system

Abstract

The system according to this embodiment aims to analyze data from the manufacturing line and propose and implement the optimal manufacturing method. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a data supply unit. The data collection unit collects data from the manufacturing line. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal manufacturing method. The data supply unit controls the manufacturing process based on the manufacturing method proposed by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the data of the production line has not been fully utilized effectively to optimize the production process, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the data of the production line and propose and implement an optimal production method.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects the data of the production line. The analysis unit analyzes the data collected by the collection unit and proposes an optimal production method. The provision unit controls the production process based on the production method proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze data from the manufacturing line and propose and implement the optimal manufacturing method. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The next-generation smart factory system according to an embodiment of the present invention is a system that innovates all processes in the manufacturing industry by integrating 3D printers, 3D scanners, AI, digital twins, XR, robots, and IoT. This system solves the challenges faced by conventional manufacturing by digitizing all processes from product design to manufacturing, quality control, improvement, and maintenance, and optimizing them with AI. For example, the next-generation smart factory system uses AI and digital twins to optimize the design. The AI ​​analyzes past design data and market trends and proposes the optimal design. By performing virtual testing using the digital twin, the design time can be significantly reduced. For example, in the design of automobile parts, the AI ​​proposes the optimal shape and material, and the strength and durability are simulated using the digital twin. Next, the next-generation smart factory system achieves flexible manufacturing using 3D printers and robots. The AI ​​selects the optimal manufacturing method and performs high-speed and flexible manufacturing by combining 3D printers and robots. This makes it possible to handle small-lot, high-mix production. For example, electronic device cases are manufactured with a 3D printer and assembled by robots. Furthermore, the next-generation smart factory system performs real-time quality control using IoT and AI. IoT sensors are installed at each stage of the manufacturing line, and AI monitors quality in real time. Anomalies are detected immediately, and adjustments or shutdowns are performed automatically. For example, in the manufacturing of medical devices, sensors measure the dimensions and surface condition of parts, and AI determines the quality. Furthermore, next-generation smart factory systems utilize XR to provide remote work support. The knowledge of skilled technicians is systematized by AI, and XR technology is used to provide visual instructions to on-site workers. This realizes knowledge transfer and improves work efficiency. For example, in aircraft maintenance work, workers wearing XR goggles perform tasks according to AI instructions. Finally, next-generation smart factory systems perform predictive maintenance using AI and digital twins. Data from product use is collected via IoT, and AI analyzes it. The digital twin predicts future failures and proposes the optimal maintenance timing. For example, in factory machinery, sensors measure vibration and temperature, and AI detects signs of failure.In this way, next-generation smart factory systems digitize all manufacturing processes and optimize them using AI, thereby shortening development and manufacturing times, reducing costs, and improving quality and safety. Furthermore, by building flexible manufacturing systems that can quickly respond to market changes and individual needs, they can significantly enhance the competitiveness of the manufacturing industry. Thus, next-generation smart factory systems can optimize all manufacturing processes, enabling efficient and flexible manufacturing.

[0029] The next-generation smart factory system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data from the manufacturing line. The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line, for example. The data collection unit can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. For example, the data collection unit can collect temperature data from the manufacturing line using a temperature sensor. The data collection unit can also collect humidity data from the manufacturing line using a humidity sensor. Furthermore, the data collection unit can also collect vibration data from the manufacturing line using a vibration sensor. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal manufacturing method. The analysis unit can analyze the collected data using AI, for example, and propose the optimal manufacturing method. The analysis unit can propose the optimal manufacturing method based on past data and market trends. For example, the analysis unit can analyze past data using AI and propose the optimal manufacturing method. Furthermore, the analysis unit can analyze market trends using AI and propose the optimal manufacturing method. Furthermore, the analysis unit can optimize the manufacturing process based on the collected data using AI. The supply unit controls the manufacturing process based on the manufacturing method proposed by the analysis unit. The supply unit can, for example, control the manufacturing process using AI. The supply unit can perform manufacturing by combining 3D printers and robots. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to field workers using XR technology. For example, the supply unit can provide visual instructions to field workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and propose optimal maintenance timing. For example, the supply unit can predict failures of machinery and equipment using digital twins and propose optimal maintenance timing.As a result, the next-generation smart factory system according to this embodiment can optimize manufacturing processes by collecting and analyzing data from the manufacturing line, proposing the optimal manufacturing method, and controlling the manufacturing process.

[0030] The data collection unit collects data from the manufacturing line. For example, the data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line. Specifically, it can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. Temperature sensors monitor temperature fluctuations in detail at each stage of the manufacturing line and detect abnormal temperature increases or decreases. Humidity sensors monitor the humidity of the manufacturing environment and detect humidity fluctuations that may affect product quality. Vibration sensors monitor the operating status of machinery and are used to detect abnormal vibrations or machine failures early. The data collected from these sensors is transmitted in real time to a central database, allowing for unified management of the entire manufacturing line's status. Furthermore, the data collection unit can integrate the data from each sensor and use anomaly detection algorithms to detect unusual patterns or abnormal data. For example, by combining and analyzing data from temperature and vibration sensors, it is possible to detect machine overheating or abnormal vibrations early, enabling a rapid response. The data collection unit can also adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect data, thereby improving the overall performance of the manufacturing line.

[0031] The analysis unit analyzes data collected by the data collection unit and proposes the optimal manufacturing method. For example, the analysis unit can use AI to analyze collected data and propose the optimal manufacturing method. Specifically, the AI ​​uses machine learning algorithms to analyze past and current data and optimize the manufacturing process. For example, based on past manufacturing data, the AI ​​can propose the optimal temperature and humidity settings for a specific product. The AI ​​can also analyze market trends and develop manufacturing plans that respond to fluctuations in demand. For example, the AI ​​can analyze past sales data and market trends to adjust production volume to coincide with periods of high demand. Furthermore, the analysis unit can use AI to detect anomalies in the manufacturing process and perform preventive maintenance based on the collected data. For example, the AI ​​can analyze vibration sensor data to detect abnormal machine vibrations early and identify signs of failure. This allows the analysis unit to not only optimize the manufacturing process but also handle anomaly detection and preventive maintenance, improving the reliability and efficiency of the manufacturing line. Additionally, the analysis unit can use data visualization tools to monitor the status of the manufacturing line in real time and provide managers with information that is easy to understand intuitively. This allows the analysis unit to support the optimization and management of manufacturing processes, thereby enhancing the competitiveness of the manufacturing industry.

