System

Abstract

An object of a system according to an embodiment is to perform efficient illumination adjustment while ensuring public safety.SOLUTION: A system includes a collection part, an analysis part, a notification part, and an illumination adjustment part. In the collection unit, a camera or a microphone collects data. The analysis unit analyzes the data collected by the collection unit. The reporting unit reports the abnormality based on the data analyzed by the analysis unit. The illumination adjustment unit adjusts illumination based on the data analyzed by the 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 do not adequately coordinate surveillance and lighting to ensure public safety, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently adjust lighting while ensuring public safety. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reporting unit, and a lighting adjustment unit. The collection unit collects data using a camera or microphone. The analysis unit analyzes the data collected by the collection unit. The reporting unit reports an abnormality based on the data analyzed by the analysis unit. The lighting adjustment unit adjusts the lighting based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently adjust lighting while ensuring public safety. [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) A street lighting system according to an embodiment of the present invention is equipped with a multimodal AI that also analyzes audio data, ensuring public safety regardless of changes in day and night, visibility, or pedestrian traffic. The street lighting system uses cameras and microphones to collect data, and multimodal AI analyzes this data to detect abnormal behavior, late-night behavior, abandoned bicycles, traffic accidents, and other issues, and then reports them to the police, fire department, and administrator. The street lighting system also adjusts the directionality and illumination range of lighting according to pedestrian and traffic volume, adjusting light intensity as needed to prevent crime and conserve energy. For example, the street lighting system uses cameras to capture high-resolution video images day and night, and microphones to collect surrounding audio. The street lighting system then uses multimodal AI to analyze the collected video and audio data. For example, the multimodal AI detects abnormal behavior, abandoned bicycles, and traffic accidents from the video data, and abnormal sounds and late-night behavior from the audio data. The street lighting system then reports the detected anomalies to the police, fire department, and administrator. For example, if abnormal behavior is detected, the police will be notified immediately, and if a traffic accident is detected, the fire department or emergency services will be notified. Furthermore, street lighting systems adjust the directionality and illumination range of lighting according to the number of people and traffic volumes. For example, the directionality of lighting is widened during times of heavy foot traffic, and the illumination range is narrowed during times of light traffic. Energy can also be saved by adjusting the intensity of the light as needed. For example, energy consumption can be reduced by reducing the intensity of the light late at night. This allows street lighting systems to ensure public safety, day and night. For example, detecting abnormal behavior or traffic accidents enables a quick response, which is expected to prevent crime and reduce accidents. Energy can also be saved by adjusting the directionality, illumination range, and intensity of lighting. For example, energy consumption can be reduced by reducing the intensity of the light late at night.

[0029] A street lighting system according to an embodiment includes a collection unit, an analysis unit, a reporting unit, and a lighting adjustment unit. The collection unit collects data using a camera or a microphone. The camera, for example, captures high-resolution video day or night. The collection unit also includes a microphone that picks up surrounding sounds. For example, the camera monitors the area around the street light, and the microphone collects people's conversations and the sounds of cars. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses multimodal AI to detect abnormal behavior, abandoned bicycles, traffic accidents, and the like from the video data. The analysis unit also detects abnormal sounds and late-night behavior from the audio data. For example, the multimodal AI detects a person falling from the video data, and detects screams and collision sounds from the audio data. The reporting unit reports an abnormality based on the data analyzed by the analysis unit. For example, the reporting unit immediately reports to the police if it detects abnormal behavior, and to the fire department or emergency services if it detects a traffic accident. This enables a rapid response. The lighting adjustment unit adjusts the lighting based on the data analyzed by the analysis unit. For example, the lighting adjustment unit widens the directionality of the lighting during busy hours and narrows the illumination range during light traffic hours. The lighting adjustment unit also adjusts the intensity of the light as needed. For example, by reducing the intensity of the light late at night, energy consumption can be reduced. As a result, the street lighting system according to the embodiment can ensure public safety and save energy.

[0030] The collection unit can be equipped with a camera that captures high-definition images day and night. The camera can, for example, capture high-resolution images during the day and capture images in the dark at night using an infrared camera. The camera can also monitor a wide area using a wide-angle lens. Furthermore, the camera has a zoom function and can capture distant objects in detail. This improves the accuracy of detecting anomalies by capturing high-resolution images day and night. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by the camera to a generation AI and have the generation AI detect anomalies from the video data.

