System

Abstract

An object of a system according to an embodiment is to monitor a traffic volume and a surrounding environment in real time and efficiently control lighting.SOLUTION: A system includes a sensor part, a data analysis part, and an illumination control part. The sensor unit monitors the traffic volume and the surrounding environment in real time. The data analysis unit analyzes the data collected by the sensor unit. The illumination control unit controls illumination on the basis of the result analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies are not sufficient to efficiently control lighting in roads and public spaces, and there is room for improvement in terms of energy efficiency and cost reduction.

[0005] The system according to the embodiment aims to monitor traffic volume and the surrounding environment in real time and to efficiently control lighting. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, a data analysis unit, and a lighting control unit. The sensor unit monitors traffic volume and the surrounding environment in real time. The data analysis unit analyzes data collected by the sensor unit. The lighting control unit controls lighting based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor traffic volume and the surrounding environment in real time and efficiently control lighting. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The lighting control system according to the embodiment of the present invention monitors traffic volume and the surrounding environment in real time, and uses a generative AI to determine the optimal lighting brightness and timing. This allows the lighting control system to improve energy efficiency and reduce costs, while also contributing to improved safety and aesthetics.

[0029] A lighting control system according to an embodiment includes a sensor unit, a data analysis unit, and a lighting control unit. The sensor unit monitors traffic volume and the surrounding environment in real time. For example, a traffic volume sensor is installed to count the number of vehicles and pedestrians. The environmental sensor detects the surrounding brightness and weather conditions. For example, the traffic volume sensor is installed on a road and counts the number of passing vehicles. The environmental sensor measures the surrounding brightness and temperature using a light sensor and a temperature sensor. The data analysis unit analyzes the data collected by the sensor unit. For example, the data analysis unit predicts peak hours based on traffic volume data. The data analysis unit predicts weather changes based on weather data. For example, the data analysis unit analyzes past traffic volume data to identify peak hours. The data analysis unit analyzes weather data to predict the occurrence of rain or fog. The lighting control unit controls the lighting based on the results of the analysis by the data analysis unit. For example, the lighting is brightened during times of heavy traffic and dimmed during times of light traffic. The lighting is brightened when the weather is bad and dimmed when the weather is good. For example, the lighting control unit turns on the lighting at maximum output during times of heavy traffic. During times of low traffic volume, the lights are dimmed. During bad weather, the lights are brightened to improve visibility. This allows the lighting control system according to the embodiment to perform optimal lighting control according to traffic volume and the environment. For example, during times of high traffic volume or bad weather, brightening the lights improves visibility and reduces the risk of accidents. Furthermore, during times of low traffic volume or good weather, dimming the lights can reduce energy consumption.

[0030] The sensor unit includes a traffic volume sensor and can measure the number of vehicles and pedestrians. The sensor unit, for example, installs a traffic volume sensor to measure the number of vehicles and pedestrians. For example, the traffic volume sensor is installed on a road and counts the passing of vehicles. The sensor unit also installs a pedestrian count sensor to measure the number of pedestrians. For example, the pedestrian count sensor is installed on a sidewalk and counts the passing of pedestrians. The sensor unit also predicts changes in traffic volume based on data from the traffic volume sensor. For example, it analyzes past traffic volume data and identifies peak time periods. This enables accurate measurement of traffic volume. For example, using a traffic volume sensor makes it possible to grasp the number of vehicles and pedestrians in real time and control lighting in response to changes in traffic volume.

[0031] The sensor unit includes an environmental sensor and can detect brightness and weather conditions. The sensor unit, for example, is provided with an environmental sensor to detect the ambient brightness and weather conditions. For example, a light sensor is used to measure the ambient brightness. Also, a temperature sensor is used to measure the air temperature. For example, the light sensor measures the ambient brightness in lux values. The temperature sensor measures the air temperature in degrees Celsius. Also, the sensor unit detects the weather conditions using a weather sensor. For example, a rain sensor is used to detect whether it is raining. This enables lighting control according to the ambient environment. For example, energy consumption can be reduced by brightening the lights when the surroundings are dark and dimming the lights when the surroundings are bright.

[0032] The data analysis unit can brighten the lights during times of heavy traffic and dim the lights during times of light traffic. For example, the data analysis unit predicts peak hours based on traffic volume data and brightens the lights during times of heavy traffic. For example, it analyzes past traffic volume data to identify peak hours. It also dims the lights during times of light traffic. For example, it dims the lights during times of light traffic. This enables optimal lighting control according to traffic volume. For example, brightening the lights during times of heavy traffic improves visibility and reduces the risk of accidents. It also reduces energy consumption by dimming the lights during times of light traffic.

[0033] The data analysis unit can brighten the lights when the weather is bad and dim the lights when the weather is good. The data analysis unit, for example, predicts weather changes based on weather data and brightens the lights when the weather is bad. For example, it analyzes data from weather sensors and predicts the occurrence of rain or fog. It also dims the lights when the weather is good. For example, it dims the lights when the weather is good. This enables optimal lighting control according to the weather. For example, brightening the lights when the weather is bad can improve visibility and reduce the risk of accidents. It can also reduce energy consumption by dimming the lights when the weather is good.

[0034] The lighting control unit can turn on lights only when necessary. The lighting control unit turns on lights only when necessary, for example, based on traffic volume and environmental data. For example, it turns on lights when traffic volume is above a certain level or during specific events. The lighting control unit also automatically controls the on / off of lights based on sensor data. For example, it can turn off lights during times of low traffic volume to reduce energy consumption. This prevents unnecessary energy consumption. For example, turning on lights only when necessary can improve energy efficiency and reduce costs.

[0035] The lighting control unit can appropriately adjust the brightness of the lighting. The lighting control unit appropriately adjusts the brightness of the lighting based on, for example, traffic volume and environmental data. For example, the lighting control unit brightens the lighting during times of heavy traffic and dimmers the lighting during times of light traffic. Also, the lighting control unit brightens the lighting when the weather is bad and dimmers the lighting when the weather is good. For example, the lighting control unit turns on the lighting at maximum output during times of heavy traffic. During times of light traffic, the lighting control unit dims the lighting. When the weather is bad, the lighting is brightened to improve visibility. This makes it possible to reduce power consumption. For example, by appropriately adjusting the brightness of the lighting, it is possible to improve energy efficiency and reduce costs.

