Electronic device for realizing polymer quality prediction and control system and control method thereof

Abstract

An electronic device for implementing a quality prediction and control system is disclosed. The electronic device includes a communication interface, a memory storing a trained neural network model and process history information of a polymer manufacturing device, and one or more processors that, when process information corresponding to a second time point is received from a polymer manufacturing device through the communication interface, identify predicted process information corresponding to a third time point a preset time after the second time point based on the process history information and the process information corresponding to the second time point, input the received process information corresponding to the second time point and the identified predicted process information corresponding to the third time point into the trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point, and provide guide information including the obtained predicted quality information.

Description

[Technical field]

[0001] The present disclosure relates to an electronic device for implementing a polymer quality prediction and control system and a control method thereof, and more particularly to an electronic device for predicting polymer quality using preprocessed data and a trained neural network model and a control method thereof. [Background technology]

[0002] 2. Description of the Related Art As electronic technology advances, various types of electronic devices are being developed and popularized, and technological development of electronic devices that provide services to users and the like is becoming more and more active.

[0003] Meanwhile, a polymer means a polymer in which one or more monomers are repeatedly linked. The polymer manufacturing process according to the present disclosure is a process of obtaining a polymer, Poly Etylene, for example, POE (Polyolefin Elastomer), by polymerizing a catalyst corresponding to the monomer, ethylene, octene, or butene. Polymers are classified into product groups according to melting point and density, and the melting index or density is an index that is affected by the amount of raw materials input into the manufacturing process, the ratio of raw materials, or the reaction temperature, and indicates the quality of the polymer.

[0004] Meanwhile, the quality and yield of polymers obtained through chemical reactions between monomers may vary due to disturbances that cannot be controlled or measured. For example, due to the characteristics of polymerization reactions, the melt index and density of the obtained polymer may be different even under the same conditions or when the same raw materials are input. In addition, in the case of a recovery process for raw materials that have not undergone a reaction, since the amount of raw materials reintroduced into the reaction is not measured, it is difficult to predict the exact quality of the obtained polymer. 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 preprocesses information for a polymer production process and inputs the preprocessed information into a trained neural network model to accurately predict the quality of a produced polymer. [Means for solving the problem]

[0006] An electronic device for implementing a quality prediction and control system according to an embodiment of the present disclosure may include a communication interface, a memory storing a trained neural network model and process history information of a polymer manufacturing equipment, and one or more processors that, when process information corresponding to a second time point is received from the polymer manufacturing equipment through the communication interface, identify predicted process information corresponding to a third time point a predetermined time after the second time point based on the process history information and the process information corresponding to the second time point.

[0007] The one or more processors may input process information corresponding to the received second time point and predicted process information corresponding to the identified third time point into the trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point.

[0008] The one or more processors may provide guide information including the obtained predicted quality information.

[0009] A method of controlling an electronic device for implementing a quality prediction and control system according to one embodiment of the present disclosure may include, when process information corresponding to a second time point is received from a polymer manufacturing equipment, identifying predicted process information corresponding to a third time point a predetermined time after the second time point based on process history information of the polymer manufacturing equipment and the process information corresponding to the second time point.

[0010] The control method may include inputting the received process information corresponding to the second time point and the predicted process information corresponding to the identified third time point into a trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point.

[0011] The control method may include providing guide information including the obtained predicted quality information.

[0012] In a non-transitory computer-readable recording medium storing computer instructions that, when executed by a processor of an electronic device for implementing a quality prediction and control system according to one embodiment of the present disclosure, cause the electronic device to perform an operation, the operation may include, when process information corresponding to a second time point is received from a polymer manufacturing equipment, identifying predicted process information corresponding to a third time point a predetermined time after the second time point based on process history information of the polymer manufacturing equipment and the process information corresponding to the second time point.

[0013] The operations may include inputting the received process information corresponding to the second time point and the predicted process information corresponding to the identified third time point into a trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point.

[0014] The operations may include providing guide information including the obtained predicted quality information. Effect of the Invention

[0015] According to the above example, the quality of a polymer obtained through a polymer manufacturing process can be accurately predicted, and guide information can be provided to a user based on the predicted quality. [Brief description of the drawings]

[0016] [Figure 1]1 is a diagram for briefly explaining a method for controlling 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 and diagram illustrating a method for controlling an electronic device according to an embodiment. [Figure 4] 1 is a diagram illustrating a method for obtaining predicted quality information according to an embodiment; [Diagram 5] 1 is a diagram illustrating a method for identifying predicted process information according to an embodiment; [Figure 6] 1 is a diagram illustrating a method for obtaining guide information according to an embodiment; [Figure 7] 1 is a diagram illustrating a method for obtaining guide information according to an embodiment; [Figure 8] 1 is a diagram illustrating a method for providing a UI according to an embodiment; [Figure 9] 1 is a diagram illustrating a method for providing a UI according to an embodiment; [Figure 10] FIG. 2 is a block diagram showing a detailed configuration of an electronic device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] The present disclosure will now be described in detail with reference to the accompanying drawings.

