System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring real-time data from sensors located in a manufacturing facility; means for filtering, de-noising, and scaling the acquired data; artificial intelligence algorithm means for using the pre-processed data as input to optimize the manufacturing process and detect anomalies; means for controlling robotic equipment based on the output of the artificial intelligence algorithm; and means for monitoring the operation of the entire system in real-time and generating and displaying alerts when anomalies are detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditional manufacturing processes are often inefficient, costly, and difficult to meet today's stringent production standards. In particular, current systems often fall short in achieving the goals of automating complex tasks, improving precision, and reducing costs. Against this backdrop, the challenges facing the manufacturing industry are as follows:

[0005] 1. The difficulty of automating complex manufacturing tasks.

[0006] 2. Maintaining a high-precision manufacturing process.

[0007] 3. Reduced manufacturing costs.

[0008] 4. Real-time anomaly detection and rapid response.

[0009] 5. Integrated data management and analysis.

[0010] A new system is needed to efficiently solve these problems. [Means for solving the problem]

[0011] In order to solve the above problems, the present invention provides the following means.

[0012] 1. Providing a means to obtain real-time data from sensors placed in manufacturing equipment, thereby providing an accurate picture of the current state of the manufacturing process.

[0013] 2. Use methods to filter, denoise, and scale the acquired data, improving its quality and enabling AI algorithms to operate with precision.

[0014] 3. The pre-processed data is used as input to optimize the manufacturing process and utilize artificial intelligence algorithms to detect anomalies, thereby optimizing the manufacturing process and ensuring high-precision and efficient production.

[0015] 4. Providing a means to control robotic equipment based on the output of artificial intelligence algorithms, thereby automating physical manufacturing tasks and improving precision.

[0016] 5. It has a means to monitor the operation of the entire system in real time and generate and display a warning if an abnormality is detected, which enables a prompt response when an abnormality occurs.

[0017] In this way, the present invention makes it possible to improve the efficiency of the manufacturing process, increase the accuracy, reduce costs, and detect abnormalities in real time and respond quickly.

[0018] "Manufacturing facilities" includes all machinery and equipment used in the production of products.

[0019] A "sensor" is a device that detects physical phenomena (position information, force acceleration, temperature, vibration, etc.) in real time.

[0020] "Real-time data" refers to data that is acquired and processed immediately based on current events or conditions.

[0021] "Filtering" is the process of removing unnecessary parts and outliers from acquired data.

[0022] "Noise removal" is a process that removes irregular fluctuations and unnecessary information contained in data to improve the quality of the data.

[0023] "Scaling" is the process of converting data measured in different units or scales into a unified standard.

[0024] "Preprocessing" refers to a series of steps that convert the acquired raw data into a format suitable for AI algorithms.

[0025] "Artificial intelligence algorithms" refer to programs and computational methods for analyzing data and automatically performing specific tasks.

[0026] "Manufacturing process optimization" refers to the adjustments and improvements made to manufacturing procedures to make them run efficiently and effectively.

[0027] "Anomaly detection" refers to finding abnormal behavior or conditions that deviate from normal patterns.

[0028] A "robotic device" is an automated mechanical device designed to perform a specific task or operation.

[0029] A "control command" is an instruction or command to a robotic device or other device to perform a specific operation.

[0030] "Operational monitoring" is the process of monitoring the performance and status of the entire system in real time to detect abnormalities and problems.

[0031] A "warning" is a message or signal that notifies the user when an abnormality is detected and prompts the user to take appropriate action. [Brief explanation of the drawings]

[0032] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0033] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0034] First, the terms used in the following description will be explained.

[0035] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0036] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0037] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0038] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0039] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0040] [First embodiment]

[0041] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0042] 1, a 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.

[0043] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0044] 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0045] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0046] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0047] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0048] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0049] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0050] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0051] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0052] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0053] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0054] Overall system configuration

[0055] The system consists of the following main components:

[0056] 1. Sensor

[0057] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0058] 2. Server

[0059] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0060] 3. Robotic Devices

[0061] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0062] 4. Terminal

[0063] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0064] Program processing

[0065] The system program is executed through the following process.

[0066] Data Acquisition

[0067] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0068] Data Preprocessing

[0069] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0070] Running AI algorithms

[0071] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0072] Robotic device control

[0073] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0074] Real-time monitoring and feedback

[0075] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0076] Specific examples

[0077] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires and processes real-time data from each sensor. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. A terminal displays the status of the assembly line to the user in real time and immediately displays a warning if an abnormality is detected. The user can operate the system and correct any abnormalities through the terminal.

[0078] In this way, the system of the present invention realizes efficiency, high precision, and cost reduction in the manufacturing process, and enables real-time detection of abnormalities and rapid response.

[0079] The processing flow will be explained below.

[0080] Step 1: Data Acquisition

[0081] The server acquires real-time data from various sensors installed in the manufacturing equipment, including position, force acceleration, temperature, vibration, etc. For example, the server collects data from the sensors 10 times per second.

[0082] Step 2: Data filtering

[0083] The server receives the acquired data, detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0084] Step 3: Noise reduction

[0085] The server applies a filtering algorithm to remove noise from the data, for example using a moving average filter to smooth the data and remove anomalous peaks.

[0086] Step 4: Data Scaling

[0087] The server converts data measured in different units to a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range.

[0088] Step 5: Data entry

[0089] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0090] Step 6: Optimize your manufacturing process

[0091] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order, to improve efficiency.

[0092] Step 7: Anomaly detection

[0093] The server uses AI algorithms to detect anomalies in real time, and if an anomaly is detected, the server analyzes the details to determine the type and cause of the anomaly.

[0094] Step 8: Generate control commands

[0095] Based on the results obtained from the AI ​​algorithm, the server generates specific control commands for the robotic equipment, such as instructing a robotic arm to rearrange parts.

[0096] Step 9: Sending control commands

[0097] The server sends the generated control commands to the robotics system to actually execute the operations, so that the robot arm accurately performs the specified operations.

[0098] Step 10: System Monitoring

[0099] The terminal monitors the operation of the entire system in real time and displays the data visually, allowing users to check the status and progress of each station.

[0100] Step 11: Notification of abnormalities

[0101] The server generates instant notifications when an anomaly is detected and alerts the user via the terminal, for example, in the event of improper component placement or unexpected vibrations.

[0102] Step 12: Manual operation

[0103] Users can manually operate the system through a terminal, and when an abnormality occurs, they can investigate the abnormality in detail and make any necessary corrections.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In conventional manufacturing processes, it is difficult to acquire data in real time, detect anomalies, and optimize processes, which has led to a demand for more efficient and accurate manufacturing lines. Rapid response when anomalies occur is also important, but a consistent system to achieve this is lacking. Furthermore, raw data acquired from multiple sensors is often insufficiently preprocessed, making it susceptible to noise and outliers. To address these challenges, a system that integrates all of these elements is required.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes means for acquiring real-time data from sensors installed in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for using the preprocessed data as input to optimize the manufacturing process and detect abnormalities. This makes it possible to improve the efficiency and accuracy of the manufacturing process. Furthermore, the operation of the entire system can be monitored in real time, and if an abnormality is detected, a warning is generated and displayed, enabling a prompt response.

[0109] A "sensor" is a device that is placed in manufacturing equipment and measures physical quantities such as position, force, temperature, and vibration in real time.

[0110] "Real-time data" is data that is continuously acquired from sensors and immediately processed and analyzed.

[0111] "Filtering" is a process of removing unnecessary information and abnormal values ​​from acquired data.

[0112] "Denoising" is the process of removing random fluctuations and errors in the data to clean the signal.

[0113] "Scaling" is the process of converting numerical values ​​of data into consistent units and ranges.

[0114] "Preprocessing" refers to a series of processes in which the acquired raw data is organized using techniques such as filtering, noise removal, and scaling, and converted into a format suitable for the algorithm.

[0115] An "artificial intelligence algorithm" is a computer program designed to analyze input data and perform a specific task, often using machine learning models.

[0116] A "robot device" is a device that receives control commands from a server and performs automated operations. Examples include industrial robot arms.

[0117] "Real-time monitoring" is the process of constantly monitoring the operating status of the entire system and immediately understanding the situation.

[0118] A "warning" is an alert that the system notifies the user when an abnormality is detected.

[0119] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0120] Overall system configuration

[0121] The system consists of the following main components:

[0122] 1. Sensor

[0123] Acquire data in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0124] 2. Server

[0125] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls automated equipment using software such as Python and TensorFlow.

[0126] 3. Robotic Devices

[0127] Automation equipment such as a robot arm that receives control commands from a server and executes the specified operations. For example, general equipment from industrial robot manufacturers is used.

[0128] 4. Terminal

[0129] It displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. Specifically, it uses a SCADA (Supervisory Control and Data Acquisition) system.

[0130] Program processing

[0131] Data Acquisition

[0132] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0133] Data Preprocessing

[0134] The server receives the raw data and converts each data set into a format suitable for AI algorithms, specifically by filtering, denoising, and scaling the data.

[0135] Running AI algorithms

[0136] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as using TensorFlow to run high-precision optimization of part alignment and anomaly detection algorithms to detect abnormal vibration patterns.

[0137] Robotic device control

[0138] The server controls the robotic device based on the output of the AI ​​algorithm, for example by sending a control command to the robotic device to move a part to a specific position.

[0139] Real-time monitoring and feedback

[0140] The terminal monitors the system's operation in real time and provides visual feedback to the user, and if an anomaly is detected, the server generates immediate feedback and displays a warning to the user via the terminal.

[0141] Specific examples

[0142] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0143] Examples of prompt statements

[0144] Below are some example prompts that explain the system's behavior:

[0145] In an automotive parts assembly line, acquire real-time data from each sensor (position, force, temperature, vibration), preprocess the data by filtering, denoising, and scaling it, then use TensorFlow to optimize the manufacturing process and detect anomalies, control a robot arm to accurately place parts, and finally use a SCADA system to display the real-time situation on a terminal and immediately issue a warning if an anomaly is detected.

[0146] By inputting this prompt into the generative AI model, specific implementation methods and operational details can be obtained.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] System program processing flow

[0149] Step 1:

[0150] The server collects data in real time from various sensors installed in the manufacturing equipment. As input, it receives position data from position sensors, pressure data from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. As output, the raw data is stored in the server's database. Specifically, the server analyzes the signals sent from the sensors, extracts the necessary data, and stores it.

[0151] Step 2:

[0152] The server preprocesses the acquired raw data. As input, it takes the raw data acquired in step 1 and filters, denoises, and scales it. Data filtering removes unwanted data and outliers. Denoising eliminates random fluctuations and errors in the data. Scaling converts the data into consistent units and ranges. The final output is preprocessed, clean data. Specifically, the server records data frames and processes them by applying filtering algorithms and denoising filters.

[0153] Step 3:

[0154] The server uses the preprocessed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies. The clean data obtained in step 2 is used as input and analyzed using a machine learning framework such as TensorFlow. The output is the optimal manufacturing process procedure and the results of anomaly detection. Specifically, the server loads the trained AI model, feeds in the data for analysis, and obtains the results.

[0155] Step 4:

[0156] The server controls the robotic device based on the output of the AI ​​algorithm. As input, it uses the optimal procedure and anomaly detection results obtained in step 3. As output, a control command is generated to be sent to the robotic device. Specifically, the server sends a control command to the robotic device to move a part to a specific position. This causes the robotic device to perform the instructed operation.

[0157] Step 5:

[0158] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. As input, it receives the system's operating status and the results of anomaly detection sent from the server. As output, it displays the real-time operating status and warning messages on the monitoring screen. Specifically, the terminal uses the SCADA system to monitor the status of the production line and issues warnings to the user as necessary.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] In manufacturing processes, the effective use of real-time data obtained from sensors for optimization and anomaly detection requires the application of advanced data preprocessing and artificial intelligence algorithms. Furthermore, rapid response is required after an anomaly is detected, but systems to achieve this are lacking. Furthermore, real-time situation assessment and countermeasure proposals via user devices are also essential.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes means for acquiring real-time data from sensors arranged in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for implementing an artificial intelligence algorithm that uses the pre-processed data as an input to optimize the manufacturing process and detect anomalies, thereby enabling optimization of the manufacturing process and detection of anomalies.

[0164] The server also includes a means for controlling the robotic devices based on the output of the artificial intelligence algorithm, a means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected, a means for displaying real-time data via a mobile device, and a means for displaying an alert when an abnormality is detected and providing information on the specific location of the abnormality and countermeasures. This allows users to grasp the status of the manufacturing process in real time and take prompt action after an abnormality is detected.

[0165] "Manufacturing equipment" means the machinery and equipment used to manufacture products.

[0166] A "sensor" is a device that senses physical conditions or changes and outputs that information as data.

[0167] "Real-time data" is data obtained instantaneously from an ongoing process and is immediately available for use.

[0168] "Filtering" is the process of removing unnecessary information and noise from data.

[0169] "Noise removal" is a process that removes unnecessary fluctuations and errors contained in data.

[0170] "Scaling" is the process of converting data into a certain range or format.

[0171] An "artificial intelligence algorithm" is a computational procedure that analyzes large amounts of data, finds patterns, and uses the results to make predictions and optimizations.

[0172] A "robotic device" is a mechanical device that is controlled by a program and performs specific tasks automatically.

[0173] "Real-time monitoring" is the act of constantly and instantly understanding the status of a system or process.

[0174] A "warning" is an alert that notifies you that an abnormality or problem has occurred.

[0175] A "mobile device" is an electronic device that can be carried and used by a user.

[0176] An "alert" is a visual or audio message that notifies you of an emergency or abnormality.

[0177] This invention provides a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, this system controls robotic equipment based on the output of the AI ​​algorithm and monitors the operation of the entire system in real time. Another feature is that it provides real-time feedback to users via mobile devices.

[0178] Hardware and software used

[0179] The system consists of the following main components:

[0180] 1. Sensor

[0181] Various sensors installed in manufacturing equipment, such as position sensors, force sensors, temperature sensors, and vibration sensors.

[0182] 2. Server

[0183] It receives data from sensors, pre-processes it and runs artificial intelligence algorithms on it.

[0184] The software used is TensorFlow, PyTorch (for running AI algorithms), and Flask (web server and API).

[0185] 3. Robotic Devices

[0186] Automated equipment such as a robot arm that receives control commands from a server and performs specified operations.

[0187] 4. Mobile devices

[0188] Real-time feedback on the situation is provided to the user via a smartphone or head-mounted display (HMD), and an alert is displayed in the event of an abnormality.

[0189] Data Preprocessing

[0190] The server acquires real-time data from sensors located at manufacturing facilities and pre-processes it by filtering, denoising, and scaling it, converting it into a format that can be properly analyzed by artificial intelligence algorithms.

[0191] Running artificial intelligence algorithms

[0192] Based on the pre-processed data, the server runs artificial intelligence algorithms that optimize and detect anomalies in the manufacturing process. By analyzing large amounts of data using machine learning models, the original manufacturing process can be maintained with high accuracy.

[0193] Robotic device control

[0194] Based on the output of the artificial intelligence algorithm, the server sends control commands to the robotic equipment, such as actions to adjust the position of parts or reduce vibrations, thereby optimizing the manufacturing process and quickly correcting any anomalies that occur.

[0195] Real-time monitoring and feedback

[0196] Using mobile devices, the system monitors the operation of the entire system in real time and provides feedback to the user. If an abnormality is detected, an alert will be displayed, with specific information on the abnormality and recommended countermeasures.

[0197] Specific examples

[0198] For example, if a temperature sensor detects an abnormally high temperature, the server preprocesses the data and inputs it into an artificial intelligence algorithm. The algorithm analyzes the abnormality and generates a command to activate the cooling system. The server then sends this command to the robotic device, which automatically activates the cooling system. At the same time, the user's smartphone is notified of the abnormality and displays the specific location and recommended countermeasures in real time.

[0199] Example prompts to input to a generative AI model:

[0200] "Please analyze today's temperature sensor data and check for any abnormalities."

[0201] "Detect abnormal patterns from vibration data and suggest solutions."

[0202] The above is a specific embodiment of the present invention. By using this system, optimization of the manufacturing process and detection of abnormalities can be performed in real time, enabling highly accurate and rapid response.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The server receives data in real time from sensors placed in the manufacturing equipment. The input is data from the sensors (e.g., temperature, vibration, force), and processes it as is. The output is the collected raw data.

[0206] Step 2:

[0207] The server pre-processes the acquired data. It filters, denoises, and scales the data. The input is the raw data acquired in the previous step, and it processes the data to convert it into an appropriate format. The output is the pre-processed data.