[0036] The lighting control unit can improve visibility by brightening the lighting during times of heavy traffic or bad weather. The lighting control unit, for example, brightens the lighting during times of heavy traffic or bad weather. For example, during times of heavy traffic, the lighting is turned on at maximum output. When the weather is bad, the lighting is brightened to improve visibility. This improves visibility and can reduce the risk of accidents. For example, by brightening the lighting during times of heavy traffic or bad weather, visibility can be improved and the risk of accidents can be reduced.

[0037] The lighting control unit brightens the lights in areas with many pedestrians, which is expected to have a deterrent effect on crime. The lighting control unit, for example, brightens the lights in areas with many pedestrians. For example, in areas with many pedestrians, the lights are turned on at maximum output. The lighting control unit also adjusts the brightness of the lights based on the number of pedestrians. For example, brightening the lights in areas with many pedestrians is expected to have a deterrent effect on crime. This is expected to have a deterrent effect on crime. For example, brightening the lights in areas with many pedestrians is expected to have a deterrent effect on crime.

[0038] The lighting control unit can appropriately adjust the lighting in parks and squares at night to create a comfortable atmosphere. The lighting control unit appropriately adjusts the lighting in parks and squares at night, for example. For example, in parks and squares at night, the lighting control unit appropriately adjusts the lighting to create a comfortable atmosphere. The lighting control unit also changes the color and pattern of the lighting depending on the season or an event. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of the area. This makes it possible to create a comfortable atmosphere. For example, in parks and squares at night, the lighting control unit can appropriately adjust the lighting to create a comfortable atmosphere.

[0039] The lighting control unit can change the color and pattern of the lighting depending on the season or an event. The lighting control unit changes the color and pattern of the lighting depending on the season or an event, for example. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area. The lighting control unit also automatically changes the color and pattern of the lighting. For example, automatically changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area. This can enhance the appeal of an area. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area.

[0040] The data analysis unit can predict changes in traffic volume and the environment based on sensor data and adjust lighting in advance based on the prediction. The data analysis unit predicts changes in traffic volume and the environment based on, for example, sensor data. For example, it analyzes past traffic volume data to predict peak hours. It also analyzes weather data to predict weather changes. For example, the data analysis unit identifies peak hours based on past traffic volume data and adjusts lighting in advance during the predicted peak hours. It also brightens lighting before rain or fog occurs based on weather data. This enables lighting control based on predictions. For example, predicting changes in traffic volume and the environment and adjusting lighting in advance based on the predictions can improve energy efficiency and reduce costs.

[0041] The sensor unit can incorporate an algorithm that dynamically changes the placement of sensors and automatically selects the optimal monitoring point. The sensor unit, for example, incorporates an algorithm for dynamically changing the placement of sensors. For example, the sensor unit automatically selects the optimal monitoring point in response to changes in traffic volume or the environment. For example, the sensor unit concentrates sensors in locations with heavy traffic. The sensor unit also moves sensors when an event is held to accurately grasp the congestion situation. For example, the sensor unit simulates the optimal sensor placement based on past data and reflects this in the actual placement. This makes it possible to automatically select the optimal monitoring point. For example, by dynamically changing the placement of sensors and automatically selecting the optimal monitoring point, it is possible to quickly respond to changes in traffic volume and the environment.

[0042] The sensor unit can simultaneously monitor noise levels and air quality, optimizing the entire environment. For example, a noise sensor can be installed to monitor noise levels in real time. For example, lights can be brightened in noisy areas and dimmed in quiet areas. An air quality sensor can also be installed to monitor airborne pollutants in real time. For example, if air quality deteriorates, lights can be brightened and a warning issued. For example, the sensor unit can use a noise sensor to measure noise levels in decibels. An air quality sensor can also be used to measure PM2.5 concentrations in the air. This makes it possible to optimize the entire environment. For example, by simultaneously monitoring noise levels and air quality and optimizing the entire environment, the quality of life of residents can be improved.

[0043] The data analysis unit can link sensor data with other urban infrastructure (e.g., traffic signals and public transportation) to achieve comprehensive city management. For example, the data analysis unit can link sensor data with a traffic signal system to adjust the timing of signals according to traffic volume. For example, when traffic volume is high, the signal is made longer. The sensor data can also be linked with public transportation to optimize bus and train operation schedules. For example, the data analysis unit can link sensor data with a traffic signal system to adjust the timing of signals according to traffic volume. The sensor data can also be linked with public transportation to optimize bus and train operation schedules. This enables comprehensive city management. For example, by linking sensor data with other urban infrastructure to achieve comprehensive city management, it is possible to improve the efficiency of the entire city.

[0044] The data analysis unit can use the generation AI to learn from past data and automatically generate optimal lighting patterns. The data analysis unit, for example, uses the generation AI to learn from past traffic volume data and automatically generate optimal lighting patterns. For example, it generates lighting patterns tailored to peak traffic hours. The generation AI also uses past weather data to automatically generate optimal lighting patterns according to the weather. For example, it generates bright lighting patterns on rainy days. For example, the data analysis unit uses the generation AI to learn from past traffic volume data and automatically generate optimal lighting patterns. It also learns from past weather data and automatically generates optimal lighting patterns according to the weather. This makes it possible to automatically generate optimal lighting patterns based on past data. For example, using the generation AI to learn from past data and automatically generate optimal lighting patterns can improve energy efficiency and reduce costs.

