system

Abstract

The system according to this embodiment aims to continuously monitor abnormal noises in a manufacturing site and detect abnormalities non-contactually. [Solution] The system according to the embodiment comprises a sound collection unit, a sound analysis unit, and an anomaly detection unit. The sound collection unit collects sounds from the manufacturing site. The sound analysis unit analyzes the sounds collected by the sound collection unit, removes noise, and emphasizes abnormal sounds. The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, abnormal sounds at the manufacturing site are detected relying on the experience of skilled workers, and there is a problem that continuous monitoring for 24 hours a day and 365 days a year is difficult.

[0005] The system according to the embodiment aims to continuously monitor abnormal sounds at the manufacturing site and detect abnormalities without contact.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sound collection unit, a sound analysis unit, and an anomaly detection unit. The sound collection unit collects sounds from the manufacturing site. The sound analysis unit analyzes the sounds collected by the sound collection unit, removes noise, and emphasizes abnormal sounds. The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. [Effects of the Invention]

[0007] The system according to this embodiment can continuously monitor abnormal noises in the manufacturing site and detect abnormalities non-contactually. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An anomaly detection system according to an embodiment of the present invention implements the "master craftsman's ear" ability of skilled workers in manufacturing sites into AI, enabling continuous 24 / 7 monitoring and non-contact anomaly detection. The anomaly detection system collects and analyzes sounds in the manufacturing site and detects signs of anomalies, thereby achieving continuous 24 / 7 monitoring and non-contact anomaly detection. For example, the anomaly detection system's sound collection unit collects sounds in the manufacturing site using an ultra-compact, high-performance microphone array. Next, the collected sounds are sent to the sound analysis unit, where noise is removed and abnormal sounds are emphasized using ambient sound enhancement technology. Furthermore, the anomaly detection unit performs pattern learning and predictive detection, analyzing changes in sound in real time. This enables the detection of signs of anomalies that cannot be perceived visually, achieving continuous 24 / 7 monitoring and non-contact anomaly detection. For example, the anomaly detection system can collect various sounds, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. Next, the collected sounds are sent to the sound analysis unit. The sound analysis unit removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. This allows for the clear detection of abnormal sounds. For example, it can detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. Furthermore, the anomaly detection unit performs pattern learning and predictive detection. The anomaly detection unit learns the patterns of collected sounds and detects signs of anomalies. For example, it can analyze abnormalities in press machines and welding quality in automotive parts manufacturing, and anomalies on assembly lines in real time. This implements the "expert ear" ability of skilled workers on the manufacturing floor into AI, enabling continuous 24 / 7 monitoring and non-contact anomaly detection. As a result, quality control on the manufacturing floor improves, and anomalies that appear as changes in sound can be detected early without relying on visual inspection.

[0029] The anomaly detection system according to this embodiment comprises a sound collection unit, a sound analysis unit, and an anomaly detection unit. The sound collection unit collects sounds from the manufacturing site. The sound collection unit collects sounds from the manufacturing site using, for example, an ultra-compact, high-performance microphone array. The sound collection unit can collect various sounds, for example, the cutting sounds of machine tools or the sounds of presses used in automobile parts manufacturing. The sound collection unit can collect sounds over a wide range with high precision. The sound analysis unit analyzes the collected sounds, removes noise, and emphasizes abnormal sounds. The sound analysis unit removes noise and emphasizes abnormal sounds using, for example, ambient sound enhancement technology. The sound analysis unit can detect, for example, changes in the cutting sounds of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit ensures that abnormal sounds are clearly detected. The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. The anomaly detection unit performs, for example, pattern learning and sign detection, and analyzes changes in sound in real time. The anomaly detection unit can analyze, for example, abnormalities in press machines used in automotive parts manufacturing, welding quality issues, and assembly line abnormalities in real time. The anomaly detection unit can learn, for example, the patterns of collected sounds and detect signs of anomalies. As a result, the anomaly detection system according to this embodiment can collect and analyze sounds from the manufacturing site and detect signs of anomalies, enabling continuous 24 / 7 monitoring and non-contact anomaly detection.

[0030] The sound collection unit collects sounds from the manufacturing site. For example, the sound collection unit uses a miniature, high-performance microphone array to collect sounds from the manufacturing site. Specifically, the microphone array is a combination of multiple microphones, enabling high-precision collection of sounds over a wide area. The microphone array is installed in various locations on the manufacturing site and can collect a variety of sounds, such as the cutting sounds of machine tools and the sounds of presses used in automotive parts manufacturing. For example, the cutting sounds of machine tools are generated when a tool cuts material, and the characteristics of the sound change depending on the tool's condition and cutting conditions. The sounds of presses used in automotive parts manufacturing are generated when metal parts are pressed, and the characteristics of the sound change depending on the press's operating state and the condition of the parts. The sound collection unit collects these sounds with high precision and transmits them to a central database in real time. Furthermore, the sound collection unit centrally manages the collected sound data and can collaborate with other systems and departments as needed. For example, the collected sound data is stored on a cloud server, making it accessible to the sound analysis unit and the anomaly detection unit. Furthermore, the sound collection unit can flexibly respond to specific situations and conditions by adjusting the collection frequency and accuracy. This allows the sound collection unit to collect sound efficiently and effectively, improving the overall performance of the system.

[0031] The sound analysis unit analyzes the collected sound, removes noise, and emphasizes abnormal sounds. Specifically, the sound analysis unit uses ambient sound enhancement technology to remove noise and emphasize abnormal sounds. Ambient sound enhancement technology is a technique that removes unwanted noise from collected sound data and clarifies abnormal sounds. For example, it can detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit analyzes the collected sound data in real time to ensure that abnormal sounds are clearly detected. Specifically, the sound analysis unit decomposes the sound data into frequency components and extracts the characteristics of abnormal sounds. For example, changes in the cutting sound of machine tools often appear as changes in specific frequency components. Tool wear and abnormal noises from bearings are also often detected as changes in specific frequency components. The sound analysis unit detects these changes in frequency components and emphasizes abnormal sounds. Furthermore, the sound analysis unit can also analyze long-term trends in abnormal sounds by utilizing past data and statistical information. For example, based on past sound data, it can analyze the frequency and patterns of abnormal sounds from specific machines or parts to detect signs of future abnormalities. Furthermore, the sound analysis unit can utilize machine learning algorithms to improve the accuracy of abnormal sound detection. This allows the sound analysis unit to quickly and accurately analyze the collected sound data and highlight abnormal sounds, thereby improving the overall reliability and safety of the system.