[0208] Step 3:

[0209] The server inputs the preprocessed data into an artificial intelligence algorithm, which uses a machine learning model (e.g., TensorFlow or PyTorch) to optimize the manufacturing process and detect anomalies. The input is the preprocessed data, which the AI ​​uses to perform data analysis and predictions. The output is the analysis result of the AI ​​algorithm.

[0210] Step 4:

[0211] The server controls the robotic equipment based on the output of the artificial intelligence algorithm. It generates control commands based on the analysis results and sends them to the robotic equipment. The input is the analysis results of the AI ​​algorithm, and the control commands are generated based on this. The output is the actual robot operation (e.g., adjusting the position of parts, activating the cooling system).

[0212] Step 5:

[0213] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. If an abnormality is detected, it generates and displays an alert. The input is data from the server (normal or abnormal information), and based on this, it displays and alerts. The output is real-time information and alerts displayed on the user's screen.

[0214] Step 6:

[0215] Users use their mobile devices to receive real-time data and feedback on abnormalities. If an abnormality is detected, they are given specific information about the location and recommended countermeasures. The input is real-time information and alerts from the device, and based on this, the abnormality is confirmed and dealt with. The output is the user's confirmation and corresponding action (e.g., on-site confirmation, system restart).

[0216] The above are the specific processing steps for carrying out the invention. This system enables efficient manufacturing processes, highly accurate management, and rapid response to abnormalities.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[0219] Overall system configuration

[0220] The system consists of the following main components:

[0221] 1. Sensor

[0222] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0223] 2. Server

[0224] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0225] 3. Robotic Devices

[0226] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0227] 4. Terminal

[0228] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0229] 5. Emotion Engine

[0230] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds back the user's emotional information to the system, dynamically adjusting system operation.

[0231] Program processing

[0232] The system program is executed through the following process.

[0233] Data Acquisition

[0234] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0235] Data Preprocessing

[0236] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0237] Running AI algorithms

[0238] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0239] Robotic device control

[0240] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0241] Real-time monitoring and feedback

[0242] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0243] emotion recognition

[0244] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0245] Emotion-Based Adjustment

[0246] The server dynamically adjusts system operations based on the user's emotions. For example, if the user is feeling stressed, the server may simplify the operation interface.

[0247] Specific examples

[0248] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0249] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[0250] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[0251] The processing flow will be explained below.

[0252] Step 1: Data Acquisition

[0253] The server acquires real-time data from multiple sensors installed in the manufacturing facility, including location information, force acceleration, temperature, vibration, etc. The server collects data from the sensors at a frequency of, for example, 10 times per second.

[0254] Step 2: Data filtering

[0255] The server receives the acquired raw data and detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0256] Step 3: Noise reduction

[0257] The server applies filtering algorithms to remove noise from the data, for example, smoothing the data using a moving average filter to remove anomalous peaks, improving the quality of the data and increasing the accuracy of the AI ​​algorithms.

[0258] Step 4: Data Scaling

[0259] The server converts data measured in different units into a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range, allowing different data sources to be treated uniformly.

[0260] Step 5: Data entry

[0261] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0262] Step 6: Optimize your manufacturing process

[0263] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order to improve efficiency. Based on the optimization results, the server updates the specific production plan.

[0264] Step 7: Anomaly detection

[0265] The server uses AI algorithms to detect anomalies in real time. If an anomaly is detected, the server analyzes the details to identify the type and cause of the anomaly. For example, if improper component placement or unexpected vibration is detected, an alert is generated.

[0266] Step 8: Generate control commands

[0267] Based on the results of the AI ​​algorithm, the server generates specific control commands for the robotic device, such as instructing a robot arm to rearrange a part. These control commands are carefully configured to ensure precision.

[0268] Step 9: Sending control commands

[0269] The server then sends the generated control commands to the robotics system, which then executes the actual operations, ensuring that the robot arm accurately performs the specified movements and optimizes the manufacturing process.

[0270] Step 10: System Monitoring

[0271] The terminal monitors the operation of the entire system in real time and displays the data visually. Users can check the status and progress of each station. For example, the dashboard screen displays the current operating rate and any abnormalities detected.

[0272] Step 11: Notification of abnormalities

[0273] The server generates a notification immediately if an abnormality is detected and warns the user through the terminal, for example, if there is improper component placement or unexpected vibration, so that the user can take corrective action immediately.

[0274] Step 12: Manual operation

[0275] Users can manually operate the system through a terminal. When an abnormality occurs, users can investigate the abnormality in detail and make the necessary corrections. For example, they can manually rearrange parts or restart the system.

[0276] Step 13: Emotion Recognition

[0277] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0278] Step 14: Emotional Adjustment

[0279] The server dynamically adjusts system operation based on the user's recognized emotions, for example, simplifying the operation interface and providing gentle audio feedback if the user is feeling stressed.

[0280] Example 2

[0281] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0282] In recent manufacturing processes, real-time data acquisition and appropriate processing, as well as the optimization of production lines and anomaly detection based on that data, have become increasingly important. Conventional systems have struggled to smoothly perform the entire process from data acquisition to processing, control, and monitoring, and also lacked user operability and emotional response. This has led to issues such as reduced manufacturing efficiency and delayed response to anomalies.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0284] In this invention, the server includes: means for acquiring real-time data from sensors installed in the manufacturing equipment; means for filtering, removing noise, and scaling the acquired data; means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input; means for controlling robotic devices based on the output of the artificial intelligence algorithm; means for monitoring the operation of the entire system in real time and generating and displaying an alert if an anomaly is detected; means for recognizing emotions by analyzing the user's voice, facial expressions, and movements; and means for dynamically adjusting system operation based on the recognized user emotions. This enables real-time data acquisition and appropriate processing, enabling rapid optimization of the manufacturing process and rapid anomaly detection, as well as the provision of a flexible operating environment that takes user emotions into consideration.

[0285] A "sensor" is a device that is installed in manufacturing equipment and acquires data by detecting physical quantities (e.g., position, force, temperature, vibration, etc.).

[0286] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0287] "Filtering" is a process of removing unnecessary components and errors from raw data.

[0288] "Denoising" is the process of removing unnecessary fluctuations and noise from data.

[0289] "Scaling" is the process of converting data of different units or ranges onto a consistent scale.

[0290] "Preprocessing" refers to a series of operations that convert the acquired raw data into a format suitable for subsequent data analysis and algorithmic processing.

[0291] An "artificial intelligence algorithm" is a computational method that allows machines to perform intelligent operations, such as data analysis, prediction, and optimization.

[0292] "Robotic equipment" is a general term for mechanical devices that operate automatically according to programmed instructions, and specifically includes robotic arms.

[0293] "Real-time monitoring" refers to continuous, immediate monitoring of the status of a system or process.

[0294] An "alert" is a message or signal that notifies the user when an abnormality occurs in the system.

[0295] "User" refers to a person who operates or monitors the system.

[0296] "Voice analysis" is the process of analyzing voice data to extract specific information (e.g., emotions, commands, etc.).

[0297] "Facial expression analysis" is the process of analyzing images of a user's face captured by a camera or other device and reading emotions from their facial expressions.

[0298] "Motion analysis" is the process of analyzing a user's physical movements to determine their meaning and intention.

[0299] "Emotion recognition" is a technology that analyzes a user's voice, facial expressions, and movements to determine their emotional state.

[0300] "Dynamic adjustment" refers to the automatic change of system behavior or settings in response to circumstances and conditions.

[0301] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[0302] Overall system configuration

[0303] The system consists of the following main components:

[0304] 1. Sensor

[0305] Real-time data is acquired from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. These sensors are essential for monitoring product quality and manufacturing processes with high precision.

[0306] 2. Server

[0307] The server receives data acquired from the sensors in real time and is responsible for preprocessing the data, running AI algorithms, and controlling the robotic equipment. Specific software examples include Python for data processing, TensorFlow and PyTorch for AI algorithms, and ROS (Robot Operating System) for controlling the robotic equipment.

[0308] 3. Robotic Devices

[0309] Robotic devices include automated equipment such as robotic arms that receive control commands from a server and execute specified operations, enabling highly accurate and efficient work.

[0310] 4. Terminal

[0311] The terminal displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. The terminal is equipped with a graphical user interface (GUI) to improve operability.

[0312] 5. Emotion Engine

[0313] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds the user's emotional information back to the system, dynamically adjusting system operation. For example, if the user is feeling stressed, the system may simplify the operation screen.

[0314] Specific examples

[0315] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0316] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[0317] Prompt Sentence Examples

[0318] Below are some example prompts to explain the behavior of this system to a generative AI model:

[0319] "Describe a system that preprocesses data from sensors in manufacturing equipment and uses AI algorithms to optimize the manufacturing process and detect anomalies. For example, explain how data is obtained from sensors to control robotic equipment on an automotive parts assembly line. Also, describe the ability to recognize user emotions and adjust system operation based on those emotions."

[0320] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1: Data Acquisition

[0323] The server acquires real-time data from various sensors installed in the manufacturing equipment. Specifically, it receives coordinate data from position sensors, force magnitude from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. Sensor data is acquired as input, and raw sensor data is output.

[0324] Step 2: Data Preprocessing

[0325] The server preprocesses the acquired raw data. Specifically, it filters the data to remove noise and performs scaling. For example, it smooths temperature data and filters out high-frequency components in vibration data. Raw sensor data is generated as input, and preprocessed data is generated as output.

[0326] Step 3: Run the AI ​​algorithm

[0327] The server uses the preprocessed data to run artificial intelligence algorithms that optimize manufacturing processes and detect anomalies. Specifically, the data is input into a machine learning model (e.g., a deep learning model) to detect abnormal patterns and calculate efficient manufacturing procedures. The preprocessed data is the input, and the analysis results of the algorithm are the output.

[0328] Step 4: Controlling the robotic device

[0329] The server controls the robotic equipment based on the output of the AI ​​algorithm. For example, it sends a control command to a robot arm to place a part in a specific position. Specifically, this includes coordinate data and movement speed. The algorithm's analysis results are input, and the control command is generated as output.

[0330] Step 5: Real-time monitoring and feedback

[0331] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. Specifically, it displays the status of each part of the production line and graphs of acquired data, and immediately issues an alarm and warning message if an abnormality is detected. Sensor data and control results are input, and dashboard displays and warnings are output.

[0332] Step 6: Emotion Recognition

[0333] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions. For example, it may determine that the user is feeling stressed using camera footage and microphone data. Voice and video data are input, and recognized emotion data is generated as output.

[0334] Step 7: Emotional Adjustment

[0335] The server dynamically adjusts system operations based on the recognized user emotions. For example, if the user is feeling stressed, it can simplify the operation interface. Emotional data is input and an adjusted operation interface is generated as output.

[0336] (Application example 2)

[0337] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] In the manufacturing industry, the importance of improving the efficiency of manufacturing processes and detecting anomalies is increasing. However, existing systems have difficulty optimizing manufacturing processes in real time and quickly detecting anomalies. Providing a flexible operating environment that takes user emotions into consideration is also a challenge. In particular, there is room for improvement in the efficient display of information and instruction delivery at the workplace.

[0339] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring real-time data from sensors, means for filtering, denoising, and scaling the acquired data, means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input, means for controlling robotic equipment based on the output of the artificial intelligence algorithm, means for recognizing user emotions, means for dynamically adjusting system operation based on the recognized emotions, and means for displaying on-site information and issuing instructions using a smart device. This enables the efficiency of the manufacturing process, highly accurate anomaly detection, and the provision of a user-friendly operating environment.

[0340] A "sensor" is a device that is installed in manufacturing equipment and measures physical parameters such as temperature, vibration, position, and force in real time and acquires the data.

[0341] "Filtering" is a process of removing unnecessary information and noise from data obtained from sensors and extracting only the necessary information.

[0342] "Noise removal" is the process of removing errors and unnecessary scattered data contained in data obtained from a sensor.

[0343] "Scaling" is a method of converting data into a uniform scale to make it easier for AI algorithms to process.

[0344] "Preprocessing" refers to a series of processes that convert the acquired raw data into a format suitable for AI algorithms, including filtering, noise removal, and scaling.

[0345] An "artificial intelligence algorithm" is a specific computational method for analyzing data and detecting patterns, including machine learning models.

[0346] "Robotic devices" are automated machines that assist in the manufacturing process and operate according to control commands from a server.

[0347] "Means for recognizing user emotions" refers to technology that analyzes the user's voice, facial expressions, movements, etc., to identify their emotional state.

[0348] "Means for dynamically adjusting system operation" refers to technology that appropriately changes the system's operation interface and functions according to the user's emotions and situation.

[0349] A "smart device" is an advanced electronic device that has the functions of displaying information, acquiring data, and issuing instructions, and is operated directly by the user.

[0350] "Real-time monitoring" is a technology that allows for immediate monitoring of system operation and status, and for immediate response when an abnormality occurs.

[0351] "Means for generating and displaying warnings" refers to technology that monitors the entire system and notifies the user with a visual or audio warning if an abnormality is detected.

[0352] This system acquires real-time data from sensors installed in manufacturing facilities, preprocesses it, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. It also has the ability to control robotic devices based on the output of the AI ​​algorithm and monitor the operation of the entire system in real time. Another feature of this system is that it also includes the ability to recognize user emotions and dynamically adjust system operation based on the recognized emotions.

[0353] Overall system configuration

[0354] The system consists of the following main components:

[0355] 1. Sensor

[0356] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. Each sensor measures a physical parameter in real time and sends the data to a server.

[0357] 2. Server

[0358] The server receives real-time sensor data, filters it, removes noise, and scales it. It uses the pre-processed data as input to run artificial intelligence algorithms that optimize the manufacturing process and detect anomalies. The server also controls robotic devices based on the output of the AI ​​algorithms and has the means to recognize user emotions.

[0359] 3. Robotic Devices

[0360] This refers to automated equipment such as robotic arms that receive control commands from a server and execute the specified operations, thereby improving the efficiency and precision of the manufacturing process.

[0361] 4. Terminal

[0362] It visually displays the overall system operating status, provides real-time feedback to the user, and displays a warning to notify the user if an abnormality is detected.

[0363] 5. Smart Devices

[0364] This is an advanced electronic device for displaying on-site information and issuing instructions. For example, it uses smart glasses, allowing users to give instructions while viewing the situation at the work site in real time.

[0365] 6. Emotion Engine

[0366] The system analyzes the user's voice, facial expressions, and movements to recognize emotions. This recognition information is fed back to the system, and the system's operation is dynamically adjusted based on the user's emotions.

[0367] Specific examples

[0368] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. The server collects real-time data from each sensor and filters, denoises, and scales it. The preprocessed data is then input into an AI algorithm to calculate the optimal steps for the manufacturing process. Based on the output of the AI ​​algorithm, the server sends control commands to robotic equipment to ensure that parts are positioned accurately.

[0369] Workers wearing the smart glasses can check on-site information and immediately respond if an abnormality occurs. Furthermore, the emotion engine recognizes the user's emotions from their voice and facial expressions, and if the worker is feeling stressed, for example, the system will simplify the operation interface.

[0370] Prompt Sentence Examples

[0371] "Design an application that uses smart glasses to manage and monitor robotic systems in a factory in real time. Preprocess sensor data and use AI algorithms to optimize the manufacturing process and detect anomalies. Also, incorporate a function that recognizes user emotions via a camera and dynamically adjusts the operating interface."

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] The server acquires data in real time from sensors deployed in the manufacturing facility. Inputs include data from position sensors, force sensors, temperature sensors, and vibration sensors. These data are sent to the server, which receives it. The received data is then preprocessed, including filtering, noise removal, and scaling. The output of this stage is the preprocessed data.

[0375] Step 2:

[0376] The server inputs the preprocessed data into an artificial intelligence algorithm. The preprocessed data from step 1 is used as input. The server then runs an AI model based on this data to optimize the manufacturing process and detect anomalies. Specific operations include data analysis and pattern recognition. The output is an optimized manufacturing procedure and detected anomalies.

[0377] Step 3:

[0378] The server controls the robotic device based on the output from the AI ​​algorithm. The optimization procedure and anomaly information from step 2 are used as input. Based on this information, the server sends commands to the robotic device to perform the required operations. The output is a control command that enables the robotic device to operate accurately.

[0379] Step 4:

[0380] The terminal monitors the operating status of the entire system in real time and provides feedback to the user. Input includes status information and abnormality warnings from the server. The terminal uses this information to display visual feedback and warnings to the user. Specific operations include updating the monitoring screen and displaying warning messages. The output is a real-time status report and warnings to the user.

[0381] Step 5:

[0382] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements to recognize emotions. Inputs include the user's voice data and camera footage. The server analyzes this data to identify emotions. The output is the user's emotional state.