[0045] The data analysis unit has an anomaly detection function using generative AI, and can detect abnormal situations based on sensor data and respond immediately. The data analysis unit, for example, has an anomaly detection function using generative AI and detects abnormal traffic volume based on sensor data. For example, it detects traffic congestion or accidents and turns on the lights. It also uses generative AI to detect abnormal environmental conditions. For example, it detects sudden changes in weather and adjusts the lighting. For example, the data analysis unit uses generative AI to detect abnormal traffic volume and detects traffic congestion or accidents. It uses generative AI to detect abnormal environmental conditions and detects sudden changes in weather. This makes it possible to immediately detect and respond to abnormal situations. For example, it has an anomaly detection function using generative AI and detects abnormal situations based on sensor data and respond immediately, thereby improving safety.

[0046] The data analysis unit uses the generation AI to simultaneously control not only lighting but also sound and video, thereby realizing comprehensive spatial presentation. The data analysis unit, for example, uses the generation AI to automatically generate a spatial presentation that links lighting and sound. For example, changing the color and brightness of lighting in accordance with the rhythm of music. The data analysis unit also uses the generation AI to automatically generate a spatial presentation that links lighting and video. For example, changing the lighting pattern in accordance with the content of the video. For example, the data analysis unit uses the generation AI to automatically generate a spatial presentation that links lighting and sound. The data analysis unit also uses the generation AI to automatically generate a spatial presentation that links lighting and video. This makes comprehensive spatial presentation possible. For example, using the generation AI to simultaneously control not only lighting but also sound and video to realize comprehensive spatial presentation can improve the satisfaction of participants at events and festivals.

[0047] The data analysis unit can use the generative AI to link with other city management systems (for example, garbage collection or water management) to improve efficiency throughout the city. For example, the data analysis unit uses the generative AI to link a lighting control system with a garbage collection system to improve efficiency throughout the city. For example, lighting can be adjusted to match the routes of garbage collection trucks. Also, the generative AI can be used to link a lighting control system with a water management system to improve efficiency throughout the city. For example, lighting can be brightened when water work is being carried out. For example, the data analysis unit uses the generative AI to link a lighting control system with a garbage collection system to adjust lighting to match the routes of garbage collection trucks. The generative AI can be used to link a lighting control system with a water management system to brighten lighting when water work is being carried out. This makes it possible to improve efficiency throughout the city. For example, by using the generative AI to link with other city management systems to improve efficiency throughout the city, energy consumption can be optimized and costs can be reduced.

[0048] The data analysis unit can monitor lighting energy consumption in real time and have the generation AI automatically generate optimal energy usage patterns. For example, the data analysis unit monitors lighting energy consumption in real time and has the generation AI automatically generate optimal energy usage patterns. For example, a pattern is generated that reduces energy consumption during peak hours. The generation AI is also used to analyze lighting energy consumption data and automatically generate optimal energy usage patterns. For example, a pattern is generated that minimizes energy consumption at night. For example, the data analysis unit monitors lighting energy consumption in real time and has the generation AI automatically generate optimal energy usage patterns. The generation AI is used to analyze lighting energy consumption data and automatically generate optimal energy usage patterns. This makes it possible to automatically generate optimal energy usage patterns. For example, by monitoring lighting energy consumption in real time and having the generation AI automatically generate optimal energy usage patterns, energy efficiency can be improved and costs can be reduced.

[0049] The data analysis unit predicts the lifespan of lighting and the generation AI determines the optimal replacement time, thereby reducing maintenance costs. The data analysis unit, for example, collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time. For example, the lifespan is predicted based on usage time and environmental conditions. The generation AI is also used to analyze the lifespan data of lighting and automatically determine the optimal replacement time. For example, the lifespan is predicted based on past replacement history. For example, the data analysis unit collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time. The generation AI is used to analyze the lifespan data of lighting and automatically determine the optimal replacement time. This makes it possible to reduce maintenance costs. For example, the data analysis unit collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time.

[0050] The data analysis unit can utilize renewable energy (for example, solar or wind power) to improve the energy efficiency of lighting. The data analysis unit, for example, introduces solar power generation into the lighting system to improve energy efficiency. For example, electricity generated by solar power during the day is used for lighting at night. Also, wind power generation is introduced into the lighting system to improve energy efficiency. For example, wind power generation is used to turn on lights in windy areas. For example, the data analysis unit introduces solar power generation into the lighting system to use electricity generated by solar power during the day for lighting at night. For example, wind power generation is introduced into the lighting system to use wind power generation to turn on lights in windy areas. This makes it possible to improve energy efficiency by utilizing renewable energy. For example, by utilizing renewable energy to improve the energy efficiency of lighting, energy consumption can be reduced and the environmental load can be lowered.

[0051] The data analysis unit can link the lighting control system with other energy consumption systems (e.g., heating / cooling and water supply) to improve overall energy efficiency. The data analysis unit, for example, links the lighting control system with a heating / cooling system to improve overall energy efficiency. For example, it adjusts lighting according to the usage status of heating / cooling. Also, it links the lighting control system with a water supply system to improve overall energy efficiency. For example, it adjusts lighting according to the operation status of a water supply pump. For example, the data analysis unit links the lighting control system with a heating / cooling system to adjust lighting according to the usage status of heating / cooling. It links the lighting control system with a water supply system to adjust lighting according to the operation status of a water supply pump. This makes it possible to improve overall energy efficiency. For example, by linking the lighting control system with other energy consumption systems to improve overall energy efficiency, it is possible to optimize energy consumption and achieve cost reductions.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The lighting control system may further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and adjust the brightness and color of the lights. For example, if the user says "Turn it up," the brightness of the lights can be increased. Alternatively, if the user says "I want to relax," the color of the lights can be changed to a warmer color. This makes it possible to control the lights according to the user's voice commands. For example, using the voice recognition unit allows the user to operate the lights without using their hands, improving convenience.

[0054] The lighting control system may further include a location information acquisition unit. The location information acquisition unit acquires location information from the user's smartphone or wearable device and can adjust lighting according to the user's location. For example, when the user approaches a specific area, the lighting in that area can be automatically turned on. Also, when the user leaves the area, the lighting can be turned off. This makes it possible to control lighting according to the user's location. For example, using the location information acquisition unit can improve energy efficiency.

