments in which the electronic device 100' performs operations corresponding to the user voice signal received via the microphone 140.

[0110] By way of example, the electronic device 100' can control the display 160 based on the user voice signal received via the microphone 140. For example, when a user voice signal for displaying content A is received, the electronic device 100' can control the display 160 to display content A.

[0111] As another example, the electronic device 100' can control an external display device connected to the electronic device 100' based on a user voice signal received via the microphone 140. Specifically, the electronic device 100' can provide a control signal for controlling the external display device so that an operation corresponding to the user voice signal is performed on the external display device, and transmit the provided control signal to the external display device. Here, the electronic device 100' can store a remote control application for controlling the external display device. Also, the electronic device 100' can transmit the provided control signal to the external display device using at least one communication method among Bluetooth, Wi-Fi, or infrared rays. For example, when a user voice signal for displaying content A is received, the electronic device 100' can transmit a control signal for controlling so that content A is displayed on the external display device to the external display device. Here, the electronic device 100' means various terminal devices capable of installing a remote control application such as a smartphone or an AI speaker.

[0112] As yet another example, the electronic device 100' can use a remote control device to control an external display device connected to the electronic device 100' based on a user voice signal received via the microphone 140. Specifically, the electronic device 100' can transmit a control signal for controlling the external display device so that an operation corresponding to the user voice signal is performed on the external display device to the remote control device. Also, the remote control device can transmit the control signal received from the electronic device 100' to the external display device. For example, when a user voice signal for displaying content A is received, the electronic device 100' transmits a control signal for controlling so that content A is displayed on the external display device to the remote control device, and the remote control device can transmit the received control signal to the external display device.

[0113] Speaker 150 includes a tweeter for high - pitch sound reproduction, a mid - range for mid - pitch sound reproduction, a woofer for low - pitch sound reproduction, a sub - woofer for ultra - low - pitch sound reproduction, an enclosure for controlling resonance, a crossover network for separating the frequency of the electrical signal input to the speaker into different bands, and so on.

[0114] Speaker 150 can output an acoustic signal to the outside of the electronic device 100'. Speaker 150 can output multimedia playback, recording playback, various notification sounds, voice messages, and so on. The electronic device 100' may include an audio output device such as Speaker 150, or may include an output device such as an audio output terminal. In particular, Speaker 150 can provide the acquired information, the information processed and produced based on the acquired information, the response result or operation result for the user voice, etc. in voice form.

[0115] The display 160 may be implemented as a display including a self-emitting element or a display including a non-self-emitting element and a backlight. For example, it may be implemented as various types of displays such as an LCD (Liquid Crystal Display), an OLED (Organic Light Emitting Diodes) display, an LED (Light Emitting Diodes), a micro LED, a Mini LED, a PDP (Plasma Display Panel), a QD (Quantum dot) display, a QLED (Quantum dot light-emitting diodes), etc. The display 160 may also include a driving circuit implemented in the form of an a-si TFT, an LTPS (low temperature poly silicon) TFT, an OTFT (organic TFT), etc., and a backlight unit together. Note that the display 160 may be implemented as a touch screen combined with a touch sensor, a flexible display, a rollable display, a 3D display (3D display), a display in which a plurality of display modules are physically connected, etc. The processor 130 can control the display 160 to output the output video obtained according to the various embodiments described above. Here, the output video may be a high-resolution video of 4K or 8K or higher.

[0116] Note that according to other embodiments, the electronic device 100' may not include the display 160. The electronic device 100' may be connected to an external display device and can transmit an image or content stored in the electronic device 100' to the external display device. Specifically, the electronic device 100' can transmit an image or content to the external display device together with a control signal for controlling the image or content to be displayed on the external display device.

[0117] Here, the external display device can be connected to the electronic device 100' via the communication interface 110 or an input / output interface (not shown). For example, the electronic device 100' may not include a display, such as a Set Top Box (STB). Further, the electronic device 100' may include only a small display that can display only simple information such as text information. Here, the electronic device 100' can transmit an image or content to the external display device either wired or wirelessly via the communication interface 110, or transmit it to the external display device via an input / output interface (not shown).

[0118] The user interface 170 is a configuration for the electronic device 100' to interact with the user. For example, the user interface 170 may include at least one of a touch sensor, a motion sensor, a button, a Jog dial, a switch, a microphone, or a speaker, but is not limited thereto.