[0031] The collection unit may include a microphone that picks up environmental sounds. The microphone may, for example, be capable of picking up ambient sounds with high sensitivity. The microphone may also have a noise-canceling function, which can eliminate unnecessary noise. Furthermore, the microphone is directional and can preferentially collect sounds from a specific direction. This allows the collection unit to analyze the audio data and detect anomalies by picking up ambient sounds. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input audio data acquired by the microphone to a generation AI and cause the generation AI to detect anomalies from the audio data.

[0032] The analysis unit can detect abnormal behavior, abandoned bicycles, or traffic accidents from the video data. The analysis unit detects abnormal behavior from the video data, for example, using multimodal AI. For example, the analysis unit can detect a person lying down or suspicious behavior. The analysis unit can also detect abandoned bicycles. For example, the analysis unit can detect a bicycle that has not moved for a certain period of time and recognize it as an abandoned bicycle. The analysis unit can also detect traffic accidents. For example, the analysis unit can detect a vehicle collision or fall and recognize it as a traffic accident. This makes it possible to detect abnormal behavior, abandoned bicycles, traffic accidents, etc. from the video data with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input video data to a generation AI and have the generation AI detect abnormal behavior, abandoned bicycles, and traffic accidents.

[0033] The analysis unit can detect abnormal sounds or late-night behavior from the audio data. The analysis unit detects abnormal sounds from the audio data, for example, using multimodal AI. For example, the analysis unit can detect screams or crashing sounds. The analysis unit can also detect late-night behavior. For example, the analysis unit detects suspicious sounds or movements occurring late at night and recognizes them as abnormal. This allows for highly accurate detection of abnormal sounds and late-night behavior from the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI detect abnormal sounds or late-night behavior.

[0034] The reporting unit can notify the police when it detects abnormal behavior. For example, when the analysis unit detects abnormal behavior, the reporting unit immediately notifies the police. For example, the reporting unit provides detailed information about the abnormal behavior to the police to encourage a prompt response. The reporting unit can also include location information and video data in the report content. This makes it easier for the police to grasp the situation at the scene. This makes it possible to quickly notify the police when abnormal behavior is detected. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input information about the abnormal behavior into a generation AI and have the generation AI generate the report content.

[0035] The reporting unit can notify the fire department or emergency services when it detects a traffic accident. For example, when the analysis unit detects a traffic accident, the reporting unit immediately notifies the fire department or emergency services. For example, the reporting unit provides detailed information about the traffic accident to the fire department or emergency services to encourage a prompt response. The reporting unit can also include location information and video data in the report content. This makes it easier for the fire department or emergency services to understand the situation at the scene. This makes it possible to quickly notify the fire department or emergency services when a traffic accident is detected. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input information about the traffic accident into a generation AI and have the generation AI generate the report content.

[0036] The lighting adjustment unit can widen the directionality of lighting during times when there is a lot of foot traffic. For example, the lighting adjustment unit widens the directionality of lighting during times when there is a lot of foot traffic. For example, the lighting adjustment unit improves visibility by illuminating a wide area. The lighting adjustment unit can also dynamically change the directionality of lighting. For example, it automatically widens the directionality when the number of foot traffic increases and narrows the directionality when the number of foot traffic decreases. In this way, visibility can be improved by widening the directionality of lighting during times when there is a lot of foot traffic. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, or without, AI, for example. For example, the lighting adjustment unit can input pedestrian traffic data into the generation AI and have the generation AI adjust the directionality of the lighting.

[0037] The lighting adjustment unit can narrow the illumination range during times when traffic volume is low. For example, the lighting adjustment unit narrows the illumination range during times when traffic volume is low. For example, the lighting adjustment unit reduces unnecessary energy consumption by narrowing the illumination range in order to save energy. The lighting adjustment unit can also dynamically change the illumination range. For example, the lighting adjustment unit automatically narrows the illumination range when traffic volume decreases and widens the illumination range when traffic volume increases. In this way, energy can be saved by narrowing the illumination range during times when traffic volume is low. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input traffic volume data into the generation AI and have the generation AI adjust the illumination range.