[0032] The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. Specifically, the anomaly detection unit performs pattern learning and sign detection, analyzing changes in sound in real time. For example, the anomaly detection unit can analyze in real time anomalies in press machines in automotive parts manufacturing, welding quality problems, and assembly line anomalies. The anomaly detection unit uses machine learning algorithms to learn the collected sound patterns and detect signs of anomalies. Specifically, the anomaly detection unit learns normal and abnormal sound patterns and compares them with the collected sound data in real time. For example, it learns the sounds of a normal and abnormal press machine and issues a warning when an abnormal sound is detected. The anomaly detection unit also continuously monitors changes in sound data to detect signs of anomalies. For example, welding quality problems and assembly line anomalies often manifest as changes in sound data. The anomaly detection unit detects these changes in real time and detects signs of anomalies early. Furthermore, the anomaly detection unit can provide information to enable appropriate countermeasures when it detects signs of an anomaly. For example, it can notify the responsible person when an anomaly is detected, prompting a quick response. The anomaly detection unit can also automatically adjust the operation of the entire system when it detects signs of an anomaly. In this way, the anomaly detection unit can improve the reliability and safety of the entire system by detecting signs of an anomaly in real time and supporting a quick and appropriate response.

[0033] The sound collection unit can collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The sound collection unit can, for example, collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The sound collection unit can, for example, collect sounds over a wide range with high precision. The sound collection unit can collect various sounds, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. This allows for the collection of sounds over a wide range with high precision using an ultra-compact, high-performance microphone array. Some or all of the above-described processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input the sound data collected using the ultra-compact, high-performance microphone array into a generating AI and have the generating AI perform analysis of the sound data.

[0034] The sound analysis unit can remove noise and enhance abnormal sounds using ambient sound enhancement technology. The sound analysis unit can, for example, remove noise and enhance abnormal sounds using ambient sound enhancement technology. The sound analysis unit can detect, for example, changes in cutting sounds of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit can, for example, clearly detect abnormal sounds. As a result, by using ambient sound enhancement technology, abnormal sounds become clearly detectable. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input collected sound data into a generating AI and have the generating AI perform noise reduction and enhancement of abnormal sounds.

[0035] The anomaly detection unit can learn the patterns of collected sounds and detect signs of anomalies. For example, the anomaly detection unit learns the patterns of collected sounds and detects signs of anomalies. For example, the anomaly detection unit performs pattern learning and sign detection and analyzes changes in sound in real time. For example, the anomaly detection unit can analyze in real time anomalies in press machines for automobile parts manufacturing, welding quality problems, anomalies in assembly lines, etc. By learning the patterns of collected sounds, it can detect signs of anomalies. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input collected sound data into a generating AI and have the generating AI perform pattern learning to detect signs of anomalies.

[0036] The anomaly detection unit can detect changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. For example, the anomaly detection unit can detect changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. For example, the anomaly detection unit can detect changes in the cutting sound of a machine tool. For example, the anomaly detection unit can detect tool wear. For example, the anomaly detection unit can detect abnormal noises from bearings. This allows for early detection of anomalies by detecting changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input cutting sound data from a machine tool into a generating AI and have the generating AI perform anomaly detection.

[0037] The anomaly detection unit can analyze abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze abnormalities in press machines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze welding quality issues in real time. For example, the anomaly detection unit can analyze abnormalities in assembly lines in real time. This allows for rapid detection of abnormalities by analyzing abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input sound data from press machines in automotive parts manufacturing into a generating AI and have the generating AI perform real-time analysis of abnormalities.

[0038] The sound collection unit can improve the accuracy of anomaly detection by focusing on collecting sound from specific machines or areas. For example, the sound collection unit can improve the accuracy of anomaly detection by focusing on collecting sound from specific machines or areas. For example, the sound collection unit can intensively collect sound from a specific machine tool and detect anomalies in that machine early. For example, the sound collection unit can collect sound from a specific area of ​​a manufacturing line and identify anomalies in that area. For example, the sound collection unit can collect sound from a specific process and detect anomalies in that process. In this way, the accuracy of anomaly detection can be improved by focusing on collecting sound from specific machines or areas. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input sound data from specific machines or areas into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0039] The sound collection unit can select and collect different types of sounds. For example, the sound collection unit can select and collect different types of sounds. For example, the sound collection unit can collect only machine sounds to detect machine abnormalities. For example, the sound collection unit can collect only ambient sounds to detect environmental abnormalities in a manufacturing site. For example, the sound collection unit can collect only human voices to detect abnormalities in workers. By selecting the types of sounds to collect, the accuracy of abnormality detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input the types of sounds to be collected into a generating AI and have the generating AI perform the sound selection.

[0040] The sound collection unit can simultaneously collect environmental data such as temperature and humidity at the manufacturing site while collecting sound. For example, the sound collection unit can simultaneously collect environmental data such as temperature and humidity at the manufacturing site while collecting sound. For example, the sound collection unit can collect temperature data to identify the cause of abnormal noise. For example, the sound collection unit can collect humidity data to identify the cause of abnormal noise. For example, the sound collection unit can comprehensively collect environmental data to identify the cause of abnormal noise. By simultaneously collecting environmental data such as temperature and humidity at the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input environmental data such as temperature and humidity into a generating AI and have the generating AI perform anomaly detection accuracy improvement.

[0041] The sound collection unit can collect sounds based on the work schedule of the manufacturing site during sound collection. For example, the sound collection unit can collect sounds based on the work schedule of the manufacturing site during sound collection. For example, the sound collection unit can collect sounds at specific time periods based on the work schedule. For example, the sound collection unit can collect sounds from specific processes based on the work schedule. For example, the sound collection unit can collect sounds from specific work areas based on the work schedule. By collecting sounds based on the work schedule of the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input work schedule data into a generating AI and have the generating AI perform sound collection.