[0383] Step 6:

[0384] The server dynamically adjusts system operations based on the recognized emotions. The emotional state data from step 5 is used as input. The server uses this data to modify the user interface and simplify the operation procedures. Specific actions include changing the layout of the operation screen and displaying help messages. The output is the adjusted user interface and operation procedures.

[0385] Step 7:

[0386] The smart device displays on-site information and provides an interface for users to issue instructions. Inputs include control information from the server and on-site status data. The smart device displays this data and allows users to issue the necessary instructions. Specific operations include displaying information and providing a user interface. Outputs include instructions from the user and confirmation of the on-site status.

[0387] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0388] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0390] [Second embodiment]

[0391] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0392] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0393] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0394] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0395] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0397] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0398] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0399] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0402] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0403] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0404] Overall system configuration

[0405] The system consists of the following main components:

[0406] 1. Sensor

[0407] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0408] 2. Server

[0409] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0410] 3. Robotic Devices

[0411] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0412] 4. Terminal

[0413] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0414] Program processing

[0415] The system program is executed through the following process.

[0416] Data Acquisition

[0417] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0418] Data Preprocessing

[0419] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0420] Running AI algorithms

[0421] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0422] Robotic device control

[0423] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0424] Real-time monitoring and feedback

[0425] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0426] Specific examples

[0427] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires and processes real-time data from each sensor. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. A terminal displays the status of the assembly line to the user in real time and immediately displays a warning if an abnormality is detected. The user can operate the system and correct any abnormalities through the terminal.

[0428] In this way, the system of the present invention realizes efficiency, high precision, and cost reduction in the manufacturing process, and enables real-time detection of abnormalities and rapid response.

[0429] The processing flow will be explained below.

[0430] Step 1: Data Acquisition

[0431] The server acquires real-time data from various sensors installed in the manufacturing equipment, including position, force acceleration, temperature, vibration, etc. For example, the server collects data from the sensors 10 times per second.

[0432] Step 2: Data filtering

[0433] The server receives the acquired data, detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0434] Step 3: Noise reduction

[0435] The server applies a filtering algorithm to remove noise from the data, for example using a moving average filter to smooth the data and remove anomalous peaks.

[0436] Step 4: Data Scaling

[0437] The server converts data measured in different units to a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range.

[0438] Step 5: Data entry

[0439] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0440] Step 6: Optimize your manufacturing process

[0441] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order, to improve efficiency.

[0442] Step 7: Anomaly detection

[0443] The server uses AI algorithms to detect anomalies in real time, and if an anomaly is detected, the server analyzes the details to determine the type and cause of the anomaly.

[0444] Step 8: Generate control commands

[0445] Based on the results obtained from the AI ​​algorithm, the server generates specific control commands for the robotic equipment, such as instructing a robotic arm to rearrange parts.

[0446] Step 9: Sending control commands

[0447] The server sends the generated control commands to the robotics system to actually execute the operations, so that the robot arm accurately performs the specified operations.

[0448] Step 10: System Monitoring

[0449] The terminal monitors the operation of the entire system in real time and displays the data visually, allowing users to check the status and progress of each station.

[0450] Step 11: Notification of abnormalities

[0451] The server generates instant notifications when an anomaly is detected and alerts the user via the terminal, for example, in the event of improper component placement or unexpected vibrations.

[0452] Step 12: Manual operation

[0453] Users can manually operate the system through a terminal, and when an abnormality occurs, they can investigate the abnormality in detail and make any necessary corrections.

[0454] Example 1

[0455] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0456] In conventional manufacturing processes, it is difficult to acquire data in real time, detect anomalies, and optimize processes, which has led to a demand for more efficient and accurate manufacturing lines. Rapid response when anomalies occur is also important, but a consistent system to achieve this is lacking. Furthermore, raw data acquired from multiple sensors is often insufficiently preprocessed, making it susceptible to noise and outliers. To address these challenges, a system that integrates all of these elements is required.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0458] In this invention, the server includes means for acquiring real-time data from sensors installed in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for using the preprocessed data as input to optimize the manufacturing process and detect abnormalities. This makes it possible to improve the efficiency and accuracy of the manufacturing process. Furthermore, the operation of the entire system can be monitored in real time, and if an abnormality is detected, a warning is generated and displayed, enabling a prompt response.

[0459] A "sensor" is a device that is placed in manufacturing equipment and measures physical quantities such as position, force, temperature, and vibration in real time.

[0460] "Real-time data" is data that is continuously acquired from sensors and immediately processed and analyzed.

[0461] "Filtering" is a process of removing unnecessary information and abnormal values ​​from acquired data.

[0462] "Denoising" is the process of removing random fluctuations and errors in the data to clean the signal.

[0463] "Scaling" is the process of converting numerical values ​​of data into consistent units and ranges.

[0464] "Preprocessing" refers to a series of processes in which the acquired raw data is organized using techniques such as filtering, noise removal, and scaling, and converted into a format suitable for the algorithm.

[0465] An "artificial intelligence algorithm" is a computer program designed to analyze input data and perform a specific task, often using machine learning models.

[0466] A "robot device" is a device that receives control commands from a server and performs automated operations. Examples include industrial robot arms.

[0467] "Real-time monitoring" is the process of constantly monitoring the operating status of the entire system and immediately understanding the situation.

[0468] A "warning" is an alert that the system notifies the user when an abnormality is detected.

[0469] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0470] Overall system configuration

[0471] The system consists of the following main components:

[0472] 1. Sensor

[0473] Acquire data in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0474] 2. Server

[0475] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls automated equipment using software such as Python and TensorFlow.

[0476] 3. Robotic Devices

[0477] Automation equipment such as a robot arm that receives control commands from a server and executes the specified operations. For example, general equipment from industrial robot manufacturers is used.

[0478] 4. Terminal

[0479] It displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. Specifically, it uses a SCADA (Supervisory Control and Data Acquisition) system.

[0480] Program processing

[0481] Data Acquisition

[0482] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0483] Data Preprocessing

[0484] The server receives the raw data and converts each data set into a format suitable for AI algorithms, specifically by filtering, denoising, and scaling the data.

[0485] Running AI algorithms

[0486] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as using TensorFlow to run high-precision optimization of part alignment and anomaly detection algorithms to detect abnormal vibration patterns.

[0487] Robotic device control

[0488] The server controls the robotic device based on the output of the AI ​​algorithm, for example by sending a control command to the robotic device to move a part to a specific position.

[0489] Real-time monitoring and feedback

[0490] The terminal monitors the system's operation in real time and provides visual feedback to the user, and if an anomaly is detected, the server generates immediate feedback and displays a warning to the user via the terminal.

[0491] Specific examples

[0492] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0493] Examples of prompt statements

[0494] Below are some example prompts that explain the system's behavior:

[0495] In an automotive parts assembly line, acquire real-time data from each sensor (position, force, temperature, vibration), preprocess the data by filtering, denoising, and scaling it, then use TensorFlow to optimize the manufacturing process and detect anomalies, control a robot arm to accurately place parts, and finally use a SCADA system to display the real-time situation on a terminal and immediately issue a warning if an anomaly is detected.

[0496] By inputting this prompt into the generative AI model, specific implementation methods and operational details can be obtained.

[0497] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0498] System program processing flow

[0499] Step 1:

[0500] The server collects data in real time from various sensors installed in the manufacturing equipment. As input, it receives position data from position sensors, pressure data from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. As output, the raw data is stored in the server's database. Specifically, the server analyzes the signals sent from the sensors, extracts the necessary data, and stores it.

[0501] Step 2:

[0502] The server preprocesses the acquired raw data. As input, it takes the raw data acquired in step 1 and filters, denoises, and scales it. Data filtering removes unwanted data and outliers. Denoising eliminates random fluctuations and errors in the data. Scaling converts the data into consistent units and ranges. The final output is preprocessed, clean data. Specifically, the server records data frames and processes them by applying filtering algorithms and denoising filters.

[0503] Step 3:

[0504] The server uses the preprocessed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies. The clean data obtained in step 2 is used as input and analyzed using a machine learning framework such as TensorFlow. The output is the optimal manufacturing process procedure and the results of anomaly detection. Specifically, the server loads the trained AI model, feeds in the data for analysis, and obtains the results.

[0505] Step 4:

[0506] The server controls the robotic device based on the output of the AI ​​algorithm. As input, it uses the optimal procedure and anomaly detection results obtained in step 3. As output, a control command is generated to be sent to the robotic device. Specifically, the server sends a control command to the robotic device to move a part to a specific position. This causes the robotic device to perform the instructed operation.

[0507] Step 5:

[0508] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. As input, it receives the system's operating status and the results of anomaly detection sent from the server. As output, it displays the real-time operating status and warning messages on the monitoring screen. Specifically, the terminal uses the SCADA system to monitor the status of the production line and issues warnings to the user as necessary.

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] In manufacturing processes, the effective use of real-time data obtained from sensors for optimization and anomaly detection requires the application of advanced data preprocessing and artificial intelligence algorithms. Furthermore, rapid response is required after an anomaly is detected, but systems to achieve this are lacking. Furthermore, real-time situation assessment and countermeasure proposals via user devices are also essential.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0513] In this invention, the server includes means for acquiring real-time data from sensors arranged in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for implementing an artificial intelligence algorithm that uses the pre-processed data as an input to optimize the manufacturing process and detect anomalies, thereby enabling optimization of the manufacturing process and detection of anomalies.

[0514] The server also includes a means for controlling the robotic devices based on the output of the artificial intelligence algorithm, a means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected, a means for displaying real-time data via a mobile device, and a means for displaying an alert when an abnormality is detected and providing information on the specific location of the abnormality and countermeasures. This allows users to grasp the status of the manufacturing process in real time and take prompt action after an abnormality is detected.

[0515] "Manufacturing equipment" means the machinery and equipment used to manufacture products.

[0516] A "sensor" is a device that senses physical conditions or changes and outputs that information as data.

[0517] "Real-time data" is data obtained instantaneously from an ongoing process and is immediately available for use.

[0518] "Filtering" is the process of removing unnecessary information and noise from data.

[0519] "Noise removal" is a process that removes unnecessary fluctuations and errors contained in data.

[0520] "Scaling" is the process of converting data into a certain range or format.

[0521] An "artificial intelligence algorithm" is a computational procedure that analyzes large amounts of data, finds patterns, and uses the results to make predictions and optimizations.

[0522] A "robotic device" is a mechanical device that is controlled by a program and performs specific tasks automatically.

[0523] "Real-time monitoring" is the act of constantly and instantly understanding the status of a system or process.

[0524] A "warning" is an alert that notifies you that an abnormality or problem has occurred.

[0525] A "mobile device" is an electronic device that can be carried and used by a user.

[0526] An "alert" is a visual or audio message that notifies you of an emergency or abnormality.

[0527] This invention provides a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, this system controls robotic equipment based on the output of the AI ​​algorithm and monitors the operation of the entire system in real time. Another feature is that it provides real-time feedback to users via mobile devices.

[0528] Hardware and software used

[0529] The system consists of the following main components:

[0530] 1. Sensor

[0531] Various sensors installed in manufacturing equipment, such as position sensors, force sensors, temperature sensors, and vibration sensors.

[0532] 2. Server

[0533] It receives data from sensors, pre-processes it and runs artificial intelligence algorithms on it.

[0534] The software used is TensorFlow, PyTorch (for running AI algorithms), and Flask (web server and API).

[0535] 3. Robotic Devices

[0536] Automated equipment such as a robot arm that receives control commands from a server and performs specified operations.

[0537] 4. Mobile devices

[0538] Real-time feedback on the situation is provided to the user via a smartphone or head-mounted display (HMD), and an alert is displayed in the event of an abnormality.

[0539] Data Preprocessing

[0540] The server acquires real-time data from sensors located at manufacturing facilities and pre-processes it by filtering, denoising, and scaling it, converting it into a format that can be properly analyzed by artificial intelligence algorithms.

[0541] Running artificial intelligence algorithms

[0542] Based on the pre-processed data, the server runs artificial intelligence algorithms that optimize and detect anomalies in the manufacturing process. By analyzing large amounts of data using machine learning models, the original manufacturing process can be maintained with high accuracy.

[0543] Robotic device control

[0544] Based on the output of the artificial intelligence algorithm, the server sends control commands to the robotic equipment, such as actions to adjust the position of parts or reduce vibrations, thereby optimizing the manufacturing process and quickly correcting any anomalies that occur.

[0545] Real-time monitoring and feedback

[0546] Using mobile devices, the system monitors the operation of the entire system in real time and provides feedback to the user. If an abnormality is detected, an alert will be displayed, with specific information on the abnormality and recommended countermeasures.

[0547] Specific examples

[0548] For example, if a temperature sensor detects an abnormally high temperature, the server preprocesses the data and inputs it into an artificial intelligence algorithm. The algorithm analyzes the abnormality and generates a command to activate the cooling system. The server then sends this command to the robotic device, which automatically activates the cooling system. At the same time, the user's smartphone is notified of the abnormality and displays the specific location and recommended countermeasures in real time.

[0549] Example prompts to input to a generative AI model:

[0550] "Please analyze today's temperature sensor data and check for any abnormalities."

[0551] "Detect abnormal patterns from vibration data and suggest solutions."

[0552] The above is a specific embodiment of the present invention. By using this system, optimization of the manufacturing process and detection of abnormalities can be performed in real time, enabling highly accurate and rapid response.

[0553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0554] Step 1:

[0555] The server receives data in real time from sensors placed in the manufacturing equipment. The input is data from the sensors (e.g., temperature, vibration, force), and processes it as is. The output is the collected raw data.

[0556] Step 2:

[0557] The server pre-processes the acquired data. It filters, denoises, and scales the data. The input is the raw data acquired in the previous step, and it processes the data to convert it into an appropriate format. The output is the pre-processed data.

[0558] Step 3:

[0559] The server inputs the preprocessed data into an artificial intelligence algorithm, which uses a machine learning model (e.g., TensorFlow or PyTorch) to optimize the manufacturing process and detect anomalies. The input is the preprocessed data, which the AI ​​uses to perform data analysis and predictions. The output is the analysis result of the AI ​​algorithm.

[0560] Step 4:

[0561] The server controls the robotic equipment based on the output of the artificial intelligence algorithm. It generates control commands based on the analysis results and sends them to the robotic equipment. The input is the analysis results of the AI ​​algorithm, and the control commands are generated based on this. The output is the actual robot operation (e.g., adjusting the position of parts, activating the cooling system).

[0562] Step 5:

[0563] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. If an abnormality is detected, it generates and displays an alert. The input is data from the server (normal or abnormal information), and based on this, it displays and alerts. The output is real-time information and alerts displayed on the user's screen.

[0564] Step 6:

[0565] Users use their mobile devices to receive real-time data and feedback on abnormalities. If an abnormality is detected, they are given specific information about the location and recommended countermeasures. The input is real-time information and alerts from the device, and based on this, the abnormality is confirmed and dealt with. The output is the user's confirmation and corresponding action (e.g., on-site confirmation, system restart).

[0566] The above are the specific processing steps for carrying out the invention. This system enables efficient manufacturing processes, highly accurate management, and rapid response to abnormalities.

[0567] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0568] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[0569] Overall system configuration

[0570] The system consists of the following main components:

[0571] 1. Sensor

[0572] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0573] 2. Server

[0574] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0575] 3. Robotic Devices

[0576] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0577] 4. Terminal

[0578] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0579] 5. Emotion Engine

[0580] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds back the user's emotional information to the system, dynamically adjusting system operation.

[0581] Program processing

[0582] The system program is executed through the following process.

[0583] Data Acquisition

[0584] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0585] Data Preprocessing

[0586] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0587] Running AI algorithms

[0588] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0589] Robotic device control

[0590] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0591] Real-time monitoring and feedback

[0592] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0593] emotion recognition

[0594] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0595] Emotion-Based Adjustment

[0596] The server dynamically adjusts system operations based on the user's emotions. For example, if the user is feeling stressed, the server may simplify the operation interface.

[0597] Specific examples

[0598] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0599] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[0600] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[0601] The processing flow will be explained below.

[0602] Step 1: Data Acquisition

[0603] The server acquires real-time data from multiple sensors installed in the manufacturing facility, including location information, force acceleration, temperature, vibration, etc. The server collects data from the sensors at a frequency of, for example, 10 times per second.

[0604] Step 2: Data filtering

[0605] The server receives the acquired raw data and detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0606] Step 3: Noise reduction

[0607] The server applies filtering algorithms to remove noise from the data, for example, smoothing the data using a moving average filter to remove anomalous peaks, improving the quality of the data and increasing the accuracy of the AI ​​algorithms.

[0608] Step 4: Data Scaling

[0609] The server converts data measured in different units into a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range, allowing different data sources to be treated uniformly.

