Electronic apparatus for embodying temperature prediction and control system and method for controlling the same

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, and more particularly to an electronic device for predicting the temperature of a bottom end of a glass melting device using a trained neural network model and controlling fuel input to the glass melting device based on the predicted temperature. [Background technology]

[0002] When glass raw materials are charged into the glass melting furnace (or glass melting equipment) through the raw material charge port (OBC, On Board Charge), heat is applied to the glass material in the melting furnace through the burner or electric booster included in the glass melting equipment. The glass material in the melting furnace is melted through the melting part.

[0003] In addition, there is an appropriate range for the temperature and stacking height of the glass objects in the glass melting furnace. In this case, there is a problem that it is difficult to maintain the temperature and stacking height of the glass objects due to frequent changes in the elution amount and uncertainties such as the moisture content of the raw materials. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Korean Patent Publication No. 10-2023-0082577 Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure is intended to solve the above-mentioned problems, and provides an electronic device and a control method thereof that predicts a temperature at a specific position in a glass melting apparatus using a trained neural network model, and provides a user with guide information for reducing the amount of fuel input while maintaining an appropriate process temperature based on the predicted temperature information. [Means for solving the problem]

[0006] An electronic device for implementing a temperature prediction and control system according to an embodiment of the present disclosure may include a communication interface, a memory storing a first trained neural network model and a second trained neural network model, and one or more processors for pre-processing the received process information, when process information including input fuel information of a glass melting apparatus is received via the communication interface.

[0007] The one or more processors may input 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 melting apparatus.

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

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

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

[0011] The control method may include inputting the pre-processed process information into a first trained neural network model to obtain first predicted temperature information corresponding to a first position of the glass melting apparatus.

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

[0013] The control method may include 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 one embodiment of the present disclosure stores 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 may include, when process information including input fuel information for a glass melting apparatus is received, pre-processing the received process information.

[0015] The operations may include inputting the pre-processed process information into a first trained neural network model to obtain first predicted temperature information corresponding to a first position of the glass melter.

[0016] The operations may include inputting the obtained first predicted temperature information and the process information into a second trained neural network model to obtain second predicted temperature information corresponding to a second position of the glass melting apparatus.

[0017] The operations may include providing guide information including the obtained first predicted temperature information and the obtained second predicted temperature information. Effect of the Invention

[0018] According to the present disclosure, the internal temperature of the glass melting apparatus and the temperature of the glass object can be predicted using a trained neural network model, and based on this, the electronic device 100 can provide a user with guidance information for minimizing fuel input while maintaining the temperature of the glass object within an appropriate range. [Brief description of the drawings]

[0019] [Figure 1] 1 is a diagram for illustrating a control method for an electronic device according to an embodiment; [Diagram 2] FIG. 1 is a block diagram showing a configuration of an electronic device according to an embodiment. [Diagram 3] 1 is a flowchart illustrating a method for controlling an electronic device according to an embodiment. [Figure 4] FIG. 2 is a diagram for explaining a first trained neural network model according to one embodiment. [Diagram 5] FIG. 13 is a diagram for explaining a second trained neural network model according to one embodiment. [Figure 6] 1 is a diagram illustrating a method for providing guide information according to an embodiment. [Figure 7] 1 is a diagram illustrating a method for acquiring temperature change information according to an embodiment. [Figure 8] 1 is a diagram illustrating a method for acquiring temperature change information according to an embodiment. [Figure 9a] 1 is a diagram illustrating a method for providing a UI according to an embodiment. [Figure 9b] Same as above. [Figure 9c] Same as above. [Figure 9d] Same as above. [Figure 10] 1 is a diagram illustrating a method for identifying and transmitting control information to a control engine according to an embodiment. [Figure 11] FIG. 2 is a block diagram showing a detailed configuration of an electronic device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0021] The terms used in this specification will be briefly explained below to specifically explain the present disclosure.

[0022] The terms used in the embodiments of the present disclosure are currently common terms that are widely used as much as possible, taking into consideration the functions in the present disclosure, but these may change depending on the intentions or precedents of engineers in the field, the emergence of new technologies, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meanings are described in detail in the description of the relevant disclosure. Therefore, the terms used in the present disclosure should be defined based on the meanings that the terms have and the overall content of the present disclosure, rather than simply the names of the terms.

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

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

[0025] As used in this specification, expressions such as "first," "second," "primary," or "second" may modify various components regardless of order and / or importance, and are only used to distinguish one component from other components, and do not limit the component in question.