[0055] The lighting control system can further include a plant growth mode. The plant growth mode can provide optimal lighting conditions for plant growth. For example, by irradiating light of a specific wavelength, plants can promote photosynthesis. The brightness and color of the lighting can also be adjusted according to the plant's growth stage. This can support the healthy growth of plants. For example, using the plant growth mode makes it easier to grow home gardens and indoor plants.

[0056] The lighting control system can further include an art exhibition mode. The art exhibition mode can provide optimal lighting conditions for the artworks on display. For example, a specific color temperature and brightness can be set to highlight the colors and textures of the artworks. The lighting pattern can also be changed depending on the type or theme of the artwork. This allows the artworks to be displayed to their fullest potential. For example, the art exhibition mode can be used to enhance the effectiveness of exhibitions in museums and galleries.

[0057] The lighting control system can further include a music-linked mode. The music-linked mode can adjust the lighting to match the rhythm and tempo of the music being played. For example, when fast-paced music is being played, the lighting can be made brighter and flash in time with the rhythm. When slow-paced music is being played, the lighting can be changed to a warmer color with a gradual change. This can enhance the entertainment effect of linking music and lighting. For example, using the music-linked mode can make the performance at a party or event more attractive.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The sensor unit monitors traffic volume and the surrounding environment in real time. For example, a traffic volume sensor is installed to measure the number of vehicles and pedestrians. Environmental sensors are also used to detect the surrounding brightness and weather conditions. For example, a traffic volume sensor is installed on a road to count the number of passing vehicles. Environmental sensors use light sensors and temperature sensors to measure the surrounding brightness and temperature. Step 2: The data analysis unit analyzes the data collected by the sensor unit. For example, it predicts peak hours based on traffic volume data. It also predicts weather changes based on weather data. For example, the data analysis unit analyzes past traffic volume data to identify peak hours. It analyzes weather data to predict the occurrence of rain or fog. Step 3: The lighting control unit controls the lighting based on the results of the analysis by the data analysis unit. For example, the lighting is brightened during times of heavy traffic and dimmed during times of light traffic. Also, the lighting is brightened when the weather is bad and dimmed when the weather is good. For example, the lighting control unit turns on the lights at maximum output during times of heavy traffic and dims the lights during times of light traffic. When the weather is bad, the lighting is brightened to improve visibility.

[0060] (Example 2) The lighting control system according to the embodiment of the present invention monitors traffic volume and the surrounding environment in real time, and uses a generative AI to determine the optimal lighting brightness and timing. This allows the lighting control system to improve energy efficiency and reduce costs, while also contributing to improved safety and aesthetics.

[0061] A lighting control system according to an embodiment includes a sensor unit, a data analysis unit, and a lighting control unit. The sensor unit monitors traffic volume and the surrounding environment in real time. For example, a traffic volume sensor is installed to count the number of vehicles and pedestrians. The environmental sensor detects the surrounding brightness and weather conditions. For example, the traffic volume sensor is installed on a road and counts the number of passing vehicles. The environmental sensor measures the surrounding brightness and temperature using a light sensor and a temperature sensor. The data analysis unit analyzes the data collected by the sensor unit. For example, the data analysis unit predicts peak hours based on traffic volume data. The data analysis unit predicts weather changes based on weather data. For example, the data analysis unit analyzes past traffic volume data to identify peak hours. The data analysis unit analyzes weather data to predict the occurrence of rain or fog. The lighting control unit controls the lighting based on the results of the analysis by the data analysis unit. For example, the lighting is brightened during times of heavy traffic and dimmed during times of light traffic. The lighting is brightened when the weather is bad and dimmed when the weather is good. For example, the lighting control unit turns on the lighting at maximum output during times of heavy traffic. During times of low traffic volume, the lights are dimmed. During bad weather, the lights are brightened to improve visibility. This allows the lighting control system according to the embodiment to perform optimal lighting control according to traffic volume and the environment. For example, during times of high traffic volume or bad weather, brightening the lights improves visibility and reduces the risk of accidents. Furthermore, during times of low traffic volume or good weather, dimming the lights can reduce energy consumption.

[0062] The sensor unit includes a traffic volume sensor and can measure the number of vehicles and pedestrians. The sensor unit, for example, installs a traffic volume sensor to measure the number of vehicles and pedestrians. For example, the traffic volume sensor is installed on a road and counts the passing of vehicles. The sensor unit also installs a pedestrian count sensor to measure the number of pedestrians. For example, the pedestrian count sensor is installed on a sidewalk and counts the passing of pedestrians. The sensor unit also predicts changes in traffic volume based on data from the traffic volume sensor. For example, it analyzes past traffic volume data and identifies peak time periods. This enables accurate measurement of traffic volume. For example, using a traffic volume sensor makes it possible to grasp the number of vehicles and pedestrians in real time and control lighting in response to changes in traffic volume.

[0063] The sensor unit includes an environmental sensor and can detect brightness and weather conditions. The sensor unit, for example, is provided with an environmental sensor to detect the ambient brightness and weather conditions. For example, a light sensor is used to measure the ambient brightness. Also, a temperature sensor is used to measure the air temperature. For example, the light sensor measures the ambient brightness in lux values. The temperature sensor measures the air temperature in degrees Celsius. Also, the sensor unit detects the weather conditions using a weather sensor. For example, a rain sensor is used to detect whether it is raining. This enables lighting control according to the ambient environment. For example, energy consumption can be reduced by brightening the lights when the surroundings are dark and dimming the lights when the surroundings are bright.

[0064] The data analysis unit can brighten the lights during times of heavy traffic and dim the lights during times of light traffic. For example, the data analysis unit predicts peak hours based on traffic volume data and brightens the lights during times of heavy traffic. For example, it analyzes past traffic volume data to identify peak hours. It also dims the lights during times of light traffic. For example, it dims the lights during times of light traffic. This enables optimal lighting control according to traffic volume. For example, brightening the lights during times of heavy traffic improves visibility and reduces the risk of accidents. It also reduces energy consumption by dimming the lights during times of light traffic.