[0018] The terms used in this specification will be briefly explained to more specifically explain the present disclosure.

[0019] The terms used in the embodiments of the present disclosure are selected as currently widely used general terms as possible while taking into consideration the functions in the present disclosure, but this may change depending on the intentions or precedents of the technicians in the field, the emergence of new technologies, etc. In addition, in certain cases, the applicant may arbitrarily select terms, and in such cases, the meanings will be described in detail in the relevant description of the 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 by the names of the terms.

[0020] In this specification, terms such as "have," "can have," "include," or "can include" indicate the presence of a given feature (e.g., a value, function, operation, or component such as a part) and do not exclude the presence of additional features.

[0021] The phrase "at least one of A and / or B" should be understood to refer to either one of "A" or "B" or "A and B."

[0022] As used in this specification, expressions such as "first," "second," "firstly," or "secondarily" may modify various elements regardless of order and / or importance, and are only used to distinguish one element from other elements, and do not limit the elements in question.

[0023] 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).

[0024] In the present disclosure, the terms "comprise" or "comprise" are intended to specify the presence of 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.

[0025] 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. Also, multiple "modules" or multiple "units" may be integrated into at least one module and embodied in at least one processor (not shown), except for a "module" or "unit" that needs to be embodied in specific hardware.

[0026] The 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) that is configured with at least one neural network layer. The artificial neural network 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 (BRDNN), or a deep Q-network, but is not limited to the above examples.

[0027] In addition, in this specification, a "parameter" is a value used in the calculation process of each layer of a neural network, and may include, for example, a weight value used when applying an input value to a predetermined calculation formula. Also, the parameter may be expressed in a matrix form. The parameter is a value set as a result of training, and may be updated through separate training data as necessary.

[0028] Meanwhile, a parameter described below means a variable corresponding to at least one layer constituting a neural network model and at least one node included in the layer.

[0029] FIG. 1 is a diagram for explaining a method for controlling an electronic device according to an embodiment.

[0030] 1, according to one embodiment, a polymer production apparatus 10 can produce a polymer (product) based on an input of a raw material. The polymer production apparatus 10 can be composed of a plurality of production sections including an input section, a reaction section, a transfer section, a molding section, a recovery section, and a purification section. Meanwhile, the polymer production apparatus 10 can include a sensor 10-1 indicating a plurality of states, and the sensor 10-1 can be included in each of the plurality of production sections.

[0031] According to one embodiment, the sensor 10-1 can obtain information on the amount and ratio of multiple types of input raw materials (e.g., hydrogen, catalyst, butene, octene, etc.) input to the polymer production equipment 10. Also, the sensor 10-1 can obtain information on the process status of the polymer production equipment 10, for example, at least one of information on the pressure, temperature, flow rate, and power consumption of the production unit of the polymer production equipment 10. Alternatively, the sensor 10-1 can obtain information on the polymer produced in the polymer production equipment 10.

[0032] Meanwhile, according to an embodiment, the electronic device 100 may receive information acquired through a sensor and predict the quality of a polymer to be produced at a preset time point based on the received information, where the quality may be, for example, the MI (Melting Index), density, or MFR (Melt flow index) type of the produced polymer.

[0033] Below, various embodiments will be described regarding a method for preprocessing information generated in a polymer production process and inputting the preprocessed information into a trained neural network model to accurately predict the quality of the polymer produced in the polymer production apparatus 10.

[0034] FIG. 2 is a block diagram illustrating a configuration of an electronic device according to an embodiment.

[0035] Referring to FIG. 2, the electronic device 100 may include a communication interface 110, a memory 120, and one or more processors 130.

[0036] According to an embodiment, the electronic device 100 may be embodied as a device that processes data and communicates with external devices, 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.

[0037] Meanwhile, the electronic device 100 may be communicatively connected to 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 the external server may also communicate with the external device using a Bluetooth module.

[0038] In other embodiments, the communication modules for communicating with the external device and the external server may be implemented separately. For example, the electronic device 100 may communicate with the external device using a Bluetooth module and communicate with the external server using an Ethernet modem or a WiFi module.

[0039] Meanwhile, the external device may be embodied as at least one of the polymer manufacturing apparatus 10 or a control engine for controlling the polymer manufacturing apparatus 10, but is not limited thereto.

[0040] 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) through communication methods such as AP-based Wi-Fi (Wireless LAN network), Bluetooth, Zigbee (registered trademark), 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, etc.

[0041] 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 a 2.4 GHz frequency band having a reach radius of approximately 10 m. However, without being limited thereto, the communication interface 110 may 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.

[0042] According to one example, the communication interface 110 may 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 may use at least one of an Ethernet module or a WiFi module to communicate with an external server, and may use a Bluetooth module to communicate with an external device, such as a remote control device. However, this is merely one example, and the communication interface 110 may use at least one of a variety of communication modules when communicating with multiple external devices or external servers.

[0043] According to one embodiment, the one or more processors 130 can receive process information from the polymer production system 10 via the communications interface 110 .