[0119] The at least one sensor 180 (hereinafter, sensor) may include a plurality of sensors of various types. The sensor 180 can measure a physical quantity or sense the operating state of the electronic device 100', and convert the measured or sensed information into an electrical signal. The sensor 180 may include a camera, and the camera may include a lens that focuses visible light or other optical signals reflected and received by an object onto an image sensor and an image sensor that can sense visible light or other optical signals. Here, the image sensor may include a 2D pixel array divided into a plurality of pixels.

[0120] According to the above-described embodiment, the internal temperature of the glass melting device and the temperature of the glass material can be predicted using the learned neural network model. Based on this, the electronic device 100' can provide the user with guidance information for minimizing the fuel input while maintaining the temperature of the glass material within an appropriate range.

[0121] In addition, the methods according to various embodiments of the present disclosure described above may be embodied in the form of an application installable in a conventional electronic device. Alternatively, the methods according to various embodiments of the present disclosure described above may be performed using a learned neural network (or a deep learned neural network) of a deep learning infrastructure, that is, a learning network model. Further, the methods according to various embodiments of the present disclosure described above may be embodied only by software upgrading or hardware upgrading of a conventional electronic device. Further, various embodiments of the present disclosure described above may also be performed via an embedded server provided in the electronic device or an external server of the electronic device.

[0122] According to an embodiment of the present disclosure, the various embodiments described above may be embodied in software including instruction words stored in a machine-readable storage media (e.g., a computer). The machine is a device that can call the stored instruction words from the storage media and operate according to the called instruction words, and may include a display device (e.g., display device (A)) according to the disclosed embodiment. When the instruction is executed by a processor, the processor can directly or use other components under the control of the processor to perform the functions corresponding to the instruction. The instruction may include code provided or executed by a compiler or an interpreter. The machine-readable storage media may be provided in the form of a non-transitory storage media. Here, "non-transitory" only means that the storage media does not include a signal and is tangible, and does not distinguish whether the data is stored semi-permanently or temporarily in the storage media.

[0123] Also, according to one embodiment, the methods according to the various embodiments described above may be provided included in a computer program product. The computer program product may be traded as a commodity between a seller and a purchaser. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or online via an application store (e.g., the App StoreTM). In the case of online distribution, at least a part of the computer program product may be at least temporarily stored or temporarily provided in a storage medium such as the memory of the manufacturer's server, the application store's server, or a relay server.

[0124] Also, each of the components (e.g., modules or programs) according to the various embodiments described above may be composed of one or more individuals, and some of the sub-components among the sub-components described above may be omitted, or other sub-components may be further included in various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated as one individual and perform the functions performed by each of the components before integration in the same or similar manner. According to various embodiments, the operations performed by a module, a program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or at least some of the operations may be executed in other orders, omitted, or other operations may be added.

[0125] As described above, the preferred embodiments of the present disclosure have been illustrated and described. However, the present disclosure is not limited to the specific embodiments described above, and it is of course possible for those having ordinary knowledge in the technical field to which the present disclosure pertains to make various modifications without departing from the gist of the present disclosure claimed in the claims. Such modifications should not be individually understood from the technical idea and perspective of the present disclosure.

Explanation of Reference Numerals

[0126] 100 Electronic device 110 Communication interface 120 Memory 130 One or more processors

Claims

1. An electronic device for implementing a temperature prediction and control system, comprising: A communication interface; a memory in which the first trained neural network model and the second trained neural network model are stored; When process information including input fuel information of the glass melting apparatus is received via the communication interface, the received process information is pre-processed; inputting the pre-processed process information into the first trained neural network model to obtain first predicted temperature information corresponding to a first position of the glass melter; inputting the obtained first predicted temperature information and the pre-processed process information into the second trained neural network model to obtain second predicted temperature information corresponding to a second position of the glass melter; and one or more processors that provide guide information including the obtained first predicted temperature information and the obtained second predicted temperature information.

2. The process information of the pretreatment is The input fuel information and the process status information are included, The input fuel information is information on the amount of fuel input to the glass melting device; The first trained neural network model When process information of the pre-processing including information on the amount of fuel input to the glass melting apparatus is input, the apparatus is trained to output first predicted temperature information according to the amount of fuel input, The second trained neural network model The electronic device according to claim 1 , wherein the electronic device is adapted to learn to output second predicted temperature information based on the amount of fuel input when the output first predicted temperature information and the pre-processed process information are input.