[0610] Step 5: Data entry

[0611] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0612] Step 6: Optimize your manufacturing process

[0613] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order to improve efficiency. Based on the optimization results, the server updates the specific production plan.

[0614] Step 7: Anomaly detection

[0615] The server uses AI algorithms to detect anomalies in real time. If an anomaly is detected, the server analyzes the details to identify the type and cause of the anomaly. For example, if improper component placement or unexpected vibration is detected, an alert is generated.

[0616] Step 8: Generate control commands

[0617] Based on the results of the AI ​​algorithm, the server generates specific control commands for the robotic device, such as instructing a robot arm to rearrange a part. These control commands are carefully configured to ensure precision.

[0618] Step 9: Sending control commands

[0619] The server then sends the generated control commands to the robotics system, which then executes the actual operations, ensuring that the robot arm accurately performs the specified movements and optimizes the manufacturing process.

[0620] Step 10: System Monitoring

[0621] The terminal monitors the operation of the entire system in real time and displays the data visually. Users can check the status and progress of each station. For example, the dashboard screen displays the current operating rate and any abnormalities detected.

[0622] Step 11: Notification of abnormalities

[0623] The server generates a notification immediately if an abnormality is detected and warns the user through the terminal, for example, if there is improper component placement or unexpected vibration, so that the user can take corrective action immediately.

[0624] Step 12: Manual operation

[0625] Users can manually operate the system through a terminal. When an abnormality occurs, users can investigate the abnormality in detail and make the necessary corrections. For example, they can manually rearrange parts or restart the system.

[0626] Step 13: Emotion Recognition

[0627] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0628] Step 14: Emotional Adjustment

[0629] The server dynamically adjusts system operation based on the user's recognized emotions, for example, simplifying the operation interface and providing gentle audio feedback if the user is feeling stressed.

[0630] Example 2

[0631] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] In recent manufacturing processes, real-time data acquisition and appropriate processing, as well as the optimization of production lines and anomaly detection based on that data, have become increasingly important. Conventional systems have struggled to smoothly perform the entire process from data acquisition to processing, control, and monitoring, and also lacked user operability and emotional response. This has led to issues such as reduced manufacturing efficiency and delayed response to anomalies.

[0633] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0634] In this invention, the server includes: means for acquiring real-time data from sensors installed in the manufacturing equipment; means for filtering, removing noise, and scaling the acquired data; means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input; means for controlling robotic devices based on the output of the artificial intelligence algorithm; means for monitoring the operation of the entire system in real time and generating and displaying an alert if an anomaly is detected; means for recognizing emotions by analyzing the user's voice, facial expressions, and movements; and means for dynamically adjusting system operation based on the recognized user emotions. This enables real-time data acquisition and appropriate processing, enabling rapid optimization of the manufacturing process and rapid anomaly detection, as well as the provision of a flexible operating environment that takes user emotions into consideration.

[0635] A "sensor" is a device that is installed in manufacturing equipment and acquires data by detecting physical quantities (e.g., position, force, temperature, vibration, etc.).

[0636] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0637] "Filtering" is a process of removing unnecessary components and errors from raw data.

[0638] "Denoising" is the process of removing unnecessary fluctuations and noise from data.

[0639] "Scaling" is the process of converting data of different units or ranges onto a consistent scale.

[0640] "Preprocessing" refers to a series of operations that convert the acquired raw data into a format suitable for subsequent data analysis and algorithmic processing.

[0641] An "artificial intelligence algorithm" is a computational method that allows machines to perform intelligent operations, such as data analysis, prediction, and optimization.

[0642] "Robotic equipment" is a general term for mechanical devices that operate automatically according to programmed instructions, and specifically includes robotic arms.

[0643] "Real-time monitoring" refers to continuous, immediate monitoring of the status of a system or process.

[0644] An "alert" is a message or signal that notifies the user when an abnormality occurs in the system.

[0645] "User" refers to a person who operates or monitors the system.

[0646] "Voice analysis" is the process of analyzing voice data to extract specific information (e.g., emotions, commands, etc.).

[0647] "Facial expression analysis" is the process of analyzing images of a user's face captured by a camera or other device and reading emotions from their facial expressions.

[0648] "Motion analysis" is the process of analyzing a user's physical movements to determine their meaning and intention.

[0649] "Emotion recognition" is a technology that analyzes a user's voice, facial expressions, and movements to determine their emotional state.

[0650] "Dynamic adjustment" refers to the automatic change of system behavior or settings in response to circumstances and conditions.

[0651] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[0652] Overall system configuration

[0653] The system consists of the following main components:

[0654] 1. Sensor

[0655] Real-time data is acquired from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. These sensors are essential for monitoring product quality and manufacturing processes with high precision.

[0656] 2. Server

[0657] The server receives data acquired from the sensors in real time and is responsible for preprocessing the data, running AI algorithms, and controlling the robotic equipment. Specific software examples include Python for data processing, TensorFlow and PyTorch for AI algorithms, and ROS (Robot Operating System) for controlling the robotic equipment.

[0658] 3. Robotic Devices

[0659] Robotic devices include automated equipment such as robotic arms that receive control commands from a server and execute specified operations, enabling highly accurate and efficient work.

[0660] 4. Terminal

[0661] The terminal displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. The terminal is equipped with a graphical user interface (GUI) to improve operability.

[0662] 5. Emotion Engine

[0663] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds the user's emotional information back to the system, dynamically adjusting system operation. For example, if the user is feeling stressed, the system may simplify the operation screen.

[0664] Specific examples

[0665] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0666] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[0667] Prompt Sentence Examples

[0668] Below are some example prompts to explain the behavior of this system to a generative AI model:

[0669] "Describe a system that preprocesses data from sensors in manufacturing equipment and uses AI algorithms to optimize the manufacturing process and detect anomalies. For example, explain how data is obtained from sensors to control robotic equipment on an automotive parts assembly line. Also, describe the ability to recognize user emotions and adjust system operation based on those emotions."

[0670] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[0671] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0672] Step 1: Data Acquisition

[0673] The server acquires real-time data from various sensors installed in the manufacturing equipment. Specifically, it receives coordinate data from position sensors, force magnitude from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. Sensor data is acquired as input, and raw sensor data is output.

[0674] Step 2: Data Preprocessing

[0675] The server preprocesses the acquired raw data. Specifically, it filters the data to remove noise and performs scaling. For example, it smooths temperature data and filters out high-frequency components in vibration data. Raw sensor data is generated as input, and preprocessed data is generated as output.

[0676] Step 3: Run the AI ​​algorithm

[0677] The server uses the preprocessed data to run artificial intelligence algorithms that optimize manufacturing processes and detect anomalies. Specifically, the data is input into a machine learning model (e.g., a deep learning model) to detect abnormal patterns and calculate efficient manufacturing procedures. The preprocessed data is the input, and the analysis results of the algorithm are the output.

[0678] Step 4: Controlling the robotic device

[0679] The server controls the robotic equipment based on the output of the AI ​​algorithm. For example, it sends a control command to a robot arm to place a part in a specific position. Specifically, this includes coordinate data and movement speed. The algorithm's analysis results are input, and the control command is generated as output.

[0680] Step 5: Real-time monitoring and feedback

[0681] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. Specifically, it displays the status of each part of the production line and graphs of acquired data, and immediately issues an alarm and warning message if an abnormality is detected. Sensor data and control results are input, and dashboard displays and warnings are output.

[0682] Step 6: Emotion Recognition

[0683] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions. For example, it may determine that the user is feeling stressed using camera footage and microphone data. Voice and video data are input, and recognized emotion data is generated as output.

[0684] Step 7: Emotional Adjustment

[0685] The server dynamically adjusts system operations based on the recognized user emotions. For example, if the user is feeling stressed, it can simplify the operation interface. Emotional data is input and an adjusted operation interface is generated as output.

[0686] (Application example 2)

[0687] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0688] In the manufacturing industry, the importance of improving the efficiency of manufacturing processes and detecting anomalies is increasing. However, existing systems have difficulty optimizing manufacturing processes in real time and quickly detecting anomalies. Providing a flexible operating environment that takes user emotions into consideration is also a challenge. In particular, there is room for improvement in the efficient display of information and instruction delivery at the workplace.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring real-time data from sensors, means for filtering, denoising, and scaling the acquired data, means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input, means for controlling robotic equipment based on the output of the artificial intelligence algorithm, means for recognizing user emotions, means for dynamically adjusting system operation based on the recognized emotions, and means for displaying on-site information and issuing instructions using a smart device. This enables the efficiency of the manufacturing process, highly accurate anomaly detection, and the provision of a user-friendly operating environment.

[0690] A "sensor" is a device that is installed in manufacturing equipment and measures physical parameters such as temperature, vibration, position, and force in real time and acquires the data.

[0691] "Filtering" is a process of removing unnecessary information and noise from data obtained from sensors and extracting only the necessary information.

[0692] "Noise removal" is the process of removing errors and unnecessary scattered data contained in data obtained from a sensor.

[0693] "Scaling" is a method of converting data into a uniform scale to make it easier for AI algorithms to process.

[0694] "Preprocessing" refers to a series of processes that convert the acquired raw data into a format suitable for AI algorithms, including filtering, noise removal, and scaling.

[0695] An "artificial intelligence algorithm" is a specific computational method for analyzing data and detecting patterns, including machine learning models.

[0696] "Robotic devices" are automated machines that assist in the manufacturing process and operate according to control commands from a server.

[0697] "Means for recognizing user emotions" refers to technology that analyzes the user's voice, facial expressions, movements, etc., to identify their emotional state.

[0698] "Means for dynamically adjusting system operation" refers to technology that appropriately changes the system's operation interface and functions according to the user's emotions and situation.

[0699] A "smart device" is an advanced electronic device that has the functions of displaying information, acquiring data, and issuing instructions, and is operated directly by the user.

[0700] "Real-time monitoring" is a technology that allows for immediate monitoring of system operation and status, and for immediate response when an abnormality occurs.

[0701] "Means for generating and displaying warnings" refers to technology that monitors the entire system and notifies the user with a visual or audio warning if an abnormality is detected.

[0702] This system acquires real-time data from sensors installed in manufacturing facilities, preprocesses it, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. It also has the ability to control robotic devices based on the output of the AI ​​algorithm and monitor the operation of the entire system in real time. Another feature of this system is that it also includes the ability to recognize user emotions and dynamically adjust system operation based on the recognized emotions.

[0703] Overall system configuration

[0704] The system consists of the following main components:

[0705] 1. Sensor

[0706] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. Each sensor measures a physical parameter in real time and sends the data to a server.

[0707] 2. Server

[0708] The server receives real-time sensor data, filters it, removes noise, and scales it. It uses the pre-processed data as input to run artificial intelligence algorithms that optimize the manufacturing process and detect anomalies. The server also controls robotic devices based on the output of the AI ​​algorithms and has the means to recognize user emotions.

[0709] 3. Robotic Devices

[0710] This refers to automated equipment such as robotic arms that receive control commands from a server and execute the specified operations, thereby improving the efficiency and precision of the manufacturing process.

[0711] 4. Terminal

[0712] It visually displays the overall system operating status, provides real-time feedback to the user, and displays a warning to notify the user if an abnormality is detected.

[0713] 5. Smart Devices

[0714] This is an advanced electronic device for displaying on-site information and issuing instructions. For example, it uses smart glasses, allowing users to give instructions while viewing the situation at the work site in real time.

[0715] 6. Emotion Engine

[0716] The system analyzes the user's voice, facial expressions, and movements to recognize emotions. This recognition information is fed back to the system, and the system's operation is dynamically adjusted based on the user's emotions.

[0717] Specific examples

[0718] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. The server collects real-time data from each sensor and filters, denoises, and scales it. The preprocessed data is then input into an AI algorithm to calculate the optimal steps for the manufacturing process. Based on the output of the AI ​​algorithm, the server sends control commands to robotic equipment to ensure that parts are positioned accurately.

[0719] Workers wearing the smart glasses can check on-site information and immediately respond if an abnormality occurs. Furthermore, the emotion engine recognizes the user's emotions from their voice and facial expressions, and if the worker is feeling stressed, for example, the system will simplify the operation interface.

[0720] Prompt Sentence Examples

[0721] "Design an application that uses smart glasses to manage and monitor robotic systems in a factory in real time. Preprocess sensor data and use AI algorithms to optimize the manufacturing process and detect anomalies. Also, incorporate a function that recognizes user emotions via a camera and dynamically adjusts the operating interface."

[0722] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0723] Step 1:

[0724] The server acquires data in real time from sensors deployed in the manufacturing facility. Inputs include data from position sensors, force sensors, temperature sensors, and vibration sensors. These data are sent to the server, which receives it. The received data is then preprocessed, including filtering, noise removal, and scaling. The output of this stage is the preprocessed data.

[0725] Step 2:

[0726] The server inputs the preprocessed data into an artificial intelligence algorithm. The preprocessed data from step 1 is used as input. The server then runs an AI model based on this data to optimize the manufacturing process and detect anomalies. Specific operations include data analysis and pattern recognition. The output is an optimized manufacturing procedure and detected anomalies.

[0727] Step 3:

[0728] The server controls the robotic device based on the output from the AI ​​algorithm. The optimization procedure and anomaly information from step 2 are used as input. Based on this information, the server sends commands to the robotic device to perform the required operations. The output is a control command that enables the robotic device to operate accurately.

[0729] Step 4:

[0730] The terminal monitors the operating status of the entire system in real time and provides feedback to the user. Input includes status information and abnormality warnings from the server. The terminal uses this information to display visual feedback and warnings to the user. Specific operations include updating the monitoring screen and displaying warning messages. The output is a real-time status report and warnings to the user.

[0731] Step 5:

[0732] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements to recognize emotions. Inputs include the user's voice data and camera footage. The server analyzes this data to identify emotions. The output is the user's emotional state.

[0733] Step 6:

[0734] The server dynamically adjusts system operations based on the recognized emotions. The emotional state data from step 5 is used as input. The server uses this data to modify the user interface and simplify the operation procedures. Specific actions include changing the layout of the operation screen and displaying help messages. The output is the adjusted user interface and operation procedures.

[0735] Step 7:

[0736] The smart device displays on-site information and provides an interface for users to issue instructions. Inputs include control information from the server and on-site status data. The smart device displays this data and allows users to issue the necessary instructions. Specific operations include displaying information and providing a user interface. Outputs include instructions from the user and confirmation of the on-site status.

[0737] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0738] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0740] [Third embodiment]

[0741] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0742] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0743] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0744] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0745] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0746] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0747] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0748] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0749] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0750] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0751] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0752] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0753] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0754] Overall system configuration

[0755] The system consists of the following main components:

[0756] 1. Sensor

[0757] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0758] 2. Server

[0759] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0760] 3. Robotic Devices

[0761] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0762] 4. Terminal

[0763] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0764] Program processing

[0765] The system program is executed through the following process.

[0766] Data Acquisition

[0767] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0768] Data Preprocessing

[0769] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0770] Running AI algorithms

[0771] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0772] Robotic device control

[0773] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0774] Real-time monitoring and feedback

[0775] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0776] Specific examples

[0777] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires and processes real-time data from each sensor. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. A terminal displays the status of the assembly line to the user in real time and immediately displays a warning if an abnormality is detected. The user can operate the system and correct any abnormalities through the terminal.

[0778] In this way, the system of the present invention realizes efficiency, high precision, and cost reduction in the manufacturing process, and enables real-time detection of abnormalities and rapid response.

[0779] The processing flow will be explained below.

[0780] Step 1: Data Acquisition

[0781] The server acquires real-time data from various sensors installed in the manufacturing equipment, including position, force acceleration, temperature, vibration, etc. For example, the server collects data from the sensors 10 times per second.

[0782] Step 2: Data filtering

[0783] The server receives the acquired data, detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0784] Step 3: Noise reduction

[0785] The server applies a filtering algorithm to remove noise from the data, for example using a moving average filter to smooth the data and remove anomalous peaks.

[0786] Step 4: Data Scaling

[0787] The server converts data measured in different units to a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range.

[0788] Step 5: Data entry

[0789] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0790] Step 6: Optimize your manufacturing process

[0791] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order, to improve efficiency.

[0792] Step 7: Anomaly detection

[0793] The server uses AI algorithms to detect anomalies in real time, and if an anomaly is detected, the server analyzes the details to determine the type and cause of the anomaly.

[0794] Step 8: Generate control commands

[0795] Based on the results obtained from the AI ​​algorithm, the server generates specific control commands for the robotic equipment, such as instructing a robotic arm to rearrange parts.

[0796] Step 9: Sending control commands

[0797] The server sends the generated control commands to the robotics system to actually execute the operations, so that the robot arm accurately performs the specified operations.

[0798] Step 10: System Monitoring

[0799] The terminal monitors the operation of the entire system in real time and displays the data visually, allowing users to check the status and progress of each station.