[0032] The supply department controls the manufacturing process based on the manufacturing method proposed by the analysis department. The supply department can, for example, control the manufacturing process using AI. Specifically, the supply department can combine 3D printers and robots for manufacturing. The 3D printer can manufacture the product's shape and structure with high precision based on the optimal manufacturing method proposed by the analysis department. Robots are used to assemble the parts manufactured by the 3D printer, improving the product's overall quality. For example, the supply department can manufacture complex-shaped parts using a 3D printer and then accurately assemble those parts using robots. Furthermore, the supply department can provide visual instructions to on-site workers using XR technology. By using XR goggles, on-site workers can visually confirm each step of the manufacturing process as they work. This improves work efficiency and accuracy, and enhances product quality. In addition, the supply department can use digital twins to predict future failures and propose optimal maintenance timing. The digital twin creates a virtual model of the physical manufacturing line and simulates data in real time, allowing for detailed monitoring of the machine equipment's condition. This allows the supply department to predict machine failures and perform maintenance at the appropriate time. For example, the supply department can use a digital twin to analyze machine vibration data, detect early signs of failure, and propose planned maintenance. This enables the supply department to improve the efficiency and reliability of the manufacturing process and enhance the competitiveness of the manufacturing industry.

[0033] The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line. For example, the data collection unit can collect data from temperature sensors installed at each stage of the manufacturing line. The data collection unit can collect temperature data from the manufacturing line using temperature sensors. The data collection unit can also collect data from humidity sensors installed at each stage of the manufacturing line. The data collection unit can collect humidity data from the manufacturing line using humidity sensors. Furthermore, the data collection unit can also collect data from vibration sensors installed at each stage of the manufacturing line. The data collection unit can collect vibration data from the manufacturing line using vibration sensors. This allows for real-time monitoring of the manufacturing process by collecting data from IoT sensors installed at each stage of the manufacturing line. IoT sensors include, but are not limited to, temperature sensors, humidity sensors, and vibration sensors. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can optimize the manufacturing process based on the collected data. For example, the analysis unit can use AI to analyze the collected data and optimize the manufacturing process. The analysis unit can optimize the manufacturing process based on historical data and market trends. For example, the analysis unit can use AI to analyze historical data and optimize the manufacturing process. The analysis unit can also use AI to analyze market trends and optimize the manufacturing process. Furthermore, the analysis unit can use AI to optimize the manufacturing process based on the collected data. This improves manufacturing efficiency by optimizing the manufacturing process based on the collected data. The optimization of the manufacturing process includes, but is not limited to, the algorithms used, the purpose of optimization, and the evaluation criteria. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the optimization of the manufacturing process.

[0035] The supply unit can control the manufacturing process based on the analysis results. The supply unit can control the manufacturing process using, for example, AI. The supply unit can perform manufacturing by combining 3D printers and robots. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to on-site workers using XR technology. For example, the supply unit can provide visual instructions to on-site workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and suggest optimal maintenance timings. For example, the supply unit can predict failures of machinery and equipment using digital twins and suggest optimal maintenance timings. This improves the accuracy and efficiency of the manufacturing process by controlling it based on the analysis results. Control of the manufacturing process includes, but is not limited to, parameters to be controlled, control means, and control frequency. Some or all of the above-described processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input the analysis results into a generating AI and have the generating AI execute the control of the manufacturing process.

[0036] The supply unit can perform manufacturing by combining a 3D printer and a robot. For example, the supply unit can manufacture a product using a 3D printer and assemble it using a robot. For example, the supply unit can manufacture a case for an electronic device using a 3D printer. Furthermore, the supply unit can assemble a component for an electronic device using a robot. In addition, the supply unit can manufacture and assemble a component with a complex shape by combining a 3D printer and a robot. This enables flexible and efficient manufacturing by combining a 3D printer and a robot. The 3D printer includes, but is not limited to, the materials used, printing accuracy, and printing speed. The robot includes, but is not limited to, the functions, operating range, and control methods. Some or all of the above processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can have a generative AI perform the control of the 3D printer and the robot.

[0037] The service provider can provide visual instructions to field workers using XR technology. For example, the service provider can provide visual instructions to field workers using XR goggles. The service provider can use XR technology to provide visual instructions to workers, for example, in aircraft maintenance work. The service provider can also use XR technology to provide visual instructions to workers, for example, in maintenance work on factory machinery. Furthermore, the service provider can use XR technology to provide visual instructions to workers, for example, at construction sites. This improves work efficiency and accuracy by providing visual instructions to field workers using XR technology. XR technology includes, but is not limited to, AR (augmented reality), VR (virtual reality), and MR (mixed reality). Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the provision of instructions using XR goggles.

[0038] The service provider can use digital twins to predict future failures and propose optimal maintenance timing. For example, the service provider can use digital twins to predict failures in machinery and equipment and propose optimal maintenance timing. The service provider can use digital twins to measure vibrations and temperatures of factory machinery and equipment and detect signs of failure. The service provider can also use digital twins to monitor the usage of automobile parts and detect signs of failure. Furthermore, the service provider can use digital twins to monitor the condition of aircraft engines and detect signs of failure. This improves the efficiency and accuracy of maintenance by using digital twins to predict future failures and propose optimal maintenance timing. Digital twins include, but are not limited to, methods for creating digital models, methods for synchronizing data, and prediction algorithms. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input digital twin data into a generating AI and have the generating AI perform failure predictions.

[0039] The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line in real time and immediately notify when an anomaly occurs. For example, if an IoT sensor detects a temperature anomaly, the data collection unit can immediately issue an alert. If an IoT sensor detects a vibration anomaly, the data collection unit can notify in real time and prompt corrective action. Furthermore, if an IoT sensor detects a pressure anomaly, the data collection unit can immediately stop the manufacturing line. This enables a rapid response by collecting data from IoT sensors installed at each stage of the manufacturing line in real time and immediately notifying when an anomaly occurs. Real-time collection includes, but is not limited to, data update frequency, communication method, and data processing speed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform anomaly detection and notification.

[0040] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can identify the most efficient collection timing from past data collection history. Based on past data collection history, the data collection unit can propose the optimal sensor placement. Furthermore, the data collection unit can analyze past data collection history and improve the accuracy of data collection. This improves the efficiency and accuracy of data collection by analyzing past data collection history and selecting the optimal data collection method. The optimal data collection method includes, but is not limited to, data importance, collection cost, and collection accuracy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can improve the accuracy of data collected from IoT sensors installed at each stage of the manufacturing line based on environmental conditions (temperature, humidity, etc.). For example, the data collection unit can determine the optimal collection timing based on temperature sensor data. The data collection unit can also correct data based on temperature sensor data. Furthermore, the data collection unit can also correct data based on humidity sensor data. In addition, the data collection unit can optimize the placement of sensors considering environmental conditions. This makes it possible to collect more accurate data by improving data accuracy based on environmental conditions. Environmental conditions include, but are not limited to, temperature, humidity, and atmospheric pressure. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental condition data into a generating AI and have the generating AI perform data accuracy improvement.