[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 storing data. For example, data for driving the electronic device 100 may be stored in a memory embedded in the electronic device 100, and data for an extended function of the electronic device 100 may be stored in a memory removable from the electronic device 100.

[0045] Meanwhile, 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), etc.). The USB memory device may be implemented in the form of an external memory that can be connected to a USB port (e.g., a USB memory).

[0046] Meanwhile, according to one embodiment, the memory 120 may store the trained neural network model and process history information of a polymer production apparatus. Here, the process history information may include, for example, the amount of input raw material input to the polymer production apparatus 10, the amount of produced polymer produced by the polymer production apparatus 10, and history information on the process status of the polymer production apparatus 10. For example, the process history information may include input raw material information, produced polymer information, process status information, and quality information on the produced polymer corresponding to each of a plurality of time points before the second time point.

[0047] Meanwhile, the trained neural network model is a neural network model trained to output quality information when process history is input, which will be explained in detail with reference to Figure 4.

[0048] One or more processors 130 (hereinafter, processors) are electrically connected to the communication interface 110 and the memory 120 to 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 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.

[0049] 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, or may be defined by the relevant term. In addition, 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).

[0050] 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 processor 130 may include, but is not limited to, 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, or may be defined by the relevant term. 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).

[0051] According to one embodiment, the processor 130 can receive process information corresponding to a second point in time from the polymer production apparatus 10 (not shown) via the communication interface 110 .

[0052] In one example, the second time point may be a current time point, but is not limited thereto. The process information may include at least one of information on input raw materials input to the polymer production apparatus 10, information on produced polymers produced (or acquired) by the polymer production apparatus 10, and information on the process status of the polymer production apparatus 10. Here, the input raw material information means the amount of raw materials input to the polymer production apparatus 10 during a preset time period, and the produced polymer information means the amount of polymer produced by the polymer production apparatus 10 during a preset time period. The information on the process status may include, but is not limited to, information on the flow rate of compounds present in the polymer production apparatus 10, the amount of power of at least one control unit included in the production apparatus, the temperature of the production apparatus, and the pressure within the production apparatus.

[0053] According to one example, the process information corresponding to the second time point may include information on input raw materials input to the polymer manufacturing equipment 10 during a predetermined time period from the second time point, information on the produced polymer produced by the polymer manufacturing equipment 10 during a predetermined time period from the second time point, and process status information of the polymer manufacturing equipment 10 at the second time point.

[0054] According to one embodiment, the processor 130 can identify predicted process information corresponding to a third point in time that is a predetermined time after the second point in time based on the process history information and the process information corresponding to the second point in time.

[0055] The process history information means history information on a process performed in the polymer production apparatus 10 before the second time point, and may include, for example, the amount of raw material input to the polymer production apparatus 10 (not shown), the amount of polymer produced in the polymer production apparatus 10 (not shown), and history information on the process status of the polymer production apparatus 10 (not shown) before the second time point. The predicted process information means process information at a third time point predicted based on the process history information and process information corresponding to the second time point. This will be described in detail with reference to FIG. 4.

[0056] According to one embodiment, the processor 130 may input the received process information corresponding to the second time point and the predicted process information corresponding to the identified third time point into the trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point. Here, the quality information refers to information on the quality of the produced polymer obtained by the polymer production apparatus 10, and according to one example, the quality information may include at least one of information on the MI (Melting Index) value, density value, or MFR (Melt flow index) value of the produced polymer.

[0057] According to an example, the trained neural network model can obtain predicted quality information of a polymer corresponding to a third time point when process information corresponding to a second time point and predicted process information corresponding to a third time point are input. Here, the predicted quality information can include at least one of information on a melting index (MI), density, or melt flow index (MFR) value of a polymer produced in the polymer production apparatus 10 at a third time point after a preset time has elapsed from the second time point.

[0058] According to an embodiment, the processor 130 may provide guide information including the acquired predicted quality information. According to an example, the guide information may be, but is not limited to, information that guides the user to make the predicted quality equal to or greater than a preset value when the predicted quality at the third time point is less than a preset value based on the predicted quality information. A method of providing the guide information will be described in detail with reference to FIGS. 6 to 9.

[0059] FIG. 3 is a flowchart illustrating a method for controlling an electronic device according to an embodiment.

[0060] 3, according to one embodiment, the control method may identify whether process information corresponding to a second time point is received from the polymer manufacturing apparatus (S310). According to one example, the processor 130 may receive process information corresponding to the second time point of the polymer manufacturing apparatus 10 from at least one of an external device (not shown), for example, the polymer manufacturing apparatus 10 or an external server (not shown), via the communication interface 110.

[0061] Next, according to one embodiment, when process information is received (Y), the control method can identify predicted process information corresponding to a third point in time that is a predetermined time after the second point in time based on the process history information of the polymer manufacturing equipment 10 and the process information corresponding to the second point in time (S320).