[0800] Step 11: Notification of abnormalities

[0801] The server generates instant notifications when an anomaly is detected and alerts the user via the terminal, for example, in the event of improper component placement or unexpected vibrations.

[0802] Step 12: Manual operation

[0803] Users can manually operate the system through a terminal, and when an abnormality occurs, they can investigate the abnormality in detail and make any necessary corrections.

[0804] Example 1

[0805] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0806] In conventional manufacturing processes, it is difficult to acquire data in real time, detect anomalies, and optimize processes, which has led to a demand for more efficient and accurate manufacturing lines. Rapid response when anomalies occur is also important, but a consistent system to achieve this is lacking. Furthermore, raw data acquired from multiple sensors is often insufficiently preprocessed, making it susceptible to noise and outliers. To address these challenges, a system that integrates all of these elements is required.

[0807] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0808] In this invention, the server includes means for acquiring real-time data from sensors installed in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for using the preprocessed data as input to optimize the manufacturing process and detect abnormalities. This makes it possible to improve the efficiency and accuracy of the manufacturing process. Furthermore, the operation of the entire system can be monitored in real time, and if an abnormality is detected, a warning is generated and displayed, enabling a prompt response.

[0809] A "sensor" is a device that is placed in manufacturing equipment and measures physical quantities such as position, force, temperature, and vibration in real time.

[0810] "Real-time data" is data that is continuously acquired from sensors and immediately processed and analyzed.

[0811] "Filtering" is a process of removing unnecessary information and abnormal values ​​from acquired data.

[0812] "Denoising" is the process of removing random fluctuations and errors in the data to clean the signal.

[0813] "Scaling" is the process of converting numerical values ​​of data into consistent units and ranges.

[0814] "Preprocessing" refers to a series of processes in which the acquired raw data is organized using techniques such as filtering, noise removal, and scaling, and converted into a format suitable for the algorithm.

[0815] An "artificial intelligence algorithm" is a computer program designed to analyze input data and perform a specific task, often using machine learning models.

[0816] A "robot device" is a device that receives control commands from a server and performs automated operations. Examples include industrial robot arms.

[0817] "Real-time monitoring" is the process of constantly monitoring the operating status of the entire system and immediately understanding the situation.

[0818] A "warning" is an alert that the system notifies the user when an abnormality is detected.

[0819] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[0820] Overall system configuration

[0821] The system consists of the following main components:

[0822] 1. Sensor

[0823] Acquire data in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0824] 2. Server

[0825] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls automated equipment using software such as Python and TensorFlow.

[0826] 3. Robotic Devices

[0827] Automation equipment such as a robot arm that receives control commands from a server and executes the specified operations. For example, general equipment from industrial robot manufacturers is used.

[0828] 4. Terminal

[0829] It displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. Specifically, it uses a SCADA (Supervisory Control and Data Acquisition) system.

[0830] Program processing

[0831] Data Acquisition

[0832] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0833] Data Preprocessing

[0834] The server receives the raw data and converts each data set into a format suitable for AI algorithms, specifically by filtering, denoising, and scaling the data.

[0835] Running AI algorithms

[0836] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as using TensorFlow to run high-precision optimization of part alignment and anomaly detection algorithms to detect abnormal vibration patterns.

[0837] Robotic device control

[0838] The server controls the robotic device based on the output of the AI ​​algorithm, for example by sending a control command to the robotic device to move a part to a specific position.

[0839] Real-time monitoring and feedback

[0840] The terminal monitors the system's operation in real time and provides visual feedback to the user, and if an anomaly is detected, the server generates immediate feedback and displays a warning to the user via the terminal.

[0841] Specific examples

[0842] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0843] Examples of prompt statements

[0844] Below are some example prompts that explain the system's behavior:

[0845] In an automotive parts assembly line, acquire real-time data from each sensor (position, force, temperature, vibration), preprocess the data by filtering, denoising, and scaling it, then use TensorFlow to optimize the manufacturing process and detect anomalies, control a robot arm to accurately place parts, and finally use a SCADA system to display the real-time situation on a terminal and immediately issue a warning if an anomaly is detected.

[0846] By inputting this prompt into the generative AI model, specific implementation methods and operational details can be obtained.

[0847] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0848] System program processing flow

[0849] Step 1:

[0850] The server collects data in real time from various sensors installed in the manufacturing equipment. As input, it receives position data from position sensors, pressure data from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. As output, the raw data is stored in the server's database. Specifically, the server analyzes the signals sent from the sensors, extracts the necessary data, and stores it.

[0851] Step 2:

[0852] The server preprocesses the acquired raw data. As input, it takes the raw data acquired in step 1 and filters, denoises, and scales it. Data filtering removes unwanted data and outliers. Denoising eliminates random fluctuations and errors in the data. Scaling converts the data into consistent units and ranges. The final output is preprocessed, clean data. Specifically, the server records data frames and processes them by applying filtering algorithms and denoising filters.

[0853] Step 3:

[0854] The server uses the preprocessed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies. The clean data obtained in step 2 is used as input and analyzed using a machine learning framework such as TensorFlow. The output is the optimal manufacturing process procedure and the results of anomaly detection. Specifically, the server loads the trained AI model, feeds in the data for analysis, and obtains the results.

[0855] Step 4:

[0856] The server controls the robotic device based on the output of the AI ​​algorithm. As input, it uses the optimal procedure and anomaly detection results obtained in step 3. As output, a control command is generated to be sent to the robotic device. Specifically, the server sends a control command to the robotic device to move a part to a specific position. This causes the robotic device to perform the instructed operation.

[0857] Step 5:

[0858] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. As input, it receives the system's operating status and the results of anomaly detection sent from the server. As output, it displays the real-time operating status and warning messages on the monitoring screen. Specifically, the terminal uses the SCADA system to monitor the status of the production line and issues warnings to the user as necessary.

[0859] (Application example 1)

[0860] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0861] In manufacturing processes, the effective use of real-time data obtained from sensors for optimization and anomaly detection requires the application of advanced data preprocessing and artificial intelligence algorithms. Furthermore, rapid response is required after an anomaly is detected, but systems to achieve this are lacking. Furthermore, real-time situation assessment and countermeasure proposals via user devices are also essential.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0863] In this invention, the server includes means for acquiring real-time data from sensors arranged in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for implementing an artificial intelligence algorithm that uses the pre-processed data as an input to optimize the manufacturing process and detect anomalies, thereby enabling optimization of the manufacturing process and detection of anomalies.

[0864] The server also includes a means for controlling the robotic devices based on the output of the artificial intelligence algorithm, a means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected, a means for displaying real-time data via a mobile device, and a means for displaying an alert when an abnormality is detected and providing information on the specific location of the abnormality and countermeasures. This allows users to grasp the status of the manufacturing process in real time and take prompt action after an abnormality is detected.

[0865] "Manufacturing equipment" means the machinery and equipment used to manufacture products.

[0866] A "sensor" is a device that senses physical conditions or changes and outputs that information as data.

[0867] "Real-time data" is data obtained instantaneously from an ongoing process and is immediately available for use.

[0868] "Filtering" is the process of removing unnecessary information and noise from data.

[0869] "Noise removal" is a process that removes unnecessary fluctuations and errors contained in data.

[0870] "Scaling" is the process of converting data into a certain range or format.

[0871] An "artificial intelligence algorithm" is a computational procedure that analyzes large amounts of data, finds patterns, and uses the results to make predictions and optimizations.

[0872] A "robotic device" is a mechanical device that is controlled by a program and performs specific tasks automatically.

[0873] "Real-time monitoring" is the act of constantly and instantly understanding the status of a system or process.

[0874] A "warning" is an alert that notifies you that an abnormality or problem has occurred.

[0875] A "mobile device" is an electronic device that can be carried and used by a user.

[0876] An "alert" is a visual or audio message that notifies you of an emergency or abnormality.

[0877] This invention provides a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, this system controls robotic equipment based on the output of the AI ​​algorithm and monitors the operation of the entire system in real time. Another feature is that it provides real-time feedback to users via mobile devices.

[0878] Hardware and software used

[0879] The system consists of the following main components:

[0880] 1. Sensor

[0881] Various sensors installed in manufacturing equipment, such as position sensors, force sensors, temperature sensors, and vibration sensors.

[0882] 2. Server

[0883] It receives data from sensors, pre-processes it and runs artificial intelligence algorithms on it.

[0884] The software used is TensorFlow, PyTorch (for running AI algorithms), and Flask (web server and API).

[0885] 3. Robotic Devices

[0886] Automated equipment such as a robot arm that receives control commands from a server and performs specified operations.

[0887] 4. Mobile devices

[0888] Real-time feedback on the situation is provided to the user via a smartphone or head-mounted display (HMD), and an alert is displayed in the event of an abnormality.

[0889] Data Preprocessing

[0890] The server acquires real-time data from sensors located at manufacturing facilities and pre-processes it by filtering, denoising, and scaling it, converting it into a format that can be properly analyzed by artificial intelligence algorithms.

[0891] Running artificial intelligence algorithms

[0892] Based on the pre-processed data, the server runs artificial intelligence algorithms that optimize and detect anomalies in the manufacturing process. By analyzing large amounts of data using machine learning models, the original manufacturing process can be maintained with high accuracy.

[0893] Robotic device control

[0894] Based on the output of the artificial intelligence algorithm, the server sends control commands to the robotic equipment, such as actions to adjust the position of parts or reduce vibrations, thereby optimizing the manufacturing process and quickly correcting any anomalies that occur.

[0895] Real-time monitoring and feedback

[0896] Using mobile devices, the system monitors the operation of the entire system in real time and provides feedback to the user. If an abnormality is detected, an alert will be displayed, with specific information on the abnormality and recommended countermeasures.

[0897] Specific examples

[0898] For example, if a temperature sensor detects an abnormally high temperature, the server preprocesses the data and inputs it into an artificial intelligence algorithm. The algorithm analyzes the abnormality and generates a command to activate the cooling system. The server then sends this command to the robotic device, which automatically activates the cooling system. At the same time, the user's smartphone is notified of the abnormality and displays the specific location and recommended countermeasures in real time.

[0899] Example prompts to input to a generative AI model:

[0900] "Please analyze today's temperature sensor data and check for any abnormalities."

[0901] "Detect abnormal patterns from vibration data and suggest solutions."

[0902] The above is a specific embodiment of the present invention. By using this system, optimization of the manufacturing process and detection of abnormalities can be performed in real time, enabling highly accurate and rapid response.

[0903] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0904] Step 1:

[0905] The server receives data in real time from sensors placed in the manufacturing equipment. The input is data from the sensors (e.g., temperature, vibration, force), and processes it as is. The output is the collected raw data.

[0906] Step 2:

[0907] The server pre-processes the acquired data. It filters, denoises, and scales the data. The input is the raw data acquired in the previous step, and it processes the data to convert it into an appropriate format. The output is the pre-processed data.

[0908] Step 3:

[0909] The server inputs the preprocessed data into an artificial intelligence algorithm, which uses a machine learning model (e.g., TensorFlow or PyTorch) to optimize the manufacturing process and detect anomalies. The input is the preprocessed data, which the AI ​​uses to perform data analysis and predictions. The output is the analysis result of the AI ​​algorithm.

[0910] Step 4:

[0911] The server controls the robotic equipment based on the output of the artificial intelligence algorithm. It generates control commands based on the analysis results and sends them to the robotic equipment. The input is the analysis results of the AI ​​algorithm, and the control commands are generated based on this. The output is the actual robot operation (e.g., adjusting the position of parts, activating the cooling system).

[0912] Step 5:

[0913] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. If an abnormality is detected, it generates and displays an alert. The input is data from the server (normal or abnormal information), and based on this, it displays and alerts. The output is real-time information and alerts displayed on the user's screen.

[0914] Step 6:

[0915] Users use their mobile devices to receive real-time data and feedback on abnormalities. If an abnormality is detected, they are given specific information about the location and recommended countermeasures. The input is real-time information and alerts from the device, and based on this, the abnormality is confirmed and dealt with. The output is the user's confirmation and corresponding action (e.g., on-site confirmation, system restart).

[0916] The above are the specific processing steps for carrying out the invention. This system enables efficient manufacturing processes, highly accurate management, and rapid response to abnormalities.

[0917] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0918] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[0919] Overall system configuration

[0920] The system consists of the following main components:

[0921] 1. Sensor

[0922] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[0923] 2. Server

[0924] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[0925] 3. Robotic Devices

[0926] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[0927] 4. Terminal

[0928] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[0929] 5. Emotion Engine

[0930] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds back the user's emotional information to the system, dynamically adjusting system operation.

[0931] Program processing

[0932] The system program is executed through the following process.

[0933] Data Acquisition

[0934] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[0935] Data Preprocessing

[0936] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[0937] Running AI algorithms

[0938] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[0939] Robotic device control

[0940] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[0941] Real-time monitoring and feedback

[0942] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[0943] emotion recognition

[0944] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0945] Emotion-Based Adjustment

[0946] The server dynamically adjusts system operations based on the user's emotions. For example, if the user is feeling stressed, the server may simplify the operation interface.

[0947] Specific examples

[0948] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[0949] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[0950] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[0951] The processing flow will be explained below.

[0952] Step 1: Data Acquisition

[0953] The server acquires real-time data from multiple sensors installed in the manufacturing facility, including location information, force acceleration, temperature, vibration, etc. The server collects data from the sensors at a frequency of, for example, 10 times per second.

[0954] Step 2: Data filtering

[0955] The server receives the acquired raw data and detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[0956] Step 3: Noise reduction

[0957] The server applies filtering algorithms to remove noise from the data, for example, smoothing the data using a moving average filter to remove anomalous peaks, improving the quality of the data and increasing the accuracy of the AI ​​algorithms.

[0958] Step 4: Data Scaling

[0959] The server converts data measured in different units into a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range, allowing different data sources to be treated uniformly.

[0960] Step 5: Data entry

[0961] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[0962] Step 6: Optimize your manufacturing process

[0963] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order to improve efficiency. Based on the optimization results, the server updates the specific production plan.

[0964] Step 7: Anomaly detection

[0965] The server uses AI algorithms to detect anomalies in real time. If an anomaly is detected, the server analyzes the details to identify the type and cause of the anomaly. For example, if improper component placement or unexpected vibration is detected, an alert is generated.

[0966] Step 8: Generate control commands

[0967] Based on the results of the AI ​​algorithm, the server generates specific control commands for the robotic device, such as instructing a robot arm to rearrange a part. These control commands are carefully configured to ensure precision.

[0968] Step 9: Sending control commands

[0969] The server then sends the generated control commands to the robotics system, which then executes the actual operations, ensuring that the robot arm accurately performs the specified movements and optimizes the manufacturing process.

[0970] Step 10: System Monitoring

[0971] The terminal monitors the operation of the entire system in real time and displays the data visually. Users can check the status and progress of each station. For example, the dashboard screen displays the current operating rate and any abnormalities detected.

[0972] Step 11: Notification of abnormalities

[0973] The server generates a notification immediately if an abnormality is detected and warns the user through the terminal, for example, if there is improper component placement or unexpected vibration, so that the user can take corrective action immediately.

[0974] Step 12: Manual operation

[0975] Users can manually operate the system through a terminal. When an abnormality occurs, users can investigate the abnormality in detail and make the necessary corrections. For example, they can manually rearrange parts or restart the system.

[0976] Step 13: Emotion Recognition

[0977] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[0978] Step 14: Emotional Adjustment

[0979] The server dynamically adjusts system operation based on the user's recognized emotions, for example, simplifying the operation interface and providing gentle audio feedback if the user is feeling stressed.

[0980] Example 2

[0981] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0982] In recent manufacturing processes, real-time data acquisition and appropriate processing, as well as the optimization of production lines and anomaly detection based on that data, have become increasingly important. Conventional systems have struggled to smoothly perform the entire process from data acquisition to processing, control, and monitoring, and also lacked user operability and emotional response. This has led to issues such as reduced manufacturing efficiency and delayed response to anomalies.

[0983] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0984] In this invention, the server includes: means for acquiring real-time data from sensors installed in the manufacturing equipment; means for filtering, removing noise, and scaling the acquired data; means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input; means for controlling robotic devices based on the output of the artificial intelligence algorithm; means for monitoring the operation of the entire system in real time and generating and displaying an alert if an anomaly is detected; means for recognizing emotions by analyzing the user's voice, facial expressions, and movements; and means for dynamically adjusting system operation based on the recognized user emotions. This enables real-time data acquisition and appropriate processing, enabling rapid optimization of the manufacturing process and rapid anomaly detection, as well as the provision of a flexible operating environment that takes user emotions into consideration.