[0042] The data collection unit can integrate data from IoT sensors installed at each stage of a manufacturing line based on its interrelationships with other manufacturing lines. For example, the data collection unit can analyze interrelationships based on data from other manufacturing lines and then integrate the data. The data collection unit can collect and integrate data from multiple manufacturing lines in real time. Furthermore, the data collection unit can select the optimal data collection method by considering data from other manufacturing lines. This enables the optimization of the entire manufacturing process by integrating data based on interrelationships with other manufacturing lines. Interrelationships with other manufacturing lines include, but are not limited to, data correlations, impacts, and methods of collaboration. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from other manufacturing lines into a generating AI and have the generating AI perform the data integration.

[0043] The analysis unit can optimize the manufacturing process and improve the product design based on the collected data. For example, the analysis unit can optimize the product design based on the collected data. The analysis unit can improve the product design based on the manufacturing process data. Furthermore, the analysis unit can optimize both the product design and the manufacturing process simultaneously based on the collected data. This improves product quality and manufacturing efficiency by optimizing the manufacturing process and improving the product design. Product design improvements include, but are not limited to, the procedures for design changes, evaluation criteria, and improvement objectives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform product design improvements.

[0044] The analysis unit can perform simulations under different manufacturing conditions when optimizing the manufacturing process based on the collected data. For example, the analysis unit can simulate the manufacturing process under different temperature conditions. The analysis unit can simulate the manufacturing process under different humidity conditions. Furthermore, the analysis unit can simulate the manufacturing process under different material conditions. This improves the accuracy of manufacturing process optimization by performing simulations under different manufacturing conditions. Different manufacturing conditions include, but are not limited to, temperature conditions, material conditions, and process conditions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for different manufacturing conditions into a generating AI and have the generating AI perform the simulation.

[0045] The analysis unit can improve the accuracy of optimization by comparing the collected data with data from other production lines when optimizing the manufacturing process. For example, the analysis unit can identify the optimal manufacturing process by comparing it with data from other production lines. The analysis unit can improve the accuracy of optimization by integrating data from multiple production lines. Furthermore, the analysis unit can identify areas for improvement in the manufacturing process based on data from other production lines. This improves the overall efficiency of the manufacturing process by improving the accuracy of optimization by comparing it with data from other production lines. The data from other production lines includes, but is not limited to, data format, comparison criteria, and analysis methods. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other production lines into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0046] The analysis unit can perform analysis based on the entire product lifecycle when optimizing the manufacturing process based on the collected data. For example, the analysis unit can identify the optimal manufacturing process by considering the entire product lifecycle. The analysis unit can identify areas for improvement in the manufacturing process based on the product lifecycle data. Furthermore, the analysis unit can optimize the manufacturing process by considering the entire product lifecycle. This improves the accuracy of manufacturing process optimization by performing analysis based on the entire product lifecycle. The entire product lifecycle includes, but is not limited to, the design phase, manufacturing phase, use phase, and disposal phase. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product lifecycle data into a generating AI and have the generating AI perform the optimization of the manufacturing process.

[0047] The supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments as needed when controlling the manufacturing process based on the analysis results. For example, the supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments if an anomaly is detected. The supply unit can select the optimal control method based on the performance of each process on the manufacturing line. Furthermore, the supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments to maximize efficiency. In this way, the efficiency and accuracy of the manufacturing process are improved by monitoring the performance of each process on the manufacturing line in real time and making adjustments as needed. Real-time monitoring includes, but is not limited to, the frequency of monitoring, the sensors used, and the data processing method. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input the performance data of the manufacturing line into a generating AI and have the generating AI perform the adjustments.

[0048] The supply unit can control the manufacturing process based on the analysis results, and can perform optimal resource allocation to maximize the efficiency of each process in the manufacturing line. For example, the supply unit can perform optimal resource allocation based on the efficiency of each process in the manufacturing line. The supply unit can optimize resource allocation based on data from each process in the manufacturing line. Furthermore, the supply unit can adjust resource allocation to maximize the efficiency of each process in the manufacturing line. This improves the efficiency of the manufacturing process by performing optimal resource allocation to maximize the efficiency of each process in the manufacturing line. Optimal resource allocation includes, but is not limited to, resource types, allocation criteria, and allocation procedures. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input manufacturing line data into a generating AI and have the generating AI perform resource allocation optimization.

[0049] The supply unit can select the optimal control method based on coordination with other manufacturing lines when controlling the manufacturing process based on the analysis results. For example, the supply unit can select the optimal control method based on data from other manufacturing lines. The supply unit can integrate data from multiple manufacturing lines and select the optimal control method. Furthermore, the supply unit can select the optimal control method considering coordination with other manufacturing lines. This improves the overall efficiency of the manufacturing process by selecting the optimal control method based on coordination with other manufacturing lines. Coordination with other manufacturing lines includes, but is not limited to, methods of data sharing, coordination procedures, and the purpose of coordination. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data from other manufacturing lines into a generating AI and have the generating AI select the optimal control method.

[0050] The supply unit can improve overall efficiency by integrating data from each stage of the manufacturing line when controlling the manufacturing process based on the analysis results. For example, the supply unit can integrate data from each stage of the manufacturing line to improve overall efficiency. The supply unit can integrate data from multiple manufacturing lines to improve overall efficiency. Furthermore, the supply unit can perform controls to improve overall efficiency based on data from each stage of the manufacturing line. This makes it possible to optimize the entire manufacturing process by integrating data from each stage of the manufacturing line to improve overall efficiency. Data integration includes, but is not limited to, the data format, integration procedure, and purpose of integration. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input manufacturing line data into a generating AI and have the generating AI perform the overall efficiency improvement.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] Next-generation smart factory systems can also be equipped with an energy management unit. This unit can monitor energy consumption at each stage of the manufacturing line in real time and propose optimal energy usage methods. For example, it can collect energy consumption data from each stage and use AI to make adjustments to maximize energy efficiency. Furthermore, it can distribute the load during peak energy consumption periods, reducing energy costs. In addition, it can optimize the use of renewable energy, reducing environmental impact. Thus, by incorporating an energy management unit, the overall energy efficiency of the manufacturing process can be improved, leading to cost reductions and environmental protection.

[0053] Next-generation smart factory systems can also be equipped with a safety management unit. This unit can monitor safety at each stage of the manufacturing line in real time and respond immediately when an anomaly occurs. For example, the safety management unit can collect data from sensors installed at each stage and use AI to evaluate safety. It can also monitor worker movements and issue warnings if dangerous actions are detected. Furthermore, the safety management unit can analyze past accident data and propose preventative measures. Thus, by incorporating a safety management unit, the safety of the manufacturing process can be improved and worker safety can be ensured.