[0065] The data analysis unit can brighten the lights when the weather is bad and dim the lights when the weather is good. The data analysis unit, for example, predicts weather changes based on weather data and brightens the lights when the weather is bad. For example, it analyzes data from weather sensors and predicts the occurrence of rain or fog. It also dims the lights when the weather is good. For example, it dims the lights when the weather is good. This enables optimal lighting control according to the weather. For example, brightening the lights when the weather is bad can improve visibility and reduce the risk of accidents. It can also reduce energy consumption by dimming the lights when the weather is good.

[0066] The lighting control unit can turn on lights only when necessary. The lighting control unit turns on lights only when necessary, for example, based on traffic volume and environmental data. For example, it turns on lights when traffic volume is above a certain level or during specific events. The lighting control unit also automatically controls the on / off of lights based on sensor data. For example, it can turn off lights during times of low traffic volume to reduce energy consumption. This prevents unnecessary energy consumption. For example, turning on lights only when necessary can improve energy efficiency and reduce costs.

[0067] The lighting control unit can appropriately adjust the brightness of the lighting. The lighting control unit appropriately adjusts the brightness of the lighting based on, for example, traffic volume and environmental data. For example, the lighting control unit brightens the lighting during times of heavy traffic and dimmers the lighting during times of light traffic. Also, the lighting control unit brightens the lighting when the weather is bad and dimmers the lighting when the weather is good. For example, the lighting control unit turns on the lighting at maximum output during times of heavy traffic. During times of light traffic, the lighting control unit dims the lighting. When the weather is bad, the lighting is brightened to improve visibility. This makes it possible to reduce power consumption. For example, by appropriately adjusting the brightness of the lighting, it is possible to improve energy efficiency and reduce costs.

[0068] The lighting control unit can improve visibility by brightening the lighting during times of heavy traffic or bad weather. The lighting control unit, for example, brightens the lighting during times of heavy traffic or bad weather. For example, during times of heavy traffic, the lighting is turned on at maximum output. When the weather is bad, the lighting is brightened to improve visibility. This improves visibility and can reduce the risk of accidents. For example, by brightening the lighting during times of heavy traffic or bad weather, visibility can be improved and the risk of accidents can be reduced.

[0069] The lighting control unit brightens the lights in areas with many pedestrians, which is expected to have a deterrent effect on crime. The lighting control unit, for example, brightens the lights in areas with many pedestrians. For example, in areas with many pedestrians, the lights are turned on at maximum output. The lighting control unit also adjusts the brightness of the lights based on the number of pedestrians. For example, brightening the lights in areas with many pedestrians is expected to have a deterrent effect on crime. This is expected to have a deterrent effect on crime. For example, brightening the lights in areas with many pedestrians is expected to have a deterrent effect on crime.

[0070] The lighting control unit can appropriately adjust the lighting in parks and squares at night to create a comfortable atmosphere. The lighting control unit appropriately adjusts the lighting in parks and squares at night, for example. For example, in parks and squares at night, the lighting control unit appropriately adjusts the lighting to create a comfortable atmosphere. The lighting control unit also changes the color and pattern of the lighting depending on the season or an event. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of the area. This makes it possible to create a comfortable atmosphere. For example, in parks and squares at night, the lighting control unit can appropriately adjust the lighting to create a comfortable atmosphere.

[0071] The lighting control unit can change the color and pattern of the lighting depending on the season or an event. The lighting control unit changes the color and pattern of the lighting depending on the season or an event, for example. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area. The lighting control unit also automatically changes the color and pattern of the lighting. For example, automatically changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area. This can enhance the appeal of an area. For example, changing the color and pattern of the lighting depending on the season or an event can enhance the appeal of an area.

[0072] The data analysis unit can predict changes in traffic volume and the environment based on sensor data and adjust lighting in advance based on the prediction. The data analysis unit predicts changes in traffic volume and the environment based on, for example, sensor data. For example, it analyzes past traffic volume data to predict peak hours. It also analyzes weather data to predict weather changes. For example, the data analysis unit identifies peak hours based on past traffic volume data and adjusts lighting in advance during the predicted peak hours. It also brightens lighting before rain or fog occurs based on weather data. This enables lighting control based on predictions. For example, predicting changes in traffic volume and the environment and adjusting lighting in advance based on the predictions can improve energy efficiency and reduce costs.

[0073] The sensor unit can incorporate an algorithm that dynamically changes the placement of sensors and automatically selects the optimal monitoring point. The sensor unit, for example, incorporates an algorithm for dynamically changing the placement of sensors. For example, the sensor unit automatically selects the optimal monitoring point in response to changes in traffic volume or the environment. For example, the sensor unit concentrates sensors in locations with heavy traffic. The sensor unit also moves sensors when an event is held to accurately grasp the congestion situation. For example, the sensor unit simulates the optimal sensor placement based on past data and reflects this in the actual placement. This makes it possible to automatically select the optimal monitoring point. For example, by dynamically changing the placement of sensors and automatically selecting the optimal monitoring point, it is possible to quickly respond to changes in traffic volume and the environment.

[0074] The data analysis unit can use the emotion estimation function to monitor the emotions of pedestrians in real time and adjust the brightness and color of lighting according to the emotions. The data analysis unit, for example, uses the emotion estimation function to monitor the emotions of pedestrians in real time. For example, a camera can be used to analyze the facial expressions of pedestrians and adjust the brightness of lighting according to their emotions. Also, a microphone can be used to analyze the tone of voice of pedestrians and change the color of lighting according to their emotions. For example, the data analysis unit can analyze the facial expressions of pedestrians and adjust the brightness of lighting according to their emotions. The tone of voice of pedestrians can be analyzed and the color of lighting according to their emotions. This makes it possible to control lighting according to pedestrian emotions. For example, by monitoring the emotions of pedestrians in real time and adjusting the brightness and color of lighting according to their emotions, pedestrian comfort can be improved.