[0062] According to one example, the processor 130 can first obtain process information including input raw material information and produced polymer information corresponding to each of a plurality of time points before the second time point based on the process history information. Then, according to one example, the processor 130 can obtain relationship information between the input raw material information and the produced polymer information using a statistical analysis application stored in the memory 120. Here, the relationship information can be a mathematical formula corresponding to the relationship between the input raw material information and the produced polymer information obtained using a regression analysis model. Then, the processor 130 can obtain predicted produced polymer information at a third time point based on the input raw material at the second time point based on the obtained relationship information.

[0063] However, without being limited thereto, it goes without saying that the processor 130 may also use a statistical analysis application to obtain relationship information between input raw materials, process status, and produced polymer information, and based thereon obtain predicted produced polymer information at a third point in time.

[0064] Alternatively, according to one example, the processor 130 may obtain predicted input material information, predicted process status information, and predicted produced polymer information corresponding to a third time point based on the process history information. For example, the processor 130 may obtain trend information for each of the input material information, process status information, and produced polymer information using a statistical analysis application. Here, the trend information may be a formula obtained using a regression analysis model. The processor 130 may obtain predicted input material information, predicted process status information, or predicted produced polymer information corresponding to a third time point based on the obtained trend information and the process information corresponding to the second time point.

[0065] Next, according to one embodiment, the control method can input the received process information corresponding to the second time point and the identified predicted process information corresponding to the third time point into the trained neural network model to obtain predicted quality information of the polymer corresponding to the third time point (S330).

[0066] According to one example, the processor 130 can input input raw material information at a second time point, process status information at the second time point, and the obtained predicted produced polymer information at a third time point into the trained neural network model to obtain predicted quality information for the produced polymer at the third time point.

[0067] Subsequently, according to an embodiment, the control method may provide guide information including the obtained predicted quality information (S340). According to an example, the electronic device 100 may further include a display (not shown), and the processor 130 may display a UI including the guide information through the display.

[0068] According to the above example, a pre-processing process is performed on the acquired process information, and the pre-processed process information is input to a trained neural network model to acquire predicted process information. As a result, the quality of a polymer acquired through a polymer manufacturing process can be accurately predicted, and guide information can be provided to a user based on the predicted quality.

[0069] FIG. 4 is a diagram illustrating a method for obtaining predicted quality information according to an embodiment.

[0070] Referring to FIG. 4, first, according to one embodiment, the control method can identify predicted produced polymer information at a third point in time using process history information, input raw material information corresponding to a second point in time, produced polymer information corresponding to the second point in time, and process status information corresponding to the second point in time (S410).

[0071] According to one example, the processor 130 can first obtain process information including input raw material information and produced polymer information corresponding to each of a plurality of time points before the second time point based on the process history information stored in the memory 120. Then, according to one example, the processor 130 can obtain relationship information between the input raw material information, the production status information, and the produced polymer information using a statistical analysis application stored in the memory 120. Here, the relationship information can be a mathematical formula obtained using a regression analysis model. Then, the processor 130 can obtain predicted produced polymer information at a third time point based on the input raw material at the second time point based on the obtained relationship information.

[0072] For example, the processor 130 may input the input raw material information and production status information at the second time point into a formula included in the acquired relationship information to acquire predicted polymer production information at a third time point.

[0073] Next, according to one embodiment, the control method can input input raw material information corresponding to the second time point, process status information corresponding to the second time point, and predicted production polymer information at the identified third time point into the trained neural network model to obtain predicted quality information corresponding to the third time point (S420).

[0074] Here, the neural network model may be a neural network model trained to output predicted quality information when input raw material information, produced polymer information, process information including process status information, and predicted produced polymer information are input. As an example, a data set including input raw material information at time n, process status information at time n, and produced polymer information at time n+1 may be input as training data to the neural network model to train the neural network model. In this case, the data set may include a label corresponding to quality information of the polymer produced at time n+1.

[0075] Meanwhile, an artificial neural network (or 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 (BRDNN), or a deep Q-network, but is not limited to the above examples.

[0076] However, without being limited thereto, the trained neural network model according to one example may also be a neural network model trained to output predicted quality information when process information including input raw material information, produced polymer information, and process status information is input. That is, even if predicted produced polymer information is not input, predicted quality information may be output upon receiving process information including input raw material information, produced polymer information, and process status information.

[0077] Meanwhile, according to one embodiment, the processor 130 can train the neural network model using the process history information. According to one example, the processor 130 can parse the process information and quality information corresponding to each of a plurality of time points before the second time point included in the process history information to obtain a data set for training the neural network model, and can train the neural network model using the obtained data set as training data. In this case, the label of each data set can be quality information of the produced polymer.

[0078] FIG. 5 is a diagram illustrating a method for identifying predicted process information according to an embodiment.

[0079] According to one embodiment, the control method may first obtain input material information and process status information corresponding to a first time point that is a preset time before a second time point based on process history information stored in memory 120 (S510). That is, the second time point may be when a preset time has passed from the first time point, and the third time point may be when a preset time has passed from the second time point, but is not limited thereto.