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

[0027] The singular expression includes the plural expression unless otherwise clearly indicated in the context. In this application, the terms "comprise" or "comprise" and the like are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0028] In this disclosure, a "module" or "unit" performs at least one function or operation and may be embodied in hardware or software, or a combination of hardware and software. Furthermore, multiple "modules" or multiple "units" may be integrated into at least one module and embodied in at least one processor (not shown), except for "modules" or "units" that need to be embodied in specific hardware.

[0029] According to an embodiment of the present disclosure, an electronic device may include an artificial intelligence model (or artificial neural network model or learning network model) that is composed of at least one neural network layer. The artificial neural network may include a deep neural network (DNN), such as, but not limited to, a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRRDNN), or a deep Q-network.

[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 weighting value used when applying an input value to a predetermined calculation formula. Also, the parameter may be expressed in the form of a matrix. The parameter is a value set as a result of training, and may be updated via separate training data as necessary.

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

[0032] 1, according to one embodiment, a glass melting apparatus 1 may include a plurality of fuel inputs including a plurality of burners 10 and an electric booster 20. The burners 10 are heat sources corresponding to a relatively upper side of the glass melting apparatus 1, and the electric booster 20 is a heat source corresponding to a relatively lower side of the glass melting apparatus 1. The temperature of the glass article contained in the glass melting apparatus 1 can be maintained depending on the amount of fuel input to the burners and the electric booster.

[0033] According to one embodiment, the electronic device 100 can obtain predicted temperature information corresponding to a specific position of the glass melting apparatus based on process information of the glass melting apparatus 1, and provide guide information for guiding the amount of fuel to be input to multiple fuel input ports based on the obtained predicted temperature information.

[0034] Various embodiments are described below in which the temperature at a specific position in the glass melting apparatus 1 is predicted using a trained neural network model, and guide information is provided to the user to reduce the amount of fuel input while maintaining an appropriate process temperature based on the predicted temperature information.

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

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

[0037] According to an embodiment, the electronic device 100 may be embodied as a device that processes data and communicates with an external device, such as a server, but is not limited thereto. For example, the electronic device 100 may be embodied 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 an embodiment of the present disclosure is not limited to the above-mentioned devices, and the electronic device 100 may be embodied as an electronic device 100 having two or more functions of the above-mentioned devices.

[0038] The electronic device 100 may communicate with an external device and an external server in various ways. According to an embodiment, a communication module for communicating with an external device and an external server may be implemented as a single module. For example, the electronic device 100 may communicate with an external device using a Bluetooth module, and may also communicate with an external server using a Bluetooth module.

[0039] In other embodiments, the communication modules for communicating with the external device and the external server may be embodied separately. For example, the electronic device 100 may communicate with the external device using a Bluetooth module and with the external server using an Ethernet modem or a Wi-Fi module.

[0040] According to an embodiment, the external device (not shown) may be embodied in 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 to and from 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 disk) via communication methods such as AP-based Wi-Fi (Wireless LAN network), Bluetooth, Zigbee, wired / wireless LAN (Local Area Network), WAN (Wide Area Network), Ethernet, IEEE 1394, HDMI (High-Definition Multimedia Interface), USB (Universal Serial Bus), MHL (Mobile High-Definition Link), AES / EBU (Audio Engineering Society / European Broadcasting Union), optical, coaxial, and the like.

[0042] As an example, the communication interface 110 may include a Bluetooth Low Energy (BLE) module. BLE refers to a Bluetooth technology capable of transmitting and receiving low-power, low-volume data in the 2.4 GHz frequency band having a reach radius of approximately 10 m. However, without being limited thereto, the communication interface 110 may also include a Wi-Fi communication module. That is, the communication interface 110 may include at least one of a Bluetooth Low Energy (BLE) module or a Wi-Fi communication module.

[0043] According to an example, the communication interface 110 can use different communication modules to communicate with an external device such as a remote control device and an external server. 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 use a Bluetooth module to communicate with an external device such as a remote control device. However, this is only one embodiment, and the communication interface 110 can use at least one of various communication modules when communicating with multiple external devices or servers.

[0044] The memory 120 may store data required for various embodiments. The memory 120 may be implemented as a memory embedded in the electronic device 100 or a memory removable from the electronic device 100 depending on the purpose of data storage. For example, data for driving the electronic device 100 is stored in a memory embedded in the electronic device 100, and data for an extended function of the electronic device 100 is stored in a memory removable from the electronic device 100.