[0985] A "sensor" is a device that is installed in manufacturing equipment and acquires data by detecting physical quantities (e.g., position, force, temperature, vibration, etc.).

[0986] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[0987] "Filtering" is a process of removing unnecessary components and errors from raw data.

[0988] "Denoising" is the process of removing unnecessary fluctuations and noise from data.

[0989] "Scaling" is the process of converting data of different units or ranges onto a consistent scale.

[0990] "Preprocessing" refers to a series of operations that convert the acquired raw data into a format suitable for subsequent data analysis and algorithmic processing.

[0991] An "artificial intelligence algorithm" is a computational method that allows machines to perform intelligent operations, such as data analysis, prediction, and optimization.

[0992] "Robotic equipment" is a general term for mechanical devices that operate automatically according to programmed instructions, and specifically includes robotic arms.

[0993] "Real-time monitoring" refers to continuous, immediate monitoring of the status of a system or process.

[0994] An "alert" is a message or signal that notifies the user when an abnormality occurs in the system.

[0995] "User" refers to a person who operates or monitors the system.

[0996] "Voice analysis" is the process of analyzing voice data to extract specific information (e.g., emotions, commands, etc.).

[0997] "Facial expression analysis" is the process of analyzing images of a user's face captured by a camera or other device and reading emotions from their facial expressions.

[0998] "Motion analysis" is the process of analyzing a user's physical movements to determine their meaning and intention.

[0999] "Emotion recognition" is a technology that analyzes a user's voice, facial expressions, and movements to determine their emotional state.

[1000] "Dynamic adjustment" refers to the automatic change of system behavior or settings in response to circumstances and conditions.

[1001] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[1002] Overall system configuration

[1003] The system consists of the following main components:

[1004] 1. Sensor

[1005] Real-time data is acquired from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. These sensors are essential for monitoring product quality and manufacturing processes with high precision.

[1006] 2. Server

[1007] The server receives data acquired from the sensors in real time and is responsible for preprocessing the data, running AI algorithms, and controlling the robotic equipment. Specific software examples include Python for data processing, TensorFlow and PyTorch for AI algorithms, and ROS (Robot Operating System) for controlling the robotic equipment.

[1008] 3. Robotic Devices

[1009] Robotic devices include automated equipment such as robotic arms that receive control commands from a server and execute specified operations, enabling highly accurate and efficient work.

[1010] 4. Terminal

[1011] The terminal displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. The terminal is equipped with a graphical user interface (GUI) to improve operability.

[1012] 5. Emotion Engine

[1013] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds the user's emotional information back to the system, dynamically adjusting system operation. For example, if the user is feeling stressed, the system may simplify the operation screen.

[1014] Specific examples

[1015] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[1016] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[1017] Prompt Sentence Examples

[1018] Below are some example prompts to explain the behavior of this system to a generative AI model:

[1019] "Describe a system that preprocesses data from sensors in manufacturing equipment and uses AI algorithms to optimize the manufacturing process and detect anomalies. For example, explain how data is obtained from sensors to control robotic equipment on an automotive parts assembly line. Also, describe the ability to recognize user emotions and adjust system operation based on those emotions."

[1020] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[1021] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1022] Step 1: Data Acquisition

[1023] The server acquires real-time data from various sensors installed in the manufacturing equipment. Specifically, it receives coordinate data from position sensors, force magnitude from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. Sensor data is acquired as input, and raw sensor data is output.

[1024] Step 2: Data Preprocessing

[1025] The server preprocesses the acquired raw data. Specifically, it filters the data to remove noise and performs scaling. For example, it smooths temperature data and filters out high-frequency components in vibration data. Raw sensor data is generated as input, and preprocessed data is generated as output.

[1026] Step 3: Run the AI ​​algorithm

[1027] The server uses the preprocessed data to run artificial intelligence algorithms that optimize manufacturing processes and detect anomalies. Specifically, the data is input into a machine learning model (e.g., a deep learning model) to detect abnormal patterns and calculate efficient manufacturing procedures. The preprocessed data is the input, and the analysis results of the algorithm are the output.

[1028] Step 4: Controlling the robotic device

[1029] The server controls the robotic equipment based on the output of the AI ​​algorithm. For example, it sends a control command to a robot arm to place a part in a specific position. Specifically, this includes coordinate data and movement speed. The algorithm's analysis results are input, and the control command is generated as output.

[1030] Step 5: Real-time monitoring and feedback

[1031] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. Specifically, it displays the status of each part of the production line and graphs of acquired data, and immediately issues an alarm and warning message if an abnormality is detected. Sensor data and control results are input, and dashboard displays and warnings are output.

[1032] Step 6: Emotion Recognition

[1033] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions. For example, it may determine that the user is feeling stressed using camera footage and microphone data. Voice and video data are input, and recognized emotion data is generated as output.

[1034] Step 7: Emotional Adjustment

[1035] The server dynamically adjusts system operations based on the recognized user emotions. For example, if the user is feeling stressed, it can simplify the operation interface. Emotional data is input and an adjusted operation interface is generated as output.

[1036] (Application example 2)

[1037] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1038] In the manufacturing industry, the importance of improving the efficiency of manufacturing processes and detecting anomalies is increasing. However, existing systems have difficulty optimizing manufacturing processes in real time and quickly detecting anomalies. Providing a flexible operating environment that takes user emotions into consideration is also a challenge. In particular, there is room for improvement in the efficient display of information and instruction delivery at the workplace.

[1039] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring real-time data from sensors, means for filtering, denoising, and scaling the acquired data, means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input, means for controlling robotic equipment based on the output of the artificial intelligence algorithm, means for recognizing user emotions, means for dynamically adjusting system operation based on the recognized emotions, and means for displaying on-site information and issuing instructions using a smart device. This enables the efficiency of the manufacturing process, highly accurate anomaly detection, and the provision of a user-friendly operating environment.

[1040] A "sensor" is a device that is installed in manufacturing equipment and measures physical parameters such as temperature, vibration, position, and force in real time and acquires the data.

[1041] "Filtering" is a process of removing unnecessary information and noise from data obtained from sensors and extracting only the necessary information.

[1042] "Noise removal" is the process of removing errors and unnecessary scattered data contained in data obtained from a sensor.

[1043] "Scaling" is a method of converting data into a uniform scale to make it easier for AI algorithms to process.

[1044] "Preprocessing" refers to a series of processes that convert the acquired raw data into a format suitable for AI algorithms, including filtering, noise removal, and scaling.

[1045] An "artificial intelligence algorithm" is a specific computational method for analyzing data and detecting patterns, including machine learning models.

[1046] "Robotic devices" are automated machines that assist in the manufacturing process and operate according to control commands from a server.

[1047] "Means for recognizing user emotions" refers to technology that analyzes the user's voice, facial expressions, movements, etc., to identify their emotional state.

[1048] "Means for dynamically adjusting system operation" refers to technology that appropriately changes the system's operation interface and functions according to the user's emotions and situation.

[1049] A "smart device" is an advanced electronic device that has the functions of displaying information, acquiring data, and issuing instructions, and is operated directly by the user.

[1050] "Real-time monitoring" is a technology that allows for immediate monitoring of system operation and status, and for immediate response when an abnormality occurs.

[1051] "Means for generating and displaying warnings" refers to technology that monitors the entire system and notifies the user with a visual or audio warning if an abnormality is detected.

[1052] This system acquires real-time data from sensors installed in manufacturing facilities, preprocesses it, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. It also has the ability to control robotic devices based on the output of the AI ​​algorithm and monitor the operation of the entire system in real time. Another feature of this system is that it also includes the ability to recognize user emotions and dynamically adjust system operation based on the recognized emotions.

[1053] Overall system configuration

[1054] The system consists of the following main components:

[1055] 1. Sensor

[1056] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. Each sensor measures a physical parameter in real time and sends the data to a server.

[1057] 2. Server

[1058] The server receives real-time sensor data, filters it, removes noise, and scales it. It uses the pre-processed data as input to run artificial intelligence algorithms that optimize the manufacturing process and detect anomalies. The server also controls robotic devices based on the output of the AI ​​algorithms and has the means to recognize user emotions.

[1059] 3. Robotic Devices

[1060] This refers to automated equipment such as robotic arms that receive control commands from a server and execute the specified operations, thereby improving the efficiency and precision of the manufacturing process.

[1061] 4. Terminal

[1062] It visually displays the overall system operating status, provides real-time feedback to the user, and displays a warning to notify the user if an abnormality is detected.

[1063] 5. Smart Devices

[1064] This is an advanced electronic device for displaying on-site information and issuing instructions. For example, it uses smart glasses, allowing users to give instructions while viewing the situation at the work site in real time.

[1065] 6. Emotion Engine

[1066] The system analyzes the user's voice, facial expressions, and movements to recognize emotions. This recognition information is fed back to the system, and the system's operation is dynamically adjusted based on the user's emotions.

[1067] Specific examples

[1068] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. The server collects real-time data from each sensor and filters, denoises, and scales it. The preprocessed data is then input into an AI algorithm to calculate the optimal steps for the manufacturing process. Based on the output of the AI ​​algorithm, the server sends control commands to robotic equipment to ensure that parts are positioned accurately.

[1069] Workers wearing the smart glasses can check on-site information and immediately respond if an abnormality occurs. Furthermore, the emotion engine recognizes the user's emotions from their voice and facial expressions, and if the worker is feeling stressed, for example, the system will simplify the operation interface.

[1070] Prompt Sentence Examples

[1071] "Design an application that uses smart glasses to manage and monitor robotic systems in a factory in real time. Preprocess sensor data and use AI algorithms to optimize the manufacturing process and detect anomalies. Also, incorporate a function that recognizes user emotions via a camera and dynamically adjusts the operating interface."

[1072] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1073] Step 1:

[1074] The server acquires data in real time from sensors deployed in the manufacturing facility. Inputs include data from position sensors, force sensors, temperature sensors, and vibration sensors. These data are sent to the server, which receives it. The received data is then preprocessed, including filtering, noise removal, and scaling. The output of this stage is the preprocessed data.

[1075] Step 2:

[1076] The server inputs the preprocessed data into an artificial intelligence algorithm. The preprocessed data from step 1 is used as input. The server then runs an AI model based on this data to optimize the manufacturing process and detect anomalies. Specific operations include data analysis and pattern recognition. The output is an optimized manufacturing procedure and detected anomalies.

[1077] Step 3:

[1078] The server controls the robotic device based on the output from the AI ​​algorithm. The optimization procedure and anomaly information from step 2 are used as input. Based on this information, the server sends commands to the robotic device to perform the required operations. The output is a control command that enables the robotic device to operate accurately.

[1079] Step 4:

[1080] The terminal monitors the operating status of the entire system in real time and provides feedback to the user. Input includes status information and abnormality warnings from the server. The terminal uses this information to display visual feedback and warnings to the user. Specific operations include updating the monitoring screen and displaying warning messages. The output is a real-time status report and warnings to the user.

[1081] Step 5:

[1082] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements to recognize emotions. Inputs include the user's voice data and camera footage. The server analyzes this data to identify emotions. The output is the user's emotional state.

[1083] Step 6:

[1084] The server dynamically adjusts system operations based on the recognized emotions. The emotional state data from step 5 is used as input. The server uses this data to modify the user interface and simplify the operation procedures. Specific actions include changing the layout of the operation screen and displaying help messages. The output is the adjusted user interface and operation procedures.

[1085] Step 7:

[1086] The smart device displays on-site information and provides an interface for users to issue instructions. Inputs include control information from the server and on-site status data. The smart device displays this data and allows users to issue the necessary instructions. Specific operations include displaying information and providing a user interface. Outputs include instructions from the user and confirmation of the on-site status.

[1087] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1089] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1090] [Fourth embodiment]

[1091] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1092] 7, a 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.

[1093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[1094] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1095] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1098] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1099] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1100] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1102] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1103] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1104] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[1105] Overall system configuration

[1106] The system consists of the following main components:

[1107] 1. Sensor

[1108] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[1109] 2. Server

[1110] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[1111] 3. Robotic Devices

[1112] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[1113] 4. Terminal

[1114] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[1115] Program processing

[1116] The system program is executed through the following process.

[1117] Data Acquisition

[1118] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[1119] Data Preprocessing

[1120] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[1121] Running AI algorithms

[1122] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[1123] Robotic device control

[1124] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[1125] Real-time monitoring and feedback

[1126] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[1127] Specific examples

[1128] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires and processes real-time data from each sensor. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. A terminal displays the status of the assembly line to the user in real time and immediately displays a warning if an abnormality is detected. The user can operate the system and correct any abnormalities through the terminal.

[1129] In this way, the system of the present invention realizes efficiency, high precision, and cost reduction in the manufacturing process, and enables real-time detection of abnormalities and rapid response.

[1130] The processing flow will be explained below.

[1131] Step 1: Data Acquisition

[1132] The server acquires real-time data from various sensors installed in the manufacturing equipment, including position, force acceleration, temperature, vibration, etc. For example, the server collects data from the sensors 10 times per second.

[1133] Step 2: Data filtering

[1134] The server receives the acquired data, detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[1135] Step 3: Noise reduction

[1136] The server applies a filtering algorithm to remove noise from the data, for example using a moving average filter to smooth the data and remove anomalous peaks.

[1137] Step 4: Data Scaling

[1138] The server converts data measured in different units to a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range.

[1139] Step 5: Data entry

[1140] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[1141] Step 6: Optimize your manufacturing process

[1142] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order, to improve efficiency.

[1143] Step 7: Anomaly detection

[1144] The server uses AI algorithms to detect anomalies in real time, and if an anomaly is detected, the server analyzes the details to determine the type and cause of the anomaly.

[1145] Step 8: Generate control commands

[1146] Based on the results obtained from the AI ​​algorithm, the server generates specific control commands for the robotic equipment, such as instructing a robotic arm to rearrange parts.

[1147] Step 9: Sending control commands

[1148] The server sends the generated control commands to the robotics system to actually execute the operations, so that the robot arm accurately performs the specified operations.

[1149] Step 10: System Monitoring

[1150] The terminal monitors the operation of the entire system in real time and displays the data visually, allowing users to check the status and progress of each station.

[1151] Step 11: Notification of abnormalities

[1152] The server generates instant notifications when an anomaly is detected and alerts the user via the terminal, for example, in the event of improper component placement or unexpected vibrations.

[1153] Step 12: Manual operation

[1154] Users can manually operate the system through a terminal, and when an abnormality occurs, they can investigate the abnormality in detail and make any necessary corrections.

[1155] Example 1

[1156] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1157] In conventional manufacturing processes, it is difficult to acquire data in real time, detect anomalies, and optimize processes, which has led to a demand for more efficient and accurate manufacturing lines. Rapid response when anomalies occur is also important, but a consistent system to achieve this is lacking. Furthermore, raw data acquired from multiple sensors is often insufficiently preprocessed, making it susceptible to noise and outliers. To address these challenges, a system that integrates all of these elements is required.

[1158] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1159] In this invention, the server includes means for acquiring real-time data from sensors installed in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for using the preprocessed data as input to optimize the manufacturing process and detect abnormalities. This makes it possible to improve the efficiency and accuracy of the manufacturing process. Furthermore, the operation of the entire system can be monitored in real time, and if an abnormality is detected, a warning is generated and displayed, enabling a prompt response.

[1160] A "sensor" is a device that is placed in manufacturing equipment and measures physical quantities such as position, force, temperature, and vibration in real time.

[1161] "Real-time data" is data that is continuously acquired from sensors and immediately processed and analyzed.

[1162] "Filtering" is a process of removing unnecessary information and abnormal values ​​from acquired data.

[1163] "Denoising" is the process of removing random fluctuations and errors in the data to clean the signal.

[1164] "Scaling" is the process of converting numerical values ​​of data into consistent units and ranges.

[1165] "Preprocessing" refers to a series of processes in which the acquired raw data is organized using techniques such as filtering, noise removal, and scaling, and converted into a format suitable for the algorithm.

[1166] An "artificial intelligence algorithm" is a computer program designed to analyze input data and perform a specific task, often using machine learning models.

[1167] A "robot device" is a device that receives control commands from a server and performs automated operations. Examples include industrial robot arms.

[1168] "Real-time monitoring" is the process of constantly monitoring the operating status of the entire system and immediately understanding the situation.

[1169] A "warning" is an alert that the system notifies the user when an abnormality is detected.

[1170] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic devices based on the output of the AI ​​algorithm, and monitors the operation of the entire system in real time.

[1171] Overall system configuration

[1172] The system consists of the following main components:

[1173] 1. Sensor

[1174] Acquire data in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[1175] 2. Server

[1176] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls automated equipment using software such as Python and TensorFlow.