[0054] Next-generation smart factory systems can also be equipped with an environmental monitoring unit. This unit can monitor environmental conditions (temperature, humidity, atmospheric pressure, etc.) at each stage of the manufacturing line in real time, maintaining an optimal manufacturing environment. For example, the environmental monitoring unit can collect data from sensors installed at each stage and evaluate environmental conditions using AI. Furthermore, it can immediately adjust the system if it detects abnormal environmental conditions. In addition, it can analyze past environmental data and propose optimal environmental conditions. Thus, incorporating an environmental monitoring unit can improve the quality and efficiency of the manufacturing process.

[0055] Next-generation smart factory systems can also include a logistics management department. This department can monitor the flow of materials and products used in the manufacturing line in real time and propose optimal logistics plans. For example, it can collect inventory data on materials used in each process and use AI to suggest the best inventory management methods. Furthermore, it can optimize product shipping schedules and shorten delivery times. It can also propose optimal transportation routes to reduce logistics costs. In short, by incorporating a logistics management department, the overall efficiency of the manufacturing process can be improved, leading to cost reductions and shorter delivery times.

[0056] Next-generation smart factory systems can also be equipped with a user feedback unit. This unit can collect product usage data and user feedback to improve the product. For example, it can collect product usage data and use AI to evaluate product performance. It can also analyze user feedback to identify areas for product improvement. Furthermore, it can understand user needs and use this information to develop new products. In short, incorporating a user feedback unit can improve product quality and user satisfaction.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects data from the manufacturing line. The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line, for example. The data collection unit can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. For example, the data collection unit can collect temperature data from the manufacturing line using a temperature sensor. The data collection unit can also collect humidity data from the manufacturing line using a humidity sensor. Furthermore, the data collection unit can collect vibration data from the manufacturing line using a vibration sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes the optimal manufacturing method. The analysis unit can, for example, use AI to analyze the collected data and propose the optimal manufacturing method. The analysis unit can propose the optimal manufacturing method based on past data and market trends. For example, the analysis unit can use AI to analyze past data and propose the optimal manufacturing method. The analysis unit can also use AI to analyze market trends and propose the optimal manufacturing method. Furthermore, the analysis unit can use AI to optimize the manufacturing process based on the collected data. Step 3: The supply unit controls the manufacturing process based on the manufacturing method proposed by the analysis unit. The supply unit can, for example, control the manufacturing process using AI. The supply unit can combine 3D printers and robots for manufacturing. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to field workers using XR technology. For example, the supply unit can provide visual instructions to field workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and propose optimal maintenance timing. For example, the supply unit can predict failures of machinery and equipment using digital twins and propose optimal maintenance timing.

[0059] (Example of form 2) The next-generation smart factory system according to an embodiment of the present invention is a system that innovates all processes in the manufacturing industry by integrating 3D printers, 3D scanners, AI, digital twins, XR, robots, and IoT. This system solves the challenges faced by conventional manufacturing by digitizing all processes from product design to manufacturing, quality control, improvement, and maintenance, and optimizing them with AI. For example, the next-generation smart factory system uses AI and digital twins to optimize the design. The AI ​​analyzes past design data and market trends and proposes the optimal design. By performing virtual testing using the digital twin, the design time can be significantly reduced. For example, in the design of automobile parts, the AI ​​proposes the optimal shape and material, and the strength and durability are simulated using the digital twin. Next, the next-generation smart factory system achieves flexible manufacturing using 3D printers and robots. The AI ​​selects the optimal manufacturing method and performs high-speed and flexible manufacturing by combining 3D printers and robots. This makes it possible to handle small-lot, high-mix production. For example, electronic device cases are manufactured with a 3D printer and assembled by robots. Furthermore, the next-generation smart factory system performs real-time quality control using IoT and AI. IoT sensors are installed at each stage of the manufacturing line, and AI monitors quality in real time. Anomalies are detected immediately, and adjustments or shutdowns are performed automatically. For example, in the manufacturing of medical devices, sensors measure the dimensions and surface condition of parts, and AI determines the quality. Furthermore, next-generation smart factory systems utilize XR to provide remote work support. The knowledge of skilled technicians is systematized by AI, and XR technology is used to provide visual instructions to on-site workers. This realizes knowledge transfer and improves work efficiency. For example, in aircraft maintenance work, workers wearing XR goggles perform tasks according to AI instructions. Finally, next-generation smart factory systems perform predictive maintenance using AI and digital twins. Data from product use is collected via IoT, and AI analyzes it. The digital twin predicts future failures and proposes the optimal maintenance timing. For example, in factory machinery, sensors measure vibration and temperature, and AI detects signs of failure.In this way, next-generation smart factory systems digitize all manufacturing processes and optimize them using AI, thereby shortening development and manufacturing times, reducing costs, and improving quality and safety. Furthermore, by building flexible manufacturing systems that can quickly respond to market changes and individual needs, they can significantly enhance the competitiveness of the manufacturing industry. Thus, next-generation smart factory systems can optimize all manufacturing processes, enabling efficient and flexible manufacturing.

[0060] The next-generation smart factory system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data from the manufacturing line. The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line, for example. The data collection unit can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. For example, the data collection unit can collect temperature data from the manufacturing line using a temperature sensor. The data collection unit can also collect humidity data from the manufacturing line using a humidity sensor. Furthermore, the data collection unit can also collect vibration data from the manufacturing line using a vibration sensor. The analysis unit analyzes the data collected by the data collection unit and proposes the optimal manufacturing method. The analysis unit can analyze the collected data using AI, for example, and propose the optimal manufacturing method. The analysis unit can propose the optimal manufacturing method based on past data and market trends. For example, the analysis unit can analyze past data using AI and propose the optimal manufacturing method. Furthermore, the analysis unit can analyze market trends using AI and propose the optimal manufacturing method. Furthermore, the analysis unit can optimize the manufacturing process based on the collected data using AI. The supply unit controls the manufacturing process based on the manufacturing method proposed by the analysis unit. The supply unit can, for example, control the manufacturing process using AI. The supply unit can perform manufacturing by combining 3D printers and robots. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to field workers using XR technology. For example, the supply unit can provide visual instructions to field workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and propose optimal maintenance timing. For example, the supply unit can predict failures of machinery and equipment using digital twins and propose optimal maintenance timing.As a result, the next-generation smart factory system according to this embodiment can optimize manufacturing processes by collecting and analyzing data from the manufacturing line, proposing the optimal manufacturing method, and controlling the manufacturing process.