[0045] In addition, the memory embedded in the electronic device 100 may be embodied as at least one of a volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)), a non-volatile memory (e.g., one time programmable ROM (OTPROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), a hard drive, or a solid state drive (SSD). In addition, the memory removable from the electronic device 100 may be embodied as at least one of a memory card (e.g., compact flash (CF), secure digital (SD), micro secure digital (Micro-SD), mini secure digital (Mini-SD), extreme digital (xD), multi-media (MMC), or a combination of a memory card and a memory card). The image forming apparatus may be implemented in the form of an external memory that can be connected to a USB port (e.g., a USB memory), or the like.

[0046] According to one embodiment, the memory 120 may store a first trained neural network model and a second trained neural network model, which are described in more detail with reference to Figures 4 and 5.

[0047] One or more processors 130 (hereinafter, processors) 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 may perform the operation 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 an embodiment, the processor 130 may be implemented as a digital signal processor (DSP) for processing digital video signals, a microprocessor, a graphics processing unit (GPU), an artificial intelligence (AI) processor, a neural processing unit (NPU), or a time controller (TCON). However, the processor 130 may be implemented as one or more of a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor, without being limited thereto. The processor 130 may be implemented as a system on chip (SoC) or large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0049] According to an embodiment, the processor 130 may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON). However, the term may include or be defined as one or more of a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor, without being limited thereto. The processor 130 may also be implemented as a system on chip (SoC) or 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 one 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, and according to one example, the process information may include input fuel information input to the glass melting apparatus 1 to perform the glass melting process or process status information corresponding to the glass melting apparatus 1.

[0051] The input fuel information may include information on the input amount of fuel input for performing the glass melting process, for example. The input fuel information may include information on the input amount of fuel corresponding to each of a plurality of fuel input units included in the glass melting apparatus 1. The process status information corresponding to the glass melting apparatus 1 may include, for example, process environment information regarding the glass melting apparatus 1, and the process status information may include, for example, at least one of information on the amount of oxygen input input to the glass melting apparatus 1, information on the amount of elution (or production amount) eluted from the glass melting apparatus 1, information on the environment around the glass melting apparatus 1, and information on the amount of power of an electronic booster included in the glass melting apparatus 1. The environment information around the glass melting apparatus 1 may include, for example, temperature information or humidity information within the glass melting apparatus 1.

[0052] According to an embodiment, when the process information is received, the processor 130 may perform pre-processing of the received process information. According to an example, the processor 130 may identify outlier data (outliers) among a plurality of data included in the received process information using a predetermined algorithm, and may obtain process information from which the identified outlier data (outliers) have been removed. Alternatively, if the process information corresponding to a predetermined time period during the time when the process is performed is not received, the processor 130 may perform pre-processing by receiving the process information that has not been received from the communication interface 110. However, the present invention is not limited to this.

[0053] According to one embodiment, the processor 130 can input the pre-processed process information into a first trained neural network model to 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 apparatus 1 may be a position corresponding to a relatively upper side (or ceiling) of the glass melting apparatus 1, according to an example. A temperature sensor may be provided at the first position of the glass melting apparatus 1, and the temperature of the relatively upper side of the glass melting apparatus 1 may be identified through the sensor corresponding to the first position. In this case, the temperature of the relatively upper side may be the temperature of the gas from which the melt to be melted is vaporized. According to an example, the processor 130 may input the pre-processed process information into the first trained neural network model stored in the memory 120 to obtain first predicted temperature information corresponding to the first position of the glass melting apparatus 1. Here, the multiple types of temperature information including the first predicted temperature information and the second predicted temperature information may include information regarding the temperature value.

[0055] The first trained neural network model will be described in detail with reference to FIG.

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

[0057] Here, the second position of the glass melting apparatus 1 may be a position corresponding to a relatively lower side of the glass melting apparatus 1 (or a bottom surface provided at the lower end and abutting against the glass object) according to an example. A temperature sensor may be provided at the second position of the glass melting apparatus 1, and the temperature of the relatively lower side of the glass melting apparatus 1 may be identified through the sensor corresponding to the second position. In this case, the temperature of the relatively lower side may be the temperature of the melt (or the glass object) to be melted. According to an example, the processor 130 may input the pre-processed process information and the first predicted temperature information into the second trained neural network model stored in the memory 120 to obtain the second predicted temperature information corresponding to the second position of the glass melting apparatus 1. That is, in order to accurately measure the temperature of the glass object, information on the temperature of the gas in the glass melting apparatus 1 (or the temperature of the upper end of the glass melting apparatus 1) is required.