[0042] The sound analysis unit can apply different analysis algorithms depending on the type of abnormal sound. For example, the sound analysis unit can apply different analysis algorithms depending on the type of abnormal sound. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal machine sounds. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal ambient sounds. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal human voices. By applying different analysis algorithms depending on the type of abnormal sound, the accuracy of the analysis can be improved. Some or all of the above-described processes in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input the type of abnormal sound to a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The sound analysis unit can improve the accuracy of its analysis by referring to past anomaly data during sound analysis. For example, the sound analysis unit can improve the accuracy of its analysis by referring to past anomaly data during sound analysis. For example, the sound analysis unit can adjust its analysis algorithm by referring to past anomaly data. For example, the sound analysis unit can learn patterns of anomaly sounds by referring to past anomaly data. For example, the sound analysis unit can improve the accuracy of its anomaly sound prediction by referring to past anomaly data. In this way, the accuracy of the analysis can be improved by referring to past anomaly data. Some or all of the above processes in the sound analysis unit may be performed using AI, for example, or without using AI. For example, the sound analysis unit can input past anomaly data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The sound analysis unit can change the focus of its analysis based on the work content at the manufacturing site during sound analysis. For example, the sound analysis unit can change the focus of its analysis based on the work content at the manufacturing site during sound analysis. For example, the sound analysis unit can change the focus of its analysis based on specific work content. For example, the sound analysis unit can change the focus of its analysis based on specific processes. For example, the sound analysis unit can change the focus of its analysis based on specific work areas. By changing the focus of the analysis based on the work content at the manufacturing site, the accuracy of the analysis can be improved. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without using AI. For example, the sound analysis unit can input work content data into a generating AI and have the generating AI execute the process of changing the focus of the analysis.

[0045] The sound analysis unit can adjust the level of detail of the analysis based on the frequency of abnormal sounds during sound analysis. For example, the sound analysis unit can adjust the level of detail of the analysis based on the frequency of abnormal sounds during sound analysis. For example, the sound analysis unit can perform a detailed analysis when the frequency of abnormal sounds is high. For example, the sound analysis unit can perform a simplified analysis when the frequency of abnormal sounds is low. The sound analysis unit can adjust the level of detail of the analysis according to the frequency of abnormal sounds. By adjusting the level of detail of the analysis based on the frequency of abnormal sounds, the efficiency of the analysis can be improved. Some or all of the above-described processes in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input abnormal sound frequency data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The anomaly detection unit can apply different detection algorithms depending on the type of anomaly. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in machine sounds. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in ambient sounds. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in human voices. By applying the most suitable detection algorithm according to the type of anomaly, the accuracy of detection can be improved. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the type of anomaly to a generating AI and have the generating AI execute the application of the detection algorithm.

[0047] The anomaly detection unit can improve the accuracy of detection by referring to past anomaly data when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of detection by referring to past anomaly data when an anomaly is detected. For example, the anomaly detection unit can adjust the detection algorithm by referring to past anomaly data. For example, the anomaly detection unit can learn patterns of anomaly sounds by referring to past anomaly data. For example, the anomaly detection unit can improve the accuracy of anomaly sound prediction by referring to past anomaly data. In this way, the accuracy of detection can be improved by referring to past anomaly data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input past anomaly data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0048] The anomaly detection unit can detect anomalies based on the work schedule of the manufacturing site when an anomaly is detected. For example, the anomaly detection unit can detect anomalies based on the work schedule of the manufacturing site when an anomaly is detected. For example, the anomaly detection unit can detect anomalies in a specific time period based on the work schedule. For example, the anomaly detection unit can detect anomalies in a specific process based on the work schedule. For example, the anomaly detection unit can detect anomalies in a specific work area based on the work schedule. By performing anomaly detection based on the work schedule of the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input work schedule data into a generating AI and have the generating AI perform anomaly detection.

[0049] The anomaly detection unit can adjust the level of detail of detection based on the location of the abnormal sound when an anomaly is detected. For example, the anomaly detection unit can adjust the level of detail of detection based on the location of the abnormal sound when an anomaly is detected. For example, the anomaly detection unit can perform detailed detection if the location of the abnormal sound is in an important area. For example, the anomaly detection unit can perform simplified detection if the location of the abnormal sound is in a general area. The anomaly detection unit can adjust the level of detail of detection according to the location of the abnormal sound. By adjusting the level of detail of detection based on the location of the abnormal sound, the efficiency of detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input abnormal sound location data to a generating AI and have the generating AI perform the adjustment of the level of detail of detection.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] An anomaly detection system can be equipped with a vibration collection unit in addition to a sound collection unit. The vibration collection unit can collect vibrations from machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the vibration collection unit can collect vibrations from machine tools to detect tool wear and bearing abnormalities. It can also collect vibrations from presses used in automotive parts manufacturing to detect press abnormalities early. Furthermore, the vibration collection unit can collect vibrations from the entire production line to identify anomalies throughout the line. By collecting both sound and vibration, the accuracy of anomaly detection can be further improved.

[0052] An anomaly detection system can be equipped with a temperature collection unit in addition to a sound collection unit. The temperature collection unit can collect the temperature of machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the temperature collection unit can collect the temperature of a machine tool and detect anomalies due to overheating. It can also collect the temperature of a press machine in automotive parts manufacturing and detect press machine anomalies at an early stage. Furthermore, the temperature collection unit can collect the temperature of a manufacturing line and identify anomalies throughout the entire line. By collecting both sound and temperature, the accuracy of anomaly detection can be further improved.

[0053] An anomaly detection system can be equipped with a humidity collection unit in addition to a sound collection unit. The humidity collection unit can collect humidity in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the humidity collection unit can collect humidity around machine tools and detect anomalies due to humidity changes. It can also collect humidity around presses in automotive parts manufacturing, enabling early detection of press malfunctions. Furthermore, the humidity collection unit can collect humidity along the entire production line, identifying anomalies across the entire line. By collecting both sound and humidity, the accuracy of anomaly detection can be further improved.

[0054] An anomaly detection system can include a light collection unit in addition to a sound collection unit. The light collection unit can collect changes in light at the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the light collection unit can collect changes in light around a machine tool and detect anomalies caused by these changes in light. It can also collect changes in light around a press machine in automotive parts manufacturing, enabling early detection of press machine malfunctions. Furthermore, the light collection unit can collect changes in light along a manufacturing line, identifying anomalies across the entire line. By collecting both sound and light, the accuracy of anomaly detection can be further improved.