[0061] The data collection unit collects data from the manufacturing line. For example, the data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line. Specifically, it can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. Temperature sensors monitor temperature fluctuations in detail at each stage of the manufacturing line and detect abnormal temperature increases or decreases. Humidity sensors monitor the humidity of the manufacturing environment and detect humidity fluctuations that may affect product quality. Vibration sensors monitor the operating status of machinery and are used to detect abnormal vibrations or machine failures early. The data collected from these sensors is transmitted in real time to a central database, allowing for unified management of the entire manufacturing line's status. Furthermore, the data collection unit can integrate the data from each sensor and use anomaly detection algorithms to detect unusual patterns or abnormal data. For example, by combining and analyzing data from temperature and vibration sensors, it is possible to detect machine overheating or abnormal vibrations early, enabling a rapid response. The data collection unit can also adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This allows the data collection unit to efficiently and effectively collect data, thereby improving the overall performance of the manufacturing line.

[0062] The analysis unit analyzes data collected by the data collection unit and proposes the optimal manufacturing method. For example, the analysis unit can use AI to analyze collected data and propose the optimal manufacturing method. Specifically, the AI ​​uses machine learning algorithms to analyze past and current data and optimize the manufacturing process. For example, based on past manufacturing data, the AI ​​can propose the optimal temperature and humidity settings for a specific product. The AI ​​can also analyze market trends and develop manufacturing plans that respond to fluctuations in demand. For example, the AI ​​can analyze past sales data and market trends to adjust production volume to coincide with periods of high demand. Furthermore, the analysis unit can use AI to detect anomalies in the manufacturing process and perform preventive maintenance based on the collected data. For example, the AI ​​can analyze vibration sensor data to detect abnormal machine vibrations early and identify signs of failure. This allows the analysis unit to not only optimize the manufacturing process but also handle anomaly detection and preventive maintenance, improving the reliability and efficiency of the manufacturing line. Additionally, the analysis unit can use data visualization tools to monitor the status of the manufacturing line in real time and provide managers with information that is easy to understand intuitively. This allows the analysis unit to support the optimization and management of manufacturing processes, thereby enhancing the competitiveness of the manufacturing industry.

[0063] The supply department controls the manufacturing process based on the manufacturing method proposed by the analysis department. The supply department can, for example, control the manufacturing process using AI. Specifically, the supply department can combine 3D printers and robots for manufacturing. The 3D printer can manufacture the product's shape and structure with high precision based on the optimal manufacturing method proposed by the analysis department. Robots are used to assemble the parts manufactured by the 3D printer, improving the product's overall quality. For example, the supply department can manufacture complex-shaped parts using a 3D printer and then accurately assemble those parts using robots. Furthermore, the supply department can provide visual instructions to on-site workers using XR technology. By using XR goggles, on-site workers can visually confirm each step of the manufacturing process as they work. This improves work efficiency and accuracy, and enhances product quality. In addition, the supply department can use digital twins to predict future failures and propose optimal maintenance timing. The digital twin creates a virtual model of the physical manufacturing line and simulates data in real time, allowing for detailed monitoring of the machine equipment's condition. This allows the supply department to predict machine failures and perform maintenance at the appropriate time. For example, the supply department can use a digital twin to analyze machine vibration data, detect early signs of failure, and propose planned maintenance. This enables the supply department to improve the efficiency and reliability of the manufacturing process and enhance the competitiveness of the manufacturing industry.

[0064] The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line. For example, the data collection unit can collect data from temperature sensors installed at each stage of the manufacturing line. The data collection unit can collect temperature data from the manufacturing line using temperature sensors. The data collection unit can also collect data from humidity sensors installed at each stage of the manufacturing line. The data collection unit can collect humidity data from the manufacturing line using humidity sensors. Furthermore, the data collection unit can also collect data from vibration sensors installed at each stage of the manufacturing line. The data collection unit can collect vibration data from the manufacturing line using vibration sensors. This allows for real-time monitoring of the manufacturing process by collecting data from IoT sensors installed at each stage of the manufacturing line. IoT sensors include, but are not limited to, temperature sensors, humidity sensors, and vibration sensors. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform data analysis.

[0065] The analysis unit can optimize the manufacturing process based on the collected data. For example, the analysis unit can use AI to analyze the collected data and optimize the manufacturing process. The analysis unit can optimize the manufacturing process based on historical data and market trends. For example, the analysis unit can use AI to analyze historical data and optimize the manufacturing process. The analysis unit can also use AI to analyze market trends and optimize the manufacturing process. Furthermore, the analysis unit can use AI to optimize the manufacturing process based on the collected data. This improves manufacturing efficiency by optimizing the manufacturing process based on the collected data. The optimization of the manufacturing process includes, but is not limited to, the algorithms used, the purpose of optimization, and the evaluation criteria. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the optimization of the manufacturing process.

[0066] The supply unit can control the manufacturing process based on the analysis results. The supply unit can control the manufacturing process using, for example, AI. The supply unit can perform manufacturing by combining 3D printers and robots. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to on-site workers using XR technology. For example, the supply unit can provide visual instructions to on-site workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and suggest optimal maintenance timings. For example, the supply unit can predict failures of machinery and equipment using digital twins and suggest optimal maintenance timings. This improves the accuracy and efficiency of the manufacturing process by controlling it based on the analysis results. Control of the manufacturing process includes, but is not limited to, parameters to be controlled, control means, and control frequency. Some or all of the above-described processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input the analysis results into a generating AI and have the generating AI execute the control of the manufacturing process.

[0067] The supply unit can perform manufacturing by combining a 3D printer and a robot. For example, the supply unit can manufacture a product using a 3D printer and assemble it using a robot. For example, the supply unit can manufacture a case for an electronic device using a 3D printer. Furthermore, the supply unit can assemble a component for an electronic device using a robot. In addition, the supply unit can manufacture and assemble a component with a complex shape by combining a 3D printer and a robot. This enables flexible and efficient manufacturing by combining a 3D printer and a robot. The 3D printer includes, but is not limited to, the materials used, printing accuracy, and printing speed. The robot includes, but is not limited to, the functions, operating range, and control methods. Some or all of the above processes in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can have a generative AI perform the control of the 3D printer and the robot.

[0068] The service provider can provide visual instructions to field workers using XR technology. For example, the service provider can provide visual instructions to field workers using XR goggles. The service provider can use XR technology to provide visual instructions to workers, for example, in aircraft maintenance work. The service provider can also use XR technology to provide visual instructions to workers, for example, in maintenance work on factory machinery. Furthermore, the service provider can use XR technology to provide visual instructions to workers, for example, at construction sites. This improves work efficiency and accuracy by providing visual instructions to field workers using XR technology. XR technology includes, but is not limited to, AR (augmented reality), VR (virtual reality), and MR (mixed reality). Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the provision of instructions using XR goggles.