[0058] The second trained neural network model will be described in detail with reference to FIG.

[0059] According to an embodiment, the processor 130 may provide guide information including the acquired first predicted temperature information and the acquired second predicted temperature information. According to an example, when the acquired second predicted temperature information is identified as being outside the predetermined range, the processor 130 may acquire guide information for guiding the second predicted temperature information to fall within the predetermined range based on the information stored in the memory 120. According to an example, the memory 120 may store information regarding reference temperature ranges corresponding to the first location and the second location, respectively.

[0060] FIG. 3 is a flowchart illustrating a method for controlling an electronic device according to an embodiment.

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

[0062] Next, according to one embodiment, when the process information is received (S310: Y), the control method may perform pre-processing of the received process information (S320). According to one example, the processor 130 may identify abnormal data (outliers) among a plurality of data included in the received process information using a predetermined algorithm, and may obtain process information from which the identified abnormal data (outliers) have been removed.

[0063] Next, according to one embodiment, the control method may input the pre-processed process information into a first trained neural network model to obtain first predicted temperature information corresponding to a first position of the glass melting apparatus 1 (S330). According to one example, the processor 130 may input the process information including the pre-processed input fuel information and process status information into the first trained neural network model stored in the memory 120 to obtain the first predicted temperature information.

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

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

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

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

[0068] According to the above-described embodiment, the internal temperature of the glass melting apparatus 1 and the temperature of the glass object can be predicted using the trained 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 object within an appropriate range.

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

[0070] The artificial neural network (or neural network model) including the first trained neural network model and the second trained neural network model according to an embodiment of the present disclosure may include a deep neural network (DNN), such as a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRRDNN), or a deep Q-network, but is not limited to the aforementioned examples.

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

[0072] 4, in one embodiment, a data set including process information 410 including input fuel information and process state information is input as training data to a first neural network model 400, and the first neural network model can be trained. In this case, the data set may include first temperature information 420, which is output based on (or caused by) the input fuel, as a label.

[0073] For example, a data set (set) including process information including fuel input amount information and process status information at time n may be input as learning data to the first neural network model, and the neural network model may be trained. In this case, the data set (set) may include temperature information corresponding to the first position of the glass melting apparatus 1 at time n+1 of the first preheating chamber measured based on the fuel input amount at time n as a label, and training may be performed.

[0074] FIG. 5 is a diagram illustrating a second trained neural network model according to one embodiment.

[0075] According to an embodiment, a second trained neural network model may be stored in the memory 120. According to an example, the second trained neural network model may be trained to output second predicted temperature information according to the amount of fuel input when the first predicted temperature information output via the first trained 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 (or glass object) of the glass melting apparatus 1.

[0076] Referring to FIG. 5, according to an embodiment, a data set including process information 520 and first predicted temperature information 530 may be input as learning data to a second neural network model 500, and the second neural network model 500 may be trained. Here, the first predicted temperature information 530 is information output from the first trained neural network model 400. According to an example, the process information 520 may include input fuel information and process status information. In this case, according to an example, the data set may include, in a label, second temperature information 510 output based on (or caused by) 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 data set (set) including process information including fuel input amount information and process status information at time point n and first predicted temperature information corresponding to a first position output from the first trained neural network model 400 may be input as training data to the second neural network model, and the neural network model may be trained. In this case, the data set (set) may include temperature information corresponding to a second position of the glass melting apparatus 1 at time point n+1 in the first preheating chamber measured based on the amount of fuel input at time point n as a label, and training may be performed.

[0078] FIG. 6 is a diagram illustrating a method for providing guide information according to an embodiment.

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

[0080] According to an example, 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 determine whether the acquired second predicted temperature is within the reference range based on the information stored in the memory 120.

[0081] Next, according to an embodiment, the control method may acquire guide information for guiding the second predicted temperature information to fall within the predetermined range based on information stored in memory 120 when the second predicted temperature information exceeds the predetermined range (S610: Y) (S620). According to an example, memory 120 may further include temperature change information corresponding to the second position according to a 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 memory 120. Processor 130 may acquire guide information for guiding the second predicted temperature information to fall within the predetermined range using the temperature change information stored in memory 120.