[0075] The sensor unit can simultaneously monitor noise levels and air quality, optimizing the entire environment. For example, a noise sensor can be installed to monitor noise levels in real time. For example, lights can be brightened in noisy areas and dimmed in quiet areas. An air quality sensor can also be installed to monitor airborne pollutants in real time. For example, if air quality deteriorates, lights can be brightened and a warning issued. For example, the sensor unit can use a noise sensor to measure noise levels in decibels. An air quality sensor can also be used to measure PM2.5 concentrations in the air. This makes it possible to optimize the entire environment. For example, by simultaneously monitoring noise levels and air quality and optimizing the entire environment, the quality of life of residents can be improved.

[0076] The data analysis unit can link sensor data with other urban infrastructure (e.g., traffic signals and public transportation) to achieve comprehensive city management. For example, the data analysis unit can link sensor data with a traffic signal system to adjust the timing of signals according to traffic volume. For example, when traffic volume is high, the signal is made longer. The sensor data can also be linked with public transportation to optimize bus and train operation schedules. For example, the data analysis unit can link sensor data with a traffic signal system to adjust the timing of signals according to traffic volume. The sensor data can also be linked with public transportation to optimize bus and train operation schedules. This enables comprehensive city management. For example, by linking sensor data with other urban infrastructure to achieve comprehensive city management, it is possible to improve the efficiency of the entire city.

[0077] The data analysis unit can use the emotion estimation function to monitor the emotions of participants at an event or festival and adjust the lighting effects in real time according to their emotions. The data analysis unit, for example, uses the emotion estimation function to monitor the emotions of participants at an event or festival. For example, a camera can be used to analyze the participants' facial expressions and adjust the color and brightness of the lighting according to their emotions. The microphone can also be used to analyze the participants' tone of voice and change the lighting pattern according to their emotions. For example, the data analysis unit can analyze the participants' facial expressions and adjust the color and brightness of the lighting according to their emotions. The tone of the participants' voices can be analyzed and change the lighting pattern according to their emotions. This makes it possible to create lighting effects in accordance with the emotions of participants at an event or festival. For example, by monitoring the emotions of participants at an event or festival and adjusting the lighting effects in real time according to their emotions, participant satisfaction can be improved.

[0078] The data analysis unit can use the generation AI to learn from past data and automatically generate optimal lighting patterns. The data analysis unit, for example, uses the generation AI to learn from past traffic volume data and automatically generate optimal lighting patterns. For example, it generates lighting patterns tailored to peak traffic hours. The generation AI also uses past weather data to automatically generate optimal lighting patterns according to the weather. For example, it generates bright lighting patterns on rainy days. For example, the data analysis unit uses the generation AI to learn from past traffic volume data and automatically generate optimal lighting patterns. It also learns from past weather data and automatically generates optimal lighting patterns according to the weather. This makes it possible to automatically generate optimal lighting patterns based on past data. For example, using the generation AI to learn from past data and automatically generate optimal lighting patterns can improve energy efficiency and reduce costs.

[0079] The data analysis unit has an anomaly detection function using generative AI, and can detect abnormal situations based on sensor data and respond immediately. The data analysis unit, for example, has an anomaly detection function using generative AI and detects abnormal traffic volume based on sensor data. For example, it detects traffic congestion or accidents and turns on the lights. It also uses generative AI to detect abnormal environmental conditions. For example, it detects sudden changes in weather and adjusts the lighting. For example, the data analysis unit uses generative AI to detect abnormal traffic volume and detects traffic congestion or accidents. It uses generative AI to detect abnormal environmental conditions and detects sudden changes in weather. This makes it possible to immediately detect and respond to abnormal situations. For example, it has an anomaly detection function using generative AI and detects abnormal situations based on sensor data and respond immediately, thereby improving safety.

[0080] The data analysis unit can use the emotion estimation function to analyze the user's emotion data and generate a lighting control program according to the emotion. The data analysis unit, for example, uses the emotion estimation function to analyze the user's emotion data. For example, a camera is used to analyze the user's facial expression and generate a lighting control program according to the emotion. Also, a microphone is used to analyze the user's tone of voice and generate a lighting control program according to the emotion. For example, the data analysis unit analyzes the user's facial expression and generates a lighting control program according to the emotion. The data analysis unit analyzes the user's tone of voice and generates a lighting control program according to the emotion. This makes it possible to generate a lighting control program according to the user's emotion. For example, by using the emotion estimation function to analyze the user's emotion data and generate a lighting control program according to the emotion, the user's comfort can be improved.

[0081] The data analysis unit uses the generation AI to simultaneously control not only lighting but also sound and video, thereby realizing comprehensive spatial presentation. The data analysis unit, for example, uses the generation AI to automatically generate a spatial presentation that links lighting and sound. For example, changing the color and brightness of lighting in accordance with the rhythm of music. The data analysis unit also uses the generation AI to automatically generate a spatial presentation that links lighting and video. For example, changing the lighting pattern in accordance with the content of the video. For example, the data analysis unit uses the generation AI to automatically generate a spatial presentation that links lighting and sound. The data analysis unit also uses the generation AI to automatically generate a spatial presentation that links lighting and video. This makes comprehensive spatial presentation possible. For example, using the generation AI to simultaneously control not only lighting but also sound and video to realize comprehensive spatial presentation can improve the satisfaction of participants at events and festivals.

[0082] The data analysis unit can use the generative AI to link with other city management systems (for example, garbage collection or water management) to improve efficiency throughout the city. For example, the data analysis unit uses the generative AI to link a lighting control system with a garbage collection system to improve efficiency throughout the city. For example, lighting can be adjusted to match the routes of garbage collection trucks. Also, the generative AI can be used to link a lighting control system with a water management system to improve efficiency throughout the city. For example, lighting can be brightened when water work is being carried out. For example, the data analysis unit uses the generative AI to link a lighting control system with a garbage collection system to adjust lighting to match the routes of garbage collection trucks. The generative AI can be used to link a lighting control system with a water management system to brighten lighting when water work is being carried out. This makes it possible to improve efficiency throughout the city. For example, by using the generative AI to link with other city management systems to improve efficiency throughout the city, energy consumption can be optimized and costs can be reduced.