3. The memory includes: Further comprising temperature change information corresponding to the second position according to a unit fuel input amount, The one or more processors: When the acquired second predicted temperature information is identified as being outside a predetermined range, guide information is acquired based on the information stored in the memory, the guide information guiding the second predicted temperature information to be within the predetermined range; The electronic device according to claim 1 , wherein the electronic device provides a UI including the acquired guide information.

4. The one or more processors: Identifying sub-fuel input information in which information regarding the input amount of fuel included in the fuel input information has been changed; inputting sub-process information including the sub-fuel input information into the second trained neural network model to obtain second sub-predicted temperature information; The electronic device according to claim 3 , further comprising: acquiring temperature change information corresponding to the second position due to the unit fuel input amount using the second predicted temperature information and the second sub-predicted temperature information.

5. The process information of the pretreatment is Further comprising process history information including history information regarding the amount of fuel input and temperature history information of the second position; The one or more processors: identifying relationship information between the amount of fuel input and the temperature at the second location based on the process history information; The electronic device according to claim 3 , further comprising: acquiring and storing temperature change information corresponding to the second position according to the unit fuel input amount based on the identified relationship information.

6. The one or more processors: The electronic device of claim 3 , further comprising a UI including process history information, the process history information including the temperature history information of the first position and the temperature history information of the second position.

7. a user interface; The one or more processors: when a user input corresponding to the obtained guide information is received via the user interface, identifying control information corresponding to the received user input; The electronic device of claim 1 , further comprising: a communication interface configured to transmit the identified control information to a control engine.

8. A method for controlling an electronic device to realize a temperature prediction and control system, comprising: When process information including input fuel information of the glass melting apparatus is received, pre-processing the received process information; inputting the pre-processed process information into a first trained neural network model to obtain first predicted temperature information corresponding to a first location of the glass melter; inputting the obtained first predicted temperature information and the pre-processed process information into a second trained neural network model to obtain second predicted temperature information corresponding to a second location of the glass melter; providing guide information including the acquired first predicted temperature information and the acquired second predicted temperature information.

9. The process information of the pretreatment is The input fuel information and the process status information are included, The input fuel information is information on the amount of fuel input to the glass melting device; The first trained neural network model When process information of the pre-processing including information on the amount of fuel input to the glass melting apparatus is input, the apparatus is trained to output first predicted temperature information according to the amount of fuel input, The second trained neural network model 9. The control method according to claim 8, further comprising: learning to output second predicted temperature information based on the amount of fuel input when the output first predicted temperature information and the pre-processed process information are input.

10. The step of providing the guide information includes: When the acquired second predicted temperature information is identified as being outside a predetermined range, guide information is acquired to guide the second predicted temperature information to be within the predetermined range based on temperature change information corresponding to the second position according to a unit fuel input amount stored in a memory; The control method includes: The method of claim 8 , further comprising providing a UI including the obtained guide information.

11. identifying sub-fuel input information in which information regarding the amount of fuel input included in the fuel input information has been changed; inputting sub-process information including the sub-fuel input information into the second trained neural network model to obtain second sub-predicted temperature information; The control method according to claim 10 , further comprising: acquiring temperature change information corresponding to the second position due to the unit fuel input amount using the second predicted temperature information and the second sub-predicted temperature information.

12. identifying relationship information between the amount of fuel input and the temperature at the second location based on process history information, the process history information including historical information regarding the amount of fuel input and historical temperature information at the second location; The method of claim 10, further comprising: acquiring and storing temperature change information corresponding to the second position according to the unit fuel input amount based on the identified relationship information.

13. The step of providing the UI includes: The method of claim 10 , further comprising providing a UI including process history information including the temperature history information of the first position and the temperature history information of the second position.

14. when a user input corresponding to the obtained guide information is received, identifying control information corresponding to the received user input; The method of claim 8 , further comprising: transmitting the identified control information to a control engine.

15. 1. A non-transitory computer-readable recording medium storing computer instructions that, when executed by a processor of an electronic device for implementing a temperature prediction and control system, cause the electronic device to perform operations, the operations including: When process information including input fuel information of the glass melting apparatus is received, pre-processing the received process information; inputting the pre-processed process information into a first trained neural network model to obtain first predicted temperature information corresponding to a first location of the glass melter; inputting the obtained first predicted temperature information and the pre-processed process information into a second trained neural network model to obtain second predicted temperature information corresponding to a second location of the glass melter; providing guide information including the obtained first predicted temperature information and the obtained second predicted temperature information.

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