[0055] An anomaly detection system can be equipped with a pressure collection unit in addition to a sound collection unit. The pressure collection unit can collect pressure from machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the pressure collection unit can collect pressure from machine tools and detect anomalies due to pressure changes. It can also collect pressure from presses in automotive parts manufacturing, enabling early detection of press malfunctions. Furthermore, the pressure collection unit can collect pressure from the entire production line, identifying anomalies across the entire line. By collecting both sound and pressure, the accuracy of anomaly detection can be further improved.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The sound collection unit collects sounds from the manufacturing site. For example, using an ultra-compact, high-performance microphone array, it can collect a wide range of sounds with high precision, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. Step 2: The sound analysis unit analyzes the collected sounds, removes noise, and emphasizes abnormal sounds. For example, by using ambient sound enhancement technology to remove noise, it is possible to clearly detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. Step 3: The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. For example, by performing pattern learning and predictive detection, it is possible to analyze in real time anomalies in press machines for automotive parts manufacturing, welding quality problems, and anomalies in assembly lines.

[0058] (Example of form 2) An anomaly detection system according to an embodiment of the present invention implements the "master craftsman's ear" ability of skilled workers in manufacturing sites into AI, enabling continuous 24 / 7 monitoring and non-contact anomaly detection. The anomaly detection system collects and analyzes sounds in the manufacturing site and detects signs of anomalies, thereby achieving continuous 24 / 7 monitoring and non-contact anomaly detection. For example, the anomaly detection system's sound collection unit collects sounds in the manufacturing site using an ultra-compact, high-performance microphone array. Next, the collected sounds are sent to the sound analysis unit, where noise is removed and abnormal sounds are emphasized using ambient sound enhancement technology. Furthermore, the anomaly detection unit performs pattern learning and predictive detection, analyzing changes in sound in real time. This enables the detection of signs of anomalies that cannot be perceived visually, achieving continuous 24 / 7 monitoring and non-contact anomaly detection. For example, the anomaly detection system can collect various sounds, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. Next, the collected sounds are sent to the sound analysis unit. The sound analysis unit removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. This allows for the clear detection of abnormal sounds. For example, it can detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. Furthermore, the anomaly detection unit performs pattern learning and predictive detection. The anomaly detection unit learns the patterns of collected sounds and detects signs of anomalies. For example, it can analyze abnormalities in press machines and welding quality in automotive parts manufacturing, and anomalies on assembly lines in real time. This implements the "expert ear" ability of skilled workers on the manufacturing floor into AI, enabling continuous 24 / 7 monitoring and non-contact anomaly detection. As a result, quality control on the manufacturing floor improves, and anomalies that appear as changes in sound can be detected early without relying on visual inspection.

[0059] The anomaly detection system according to this embodiment comprises a sound collection unit, a sound analysis unit, and an anomaly detection unit. The sound collection unit collects sounds from the manufacturing site. The sound collection unit collects sounds from the manufacturing site using, for example, an ultra-compact, high-performance microphone array. The sound collection unit can collect various sounds, for example, the cutting sounds of machine tools or the sounds of presses used in automobile parts manufacturing. The sound collection unit can collect sounds over a wide range with high precision. The sound analysis unit analyzes the collected sounds, removes noise, and emphasizes abnormal sounds. The sound analysis unit removes noise and emphasizes abnormal sounds using, for example, ambient sound enhancement technology. The sound analysis unit can detect, for example, changes in the cutting sounds of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit ensures that abnormal sounds are clearly detected. The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. The anomaly detection unit performs, for example, pattern learning and sign detection, and analyzes changes in sound in real time. The anomaly detection unit can analyze, for example, abnormalities in press machines used in automotive parts manufacturing, welding quality issues, and assembly line abnormalities in real time. The anomaly detection unit can learn, for example, the patterns of collected sounds and detect signs of anomalies. As a result, the anomaly detection system according to this embodiment can collect and analyze sounds from the manufacturing site and detect signs of anomalies, enabling continuous 24 / 7 monitoring and non-contact anomaly detection.

[0060] The sound collection unit collects sounds from the manufacturing site. For example, the sound collection unit uses a miniature, high-performance microphone array to collect sounds from the manufacturing site. Specifically, the microphone array is a combination of multiple microphones, enabling high-precision collection of sounds over a wide area. The microphone array is installed in various locations on the manufacturing site and can collect a variety of sounds, such as the cutting sounds of machine tools and the sounds of presses used in automotive parts manufacturing. For example, the cutting sounds of machine tools are generated when a tool cuts material, and the characteristics of the sound change depending on the tool's condition and cutting conditions. The sounds of presses used in automotive parts manufacturing are generated when metal parts are pressed, and the characteristics of the sound change depending on the press's operating state and the condition of the parts. The sound collection unit collects these sounds with high precision and transmits them to a central database in real time. Furthermore, the sound collection unit centrally manages the collected sound data and can collaborate with other systems and departments as needed. For example, the collected sound data is stored on a cloud server, making it accessible to the sound analysis unit and the anomaly detection unit. Furthermore, the sound collection unit can flexibly respond to specific situations and conditions by adjusting the collection frequency and accuracy. This allows the sound collection unit to collect sound efficiently and effectively, improving the overall performance of the system.

[0061] The sound analysis unit analyzes the collected sound, removes noise, and emphasizes abnormal sounds. Specifically, the sound analysis unit uses ambient sound enhancement technology to remove noise and emphasize abnormal sounds. Ambient sound enhancement technology is a technique that removes unwanted noise from collected sound data and clarifies abnormal sounds. For example, it can detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit analyzes the collected sound data in real time to ensure that abnormal sounds are clearly detected. Specifically, the sound analysis unit decomposes the sound data into frequency components and extracts the characteristics of abnormal sounds. For example, changes in the cutting sound of machine tools often appear as changes in specific frequency components. Tool wear and abnormal noises from bearings are also often detected as changes in specific frequency components. The sound analysis unit detects these changes in frequency components and emphasizes abnormal sounds. Furthermore, the sound analysis unit can also analyze long-term trends in abnormal sounds by utilizing past data and statistical information. For example, based on past sound data, it can analyze the frequency and patterns of abnormal sounds from specific machines or parts to detect signs of future abnormalities. Furthermore, the sound analysis unit can utilize machine learning algorithms to improve the accuracy of abnormal sound detection. This allows the sound analysis unit to quickly and accurately analyze the collected sound data and highlight abnormal sounds, thereby improving the overall reliability and safety of the system.