[0080] Next, according to one embodiment, the control method can update input raw material information corresponding to the second time point and process status information corresponding to the second time point using input raw material information and process status information corresponding to the first time point and produced polymer information corresponding to the second time point (S520).

[0081] According to one example, the processor 130 can obtain input raw material information and process status information reflecting the accumulated process prior to a second point in time based on input raw material information, process status information, and produced polymer information corresponding to each of the multiple points in time included in the process history information.

[0082] For example, when the input raw material corresponding to the nth time point is 1 and there is no disturbance, the process is assumed in which the amount of produced polymer at the n+1th time point due to the input raw material at the nth time point is 2 (where n is an integer). When the processor 130 identifies that the input raw material corresponding to the first time point is 1 and the amount of produced polymer corresponding to the second time point is 1.8 (i.e., when only 90% of the input raw material is produced as polymer) based on the process history information, the processor 130 identifies that the amount of recovered raw material is 10% of the total input amount, and accordingly updates the amount of input raw material at the second time point to 1.1. Subsequently, when the processor 130 identifies the input raw material information and process status information at the updated second time point, it becomes possible to identify predicted produced polymer information at the third time point using the acquired relationship information.

[0083] However, this is not limited to the above, and it goes without saying that the updated value of the input raw material corresponding to the first time point can be identified depending on each process corresponding to each of multiple time points prior to the first time point, and accordingly, the amount of input raw material and process status information at the second time point can be updated to different values.

[0084] Next, according to one embodiment, the control method may identify predicted process information, including predicted polymer production information, at a third time point using the updated input material information and the updated process status information (S530).

[0085] In one example, the processor 130 can identify predicted process information including predicted produced polymer information at a third time point using process information obtained based on updated input raw material information and process status information at a second time point and process history information.

[0086] According to the above example, data input to the trained neural network model can be updated to reflect accumulated process history information, and the updated data can be input to the trained neural network model to predict quality more accurately.

[0087] FIG. 6 is a diagram illustrating a method for obtaining guide information according to an embodiment.

[0088] Referring to FIG. 6, in one embodiment, the control method can identify whether the predicted quality information obtained based on the target value information corresponding to each of a plurality of quality types stored in memory 120 differs from the target value by more than a preset value (S610).

[0089] According to an example, the memory 120 may store information on target values ​​corresponding to each of a plurality of quality types. The processor 130 may compare the predicted quality information acquired through the neural network model with the target values ​​stored in the memory 120 to identify whether the acquired predicted quality information is different from the target value by a preset value or more. For example, at least one of information on the MI (Melting Index) value, density value, or MFR (Melt flow index) value of the produced polymer included in the predicted quality information may be compared with the corresponding MI (Melting Index) target value, density target value, or MFR (Melt flow index) target value of the produced polymer stored in the memory 120 to identify whether the acquired predicted quality information is different from the target value by a preset value or more.

[0090] Next, the processor 130 can acquire guide information that guides the difference between the acquired predicted quality information and the target value to be less than a preset value, based on priority information of raw materials corresponding to each of the multiple quality types included in the quality information stored in the memory 120 (S620).

[0091] According to one example, the memory 120 may store priority information for the feedstocks corresponding to each quality type. For example, the highest priority corresponding to MI may be catalyst, and the highest priority corresponding to density may be the ratio of butene to ethylene.

[0092] According to one example, if the difference between the predicted MI value and the target value is equal to or greater than a preset value, the processor 130 can identify an amount of catalyst that will cause the difference between the MI value and the target value to be less than the preset value, and obtain guide information including information on the identified amount of catalyst.

[0093] In this case, according to an example, the memory 120 may pre-store information regarding the change in quality due to a change in unit input amount corresponding to each of a plurality of raw material types, and the processor 130 may identify guide information for determining whether the predicted quality value differs from the target value by less than a preset value based on the information stored in the memory 120.

[0094] However, without being limited thereto, the processor 130 may obtain guide information corresponding to each of the multiple types of quality information predicted using the trained neural network model, which will be described in detail with reference to FIG.

[0095] Next, according to one embodiment, the control method may provide a UI including the acquired guide information (S630). According to one example, the UI may include process information corresponding to the second time point, process history information, and predicted quality information of the polymer corresponding to the third time point.

[0096] Meanwhile, according to an embodiment, the processor 130 may further include a user interface (not shown), and when a user input corresponding to guide information acquired through the user interface (not shown) is received, the processor 130 may identify control information corresponding to the received user input and transmit the identified control information to a control engine through the communication interface 110. Here, the control engine refers to an engine that controls the polymer manufacturing apparatus 10.

[0097] According to one example, after a UI including guide information is provided, when a user input to reduce the catalyst input amount to a preset value is received through the user interface, the processor 130 can identify control information for reducing the catalyst input amount to the preset value and transmit it to the control engine via the communication interface 110.

[0098] FIG. 7 is a diagram illustrating a method for obtaining guide information according to an embodiment.