[0082] It should be noted that, according to one embodiment, the processor 130 may also obtain the temperature change information using a trained neural network model, which will be described in more detail with reference to FIG.

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

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

[0085] 7, according to one embodiment, the control method may identify sub-fuel input information in which information regarding the input amount of fuel included in the fuel input information has been changed (S710). Here, the sub-fuel input information may be process information in which the input amount of fuel has been changed by a predetermined value.

[0086] Next, according to one embodiment, the control method may input the sub-process information including the sub-fuel input information to a second trained neural network model to obtain second sub-predicted temperature information (S720). According to one example, when the sub-fuel input information is identified, the processor 130 may obtain sub-process information in which the magnitude of the identified input fuel amount is updated. The processor 130 may input the sub-process information to a second trained 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-fuel input information in which the input fuel amount has been changed.

[0087] Next, according to one embodiment, the control method can obtain temperature change information corresponding to the second location due to the unit fuel input amount using the second predicted temperature information and the second sub-predicted temperature information (S730). According to one example, the processor 130 can obtain second temperature change information corresponding to the second location due to the unit fuel input amount by comparing the second predicted temperature information and the second sub-predicted temperature information output through the second trained neural network model.

[0088] However, without being limited thereto, according to an embodiment, the second temperature change information corresponding to the second position according to the unit fuel input amount may be already stored in the memory 120. Alternatively, according to an embodiment, the processor 130 may obtain the second temperature change information corresponding to the second position according to the unit fuel input amount by using the process history information of the glass melting apparatus 1 through a predetermined algorithm. Here, the process history information may include history information on the input fuel and history information on the process state.

[0089] FIG. 8 is a diagram for explaining a method for acquiring temperature change information according to an embodiment.

[0090] 8, according to one embodiment, the control method may identify relationship information between the amount of fuel input and the temperature at the second location based on the process history information (S810), where the process history information may include history information on input fuel, history information on a process state, and temperature history information at the second location.

[0091] In addition, the relationship information may be a mathematical formula corresponding to the relationship between the input fuel information and the temperature value at the second location obtained using a regression analysis model. According to one example, the processor 130 can use a predefined algorithm (e.g., a regression analysis model) to obtain the relationship information between the input fuel information and the temperature value at the second location from process history information including the input fuel history information, the process state history information, and the temperature history information at the second location.

[0092] Next, according to one embodiment, the control method may obtain and store temperature change information corresponding to the second location due to the unit fuel input amount based on the identified relationship information (S820). According to one example, the processor 130 may obtain temperature change information corresponding to the second location due to the unit fuel input amount based on the relationship information between the input fuel information and the temperature value of the second location, and may use the same to provide guidance information.

[0093] 9a to 9d are diagrams illustrating a method for providing a UI according to an embodiment.

[0094] According to an embodiment, the memory 120 may store process history information. The process history information may include, for example, temperature history information of a first position and temperature history information of a second position. Alternatively, according to an example, the process history information may include history information on input fuel and history information on a process state.

[0095] 9a, according to an embodiment, the processor 130 may provide a UI 900 including process history information. According to an example, the processor 130 may obtain temperature history information of a first position (ARCH #3) and temperature history information of a second position (MELTER BT #11) included in the process history information based on information stored in the memory 120. For example, the processor 130 may provide a UI 900 including graph information corresponding to the temperature history of the first position. Alternatively, for example, the processor 130 may provide a UI 900 including graph information corresponding to the temperature history of the second position.

[0096] It should be noted that, according to one embodiment, the electronic device 100 may further include a display (not shown), and according to one example, the processor 130 may display the UI 900 via a display (not shown).

[0097] 9b, according to an embodiment, the processor 130 may provide a UI 910 including guide information regarding the amount of fuel to be input. According to an example, the guide information may include information 911 regarding the amount of fuel currently input corresponding to each of a plurality of fuel input units in the glass melting apparatus 1 including the first fuel input unit and the second fuel input unit. Alternatively, according to an example, the guide information regarding the amount of fuel to be input may include guidance information 912 regarding the amount of fuel to be input to each of a plurality of fuel input units in the glass melting apparatus 1 including the first fuel input unit and the second fuel input unit. Here, each item displayed on the UI (e.g., "MAIN", "1L", "2L", "3L", ..., "4R") corresponds to a plurality of fuel input units including a plurality of burners 10 included in the glass melting apparatus 1.