[0062] The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. Specifically, the anomaly detection unit performs pattern learning and sign detection, analyzing changes in sound in real time. For example, the anomaly detection unit can analyze in real time anomalies in press machines in automotive parts manufacturing, welding quality problems, and assembly line anomalies. The anomaly detection unit uses machine learning algorithms to learn the collected sound patterns and detect signs of anomalies. Specifically, the anomaly detection unit learns normal and abnormal sound patterns and compares them with the collected sound data in real time. For example, it learns the sounds of a normal and abnormal press machine and issues a warning when an abnormal sound is detected. The anomaly detection unit also continuously monitors changes in sound data to detect signs of anomalies. For example, welding quality problems and assembly line anomalies often manifest as changes in sound data. The anomaly detection unit detects these changes in real time and detects signs of anomalies early. Furthermore, the anomaly detection unit can provide information to enable appropriate countermeasures when it detects signs of an anomaly. For example, it can notify the responsible person when an anomaly is detected, prompting a quick response. The anomaly detection unit can also automatically adjust the operation of the entire system when it detects signs of an anomaly. In this way, the anomaly detection unit can improve the reliability and safety of the entire system by detecting signs of an anomaly in real time and supporting a quick and appropriate response.

[0063] The sound collection unit can collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The sound collection unit can, for example, collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The sound collection unit can, for example, collect sounds over a wide range with high precision. The sound collection unit can collect various sounds, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. This allows for the collection of sounds over a wide range with high precision using an ultra-compact, high-performance microphone array. Some or all of the above-described processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input the sound data collected using the ultra-compact, high-performance microphone array into a generating AI and have the generating AI perform analysis of the sound data.

[0064] The sound analysis unit can remove noise and enhance abnormal sounds using ambient sound enhancement technology. The sound analysis unit can, for example, remove noise and enhance abnormal sounds using ambient sound enhancement technology. The sound analysis unit can detect, for example, changes in cutting sounds of machine tools, tool wear, and abnormal noises from bearings. The sound analysis unit can, for example, clearly detect abnormal sounds. As a result, by using ambient sound enhancement technology, abnormal sounds become clearly detectable. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input collected sound data into a generating AI and have the generating AI perform noise reduction and enhancement of abnormal sounds.

[0065] The anomaly detection unit can learn the patterns of collected sounds and detect signs of anomalies. For example, the anomaly detection unit learns the patterns of collected sounds and detects signs of anomalies. For example, the anomaly detection unit performs pattern learning and sign detection and analyzes changes in sound in real time. For example, the anomaly detection unit can analyze in real time anomalies in press machines for automobile parts manufacturing, welding quality problems, anomalies in assembly lines, etc. By learning the patterns of collected sounds, it can detect signs of anomalies. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input collected sound data into a generating AI and have the generating AI perform pattern learning to detect signs of anomalies.

[0066] The anomaly detection unit can detect changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. For example, the anomaly detection unit can detect changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. For example, the anomaly detection unit can detect changes in the cutting sound of a machine tool. For example, the anomaly detection unit can detect tool wear. For example, the anomaly detection unit can detect abnormal noises from bearings. This allows for early detection of anomalies by detecting changes in the cutting sound of a machine tool, tool wear, and abnormal noises from bearings. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input cutting sound data from a machine tool into a generating AI and have the generating AI perform anomaly detection.

[0067] The anomaly detection unit can analyze abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze abnormalities in press machines in automotive parts manufacturing in real time. For example, the anomaly detection unit can analyze welding quality issues in real time. For example, the anomaly detection unit can analyze abnormalities in assembly lines in real time. This allows for rapid detection of abnormalities by analyzing abnormalities in press machines, welding quality issues, and assembly lines in automotive parts manufacturing in real time. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input sound data from press machines in automotive parts manufacturing into a generating AI and have the generating AI perform real-time analysis of abnormalities.

[0068] The sound collection unit can estimate the user's emotions and adjust the timing of sound collection based on the estimated user emotions. For example, the sound collection unit can estimate the user's emotions and adjust the timing of sound collection based on the estimated user emotions. For example, if the user is stressed, the sound collection unit can increase the frequency of sound collection to improve the accuracy of anomaly detection. For example, if the user is relaxed, the sound collection unit can decrease the frequency of sound collection to reduce the system load. For example, if the user is in a hurry, the sound collection unit can prioritize collecting only important sounds to perform rapid anomaly detection. This improves the accuracy of anomaly detection by adjusting the timing of sound collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input user emotion data into a generating AI, which can then adjust the timing of sound collection.

[0069] The sound collection unit can improve the accuracy of anomaly detection by focusing on collecting sound from specific machines or areas. For example, the sound collection unit can improve the accuracy of anomaly detection by focusing on collecting sound from specific machines or areas. For example, the sound collection unit can intensively collect sound from a specific machine tool and detect anomalies in that machine early. For example, the sound collection unit can collect sound from a specific area of ​​a manufacturing line and identify anomalies in that area. For example, the sound collection unit can collect sound from a specific process and detect anomalies in that process. In this way, the accuracy of anomaly detection can be improved by focusing on collecting sound from specific machines or areas. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input sound data from specific machines or areas into a generating AI and have the generating AI perform the anomaly detection accuracy improvement.

[0070] The sound collection unit can select and collect different types of sounds. For example, the sound collection unit can select and collect different types of sounds. For example, the sound collection unit can collect only machine sounds to detect machine abnormalities. For example, the sound collection unit can collect only ambient sounds to detect environmental abnormalities in a manufacturing site. For example, the sound collection unit can collect only human voices to detect abnormalities in workers. By selecting the types of sounds to collect, the accuracy of abnormality detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input the types of sounds to be collected into a generating AI and have the generating AI perform the sound selection.