[0083] The data analysis unit can use the emotion estimation function to automatically generate lighting effects based on emotions corresponding to specific events or seasons. The data analysis unit, for example, uses the emotion estimation function to analyze the emotions of event participants and automatically generate lighting effects based on their emotions. For example, if participants are excited, colorful lighting is generated. The emotion estimation function is also used to analyze emotions according to the season and automatically generate lighting effects based on their emotions. For example, warm lighting is generated for the Christmas season. For example, the data analysis unit uses the emotion estimation function to analyze the emotions of event participants and automatically generate lighting effects based on their emotions. The emotion estimation function is used to analyze emotions according to the season and automatically generate lighting effects based on their emotions. This makes it possible to perform lighting effects based on emotions corresponding to specific events or seasons. For example, the emotion estimation function can be used to automatically generate lighting effects based on emotions corresponding to specific events or seasons, thereby enhancing the appeal of a region.

[0084] The data analysis unit can monitor lighting energy consumption in real time and have the generation AI automatically generate optimal energy usage patterns. For example, the data analysis unit monitors lighting energy consumption in real time and has the generation AI automatically generate optimal energy usage patterns. For example, a pattern is generated that reduces energy consumption during peak hours. The generation AI is also used to analyze lighting energy consumption data and automatically generate optimal energy usage patterns. For example, a pattern is generated that minimizes energy consumption at night. For example, the data analysis unit monitors lighting energy consumption in real time and has the generation AI automatically generate optimal energy usage patterns. The generation AI is used to analyze lighting energy consumption data and automatically generate optimal energy usage patterns. This makes it possible to automatically generate optimal energy usage patterns. For example, by monitoring lighting energy consumption in real time and having the generation AI automatically generate optimal energy usage patterns, energy efficiency can be improved and costs can be reduced.

[0085] The data analysis unit predicts the lifespan of lighting and the generation AI determines the optimal replacement time, thereby reducing maintenance costs. The data analysis unit, for example, collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time. For example, the lifespan is predicted based on usage time and environmental conditions. The generation AI is also used to analyze the lifespan data of lighting and automatically determine the optimal replacement time. For example, the lifespan is predicted based on past replacement history. For example, the data analysis unit collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time. The generation AI is used to analyze the lifespan data of lighting and automatically determine the optimal replacement time. This makes it possible to reduce maintenance costs. For example, the data analysis unit collects data to predict the lifespan of lighting, and the generation AI determines the optimal replacement time.

[0086] The data analysis unit can use the emotion estimation function to optimize energy efficiency and generate a lighting pattern that elicits positive emotions based on the user's emotions. The data analysis unit, for example, uses the emotion estimation function to analyze the user's emotion data and generate a lighting pattern that optimizes energy efficiency. For example, a lighting pattern that allows the user to relax is generated. The emotion estimation function can also be used to generate a lighting pattern that optimizes energy efficiency based on the user's emotions. For example, a lighting pattern that allows the user to concentrate is generated. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotion data and generate a lighting pattern that optimizes energy efficiency. The emotion estimation function can be used to generate a lighting pattern that optimizes energy efficiency based on the user's emotions. This makes it possible to optimize energy efficiency based on the user's emotions. For example, the emotion estimation function can be used to generate a lighting pattern that optimizes energy efficiency based on the user's emotions and elicits positive emotions, thereby improving the user's comfort.

[0087] The data analysis unit can utilize renewable energy (for example, solar or wind power) to improve the energy efficiency of lighting. The data analysis unit, for example, introduces solar power generation into the lighting system to improve energy efficiency. For example, electricity generated by solar power during the day is used for lighting at night. Also, wind power generation is introduced into the lighting system to improve energy efficiency. For example, wind power generation is used to turn on lights in windy areas. For example, the data analysis unit introduces solar power generation into the lighting system to use electricity generated by solar power during the day for lighting at night. For example, wind power generation is introduced into the lighting system to use wind power generation to turn on lights in windy areas. This makes it possible to improve energy efficiency by utilizing renewable energy. For example, by utilizing renewable energy to improve the energy efficiency of lighting, energy consumption can be reduced and the environmental load can be lowered.

[0088] The data analysis unit can link the lighting control system with other energy consumption systems (e.g., heating / cooling and water supply) to improve overall energy efficiency. The data analysis unit, for example, links the lighting control system with a heating / cooling system to improve overall energy efficiency. For example, it adjusts lighting according to the usage status of heating / cooling. Also, it links the lighting control system with a water supply system to improve overall energy efficiency. For example, it adjusts lighting according to the operation status of a water supply pump. For example, the data analysis unit links the lighting control system with a heating / cooling system to adjust lighting according to the usage status of heating / cooling. It links the lighting control system with a water supply system to adjust lighting according to the operation status of a water supply pump. This makes it possible to improve overall energy efficiency. For example, by linking the lighting control system with other energy consumption systems to improve overall energy efficiency, it is possible to optimize energy consumption and achieve cost reductions.

[0089] The data analysis unit can use the emotion estimation function to develop a new energy management system that simultaneously optimizes energy efficiency and user emotions. The data analysis unit, for example, uses the emotion estimation function to analyze user emotion data and build an energy management system that simultaneously optimizes energy efficiency and emotions. For example, it generates a lighting pattern that makes the user feel comfortable. The emotion estimation function is also used to develop an energy management system that optimizes energy efficiency based on the user's emotions. For example, it provides an environment in which the user can relax. For example, the data analysis unit uses the emotion estimation function to analyze user emotion data and build an energy management system that simultaneously optimizes energy efficiency and emotions. The emotion estimation function is used to develop an energy management system that optimizes energy efficiency based on the user's emotions. This makes it possible to simultaneously optimize energy efficiency and user emotions. For example, by using the emotion estimation function to develop a new energy management system that simultaneously optimizes energy efficiency and user emotions, it is possible to improve user comfort.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The lighting control system may further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and adjust the brightness and color of the lights. For example, if the user says "Turn it up," the brightness of the lights can be increased. Alternatively, if the user says "I want to relax," the color of the lights can be changed to a warmer color. This makes it possible to control the lights according to the user's voice commands. For example, using the voice recognition unit allows the user to operate the lights without using their hands, improving convenience.