[0099] According to one embodiment, the control method may first identify sub-process information in which input raw material information has been changed from information included in the process information corresponding to the received second time point (S710). Here, the sub-process information may be process information in which the ratio of input raw materials has been changed by a preset value, but is not limited thereto. According to one example, the sub-process information may be process information in which a process state (e.g., the magnitude of pressure in the polymer manufacturing apparatus 10) has been changed.

[0100] Next, according to one embodiment, the control method may identify sub-predicted process information based on the sub-process information (S720).

[0101] Next, according to one embodiment, the control method may input the sub-process information and the sub-predicted process information into the trained neural network model to obtain at least one sub-predicted quality information corresponding to a third time point (S730).

[0102] Subsequently, according to one embodiment, the control method may obtain guide information using the obtained predicted quality information and the obtained at least one sub-predicted quality information (S740).

[0103] According to an example, the processor 130 can obtain guide information by comparing the obtained predicted quality information and the sub-predicted quality information. For example, the processor 130 can obtain information on the change amount of the MI value due to a change in the unit input amount of the catalyst by comparing the obtained sub-predicted quality information and the predicted quality information.

[0104] However, without being limited thereto, the processor 130 may acquire guidance information through the predicted quality information and the sub-predicted quality information acquired using a preset algorithm. The processor 130 may acquire guide information including the acquired guidance information.

[0105] FIG. 8 is a diagram illustrating a method for providing a UI according to an embodiment.

[0106] 8, according to one embodiment, the processor 130 may provide a UI 800 including process history information. According to one example, the processor 130 may provide a UI 800 including information in which input raw material information (e.g., input amounts corresponding to each of a plurality of types of raw materials), process status information, and produced polymer information corresponding to each of a plurality of time points before the second time point are embodied in a graph form.

[0107] In this case, according to an example, the electronic device 100 may further include a display (not shown), and the processor 130 may display the UI through the display (not shown).

[0108] FIG. 9 is a diagram illustrating a method for providing a UI according to an embodiment.

[0109] Referring to FIG. 9, in one embodiment, the processor 130 can provide a UI 900 including guide information including predicted quality information 910 corresponding to a third time point, trend information 920 corresponding to each of the multiple types of qualities, guidance information 930 for control variables, and explanation function information 940 corresponding to each of the multiple qualities.

[0110] Here, the predicted quality information 910 may include predicted quality information and previous quality information when the current input amount is completed into the production polymer, reflecting the latest process state. The trend information 920 may include history information for each of the multiple qualities. The processor 130 may acquire the history information for each of the multiple qualities based on the process history information. The controlled variable guidance information 930 may include information on the input amount corresponding to each of the multiple types of controlled variables and guidance information corresponding to each of the multiple types of quality information predicted using the trained neural network model. Alternatively, in one example, it may include guidance information corresponding to each of the multiple types of quality information stored in the memory 120. The explanation function information 940 may include information on the target value corresponding to each of the multiple qualities and the change amount of the quality value due to the change in the unit input amount of each of the multiple qualities. In this case, information on the highest priority controlled variable corresponding to each of the multiple qualities may be included.

[0111] FIG. 10 is a block diagram showing a detailed configuration of an electronic device according to an embodiment.

[0112] 10, 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. Among the components illustrated in FIG. 10, detailed descriptions of components that overlap with the components illustrated in FIG. 2 will be omitted.

[0113] The microphone 140 may refer to a module that captures sound and converts it into an electrical 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.

[0114] There are various embodiments in which the electronic device 100 ′ performs an operation corresponding to a user's voice signal received through the microphone 140 .

[0115] As an example, the electronic device 100′ may control the display 160 based on a user voice signal received through the microphone 140. For example, when a user voice signal for displaying content A is received, the electronic device 100′ may control the display 160 to display content A.

[0116] 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. The electronic device 100' may transmit the provided control signal to the external display device using at least one communication method of Bluetooth, WiFi, 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 so that content A is displayed on the external display device. Here, the electronic device 100' may refer to various terminal devices on which a remote control application can be installed, such as a smartphone, an AI speaker, etc.

[0117] 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 through 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. 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.

[0118] The speaker 150 may include a tweeter for reproducing high-frequency sounds, a midrange for reproducing mid-frequency sounds, a woofer for reproducing low-frequency sounds, a subwoofer for reproducing extremely low-frequency sounds, an enclosure for controlling resonance, and a crossover network that divides the electrical signal frequencies input to the speaker into bands.

[0119] 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' can include an audio output device such as the speaker 150, and can also 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.

[0120] 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, which may be implemented as an a-si TFT, a low temperature poly silicon (LTPS) TFT, an organic TFT (OTFT), and the like. Meanwhile, the display 160 may 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 the output image obtained according to the various embodiments described above. Here, the output image may be a high-resolution image of 4K or 8K or higher.

[0121] Meanwhile, 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.

[0122] Here, the external display device may be connected to the electronic device 100' through the 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). Alternatively, 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 through the communication interface 110 in a wired or wireless manner, or may transmit the image or content to the external display device through the input / output interface (not shown).