[0071] The sound collection unit can estimate the user's emotions and determine the priority of sounds to collect based on the estimated user emotions. For example, the sound collection unit can estimate the user's emotions and determine the priority of sounds to collect based on the estimated user emotions. For example, if the user is stressed, the sound collection unit can prioritize the collection of important sounds to improve the accuracy of anomaly detection. For example, if the user is relaxed, the sound collection unit can collect all sounds evenly to reduce the system load. For example, if the user is in a hurry, the sound collection unit can prioritize the collection of specific sounds to perform rapid anomaly detection. This improves the accuracy of anomaly detection by determining the priority of sounds to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input user emotion data into a generating AI, which can then prioritize the sounds to be collected.

[0072] The sound collection unit can simultaneously collect environmental data such as temperature and humidity at the manufacturing site while collecting sound. For example, the sound collection unit can simultaneously collect environmental data such as temperature and humidity at the manufacturing site while collecting sound. For example, the sound collection unit can collect temperature data to identify the cause of abnormal noise. For example, the sound collection unit can collect humidity data to identify the cause of abnormal noise. For example, the sound collection unit can comprehensively collect environmental data to identify the cause of abnormal noise. By simultaneously collecting environmental data such as temperature and humidity at the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input environmental data such as temperature and humidity into a generating AI and have the generating AI perform anomaly detection accuracy improvement.

[0073] The sound collection unit can collect sounds based on the work schedule of the manufacturing site during sound collection. For example, the sound collection unit can collect sounds based on the work schedule of the manufacturing site during sound collection. For example, the sound collection unit can collect sounds at specific time periods based on the work schedule. For example, the sound collection unit can collect sounds from specific processes based on the work schedule. For example, the sound collection unit can collect sounds from specific work areas based on the work schedule. By collecting sounds based on the work schedule of the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input work schedule data into a generating AI and have the generating AI perform sound collection.

[0074] The sound analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the sound analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the sound analysis unit can provide a simple display method to make the analysis results easy to understand. For example, if the user is relaxed, the sound analysis unit can provide a detailed display method to explain the analysis results in detail. For example, if the user is in a hurry, the sound analysis unit can provide a concise display method to quickly convey the analysis results. In this way, by adjusting the display method of the analysis results based on the user's emotions, the analysis results can be conveyed in an easy-to-understand manner. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without using AI. For example, the sound analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0075] The sound analysis unit can apply different analysis algorithms depending on the type of abnormal sound. For example, the sound analysis unit can apply different analysis algorithms depending on the type of abnormal sound. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal machine sounds. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal ambient sounds. For example, the sound analysis unit can apply a specific analysis algorithm to abnormal human voices. By applying different analysis algorithms depending on the type of abnormal sound, the accuracy of the analysis can be improved. Some or all of the above-described processes in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input the type of abnormal sound to a generating AI and have the generating AI execute the application of the analysis algorithm.

[0076] The sound analysis unit can improve the accuracy of its analysis by referring to past anomaly data during sound analysis. For example, the sound analysis unit can improve the accuracy of its analysis by referring to past anomaly data during sound analysis. For example, the sound analysis unit can adjust its analysis algorithm by referring to past anomaly data. For example, the sound analysis unit can learn patterns of anomaly sounds by referring to past anomaly data. For example, the sound analysis unit can improve the accuracy of its anomaly sound prediction by referring to past anomaly data. In this way, the accuracy of the analysis can be improved by referring to past anomaly data. Some or all of the above processes in the sound analysis unit may be performed using AI, for example, or without using AI. For example, the sound analysis unit can input past anomaly data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0077] The sound analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, the sound analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the sound analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the sound analysis unit can display all analysis results evenly. For example, if the user is in a hurry, the sound analysis unit can prioritize displaying specific analysis results. In this way, by prioritizing analysis results based on the user's emotions, important analysis results can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input user emotion data into a generative AI and have the generative AI perform the priority determination of analysis results.

[0078] The sound analysis unit can change the focus of its analysis based on the work content at the manufacturing site during sound analysis. For example, the sound analysis unit can change the focus of its analysis based on the work content at the manufacturing site during sound analysis. For example, the sound analysis unit can change the focus of its analysis based on specific work content. For example, the sound analysis unit can change the focus of its analysis based on specific processes. For example, the sound analysis unit can change the focus of its analysis based on specific work areas. By changing the focus of the analysis based on the work content at the manufacturing site, the accuracy of the analysis can be improved. Some or all of the above processing in the sound analysis unit may be performed using AI, for example, or without using AI. For example, the sound analysis unit can input work content data into a generating AI and have the generating AI execute the process of changing the focus of the analysis.

[0079] The sound analysis unit can adjust the level of detail of the analysis based on the frequency of abnormal sounds during sound analysis. For example, the sound analysis unit can adjust the level of detail of the analysis based on the frequency of abnormal sounds during sound analysis. For example, the sound analysis unit can perform a detailed analysis when the frequency of abnormal sounds is high. For example, the sound analysis unit can perform a simplified analysis when the frequency of abnormal sounds is low. The sound analysis unit can adjust the level of detail of the analysis according to the frequency of abnormal sounds. By adjusting the level of detail of the analysis based on the frequency of abnormal sounds, the efficiency of the analysis can be improved. Some or all of the above-described processes in the sound analysis unit may be performed using AI, for example, or without AI. For example, the sound analysis unit can input abnormal sound frequency data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The anomaly detection unit can estimate the user's emotions and adjust the anomaly detection alert method based on the estimated user emotions. For example, the anomaly detection unit can estimate the user's emotions and adjust the anomaly detection alert method based on the estimated user emotions. For example, if the user is stressed, the anomaly detection unit can provide a simple alert method to clearly communicate the anomaly. For example, if the user is relaxed, the anomaly detection unit can provide a detailed alert method to explain the details of the anomaly. For example, if the user is in a hurry, the anomaly detection unit can provide a concise alert method to quickly communicate the anomaly. In this way, by adjusting the anomaly detection alert method based on the user's emotions, the anomaly can be clearly communicated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input user emotion data into a generating AI and have the generating AI adjust the alert method.

[0081] The anomaly detection unit can apply different detection algorithms depending on the type of anomaly. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in machine sounds. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in ambient sounds. For example, the anomaly detection unit can apply a specific detection algorithm to anomalies in human voices. By applying the most suitable detection algorithm according to the type of anomaly, the accuracy of detection can be improved. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input the type of anomaly to a generating AI and have the generating AI execute the application of the detection algorithm.