[1177] 3. Robotic Devices

[1178] Automation equipment such as a robot arm that receives control commands from a server and executes the specified operations. For example, general equipment from industrial robot manufacturers is used.

[1179] 4. Terminal

[1180] It displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. Specifically, it uses a SCADA (Supervisory Control and Data Acquisition) system.

[1181] Program processing

[1182] Data Acquisition

[1183] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[1184] Data Preprocessing

[1185] The server receives the raw data and converts each data set into a format suitable for AI algorithms, specifically by filtering, denoising, and scaling the data.

[1186] Running AI algorithms

[1187] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as using TensorFlow to run high-precision optimization of part alignment and anomaly detection algorithms to detect abnormal vibration patterns.

[1188] Robotic device control

[1189] The server controls the robotic device based on the output of the AI ​​algorithm, for example by sending a control command to the robotic device to move a part to a specific position.

[1190] Real-time monitoring and feedback

[1191] The terminal monitors the system's operation in real time and provides visual feedback to the user, and if an anomaly is detected, the server generates immediate feedback and displays a warning to the user via the terminal.

[1192] Specific examples

[1193] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[1194] Examples of prompt statements

[1195] Below are some example prompts that explain the system's behavior:

[1196] In an automotive parts assembly line, acquire real-time data from each sensor (position, force, temperature, vibration), preprocess the data by filtering, denoising, and scaling it, then use TensorFlow to optimize the manufacturing process and detect anomalies, control a robot arm to accurately place parts, and finally use a SCADA system to display the real-time situation on a terminal and immediately issue a warning if an anomaly is detected.

[1197] By inputting this prompt into the generative AI model, specific implementation methods and operational details can be obtained.

[1198] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1199] System program processing flow

[1200] Step 1:

[1201] The server collects data in real time from various sensors installed in the manufacturing equipment. As input, it receives position data from position sensors, pressure data from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. As output, the raw data is stored in the server's database. Specifically, the server analyzes the signals sent from the sensors, extracts the necessary data, and stores it.

[1202] Step 2:

[1203] The server preprocesses the acquired raw data. As input, it takes the raw data acquired in step 1 and filters, denoises, and scales it. Data filtering removes unwanted data and outliers. Denoising eliminates random fluctuations and errors in the data. Scaling converts the data into consistent units and ranges. The final output is preprocessed, clean data. Specifically, the server records data frames and processes them by applying filtering algorithms and denoising filters.

[1204] Step 3:

[1205] The server uses the preprocessed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies. The clean data obtained in step 2 is used as input and analyzed using a machine learning framework such as TensorFlow. The output is the optimal manufacturing process procedure and the results of anomaly detection. Specifically, the server loads the trained AI model, feeds in the data for analysis, and obtains the results.

[1206] Step 4:

[1207] The server controls the robotic device based on the output of the AI ​​algorithm. As input, it uses the optimal procedure and anomaly detection results obtained in step 3. As output, a control command is generated to be sent to the robotic device. Specifically, the server sends a control command to the robotic device to move a part to a specific position. This causes the robotic device to perform the instructed operation.

[1208] Step 5:

[1209] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. As input, it receives the system's operating status and the results of anomaly detection sent from the server. As output, it displays the real-time operating status and warning messages on the monitoring screen. Specifically, the terminal uses the SCADA system to monitor the status of the production line and issues warnings to the user as necessary.

[1210] (Application example 1)

[1211] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1212] In manufacturing processes, the effective use of real-time data obtained from sensors for optimization and anomaly detection requires the application of advanced data preprocessing and artificial intelligence algorithms. Furthermore, rapid response is required after an anomaly is detected, but systems to achieve this are lacking. Furthermore, real-time situation assessment and countermeasure proposals via user devices are also essential.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1214] In this invention, the server includes means for acquiring real-time data from sensors arranged in the manufacturing equipment, means for filtering, removing noise, and scaling the acquired data, and means for implementing an artificial intelligence algorithm that uses the pre-processed data as an input to optimize the manufacturing process and detect anomalies, thereby enabling optimization of the manufacturing process and detection of anomalies.

[1215] The server also includes a means for controlling the robotic devices based on the output of the artificial intelligence algorithm, a means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected, a means for displaying real-time data via a mobile device, and a means for displaying an alert when an abnormality is detected and providing information on the specific location of the abnormality and countermeasures. This allows users to grasp the status of the manufacturing process in real time and take prompt action after an abnormality is detected.

[1216] "Manufacturing equipment" means the machinery and equipment used to manufacture products.

[1217] A "sensor" is a device that senses physical conditions or changes and outputs that information as data.

[1218] "Real-time data" is data obtained instantaneously from an ongoing process and is immediately available for use.

[1219] "Filtering" is the process of removing unnecessary information and noise from data.

[1220] "Noise removal" is a process that removes unnecessary fluctuations and errors contained in data.

[1221] "Scaling" is the process of converting data into a certain range or format.

[1222] An "artificial intelligence algorithm" is a computational procedure that analyzes large amounts of data, finds patterns, and uses the results to make predictions and optimizations.

[1223] A "robotic device" is a mechanical device that is controlled by a program and performs specific tasks automatically.

[1224] "Real-time monitoring" is the act of constantly and instantly understanding the status of a system or process.

[1225] A "warning" is an alert that notifies you that an abnormality or problem has occurred.

[1226] A "mobile device" is an electronic device that can be carried and used by a user.

[1227] An "alert" is a visual or audio message that notifies you of an emergency or abnormality.

[1228] This invention provides a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. Furthermore, this system controls robotic equipment based on the output of the AI ​​algorithm and monitors the operation of the entire system in real time. Another feature is that it provides real-time feedback to users via mobile devices.

[1229] Hardware and software used

[1230] The system consists of the following main components:

[1231] 1. Sensor

[1232] Various sensors installed in manufacturing equipment, such as position sensors, force sensors, temperature sensors, and vibration sensors.

[1233] 2. Server

[1234] It receives data from sensors, pre-processes it and runs artificial intelligence algorithms on it.

[1235] The software used is TensorFlow, PyTorch (for running AI algorithms), and Flask (web server and API).

[1236] 3. Robotic Devices

[1237] Automated equipment such as a robot arm that receives control commands from a server and performs specified operations.

[1238] 4. Mobile devices

[1239] Real-time feedback on the situation is provided to the user via a smartphone or head-mounted display (HMD), and an alert is displayed in the event of an abnormality.

[1240] Data Preprocessing

[1241] The server acquires real-time data from sensors located at manufacturing facilities and pre-processes it by filtering, denoising, and scaling it, converting it into a format that can be properly analyzed by artificial intelligence algorithms.

[1242] Running artificial intelligence algorithms

[1243] Based on the pre-processed data, the server runs artificial intelligence algorithms that optimize and detect anomalies in the manufacturing process. By analyzing large amounts of data using machine learning models, the original manufacturing process can be maintained with high accuracy.

[1244] Robotic device control

[1245] Based on the output of the artificial intelligence algorithm, the server sends control commands to the robotic equipment, such as actions to adjust the position of parts or reduce vibrations, thereby optimizing the manufacturing process and quickly correcting any anomalies that occur.

[1246] Real-time monitoring and feedback

[1247] Using mobile devices, the system monitors the operation of the entire system in real time and provides feedback to the user. If an abnormality is detected, an alert will be displayed, with specific information on the abnormality and recommended countermeasures.

[1248] Specific examples

[1249] For example, if a temperature sensor detects an abnormally high temperature, the server preprocesses the data and inputs it into an artificial intelligence algorithm. The algorithm analyzes the abnormality and generates a command to activate the cooling system. The server then sends this command to the robotic device, which automatically activates the cooling system. At the same time, the user's smartphone is notified of the abnormality and displays the specific location and recommended countermeasures in real time.

[1250] Example prompts to input to a generative AI model:

[1251] "Please analyze today's temperature sensor data and check for any abnormalities."

[1252] "Detect abnormal patterns from vibration data and suggest solutions."

[1253] The above is a specific embodiment of the present invention. By using this system, optimization of the manufacturing process and detection of abnormalities can be performed in real time, enabling highly accurate and rapid response.

[1254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1255] Step 1:

[1256] The server receives data in real time from sensors placed in the manufacturing equipment. The input is data from the sensors (e.g., temperature, vibration, force), and processes it as is. The output is the collected raw data.

[1257] Step 2:

[1258] The server pre-processes the acquired data. It filters, denoises, and scales the data. The input is the raw data acquired in the previous step, and it processes the data to convert it into an appropriate format. The output is the pre-processed data.

[1259] Step 3:

[1260] The server inputs the preprocessed data into an artificial intelligence algorithm, which uses a machine learning model (e.g., TensorFlow or PyTorch) to optimize the manufacturing process and detect anomalies. The input is the preprocessed data, which the AI ​​uses to perform data analysis and predictions. The output is the analysis result of the AI ​​algorithm.

[1261] Step 4:

[1262] The server controls the robotic equipment based on the output of the artificial intelligence algorithm. It generates control commands based on the analysis results and sends them to the robotic equipment. The input is the analysis results of the AI ​​algorithm, and the control commands are generated based on this. The output is the actual robot operation (e.g., adjusting the position of parts, activating the cooling system).

[1263] Step 5:

[1264] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. If an abnormality is detected, it generates and displays an alert. The input is data from the server (normal or abnormal information), and based on this, it displays and alerts. The output is real-time information and alerts displayed on the user's screen.

[1265] Step 6:

[1266] Users use their mobile devices to receive real-time data and feedback on abnormalities. If an abnormality is detected, they are given specific information about the location and recommended countermeasures. The input is real-time information and alerts from the device, and based on this, the abnormality is confirmed and dealt with. The output is the user's confirmation and corresponding action (e.g., on-site confirmation, system restart).

[1267] The above are the specific processing steps for carrying out the invention. This system enables efficient manufacturing processes, highly accurate management, and rapid response to abnormalities.

[1268] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1269] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[1270] Overall system configuration

[1271] The system consists of the following main components:

[1272] 1. Sensor

[1273] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing equipment.

[1274] 2. Server

[1275] It receives data from sensors in real time, preprocesses the data, runs AI algorithms, and controls the robotic equipment.

[1276] 3. Robotic Devices

[1277] Automated equipment such as a robot arm that receives control commands from a server and performs the specified operations.

[1278] 4. Terminal

[1279] It displays the overall system operating status, provides real-time feedback to the user, and displays a warning if an abnormality is detected.

[1280] 5. Emotion Engine

[1281] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds back the user's emotional information to the system, dynamically adjusting system operation.

[1282] Program processing

[1283] The system program is executed through the following process.

[1284] Data Acquisition

[1285] The server acquires real-time data from sensors located in the manufacturing equipment, including data from position sensors, force sensors, temperature sensors, and vibration sensors.

[1286] Data Preprocessing

[1287] The server receives the raw data and converts it into a format suitable for AI algorithms, specifically filtering, denoising, and scaling the data.

[1288] Running AI algorithms

[1289] The server uses the pre-processed data as input to run artificial intelligence algorithms to optimize the manufacturing process and detect anomalies, such as optimizing part alignment for high precision or anomaly detection for detecting abnormal vibration patterns.

[1290] Robotic device control

[1291] The server controls the robotic equipment based on the output of the AI ​​algorithm, for example by sending a control command to a robotic arm to place a part in a specific position.

[1292] Real-time monitoring and feedback

[1293] The terminal monitors the overall system operation in real time and provides visual feedback to the user, and if an abnormality is detected, the server generates immediate feedback and displays a warning to the user through the terminal.

[1294] emotion recognition

[1295] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[1296] Emotion-Based Adjustment

[1297] The server dynamically adjusts system operations based on the user's emotions. For example, if the user is feeling stressed, the server may simplify the operation interface.

[1298] Specific examples

[1299] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[1300] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[1301] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[1302] The processing flow will be explained below.

[1303] Step 1: Data Acquisition

[1304] The server acquires real-time data from multiple sensors installed in the manufacturing facility, including location information, force acceleration, temperature, vibration, etc. The server collects data from the sensors at a frequency of, for example, 10 times per second.

[1305] Step 2: Data filtering

[1306] The server receives the acquired raw data and detects and removes outliers and missing values. For example, if a sensor sends a temporarily abnormal value, it removes that value. This improves the accuracy of the data.

[1307] Step 3: Noise reduction

[1308] The server applies filtering algorithms to remove noise from the data, for example, smoothing the data using a moving average filter to remove anomalous peaks, improving the quality of the data and increasing the accuracy of the AI ​​algorithms.

[1309] Step 4: Data Scaling

[1310] The server converts data measured in different units into a unified scale, for example converting temperature data from Celsius to Kelvin and normalizing force acceleration to a certain range, allowing different data sources to be treated uniformly.

[1311] Step 5: Data entry

[1312] The server then feeds the pre-processed data into artificial intelligence algorithms, which then initiate analysis to optimize the manufacturing process and detect anomalies.

[1313] Step 6: Optimize your manufacturing process

[1314] The server uses AI algorithms to generate suggestions for optimizing specific manufacturing steps, such as optimizing part placement and assembly order to improve efficiency. Based on the optimization results, the server updates the specific production plan.

[1315] Step 7: Anomaly detection

[1316] The server uses AI algorithms to detect anomalies in real time. If an anomaly is detected, the server analyzes the details to identify the type and cause of the anomaly. For example, if improper component placement or unexpected vibration is detected, an alert is generated.

[1317] Step 8: Generate control commands

[1318] Based on the results of the AI ​​algorithm, the server generates specific control commands for the robotic device, such as instructing a robot arm to rearrange a part. These control commands are carefully configured to ensure precision.

[1319] Step 9: Sending control commands

[1320] The server then sends the generated control commands to the robotics system, which then executes the actual operations, ensuring that the robot arm accurately performs the specified movements and optimizes the manufacturing process.

[1321] Step 10: System Monitoring

[1322] The terminal monitors the operation of the entire system in real time and displays the data visually. Users can check the status and progress of each station. For example, the dashboard screen displays the current operating rate and any abnormalities detected.

[1323] Step 11: Notification of abnormalities

[1324] The server generates a notification immediately if an abnormality is detected and warns the user through the terminal, for example, if there is improper component placement or unexpected vibration, so that the user can take corrective action immediately.

[1325] Step 12: Manual operation

[1326] Users can manually operate the system through a terminal. When an abnormality occurs, users can investigate the abnormality in detail and make the necessary corrections. For example, they can manually rearrange parts or restart the system.

[1327] Step 13: Emotion Recognition

[1328] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions, for example, whether the user is stressed or happy.

[1329] Step 14: Emotional Adjustment

[1330] The server dynamically adjusts system operation based on the user's recognized emotions, for example, simplifying the operation interface and providing gentle audio feedback if the user is feeling stressed.

[1331] Example 2

[1332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] In recent manufacturing processes, real-time data acquisition and appropriate processing, as well as the optimization of production lines and anomaly detection based on that data, have become increasingly important. Conventional systems have struggled to smoothly perform the entire process from data acquisition to processing, control, and monitoring, and also lacked user operability and emotional response. This has led to issues such as reduced manufacturing efficiency and delayed response to anomalies.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1335] In this invention, the server includes: means for acquiring real-time data from sensors installed in the manufacturing equipment; means for filtering, removing noise, and scaling the acquired data; means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input; means for controlling robotic devices based on the output of the artificial intelligence algorithm; means for monitoring the operation of the entire system in real time and generating and displaying an alert if an anomaly is detected; means for recognizing emotions by analyzing the user's voice, facial expressions, and movements; and means for dynamically adjusting system operation based on the recognized user emotions. This enables real-time data acquisition and appropriate processing, enabling rapid optimization of the manufacturing process and rapid anomaly detection, as well as the provision of a flexible operating environment that takes user emotions into consideration.

[1336] A "sensor" is a device that is installed in manufacturing equipment and acquires data by detecting physical quantities (e.g., position, force, temperature, vibration, etc.).

[1337] "Real-time data" refers to data that immediately reflects the current situation and is collected without delay.

[1338] "Filtering" is a process of removing unnecessary components and errors from raw data.

[1339] "Denoising" is the process of removing unnecessary fluctuations and noise from data.

[1340] "Scaling" is the process of converting data of different units or ranges onto a consistent scale.

[1341] "Preprocessing" refers to a series of operations that convert the acquired raw data into a format suitable for subsequent data analysis and algorithmic processing.

[1342] An "artificial intelligence algorithm" is a computational method that allows machines to perform intelligent operations, such as data analysis, prediction, and optimization.

[1343] "Robotic equipment" is a general term for mechanical devices that operate automatically according to programmed instructions, and specifically includes robotic arms.

[1344] "Real-time monitoring" refers to continuous, immediate monitoring of the status of a system or process.

[1345] An "alert" is a message or signal that notifies the user when an abnormality occurs in the system.