[0123] The user interface 170 is a component that allows the electronic device 100' to interact with a user. For example, the user interface 170 may include, but is not limited to, at least one of a touch sensor, a motion sensor, a button, a jog dial, a switch, a microphone, or a speaker.

[0124] 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, which may include a lens that focuses visible light or other optical signals reflected and received by an object on the image sensor, and an image sensor that may sense visible light or other optical signals. Here, the image sensor may include a 2D pixel array that is divided into a plurality of pixels.

[0125] According to the above example, a pre-processing process is performed on the acquired process information, and the pre-processed process information is input to a trained neural network model to acquire predicted process information. As a result, the quality of a polymer acquired through a polymer manufacturing process can be accurately predicted, and guide information can be provided to a user based on the predicted quality.

[0126] Meanwhile, 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 an existing electronic device. Alternatively, the methods according to the various embodiments of the present disclosure may be performed using a deep learning-based trained neural network (or a deep trained neural network), i.e., a trained network model. Also, the methods according to the various embodiments of the present disclosure may be implemented only by a software upgrade or hardware upgrade for an existing electronic device. Also, the various embodiments of the present disclosure may be performed through an embedded server provided in an electronic device or an external server of the electronic device.

[0127] Meanwhile, 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 instructions stored in the storage medium and operating according to the called instructions, and may include a display device according to the disclosed embodiments (e.g., display device (A)). 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 in the storage medium semi-permanently or temporarily.

[0128] 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 device-readable storage medium (e.g., compact disc read only memory (CD-ROM)) or online through an application store (e.g., Play Store™). In the case of online distribution, at least a portion 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.

[0129] In addition, 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 corresponding subcomponents described above may be omitted, or other subcomponents may be further included in the various embodiments. In general, or in addition, some components (e.g., modules or programs) may be integrated into one entity to perform the same or similar functions as those performed by the corresponding components before being integrated. Operations performed by modules, programs, or other components according to the various embodiments may be performed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be performed in a different order, or other operations may be omitted or added.

[0130] 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 it goes without saying that various modifications can be made by a person having ordinary skill in the art 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. [Industrial Applicability]

[0131] The electronic device for implementing the polymer quality prediction and control system and the control method thereof as described above can be applied to the polymer manufacturing process field.

Claims

1. 1. An electronic device for implementing a quality prediction and control system, Communication interface; a memory storing process history information of the trained neural network model and a polymer manufacturing apparatus, the process history information including input raw material information, produced polymer information, process status information, and quality information for the produced polymer corresponding to a first time point; raw material priority information corresponding to each of a plurality of types of quality information included in the quality information and target value information corresponding to each of the plurality of types of quality information, the plurality of types of quality information including at least one of information on the MI (Melting Index) value, density value, and MFR (Melt flow index) value of the produced polymer; and When process information including input material information, produced polymer information, and process status information corresponding to a second time point is received from the polymer production apparatus through the communication interface, input material information corresponding to the first time point prior to a preset time from the second time point and process status information corresponding to the first time point are acquired based on the process history information; updating the input raw material information corresponding to the second time point and the process status information corresponding to the second time point using input raw material information corresponding to the first time point, process status information corresponding to the first time point, and produced polymer information corresponding to the second time point - updating the input raw material information corresponding to the second time point means reflecting the amount of raw material recovered from the amount of input raw material at the first time point in the amount of input raw material at the second time point -; identifying predicted process information including predicted polymer production information at a third time point a predetermined time after the second time point using the updated input material information and the updated process status information; inputting the updated input material information, the updated process status information, and the predicted polymer production information at the third time point into the trained neural network model to obtain predicted polymer quality information corresponding to the third time point; When the difference between the acquired predicted quality information and the target value is equal to or greater than a preset value, guide information is acquired based on priority order information of the raw material to guide the difference between the acquired predicted quality information and the target value to be less than the preset value; one or more processors for providing a UI including the retrieved guide information; The UI includes: an electronic device including at least one of the process history information, predicted quality information of the polymer corresponding to the third time point, trend information corresponding to each of the plurality of types of quality information, explanatory function information corresponding to each of the plurality of types of quality information, and controlled variable guidance information corresponding to each of the plurality of types of quality information.

2. The process history information includes: The information includes input raw material information, produced polymer information, process status information, and produced polymer quality information corresponding to each of a plurality of time points before the second time point, The trained neural network model is The electronic device of claim 1 , trained to output predicted quality information when process information and predicted produced polymer information, including input raw material information, produced polymer information, and process status information, are input.

3. The UI includes: The electronic device of claim 1 , further comprising process information corresponding to the second point in time.

4. The one or more processors: Identifying sub-process information in which input material information has been changed from information included in the process information corresponding to the received second time point; Identifying sub-predicted process information based on the sub-process information; inputting the sub-process information and the sub-predicted process information into the trained neural network model to obtain at least one sub-predicted quality information corresponding to the third time point; The electronic device of claim 3 , further comprising: an electronic device for acquiring the guide information by utilizing the acquired predicted quality information and the acquired at least one sub-predicted quality information.