[0082] The anomaly detection unit can improve the accuracy of detection by referring to past anomaly data when an anomaly is detected. For example, the anomaly detection unit can improve the accuracy of detection by referring to past anomaly data when an anomaly is detected. For example, the anomaly detection unit can adjust the detection algorithm by referring to past anomaly data. For example, the anomaly detection unit can learn patterns of anomaly sounds by referring to past anomaly data. For example, the anomaly detection unit can improve the accuracy of anomaly sound prediction by referring to past anomaly data. In this way, the accuracy of detection can be improved by referring to past anomaly data. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input past anomaly data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0083] The anomaly detection unit can estimate the user's emotions and determine the priority of anomaly detection based on the estimated user emotions. For example, the anomaly detection unit can prioritize detecting important anomalies when the user is stressed. For example, the anomaly detection unit can detect anomalies evenly across the board when the user is relaxed. For example, the anomaly detection unit can prioritize detecting specific anomalies when the user is in a hurry. This allows for the priority detection of important anomalies by determining the priority of anomaly detection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the anomaly detection unit may be performed using AI or not. For example, the anomaly detection unit can input user emotion data into a generative AI and have the generative AI perform the anomaly detection priority determination.

[0084] The anomaly detection unit can detect anomalies based on the work schedule of the manufacturing site when an anomaly is detected. For example, the anomaly detection unit can detect anomalies based on the work schedule of the manufacturing site when an anomaly is detected. For example, the anomaly detection unit can detect anomalies in a specific time period based on the work schedule. For example, the anomaly detection unit can detect anomalies in a specific process based on the work schedule. For example, the anomaly detection unit can detect anomalies in a specific work area based on the work schedule. By performing anomaly detection based on the work schedule of the manufacturing site, the accuracy of anomaly detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can input work schedule data into a generating AI and have the generating AI perform anomaly detection.

[0085] The anomaly detection unit can adjust the level of detail of detection based on the location of the abnormal sound when an anomaly is detected. For example, the anomaly detection unit can adjust the level of detail of detection based on the location of the abnormal sound when an anomaly is detected. For example, the anomaly detection unit can perform detailed detection if the location of the abnormal sound is in an important area. For example, the anomaly detection unit can perform simplified detection if the location of the abnormal sound is in a general area. The anomaly detection unit can adjust the level of detail of detection according to the location of the abnormal sound. By adjusting the level of detail of detection based on the location of the abnormal sound, the efficiency of detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input abnormal sound location data to a generating AI and have the generating AI perform the adjustment of the level of detail of detection.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] An anomaly detection system can be equipped with a vibration collection unit in addition to a sound collection unit. The vibration collection unit can collect vibrations from machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the vibration collection unit can collect vibrations from machine tools to detect tool wear and bearing abnormalities. It can also collect vibrations from presses used in automotive parts manufacturing to detect press abnormalities early. Furthermore, the vibration collection unit can collect vibrations from the entire production line to identify anomalies throughout the line. By collecting both sound and vibration, the accuracy of anomaly detection can be further improved.

[0088] An anomaly detection system can be equipped with a temperature collection unit in addition to a sound collection unit. The temperature collection unit can collect the temperature of machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the temperature collection unit can collect the temperature of a machine tool and detect anomalies due to overheating. It can also collect the temperature of a press machine in automotive parts manufacturing and detect press machine anomalies at an early stage. Furthermore, the temperature collection unit can collect the temperature of a manufacturing line and identify anomalies throughout the entire line. By collecting both sound and temperature, the accuracy of anomaly detection can be further improved.

[0089] An anomaly detection system can be equipped with a humidity collection unit in addition to a sound collection unit. The humidity collection unit can collect humidity in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the humidity collection unit can collect humidity around machine tools and detect anomalies due to humidity changes. It can also collect humidity around presses in automotive parts manufacturing, enabling early detection of press malfunctions. Furthermore, the humidity collection unit can collect humidity along the entire production line, identifying anomalies across the entire line. By collecting both sound and humidity, the accuracy of anomaly detection can be further improved.

[0090] An anomaly detection system can include a light collection unit in addition to a sound collection unit. The light collection unit can collect changes in light at the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the light collection unit can collect changes in light around a machine tool and detect anomalies caused by these changes in light. It can also collect changes in light around a press machine in automotive parts manufacturing, enabling early detection of press machine malfunctions. Furthermore, the light collection unit can collect changes in light along a manufacturing line, identifying anomalies across the entire line. By collecting both sound and light, the accuracy of anomaly detection can be further improved.

[0091] An anomaly detection system can be equipped with a pressure collection unit in addition to a sound collection unit. The pressure collection unit can collect pressure from machinery and equipment in the manufacturing site with high precision, improving the accuracy of anomaly detection. For example, the pressure collection unit can collect pressure from machine tools and detect anomalies due to pressure changes. It can also collect pressure from presses in automotive parts manufacturing, enabling early detection of press malfunctions. Furthermore, the pressure collection unit can collect pressure from the entire production line, identifying anomalies across the entire line. By collecting both sound and pressure, the accuracy of anomaly detection can be further improved.

[0092] In addition to sound collection, the anomaly detection system can estimate the user's emotions and adjust the anomaly detection alert method based on the estimated emotions. For example, if the user is stressed, a simple alert method can be provided to clearly communicate the anomaly. If the user is relaxed, a detailed alert method can be provided to explain the details of the anomaly. Furthermore, if the user is in a hurry, a concise alert method can be provided to quickly communicate the anomaly. In this way, by adjusting the anomaly detection alert method based on the user's emotions, the system can communicate anomalies in an easy-to-understand manner.

[0093] In addition to sound collection, the anomaly detection system can estimate the user's emotions and determine the priority of anomaly detection based on those emotions. For example, if the user is stressed, important anomalies can be detected preferentially. If the user is relaxed, anomalies can be detected evenly across the board. Furthermore, if the user is in a hurry, specific anomalies can be detected preferentially. In this way, by determining the priority of anomaly detection based on the user's emotions, important anomalies can be detected preferentially.

[0094] In addition to sound collection, the anomaly detection system can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple display method can be provided to make the analysis results easy to understand. If the user is relaxed, a detailed display method can be provided to explain the analysis results in detail. Furthermore, if the user is in a hurry, a concise display method can be provided to quickly convey the analysis results. In this way, by adjusting the display method of the analysis results based on the user's emotions, the analysis results can be communicated in an easy-to-understand manner.