[0092] The lighting control system may further include a location information acquisition unit. The location information acquisition unit acquires location information from the user's smartphone or wearable device and can adjust lighting according to the user's location. For example, when the user approaches a specific area, the lighting in that area can be automatically turned on. Also, when the user leaves the area, the lighting can be turned off. This makes it possible to control lighting according to the user's location. For example, using the location information acquisition unit can improve energy efficiency.

[0093] The lighting control system can further include a health monitoring unit. The health monitoring unit can monitor the user's health condition and adjust the lighting according to the health condition. For example, the health monitoring unit can monitor the user's heart rate and stress level, and change the lighting to a warmer color when the user needs to relax. Also, the lighting can be brightened when the user needs to concentrate. This makes it possible to control the lighting according to the user's health condition. For example, the health monitoring unit can be used to support the user's health.

[0094] The lighting control system can further include a plant growth mode. The plant growth mode can provide optimal lighting conditions for plant growth. For example, by irradiating light of a specific wavelength, plants can promote photosynthesis. The brightness and color of the lighting can also be adjusted according to the plant's growth stage. This can support the healthy growth of plants. For example, using the plant growth mode makes it easier to grow home gardens and indoor plants.

[0095] The lighting control system can further include an art exhibition mode. The art exhibition mode can provide optimal lighting conditions for the artworks on display. For example, a specific color temperature and brightness can be set to highlight the colors and textures of the artworks. The lighting pattern can also be changed depending on the type or theme of the artwork. This allows the artworks to be displayed to their fullest potential. For example, the art exhibition mode can be used to enhance the effectiveness of exhibitions in museums and galleries.

[0096] The lighting control system can further include a music-linked mode. The music-linked mode can adjust the lighting to match the rhythm and tempo of the music being played. For example, when fast-paced music is being played, the lighting can be made brighter and flash in time with the rhythm. When slow-paced music is being played, the lighting can be changed to a warmer color with a gradual change. This can enhance the entertainment effect of linking music and lighting. For example, using the music-linked mode can make the performance at a party or event more attractive.

[0097] The lighting control system can further use an emotion estimation function to adjust the color and brightness of lighting based on the user's emotions. For example, if the user is feeling stressed, the lighting can be changed to a warmer color to enhance relaxation. Also, if the user is concentrating, the lighting can be brightened to improve work efficiency. This makes it possible to provide an optimal lighting environment according to the user's emotions. For example, by using the emotion estimation function, the user's comfort can be improved.

[0098] The lighting control system can further use the emotion estimation function to monitor the emotions of crowds in public places and adjust the lighting according to their emotions. For example, if the crowd is excited, the lighting can be changed to more colorful colors to help liven up the event. On the other hand, if the crowd is relaxed, the lighting can be changed to warmer colors to create a calm atmosphere. This makes it possible to control lighting according to the emotions of crowds in public places. For example, using the emotion estimation function can improve the satisfaction of participants at events and festivals.

[0099] The lighting control system can further use emotion estimation to monitor the emotions of employees in an office environment and adjust the lighting according to their emotions. For example, if an employee is tired, the lighting can be changed to a warmer color to enhance relaxation. Also, if an employee is concentrating, the lighting can be brightened to improve work efficiency. This makes it possible to control lighting according to the emotions of employees in an office environment. For example, using the emotion estimation function can improve employee comfort and productivity.

[0100] The lighting control system can further use the emotion estimation function to monitor the emotions of family members in the home and adjust the lighting according to their emotions. For example, if the family is relaxing, the lighting can be changed to a warmer color to create a cozy atmosphere. On the other hand, if the family is active, the lighting can be brightened to provide a lively environment. This makes it possible to control the lighting according to the emotions of family members in the home. For example, the emotion estimation function can be used to enhance comfort in the home and deepen family bonds.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The sensor unit monitors traffic volume and the surrounding environment in real time. For example, a traffic volume sensor is installed to measure the number of vehicles and pedestrians. Environmental sensors are also used to detect the surrounding brightness and weather conditions. For example, a traffic volume sensor is installed on a road to count the number of passing vehicles. Environmental sensors use light sensors and temperature sensors to measure the surrounding brightness and temperature. Step 2: The data analysis unit analyzes the data collected by the sensor unit. For example, it predicts peak hours based on traffic volume data. It also predicts weather changes based on weather data. For example, the data analysis unit analyzes past traffic volume data to identify peak hours. It analyzes weather data to predict the occurrence of rain or fog. Step 3: The lighting control unit controls the lighting based on the results of the analysis by the data analysis unit. For example, the lighting is brightened during times of heavy traffic and dimmed during times of light traffic. Also, the lighting is brightened when the weather is bad and dimmed when the weather is good. For example, the lighting control unit turns on the lights at maximum output during times of heavy traffic and dims the lights during times of light traffic. When the weather is bad, the lighting is brightened to improve visibility.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sensor unit that monitors traffic volume and the surrounding environment in real time, a data analysis unit that analyzes the data collected by the sensor unit; a lighting control unit that controls lighting based on the results of analysis by the data analysis unit. A system characterized by:

2. The sensor unit Includes traffic sensors to count vehicles and pedestrians 2. The system of claim 1.

3. The sensor unit An environmental sensor is included to detect the brightness and weather conditions.

2. The system of claim 1.

4. The data analysis unit The lighting is brightened during the time periods when the traffic volume is high, and the lighting is dimmed during the time periods when the traffic volume is low.

2. The system of claim 1.

5. The data analysis unit When the weather is bad, the lights are brightened, and when the weather is good, the lights are dimmed.

2. The system of claim 1.

Images