[1346] "User" refers to a person who operates or monitors the system.

[1347] "Voice analysis" is the process of analyzing voice data to extract specific information (e.g., emotions, commands, etc.).

[1348] "Facial expression analysis" is the process of analyzing images of a user's face captured by a camera or other device and reading emotions from their facial expressions.

[1349] "Motion analysis" is the process of analyzing a user's physical movements to determine their meaning and intention.

[1350] "Emotion recognition" is a technology that analyzes a user's voice, facial expressions, and movements to determine their emotional state.

[1351] "Dynamic adjustment" refers to the automatic change of system behavior or settings in response to circumstances and conditions.

[1352] This invention relates to a system that acquires real-time data from sensors installed in manufacturing facilities, preprocesses the data, and then uses an artificial intelligence algorithm to optimize the manufacturing process and detect anomalies. Furthermore, the system controls robotic equipment based on the output of the artificial intelligence algorithm and monitors the operation of the entire system in real time. Additionally, this invention combines an emotion engine that recognizes the user's emotions, providing a more intuitive and user-friendly operating environment.

[1353] Overall system configuration

[1354] The system consists of the following main components:

[1355] 1. Sensor

[1356] Real-time data is acquired from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. These sensors are essential for monitoring product quality and manufacturing processes with high precision.

[1357] 2. Server

[1358] The server receives data acquired from the sensors in real time and is responsible for preprocessing the data, running AI algorithms, and controlling the robotic equipment. Specific software examples include Python for data processing, TensorFlow and PyTorch for AI algorithms, and ROS (Robot Operating System) for controlling the robotic equipment.

[1359] 3. Robotic Devices

[1360] Robotic devices include automated equipment such as robotic arms that receive control commands from a server and execute specified operations, enabling highly accurate and efficient work.

[1361] 4. Terminal

[1362] The terminal displays the operating status of the entire system and provides real-time feedback to the user. It also displays a warning if an abnormality is detected. The terminal is equipped with a graphical user interface (GUI) to improve operability.

[1363] 5. Emotion Engine

[1364] This engine recognizes emotions by analyzing the user's voice, facial expressions, and movements, and feeds the user's emotional information back to the system, dynamically adjusting system operation. For example, if the user is feeling stressed, the system may simplify the operation screen.

[1365] Specific examples

[1366] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. When a user starts the system, the server acquires real-time data from each sensor and processes it. The data is filtered, noise is removed, and it is converted to a unified scale. An AI algorithm then analyzes the data and calculates the optimal procedure for the manufacturing process. Based on the optimal procedure, the server sends control commands to the robot arm to ensure that the parts are positioned accurately. The terminal displays the status of the assembly line to the user in real time and immediately displays an alert if an abnormality is detected.

[1367] Furthermore, the system incorporates an emotion engine that recognizes emotions from the user's voice and facial expressions. For example, if the user is feeling stressed, the device will provide a calming voice and simplified operation screen to reduce the user's stress. It also analyzes the user's long-term emotional data and provides feedback to improve the system's usability and performance.

[1368] Prompt Sentence Examples

[1369] Below are some example prompts to explain the behavior of this system to a generative AI model:

[1370] "Describe a system that preprocesses data from sensors in manufacturing equipment and uses AI algorithms to optimize the manufacturing process and detect anomalies. For example, explain how data is obtained from sensors to control robotic equipment on an automotive parts assembly line. Also, describe the ability to recognize user emotions and adjust system operation based on those emotions."

[1371] In this way, the system of the present invention realizes efficient manufacturing processes, high precision, and cost reduction, and further provides a flexible operating environment that takes user emotions into consideration.

[1372] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1373] Step 1: Data Acquisition

[1374] The server acquires real-time data from various sensors installed in the manufacturing equipment. Specifically, it receives coordinate data from position sensors, force magnitude from force sensors, temperature data from temperature sensors, and vibration data from vibration sensors. Sensor data is acquired as input, and raw sensor data is output.

[1375] Step 2: Data Preprocessing

[1376] The server preprocesses the acquired raw data. Specifically, it filters the data to remove noise and performs scaling. For example, it smooths temperature data and filters out high-frequency components in vibration data. Raw sensor data is generated as input, and preprocessed data is generated as output.

[1377] Step 3: Run the AI ​​algorithm

[1378] The server uses the preprocessed data to run artificial intelligence algorithms that optimize manufacturing processes and detect anomalies. Specifically, the data is input into a machine learning model (e.g., a deep learning model) to detect abnormal patterns and calculate efficient manufacturing procedures. The preprocessed data is the input, and the analysis results of the algorithm are the output.

[1379] Step 4: Controlling the robotic device

[1380] The server controls the robotic equipment based on the output of the AI ​​algorithm. For example, it sends a control command to a robot arm to place a part in a specific position. Specifically, this includes coordinate data and movement speed. The algorithm's analysis results are input, and the control command is generated as output.

[1381] Step 5: Real-time monitoring and feedback

[1382] The terminal monitors the operation of the entire system in real time and provides visual feedback to the user. Specifically, it displays the status of each part of the production line and graphs of acquired data, and immediately issues an alarm and warning message if an abnormality is detected. Sensor data and control results are input, and dashboard displays and warnings are output.

[1383] Step 6: Emotion Recognition

[1384] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements in real time to recognize the user's emotions. For example, it may determine that the user is feeling stressed using camera footage and microphone data. Voice and video data are input, and recognized emotion data is generated as output.

[1385] Step 7: Emotional Adjustment

[1386] The server dynamically adjusts system operations based on the recognized user emotions. For example, if the user is feeling stressed, it can simplify the operation interface. Emotional data is input and an adjusted operation interface is generated as output.

[1387] (Application example 2)

[1388] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1389] In the manufacturing industry, the importance of improving the efficiency of manufacturing processes and detecting anomalies is increasing. However, existing systems have difficulty optimizing manufacturing processes in real time and quickly detecting anomalies. Providing a flexible operating environment that takes user emotions into consideration is also a challenge. In particular, there is room for improvement in the efficient display of information and instruction delivery at the workplace.

[1390] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring real-time data from sensors, means for filtering, denoising, and scaling the acquired data, means for optimizing the manufacturing process and detecting anomalies using the preprocessed data as input, means for controlling robotic equipment based on the output of the artificial intelligence algorithm, means for recognizing user emotions, means for dynamically adjusting system operation based on the recognized emotions, and means for displaying on-site information and issuing instructions using a smart device. This enables the efficiency of the manufacturing process, highly accurate anomaly detection, and the provision of a user-friendly operating environment.

[1391] A "sensor" is a device that is installed in manufacturing equipment and measures physical parameters such as temperature, vibration, position, and force in real time and acquires the data.

[1392] "Filtering" is a process of removing unnecessary information and noise from data obtained from sensors and extracting only the necessary information.

[1393] "Noise removal" is the process of removing errors and unnecessary scattered data contained in data obtained from a sensor.

[1394] "Scaling" is a method of converting data into a uniform scale to make it easier for AI algorithms to process.

[1395] "Preprocessing" refers to a series of processes that convert the acquired raw data into a format suitable for AI algorithms, including filtering, noise removal, and scaling.

[1396] An "artificial intelligence algorithm" is a specific computational method for analyzing data and detecting patterns, including machine learning models.

[1397] "Robotic devices" are automated machines that assist in the manufacturing process and operate according to control commands from a server.

[1398] "Means for recognizing user emotions" refers to technology that analyzes the user's voice, facial expressions, movements, etc., to identify their emotional state.

[1399] "Means for dynamically adjusting system operation" refers to technology that appropriately changes the system's operation interface and functions according to the user's emotions and situation.

[1400] A "smart device" is an advanced electronic device that has the functions of displaying information, acquiring data, and issuing instructions, and is operated directly by the user.

[1401] "Real-time monitoring" is a technology that allows for immediate monitoring of system operation and status, and for immediate response when an abnormality occurs.

[1402] "Means for generating and displaying warnings" refers to technology that monitors the entire system and notifies the user with a visual or audio warning if an abnormality is detected.

[1403] This system acquires real-time data from sensors installed in manufacturing facilities, preprocesses it, and then uses an AI algorithm to optimize the manufacturing process and detect anomalies. It also has the ability to control robotic devices based on the output of the AI ​​algorithm and monitor the operation of the entire system in real time. Another feature of this system is that it also includes the ability to recognize user emotions and dynamically adjust system operation based on the recognized emotions.

[1404] Overall system configuration

[1405] The system consists of the following main components:

[1406] 1. Sensor

[1407] Data is acquired in real time from various sensors (e.g., position sensors, force sensors, temperature sensors, vibration sensors) installed in manufacturing facilities. Each sensor measures a physical parameter in real time and sends the data to a server.

[1408] 2. Server

[1409] The server receives real-time sensor data, filters it, removes noise, and scales it. It uses the pre-processed data as input to run artificial intelligence algorithms that optimize the manufacturing process and detect anomalies. The server also controls robotic devices based on the output of the AI ​​algorithms and has the means to recognize user emotions.

[1410] 3. Robotic Devices

[1411] This refers to automated equipment such as robotic arms that receive control commands from a server and execute the specified operations, thereby improving the efficiency and precision of the manufacturing process.

[1412] 4. Terminal

[1413] It visually displays the overall system operating status, provides real-time feedback to the user, and displays a warning to notify the user if an abnormality is detected.

[1414] 5. Smart Devices

[1415] This is an advanced electronic device for displaying on-site information and issuing instructions. For example, it uses smart glasses, allowing users to give instructions while viewing the situation at the work site in real time.

[1416] 6. Emotion Engine

[1417] The system analyzes the user's voice, facial expressions, and movements to recognize emotions. This recognition information is fed back to the system, and the system's operation is dynamically adjusted based on the user's emotions.

[1418] Specific examples

[1419] For example, consider the application of this system to an automobile parts assembly line. The assembly line is equipped with multiple position sensors, force sensors, temperature sensors, and vibration sensors. The server collects real-time data from each sensor and filters, denoises, and scales it. The preprocessed data is then input into an AI algorithm to calculate the optimal steps for the manufacturing process. Based on the output of the AI ​​algorithm, the server sends control commands to robotic equipment to ensure that parts are positioned accurately.

[1420] Workers wearing the smart glasses can check on-site information and immediately respond if an abnormality occurs. Furthermore, the emotion engine recognizes the user's emotions from their voice and facial expressions, and if the worker is feeling stressed, for example, the system will simplify the operation interface.

[1421] Prompt Sentence Examples

[1422] "Design an application that uses smart glasses to manage and monitor robotic systems in a factory in real time. Preprocess sensor data and use AI algorithms to optimize the manufacturing process and detect anomalies. Also, incorporate a function that recognizes user emotions via a camera and dynamically adjusts the operating interface."

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] The server acquires data in real time from sensors deployed in the manufacturing facility. Inputs include data from position sensors, force sensors, temperature sensors, and vibration sensors. These data are sent to the server, which receives it. The received data is then preprocessed, including filtering, noise removal, and scaling. The output of this stage is the preprocessed data.

[1426] Step 2:

[1427] The server inputs the preprocessed data into an artificial intelligence algorithm. The preprocessed data from step 1 is used as input. The server then runs an AI model based on this data to optimize the manufacturing process and detect anomalies. Specific operations include data analysis and pattern recognition. The output is an optimized manufacturing procedure and detected anomalies.

[1428] Step 3:

[1429] The server controls the robotic device based on the output from the AI ​​algorithm. The optimization procedure and anomaly information from step 2 are used as input. Based on this information, the server sends commands to the robotic device to perform the required operations. The output is a control command that enables the robotic device to operate accurately.

[1430] Step 4:

[1431] The terminal monitors the operating status of the entire system in real time and provides feedback to the user. Input includes status information and abnormality warnings from the server. The terminal uses this information to display visual feedback and warnings to the user. Specific operations include updating the monitoring screen and displaying warning messages. The output is a real-time status report and warnings to the user.

[1432] Step 5:

[1433] The server uses an emotion engine to analyze the user's voice, facial expressions, and movements to recognize emotions. Inputs include the user's voice data and camera footage. The server analyzes this data to identify emotions. The output is the user's emotional state.

[1434] Step 6:

[1435] The server dynamically adjusts system operations based on the recognized emotions. The emotional state data from step 5 is used as input. The server uses this data to modify the user interface and simplify the operation procedures. Specific actions include changing the layout of the operation screen and displaying help messages. The output is the adjusted user interface and operation procedures.

[1436] Step 7:

[1437] The smart device displays on-site information and provides an interface for users to issue instructions. Inputs include control information from the server and on-site status data. The smart device displays this data and allows users to issue the necessary instructions. Specific operations include displaying information and providing a user interface. Outputs include instructions from the user and confirmation of the on-site status.

[1438] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1440] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1441] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1442] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1443] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1444] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1445] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1446] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1447] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1448] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1449] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1450] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1451] 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.

[1452] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1453] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1454] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1455] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1456] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1457] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1458] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1459] The following is further disclosed regarding the above embodiment.

[1460] (Claim 1)

[1461] a means for acquiring real-time data from sensors located at the manufacturing facility;

[1462] means for filtering, denoising and scaling the acquired data;

[1463] an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input;

[1464] means for controlling the robotic device based on the output of the artificial intelligence algorithm;

[1465] A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected;

[1466] A system including:

[1467] (Claim 2)

[1468] 10. The system of claim 1, further comprising means for converting the preprocessed data to a unified scale.

[1469] (Claim 3)

[1470] 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection.

[1471] "Example 1"

[1472] (Claim 1)

[1473] a means for acquiring real-time data from sensors located at the manufacturing facility;

[1474] means for filtering, denoising and scaling the acquired data;

[1475] an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input;

[1476] means for controlling automated equipment based on the output of an artificial intelligence algorithm;

[1477] A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected;

[1478] A system including:

[1479] (Claim 2)

[1480] 10. The system of claim 1, further comprising means for converting the preprocessed data to a unified scale.

[1481] (Claim 3)

[1482] 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection.

[1483] "Application Example 1"

[1484] (Claim 1)

[1485] a means for acquiring real-time data from sensors located at the manufacturing facility;

[1486] means for filtering, denoising and scaling the acquired data;

[1487] an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input;

[1488] means for controlling the robotic device based on the output of the artificial intelligence algorithm;

[1489] A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected;

[1490] a means for displaying real-time data via a mobile device;

[1491] When an abnormality is detected, an alert is displayed and the specific location of the abnormality and countermeasures are provided.

[1492] A system including:

[1493] (Claim 2)

[1494] 10. The system of claim 1, further comprising means for converting the preprocessed data to a unified scale.

[1495] (Claim 3)

[1496] 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection.

[1497] "Example 2: Combining Emotion Engines"

[1498] (Claim 1)

[1499] a means for acquiring real-time data from sensors located at the manufacturing facility;

[1500] means for filtering, denoising and scaling the acquired data;

[1501] an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input;

[1502] means for controlling the robotic device based on the output of the artificial intelligence algorithm;

[1503] A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected;

[1504] A means for recognizing emotions by analyzing the user's voice, facial expressions, and movements;

[1505] means for dynamically adjusting system operation based on the recognized user emotions;

[1506] A system including:

[1507] (Claim 2)

[1508] 10. The system of claim 1, further comprising means for converting the preprocessed data to a unified scale.

[1509] (Claim 3)

[1510] 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection.

[1511] "Application example 2 when combining emotion engines"

[1512] (Claim 1)

[1513] a means for acquiring real-time data from sensors located at the manufacturing facility;

[1514] means for filtering, denoising and scaling the acquired data;

[1515] an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input;

[1516] means for controlling the robotic device based on the output of the artificial intelligence algorithm;

[1517] A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected;

[1518] means for recognizing a user's emotion;

[1519] means for dynamically adjusting system operation based on the recognized emotion;

[1520] A means of displaying on-site information and issuing instructions using a smart device;

[1521] A system including:

[1522] (Claim 2)

[1523] 10. The system of claim 1, further comprising means for converting the preprocessed data to a unified scale.

[1524] (Claim 3)

[1525] 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection. [Explanation of symbols]

[1526] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring real-time data from sensors located at the manufacturing facility; means for filtering, denoising and scaling the acquired data; an artificial intelligence algorithm means for optimizing the manufacturing process and detecting anomalies using the pre-processed data as input; means for controlling the robotic device based on the output of the artificial intelligence algorithm; A means for monitoring the operation of the entire system in real time and generating and displaying an alert when an abnormality is detected; A system including:

2. The system of claim 1 further comprising means for converting the preprocessed data to a unified scale.

3. 10. The system of claim 1, wherein the artificial intelligence algorithm means further comprises means for utilizing machine learning models for manufacturing process optimization and anomaly detection.

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