[0098] Here, the guidance information 912 on the fuel amount to be input means information on a recommended input amount corresponding to the fuel to be input to each of a plurality of positions (or a plurality of fuel input units) where the input fuel amount decreases under a condition where the temperature of the second position maintains within a predetermined range. That is, the guidance information 912 on the fuel amount to be input means a recommended input amount that minimizes the amount of fuel input to each of a plurality of positions under a condition where the temperature of the second position maintains within a predetermined range. In addition, according to an example, the processor 130 can obtain the guidance information 912 on 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 on the fuel amount to be input may be already stored in the memory 120.

[0099] 9c, according to an embodiment, the processor 130 may provide a UI 910-1 including guide information on the amount of oxygen corresponding to each of the plurality of fuel inputs. According to an example, the guide information on the amount of oxygen corresponding to each of the plurality of fuel inputs may include information 911-1 on the current amount of oxygen corresponding to each of the plurality of fuel inputs in the glass melting apparatus 1 including the first fuel input and the second fuel input. Alternatively, according to an example, the guide information on the amount of oxygen corresponding to each of the plurality of fuel inputs may include guidance information 912-1 on the amount of oxygen input to each of the plurality of fuel inputs in the glass melting apparatus 1 including the first fuel input and the second fuel input. Here, each item displayed on the UI (e.g., "MAIN", "1L", "2L", "3L", ..., "4R") corresponds to a plurality of fuel inputs including a plurality of burners 10 included in the glass melting apparatus 1.

[0100] Here, the guidance information 912-1 on the amount of oxygen corresponding to each of the fuel input units means information on the recommended amount of oxygen corresponding to each of the fuel input units in which the amount of fuel input is reduced when the temperature at the second position maintains a predetermined range. That is, the guidance information 912-1 on the amount of oxygen corresponding to each of the fuel input units means a recommended input amount that minimizes the recommended amount of oxygen corresponding to each of the fuel input units under the condition that the temperature at the second position maintains a predetermined range. In addition, according to an example, the processor 130 can obtain the guidance information 912-1 on the amount of oxygen corresponding to each of the 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 on the amount of oxygen corresponding to each of the fuel input units may be already stored in the memory 120.

[0101] In addition, according to one embodiment, the processor 130 can provide both a UI910 including guide information on the amount of fuel to be input, as shown in FIG. 9b, and a UI910-1 including guide information on the amount of oxygen corresponding to each of the multiple fuel input ports, or can provide a UI910 including guide information on the amount of fuel to be input and a UI910-1 including guide information on the amount of oxygen corresponding to each of the multiple fuel input ports.

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

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

[0104] 10, according to an embodiment, when a user input corresponding to the acquired guide information is received, the control method may identify control information corresponding to the received user input (S1010). Here, the control information refers to a control signal for controlling a control engine, and the control engine refers to an engine for controlling the glass melting apparatus 1. According to an example, the electronic device 100 may further include a user interface (not shown), and the processor 130 may receive the user input corresponding to the acquired guide information via the user interface (not shown). Then, the processor 130 may identify the 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 may transmit the identified control information to a control engine (Engine) (S1020). According to one example, the processor 130 may 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] 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. A detailed description of the configurations shown in FIG. 11 that overlap with the configurations shown in FIG. 2 will be omitted.

[0108] The microphone 140 refers to a module that captures sound and converts it into an electric signal, and may be a condenser microphone, a ribbon microphone, a moving coil microphone, a piezoelectric element microphone, a carbon microphone, or a Micro Electro Mechanical System (MEMS) microphone. Also, it may be implemented in an omnidirectional, bidirectional, unidirectional, subcardioid, supercardioid, or hypercardioid type.

[0109] Various embodiments are possible in which the electronic device 100 ′ performs an action in response to a user voice signal received via the microphone 140 .

[0110] As an example, the electronic device 100′ may control the display 160 based on a user voice signal received via the microphone 140. For example, when a user voice signal for displaying the A content is received, the electronic device 100′ may control the display 160 to display the A content.

[0111] As another example, the electronic device 100' may control an external display device connected to the electronic device 100' based on a user voice signal received through the microphone 140. Specifically, the electronic device 100' may 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 may transmit the provided control signal to the external display device. Here, the electronic device 100' may store a remote control application for controlling the external display device. Also, the electronic device 100' may transmit the provided control signal to the external display device using at least one communication method of Bluetooth, Wi-Fi, or infrared. For example, when a user voice signal for displaying content A is received, the electronic device 100' may transmit a control signal to the external display device for controlling content A to be displayed on the external display device. Here, the electronic device 100' refers to various terminal devices on which a remote control application can be installed, such as a smartphone, an AI speaker, etc.