[0069] The service provider can use digital twins to predict future failures and propose optimal maintenance timing. For example, the service provider can use digital twins to predict failures in machinery and equipment and propose optimal maintenance timing. The service provider can use digital twins to measure vibrations and temperatures of factory machinery and equipment and detect signs of failure. The service provider can also use digital twins to monitor the usage of automobile parts and detect signs of failure. Furthermore, the service provider can use digital twins to monitor the condition of aircraft engines and detect signs of failure. This improves the efficiency and accuracy of maintenance by using digital twins to predict future failures and propose optimal maintenance timing. Digital twins include, but are not limited to, methods for creating digital models, methods for synchronizing data, and prediction algorithms. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input digital twin data into a generating AI and have the generating AI perform failure predictions.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. If the user is relaxed, the data collection unit can perform detailed data collection to improve accuracy. Also, if the user is in a hurry, the data collection unit can prioritize collecting only important data. In this way, by adjusting the timing of data collection based on the user's emotions, the burden on the user is reduced and the accuracy of data collection is improved. Specific methods for estimating the user's emotions include, but are not limited to, facial recognition, voice analysis, and biosensors. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0071] The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line in real time and immediately notify when an anomaly occurs. For example, if an IoT sensor detects a temperature anomaly, the data collection unit can immediately issue an alert. If an IoT sensor detects a vibration anomaly, the data collection unit can notify in real time and prompt corrective action. Furthermore, if an IoT sensor detects a pressure anomaly, the data collection unit can immediately stop the manufacturing line. This enables a rapid response by collecting data from IoT sensors installed at each stage of the manufacturing line in real time and immediately notifying when an anomaly occurs. Real-time collection includes, but is not limited to, data update frequency, communication method, and data processing speed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform anomaly detection and notification.

[0072] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can identify the most efficient collection timing from past data collection history. Based on past data collection history, the data collection unit can propose the optimal sensor placement. Furthermore, the data collection unit can analyze past data collection history and improve the accuracy of data collection. This improves the efficiency and accuracy of data collection by analyzing past data collection history and selecting the optimal data collection method. The optimal data collection method includes, but is not limited to, data importance, collection cost, and collection accuracy. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. Also, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This reduces the burden on the user and improves the accuracy of data collection by prioritizing data collection based on the user's emotions. Specific criteria for determining data priority include, but are not limited to, data importance, urgency, and user needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the data priority determination.

[0074] The data collection unit can improve the accuracy of data collected from IoT sensors installed at each stage of the manufacturing line based on environmental conditions (temperature, humidity, etc.). For example, the data collection unit can determine the optimal collection timing based on temperature sensor data. The data collection unit can also correct data based on temperature sensor data. Furthermore, the data collection unit can also correct data based on humidity sensor data. In addition, the data collection unit can optimize the placement of sensors considering environmental conditions. This makes it possible to collect more accurate data by improving data accuracy based on environmental conditions. Environmental conditions include, but are not limited to, temperature, humidity, and atmospheric pressure. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental condition data into a generating AI and have the generating AI perform data accuracy improvement.

[0075] The data collection unit can integrate data from IoT sensors installed at each stage of a manufacturing line based on its interrelationships with other manufacturing lines. For example, the data collection unit can analyze interrelationships based on data from other manufacturing lines and then integrate the data. The data collection unit can collect and integrate data from multiple manufacturing lines in real time. Furthermore, the data collection unit can select the optimal data collection method by considering data from other manufacturing lines. This enables the optimization of the entire manufacturing process by integrating data based on interrelationships with other manufacturing lines. Interrelationships with other manufacturing lines include, but are not limited to, data correlations, impacts, and methods of collaboration. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from other manufacturing lines into a generating AI and have the generating AI perform the data integration.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis results based on the user's emotions, the analysis unit can provide results that are easy for the user to understand. Presentation methods for analysis results include, but are not limited to, graph displays, text displays, and interactive displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis results.

[0077] The analysis unit can optimize the manufacturing process and improve the product design based on the collected data. For example, the analysis unit can optimize the product design based on the collected data. The analysis unit can improve the product design based on the manufacturing process data. Furthermore, the analysis unit can optimize both the product design and the manufacturing process simultaneously based on the collected data. This improves product quality and manufacturing efficiency by optimizing the manufacturing process and improving the product design. Product design improvements include, but are not limited to, the procedures for design changes, evaluation criteria, and improvement objectives. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform product design improvements.

[0078] The analysis unit can perform simulations under different manufacturing conditions when optimizing the manufacturing process based on the collected data. For example, the analysis unit can simulate the manufacturing process under different temperature conditions. The analysis unit can simulate the manufacturing process under different humidity conditions. Furthermore, the analysis unit can simulate the manufacturing process under different material conditions. This improves the accuracy of manufacturing process optimization by performing simulations under different manufacturing conditions. Different manufacturing conditions include, but are not limited to, temperature conditions, material conditions, and process conditions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for different manufacturing conditions into a generating AI and have the generating AI perform the simulation.

[0079] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying only the most important analysis results. If the user is relaxed, the analysis unit can display detailed analysis results. If the user is in a hurry, the analysis unit can prioritize analysis results that can be quickly reviewed. This allows the system to prioritize information that is important to the user by prioritizing the analysis results based on their emotions. Specific criteria for prioritizing analysis results include, but are not limited to, the importance, urgency, and user needs of the results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis results.

[0080] The analysis unit can improve the accuracy of optimization by comparing the collected data with data from other production lines when optimizing the manufacturing process. For example, the analysis unit can identify the optimal manufacturing process by comparing it with data from other production lines. The analysis unit can improve the accuracy of optimization by integrating data from multiple production lines. Furthermore, the analysis unit can identify areas for improvement in the manufacturing process based on data from other production lines. This improves the overall efficiency of the manufacturing process by improving the accuracy of optimization by comparing it with data from other production lines. The data from other production lines includes, but is not limited to, data format, comparison criteria, and analysis methods. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from other production lines into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0081] The analysis unit can perform analysis based on the entire product lifecycle when optimizing the manufacturing process based on the collected data. For example, the analysis unit can identify the optimal manufacturing process by considering the entire product lifecycle. The analysis unit can identify areas for improvement in the manufacturing process based on the product lifecycle data. Furthermore, the analysis unit can optimize the manufacturing process by considering the entire product lifecycle. This improves the accuracy of manufacturing process optimization by performing analysis based on the entire product lifecycle. The entire product lifecycle includes, but is not limited to, the design phase, manufacturing phase, use phase, and disposal phase. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product lifecycle data into a generating AI and have the generating AI perform the optimization of the manufacturing process.