[0095] In addition to sound collection, the anomaly detection system can estimate the user's emotions and prioritize the sounds to collect based on those emotions. For example, if the user is stressed, important sounds can be prioritized for collection, improving the accuracy of anomaly detection. If the user is relaxed, sounds can be collected evenly, reducing the system load. Furthermore, if the user is in a hurry, specific sounds can be prioritized for collection, enabling rapid anomaly detection. In this way, prioritizing the sounds to collect based on the user's emotions can improve the accuracy of anomaly detection.

[0096] In addition to sound collection, the anomaly detection system can estimate the user's emotions and adjust the timing of sound collection based on those emotions. For example, if the user is stressed, the frequency of sound collection can be increased to improve the accuracy of anomaly detection. Conversely, if the user is relaxed, the frequency of sound collection can be decreased to reduce the system load. Furthermore, if the user is in a hurry, only important sounds can be prioritized for collection, enabling rapid anomaly detection. In this way, adjusting the timing of sound collection based on the user's emotions can improve the accuracy of anomaly detection.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The sound collection unit collects sounds from the manufacturing site. For example, using an ultra-compact, high-performance microphone array, it can collect a wide range of sounds with high precision, such as the cutting sounds of machine tools and the sounds of presses used in automobile parts manufacturing. Step 2: The sound analysis unit analyzes the collected sounds, removes noise, and emphasizes abnormal sounds. For example, by using ambient sound enhancement technology to remove noise, it is possible to clearly detect changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. Step 3: The anomaly detection unit learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. For example, by performing pattern learning and predictive detection, it is possible to analyze in real time anomalies in press machines for automotive parts manufacturing, welding quality problems, and anomalies in assembly lines.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the sound acquisition unit, sound analysis unit, and anomaly detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sound acquisition unit collects sounds from the manufacturing site using the microphone 38B of the smart device 14. The sound analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs pattern learning and predictive detection to analyze changes in sound in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the sound acquisition unit, sound analysis unit, and anomaly detection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sound acquisition unit collects sounds from the manufacturing site using the microphone 238 of the smart glasses 214. The sound analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs pattern learning and predictive detection to analyze changes in sound in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the sound acquisition unit, sound analysis unit, and anomaly detection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sound acquisition unit collects sounds from the manufacturing site using the microphone 238 of the headset terminal 314. The sound analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs pattern learning and predictive detection to analyze changes in sound in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the sound acquisition unit, sound analysis unit, and anomaly detection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sound acquisition unit collects sounds from the manufacturing site using the microphone 238 of the robot 414. The sound analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and removes noise and emphasizes abnormal sounds using ambient sound enhancement technology. The anomaly detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and performs pattern learning and predictive detection to analyze changes in sound in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The sound collection unit collects sounds from the manufacturing site, A sound analysis unit analyzes the sound collected by the sound collection unit, removes noise, and emphasizes abnormal sounds. The system includes an anomaly detection unit that learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. A system characterized by the following features. (Note 2) The sound collection unit is We collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sound analysis unit, Using ambient sound enhancement technology, noise is removed and abnormal sounds are emphasized. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned abnormality detection unit, It learns the patterns of collected sounds and detects signs of anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned abnormality detection unit, It detects changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned abnormality detection unit, This system analyzes press machine malfunctions, welding quality issues, and assembly line abnormalities in automotive parts manufacturing in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The sound collection unit is The system estimates the user's emotions and adjusts the timing of sound collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The sound collection unit is By focusing on specific machines or areas to collect sound, the accuracy of anomaly detection can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 9) The sound collection unit is Select and collect the types of sounds to be collected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The sound collection unit is It estimates the user's emotions and determines the priority of sounds to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The sound collection unit is When collecting sound, environmental data such as temperature and humidity at the manufacturing site are also collected simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 12) The sound collection unit is Sound collection is performed based on the work schedule at the manufacturing site. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned sound analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned sound analysis unit, Apply different analysis algorithms depending on the type of abnormal sound. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned sound analysis unit, The accuracy of sound analysis is improved by referring to past anomaly data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned sound analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned sound analysis unit, When analyzing sound, change the focus of the analysis based on the work content at the manufacturing site. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned sound analysis unit, During sound analysis, the level of detail of the analysis is adjusted based on the frequency of abnormal sounds. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned abnormality detection unit, It estimates the user's emotions and adjusts the anomaly detection alert method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned abnormality detection unit, Apply different detection algorithms depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned abnormality detection unit, When an anomaly is detected, past anomaly data is referenced to improve the accuracy of the detection. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned abnormality detection unit, The system estimates the user's emotions and determines the priority of anomaly detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned abnormality detection unit, When an anomaly is detected, the system performs anomaly detection based on the work schedule at the manufacturing site. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned abnormality detection unit, When an anomaly is detected, the level of detail of the detection is adjusted based on the location where the abnormal sound originated. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The sound collection unit collects sounds from the manufacturing site, A sound analysis unit analyzes the sound collected by the sound collection unit, removes noise, and emphasizes abnormal sounds. The system includes an anomaly detection unit that learns the sound patterns analyzed by the sound analysis unit and detects signs of anomalies. A system characterized by the following features.

2. The sound collection unit is We collect sounds from the manufacturing site using an ultra-compact, high-performance microphone array. The system according to feature 1.

3. The aforementioned sound analysis unit, Using ambient sound enhancement technology, noise is removed and abnormal sounds are emphasized. The system according to feature 1.

4. The aforementioned abnormality detection unit, It learns the patterns of collected sounds and detects signs of anomalies. The system according to feature 1.

5. The aforementioned abnormality detection unit, It detects changes in the cutting sound of machine tools, tool wear, and abnormal noises from bearings. The system according to feature 1.

6. The aforementioned abnormality detection unit, This system analyzes press machine malfunctions, welding quality issues, and assembly line abnormalities in automotive parts manufacturing in real time. The system according to feature 1.

7. The sound collection unit is The system estimates the user's emotions and adjusts the timing of sound collection based on those emotions. The system according to feature 1.

8. The sound collection unit is By focusing on specific machines or areas to collect sound, the accuracy of anomaly detection can be improved. The system according to feature 1.

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