[0112] As yet another example, the electronic device 100' may 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' may transmit a control signal to the remote control device for controlling the external display device so that an operation corresponding to the user voice signal is performed on the external display device. Also, the remote control device may 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' may transmit a control signal to the remote control device for controlling content A to be displayed on the external display device, and the remote control device may transmit the received control signal to the external display device.

[0113] The speaker 150 comprises a tweeter for reproducing high tones, a midrange for reproducing mid tones, a woofer for reproducing low tones, a subwoofer for reproducing extremely low tones, an enclosure for controlling resonance, and a crossover network that divides the electrical signal frequencies input to the speaker into bands.

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

[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, the display may be implemented as various types of displays such as a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a light emitting diode (LED), a micro LED, a mini LED, a plasma display panel (PDP), a quantum dot (QD) display, and quantum dot light-emitting diodes (QLED). The display 160 may also include a driving circuit and a backlight unit implemented as an a-si TFT, a low temperature poly silicon (LTPS) TFT, an organic TFT (OTFT), and the like. The display 160 may also be implemented as a touch screen combined with a touch sensor, a flexible display, a rollable display, a 3D display, a display in which a plurality of display modules are physically connected, and the like. The processor 130 may control the display 160 to output an output image acquired according to the various embodiments described above. Here, the output image may be a high-resolution image of 4K or 8K or higher.

[0116] According to another embodiment, the electronic device 100' may not include the display 160. The electronic device 100' may be connected to an external display device and may transmit images or content stored in the electronic device 100' to the external display device. Specifically, the electronic device 100' may transmit images 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 may be connected to the electronic device 100' via a communication interface 110 or an input / output interface (not shown). For example, the electronic device 100' may not include a display like a set top box (STB). Also, the electronic device 100' may only include a small display capable of displaying simple information such as text information. Here, the electronic device 100' may transmit an image or content to the external display device via the communication interface 110 in a wired or wireless manner, or may transmit the image or content to the external display device via the input / output interface (not shown).

[0118] The user interface 170 is a configuration for allowing the electronic device 100′ to interact with a 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] At least one sensor 180 (hereinafter, sensor) may include a plurality of sensors of various types. The sensor 180 may measure a physical quantity or sense an 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 the 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 apparatus and the temperature of the glass object can be predicted using a trained neural network model. Based on this, the electronic device 100' can provide a user with guidance information for minimizing fuel input while maintaining the temperature of the glass object within an appropriate range.

[0121] The methods according to the various embodiments of the present disclosure may be implemented in the form of an application that can be installed in a conventional electronic device. Alternatively, the methods according to the various embodiments of the present disclosure may be implemented using a deep learning-based trained neural network (or a deep trained neural network), i.e., a trained network model. The methods according to the various embodiments of the present disclosure may be implemented only as a software upgrade or hardware upgrade for a conventional electronic device. The various embodiments of the present disclosure may be implemented via an embedded server provided in an 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 instructions stored in a machine-readable storage medium. The machine is a device capable of calling up the stored instructions from the storage medium and operating according to the called instructions, and may include a display device (e.g., display device (A)) according to the disclosed embodiments. When instructions are executed by a processor, the processor may perform a function corresponding to the instructions directly or using other components under the control of the processor. The instructions may include code provided or executed by a compiler or an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" only means that the storage medium does not include a signal and is tangible, and does not distinguish between data being stored semi-permanently or temporarily in the storage medium.

[0123] According to an embodiment, the methods according to the various embodiments described above may be provided in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)) or online via an application store (e.g., Play Store™). 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 a memory of a manufacturer's server, an application store 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 entities, and some of the subcomponents described above may be omitted, or other subcomponents may be further included in the various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated into one entity and perform the same or similar functions as those performed by the respective components before being integrated. According to various embodiments, operations performed by modules, programs, or other components may be performed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be performed in other orders, omitted, or other operations may be added.

[0125] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary knowledge in the technical field to which the disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical ideas and perspectives of the present disclosure. [Explanation of symbols]

[0126] 100 Electronic equipment 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 being used to guide 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 on 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 comprises: 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.

Images