[0082] The service provider can estimate the user's emotions and adjust the control method of the manufacturing process based on the estimated user emotions. For example, if the user is tense, the service provider can provide a simple and highly visible control method. If the user is relaxed, the service provider can provide a detailed control method. Furthermore, if the user is in a hurry, the service provider can provide a control method that can be quickly confirmed. In this way, by adjusting the control method of the manufacturing process based on the user's emotions, the optimal control method for the user can be provided. The control method of the manufacturing process includes, but is not limited to, control parameters, control means, and control frequency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the control method of the manufacturing process.

[0083] The supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments as needed when controlling the manufacturing process based on the analysis results. For example, the supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments if an anomaly is detected. The supply unit can select the optimal control method based on the performance of each process on the manufacturing line. Furthermore, the supply unit can monitor the performance of each process on the manufacturing line in real time and make adjustments to maximize efficiency. In this way, the efficiency and accuracy of the manufacturing process are improved by monitoring the performance of each process on the manufacturing line in real time and making adjustments as needed. Real-time monitoring includes, but is not limited to, the frequency of monitoring, the sensors used, and the data processing method. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input the performance data of the manufacturing line into a generating AI and have the generating AI perform the adjustments.

[0084] The supply unit can control the manufacturing process based on the analysis results, and can perform optimal resource allocation to maximize the efficiency of each process in the manufacturing line. For example, the supply unit can perform optimal resource allocation based on the efficiency of each process in the manufacturing line. The supply unit can optimize resource allocation based on data from each process in the manufacturing line. Furthermore, the supply unit can adjust resource allocation to maximize the efficiency of each process in the manufacturing line. This improves the efficiency of the manufacturing process by performing optimal resource allocation to maximize the efficiency of each process in the manufacturing line. Optimal resource allocation includes, but is not limited to, resource types, allocation criteria, and allocation procedures. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input manufacturing line data into a generating AI and have the generating AI perform resource allocation optimization.

[0085] The service provider can estimate the user's emotions and determine the priority of manufacturing process controls based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize only critical controls. If the user is relaxed, the service provider can perform detailed controls. If the user is in a hurry, the service provider can prioritize controls that can be quickly verified. This allows the service provider to prioritize controls that are important to the user by determining the priority of manufacturing process controls based on the user's emotions. Specific criteria for determining control priorities include, but are not limited to, the importance and urgency of the controls and the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the control priorities.

[0086] The supply unit can select the optimal control method based on coordination with other manufacturing lines when controlling the manufacturing process based on the analysis results. For example, the supply unit can select the optimal control method based on data from other manufacturing lines. The supply unit can integrate data from multiple manufacturing lines and select the optimal control method. Furthermore, the supply unit can select the optimal control method considering coordination with other manufacturing lines. This improves the overall efficiency of the manufacturing process by selecting the optimal control method based on coordination with other manufacturing lines. Coordination with other manufacturing lines includes, but is not limited to, methods of data sharing, coordination procedures, and the purpose of coordination. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input data from other manufacturing lines into a generating AI and have the generating AI select the optimal control method.

[0087] The supply unit can improve overall efficiency by integrating data from each stage of the manufacturing line when controlling the manufacturing process based on the analysis results. For example, the supply unit can integrate data from each stage of the manufacturing line to improve overall efficiency. The supply unit can integrate data from multiple manufacturing lines to improve overall efficiency. Furthermore, the supply unit can perform controls to improve overall efficiency based on data from each stage of the manufacturing line. This makes it possible to optimize the entire manufacturing process by integrating data from each stage of the manufacturing line to improve overall efficiency. Data integration includes, but is not limited to, the data format, integration procedure, and purpose of integration. Some or all of the above processing in the supply unit may be performed using, for example, AI, or not using AI. For example, the supply unit can input manufacturing line data into a generating AI and have the generating AI perform the overall efficiency improvement.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] Next-generation smart factory systems can also be equipped with an energy management unit. This unit can monitor energy consumption at each stage of the manufacturing line in real time and propose optimal energy usage methods. For example, it can collect energy consumption data from each stage and use AI to make adjustments to maximize energy efficiency. Furthermore, it can distribute the load during peak energy consumption periods, reducing energy costs. In addition, it can optimize the use of renewable energy, reducing environmental impact. Thus, by incorporating an energy management unit, the overall energy efficiency of the manufacturing process can be improved, leading to cost reductions and environmental protection.

[0090] Next-generation smart factory systems can also be equipped with a safety management unit. This unit can monitor safety at each stage of the manufacturing line in real time and respond immediately when an anomaly occurs. For example, the safety management unit can collect data from sensors installed at each stage and use AI to evaluate safety. It can also monitor worker movements and issue warnings if dangerous actions are detected. Furthermore, the safety management unit can analyze past accident data and propose preventative measures. Thus, by incorporating a safety management unit, the safety of the manufacturing process can be improved and worker safety can be ensured.

[0091] Next-generation smart factory systems can also be equipped with an environmental monitoring unit. This unit can monitor environmental conditions (temperature, humidity, atmospheric pressure, etc.) at each stage of the manufacturing line in real time, maintaining an optimal manufacturing environment. For example, the environmental monitoring unit can collect data from sensors installed at each stage and evaluate environmental conditions using AI. Furthermore, it can immediately adjust the system if it detects abnormal environmental conditions. In addition, it can analyze past environmental data and propose optimal environmental conditions. Thus, incorporating an environmental monitoring unit can improve the quality and efficiency of the manufacturing process.

[0092] Next-generation smart factory systems can also include a logistics management department. This department can monitor the flow of materials and products used in the manufacturing line in real time and propose optimal logistics plans. For example, it can collect inventory data on materials used in each process and use AI to suggest the best inventory management methods. Furthermore, it can optimize product shipping schedules and shorten delivery times. It can also propose optimal transportation routes to reduce logistics costs. In short, by incorporating a logistics management department, the overall efficiency of the manufacturing process can be improved, leading to cost reductions and shorter delivery times.

[0093] Next-generation smart factory systems can also be equipped with a user feedback unit. This unit can collect product usage data and user feedback to improve the product. For example, it can collect product usage data and use AI to evaluate product performance. It can also analyze user feedback to identify areas for product improvement. Furthermore, it can understand user needs and use this information to develop new products. In short, incorporating a user feedback unit can improve product quality and user satisfaction.