5. a user interface; The one or more processors: When a user input corresponding to the acquired guide information is received through the user interface, control information corresponding to the received user input is identified; The electronic device of claim 3 , further comprising: a communication interface configured to transmit the identified control information to a control engine.

6. 1. A method for controlling an electronic device to realize a quality prediction and control system, comprising: storing process history information of a polymer manufacturing apparatus, the process history information including input raw material information, produced polymer information, process status information, and quality information for the produced polymer corresponding to a first time point, raw material priority information corresponding to each of a plurality of types of quality information included in the quality information, and target value information corresponding to each of the plurality of types of quality information in a memory, the plurality of types of quality information including at least one of information on a melting index (MI) value, a density value, and a melt flow index (MFR) value of the produced polymer; When process information including input material information, produced polymer information, and process status information corresponding to a second time point is received from the polymer production apparatus, acquiring input material information corresponding to the first time point, which is a predetermined time before the second time point, and process status information corresponding to the first time point, based on process history information of the polymer production apparatus; updating the input raw material information corresponding to the second time point and the process status information corresponding to the second time point using the input raw material information corresponding to the first time point, the process status information corresponding to the first time point, and the produced polymer information corresponding to the second time point - updating the input raw material information corresponding to the second time point means reflecting the amount of the raw material recovered from the amount of the input raw material at the first time point in the amount of the input raw material at the second time point -; identifying predicted process information including predicted polymer production information at a third time point a predetermined time after the second time point using the updated input material information and the updated process status information; inputting the updated input material information, the updated process status information, and the predicted polymer production information at the third time point into a trained neural network model to obtain predicted polymer quality information corresponding to the third time point; When the difference between the acquired predicted quality information and the target value is equal to or greater than a preset value, acquiring guide information for guiding the difference between the acquired predicted quality information and the target value to be less than the preset value based on priority information of the raw material; and providing a UI including the acquired guide information; The UI includes: the control method including at least one of the process history information, predicted quality information of the polymer corresponding to the third time point, trend information corresponding to each of the plurality of types of quality information, explanatory function information corresponding to each of the plurality of types of quality information, and controlled variable guidance information corresponding to each of the plurality of types of quality information.

7. The process history information includes: The information includes input raw material information, produced polymer information, process status information, and produced polymer quality information corresponding to each of a plurality of time points before the second time point, The trained neural network model is A control method, which is trained to output the predicted quality information according to claim 6 when process information including input raw material information, produced polymer information, and process status information, and predicted produced polymer information are input.

8. The UI includes: The method of claim 6 , further comprising process information corresponding to the second time point.

9. The step of acquiring guide information includes: identifying sub-process information in which input material information has been changed from information included in the process information corresponding to the received second time point; identifying sub-predicted process information based on the sub-process information; inputting the sub-process information and the sub-predicted process information into the trained neural network model to obtain at least one sub-predicted quality information corresponding to the third time point; and The control method of claim 8 , further comprising: obtaining the guide information by using the obtained predicted quality information and the obtained at least one sub-predicted quality information.

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

11. A non-transitory computer-readable medium storing computer instructions that, when executed by a processor of an electronic device for implementing a quality prediction and control system, cause the electronic device to perform operations, the operations comprising: storing process history information of a polymer manufacturing apparatus, the process history information including input raw material information, produced polymer information, process status information, and quality information for the produced polymer corresponding to a first time point, raw material priority information corresponding to each of a plurality of types of quality information included in the quality information, and target value information corresponding to each of the plurality of types of quality information in a memory, the plurality of types of quality information including at least one of information on a melting index (MI) value, a density value, and a melt flow index (MFR) value of the produced polymer; When process information including input material information, produced polymer information, and process status information corresponding to a second time point is received from the polymer production apparatus, acquiring input material information corresponding to the first time point, which is a predetermined time before the second time point, and process status information corresponding to the first time point, based on process history information of the polymer production apparatus; updating the input raw material information corresponding to the second time point and the process status information corresponding to the second time point using the input raw material information corresponding to the first time point, the process status information corresponding to the first time point, and the produced polymer information corresponding to the second time point - updating the input raw material information corresponding to the second time point means reflecting the amount of the raw material recovered from the amount of the input raw material at the first time point in the amount of the input raw material at the second time point -; identifying predicted process information including predicted polymer production information at a third time point a predetermined time after the second time point using the updated input material information and the updated process status information; inputting the updated input material information, the updated process status information, and the predicted polymer production information at the third time point into a trained neural network model to obtain predicted polymer quality information corresponding to the third time point; When the difference between the acquired predicted quality information and the target value is equal to or greater than a preset value, acquiring guide information for guiding the difference between the acquired predicted quality information and the target value to be less than the preset value based on priority information of the raw material; and providing a UI including the acquired guide information; The UI includes: a computer readable storage medium comprising at least one of the process history information, predicted quality information for the polymer corresponding to the third time point, trend information corresponding to each of the plurality of types of quality information, explanatory function information corresponding to each of the plurality of types of quality information, and controlled variable guidance information corresponding to each of the plurality of types of quality information.

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