[0094] Next-generation smart factory systems can estimate user emotions and adjust manufacturing processes based on those emotions. For example, if a user is stressed, the speed of the manufacturing process can be adjusted to reduce their burden. If a user is relaxed, the manufacturing process can be optimized to improve efficiency. Furthermore, if a user is in a hurry, critical processes can be prioritized. By adjusting the manufacturing process based on user emotions, the burden on users can be reduced and the efficiency of the manufacturing process can be improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] Next-generation smart factory systems can estimate user emotions and customize products based on those emotions. For example, if a user is happy, the product design and functions can be customized to improve user satisfaction. If a user is dissatisfied, areas for product improvement can be identified and addressed quickly. Furthermore, if a user is excited, new features and designs can be suggested to meet user expectations. In this way, customizing products based on user emotions can improve user satisfaction. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] Next-generation smart factory systems can estimate user emotions and adjust maintenance timing based on those emotions. For example, if a user is stressed, the frequency of maintenance can be reduced to lessen their burden. Conversely, if a user is relaxed, more detailed maintenance can be performed to extend the equipment's lifespan. Furthermore, if a user is in a hurry, only critical maintenance can be prioritized. By adjusting maintenance timing based on user emotions, this reduces user burden and improves equipment efficiency. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] Next-generation smart factory systems can estimate user emotions and adjust training programs based on those emotions. For example, if a user is nervous, a simple and easy-to-understand training program can be provided. If a user is relaxed, a detailed training program can be provided to improve their skills. Furthermore, if a user is in a hurry, a training program that focuses on key points can be provided. By adjusting training programs based on user emotions, the learning efficiency of users can be improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] Next-generation smart factory systems can estimate user emotions and adjust feedback in the manufacturing process based on those emotions. For example, if a user is stressed, simple, positive feedback can be provided. If a user is relaxed, detailed feedback can be provided to clearly identify areas for improvement. Furthermore, if a user is in a hurry, concise feedback can be provided. By adjusting feedback in the manufacturing process based on user emotions, it is possible to improve user understanding and motivation for improvement. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects data from the manufacturing line. The data collection unit can collect data from IoT sensors installed at each stage of the manufacturing line, for example. The data collection unit can collect data from each stage of the manufacturing line in real time using temperature sensors, humidity sensors, vibration sensors, etc. For example, the data collection unit can collect temperature data from the manufacturing line using a temperature sensor. The data collection unit can also collect humidity data from the manufacturing line using a humidity sensor. Furthermore, the data collection unit can collect vibration data from the manufacturing line using a vibration sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes the optimal manufacturing method. The analysis unit can, for example, use AI to analyze the collected data and propose the optimal manufacturing method. The analysis unit can propose the optimal manufacturing method based on past data and market trends. For example, the analysis unit can use AI to analyze past data and propose the optimal manufacturing method. The analysis unit can also use AI to analyze market trends and propose the optimal manufacturing method. Furthermore, the analysis unit can use AI to optimize the manufacturing process based on the collected data. Step 3: The supply unit controls the manufacturing process based on the manufacturing method proposed by the analysis unit. The supply unit can, for example, control the manufacturing process using AI. The supply unit can combine 3D printers and robots for manufacturing. For example, the supply unit can manufacture products using 3D printers and assemble them using robots. The supply unit can also provide visual instructions to field workers using XR technology. For example, the supply unit can provide visual instructions to field workers using XR goggles. Furthermore, the supply unit can predict future failures using digital twins and propose optimal maintenance timing. For example, the supply unit can predict failures of machinery and equipment using digital twins and propose optimal maintenance timing.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect data from the manufacturing line using the IoT sensors of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and proposes the optimal manufacturing method. The data provision unit is implemented in the control unit 46A of the smart device 14, which controls the manufacturing process using a 3D printer or robot. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect data from the manufacturing line using the IoT sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and proposes the optimal manufacturing method. The data provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, which controls the manufacturing process using a 3D printer or robot. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect data from the manufacturing line using the IoT sensors of the headset terminal 314. The analysis unit is implemented in the following way: for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and proposes the optimal manufacturing method. The data provision unit is implemented in the following way: for example, by the control unit 46A of the headset terminal 314, which controls the manufacturing process using a 3D printer or robot. The correspondence between each unit and the devices or control units is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect data from the manufacturing line using the IoT sensors of the robot 414. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using AI and proposes the optimal manufacturing method. The data provision unit is implemented in, for example, the control unit 46A of the robot 414, which controls the manufacturing process using a 3D printer or robot. The correspondence between each unit and the equipment or control unit is not limited to the examples described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A data collection unit that collects data from the manufacturing line, An analysis unit analyzes the data collected by the aforementioned collection unit and proposes the optimal manufacturing method, The system includes a supply unit that controls the manufacturing process based on the manufacturing method proposed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from IoT sensors installed at each stage of the manufacturing line. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The manufacturing process is optimized based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Control the manufacturing process based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Manufacturing is carried out by combining 3D printers and robots. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Using XR technology to provide visual instructions to field workers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Using a digital twin, we predict future failures and propose the optimal maintenance timing. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system collects data in real time from IoT sensors installed at each stage of the manufacturing line and immediately notifies when an anomaly occurs. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze past data collection history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data from IoT sensors installed at each stage of the manufacturing line, improve the accuracy of the data based on environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data from IoT sensors installed at each stage of a manufacturing line, the data is integrated based on its interrelationship with other manufacturing lines. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Based on the collected data, we not only optimize the manufacturing process but also improve the product design. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Based on the collected data, simulations are performed under different manufacturing conditions to optimize the manufacturing process. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Based on the collected data, when optimizing the manufacturing process, the accuracy of the optimization is improved by comparing it with data from other manufacturing lines. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When optimizing the manufacturing process based on the collected data, analysis is performed based on the entire product lifecycle. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates user emotions and adjusts the control methods of the manufacturing process based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When controlling the manufacturing process based on the analysis results, the performance of each process on the manufacturing line is monitored in real time, and adjustments are made as needed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When controlling the manufacturing process based on the analysis results, the optimal resource allocation is performed to maximize the efficiency of each process in the manufacturing line. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates user emotions and determines the priority of manufacturing process control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When controlling the manufacturing process based on the analysis results, the optimal control method is selected based on coordination with other manufacturing lines. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When controlling the manufacturing process based on analysis results, data from each stage of the manufacturing line is integrated to improve overall efficiency. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from the manufacturing line, An analysis unit analyzes the data collected by the aforementioned collection unit and proposes the optimal manufacturing method, The system includes a supply unit that controls the manufacturing process based on the manufacturing method proposed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from IoT sensors installed at each stage of the manufacturing line. The system according to feature 1.

3. The aforementioned analysis unit, The manufacturing process is optimized based on the collected data. The system according to feature 1.

4. The aforementioned supply unit is, Control the manufacturing process based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Manufacturing is carried out by combining 3D printers and robots. The system according to feature 1.

6. The aforementioned supply unit is, Using XR technology to provide visual instructions to field workers. The system according to feature 1.

7. The aforementioned supply unit is, Using a digital twin, we predict future failures and propose the optimal maintenance timing. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

Images