[0038] The lighting adjustment unit can adjust the light intensity as needed. For example, the lighting adjustment unit adjusts the light intensity as needed. For example, the lighting adjustment unit reduces energy consumption by dimming the light intensity late at night. The lighting adjustment unit can also dynamically change the light intensity. For example, the lighting adjustment unit automatically adjusts the light intensity according to the surrounding brightness and the number of people passing by. This allows energy to be saved by adjusting the light intensity as needed. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input data on the surrounding brightness and the number of people passing by into the generation AI and have the generation AI adjust the light intensity.

[0039] The collection unit can automatically adjust the camera exposure or microphone sensitivity in response to changes in the daytime and nighttime environment. The collection unit automatically adjusts the camera exposure in response to changes in the daytime and nighttime environment. For example, the collection unit can lower the camera exposure during the day and increase it at night to capture high-resolution video. The collection unit can also automatically adjust the microphone sensitivity in response to changes in the daytime and nighttime environment. For example, the collection unit can lower the microphone sensitivity during the day and increase it at night to pick up ambient sounds. The collection unit can also adjust the camera exposure and microphone sensitivity to a medium level during dim light hours such as evening and early morning. This allows optimal data to be collected at all times by automatically adjusting the camera exposure and microphone sensitivity in response to changes in the daytime and nighttime environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input daytime and nighttime environmental data into the generation AI and cause the generation AI to adjust the camera exposure and microphone sensitivity.

[0040] The collection unit can dynamically change the type or amount of collected data depending on a specific event. The collection unit dynamically changes the type and amount of collected data depending on the specific event. For example, when there is a large gathering, the camera resolution can be increased to collect detailed video. The collection unit can also increase the microphone sensitivity during a festival to collect detailed audio data. Furthermore, after the specific event ends, the amount of collected data can be returned to normal. This allows for efficient collection of necessary data by dynamically changing the type and amount of collected data depending on the specific event. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input event data into a generation AI and have the generation AI change the type and amount of collected data.

[0041] The collection unit can activate additional sensors to collect detailed data when it detects an abnormal sound or movement. For example, when it detects an abnormal sound, the collection unit activates additional sensors to collect detailed data. For example, when it detects an abnormal sound, the collection unit can activate a temperature sensor to measure the ambient temperature. Furthermore, when it detects abnormal movement, the collection unit can activate a vibration sensor to measure ground vibrations. Furthermore, when it detects an abnormal sound or movement, the collection unit can simultaneously activate multiple sensors to collect detailed data. In this way, detailed data can be collected by activating additional sensors when it detects an abnormal sound or movement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on abnormal sounds or movements to the generation AI and cause the generation AI to activate additional sensors.

[0042] When the collection unit detects a specified sound pattern, it can collect data in cooperation with other surrounding streetlights. For example, when the collection unit detects a specified sound pattern, it collects data in cooperation with other surrounding streetlights. For example, when the collection unit detects a shout, it simultaneously collects video from the cameras of surrounding streetlights. Furthermore, when the collection unit detects a collision sound, it can simultaneously collect audio from the microphones of surrounding streetlights. Furthermore, when the collection unit detects a specified sound pattern, it can also collect data in cooperation with surrounding streetlights to identify the extent of the abnormality. In this way, by collecting data in cooperation with other surrounding streetlights when a specified sound pattern is detected, it is possible to identify the extent of the abnormality. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of a specified sound pattern to a generation AI and cause the generation AI to execute coordination with other streetlights.

[0043] When the collection unit detects a specified movement, it can activate a drone to collect video data from the sky. For example, when the collection unit detects a specific movement, it activates a drone to collect video data from the sky. For example, when the collection unit detects abnormal movement, it activates a drone to collect video from the sky. Furthermore, when the collection unit detects a specific movement, it can activate a drone to collect video over a wide area. Furthermore, when the collection unit detects abnormal movement, it can activate a drone to collect detailed video and identify the range of the abnormality. In this way, by activating a drone when a specific movement is detected, it is possible to collect video data over a wide area. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of a specific movement to a generation AI and have the generation AI activate a drone.

[0044] The collection unit can collect additional data from nearby smart devices when it detects a specified abnormality. For example, the collection unit collects additional data from nearby smart devices when it detects a specific abnormality. For example, the collection unit collects location information from nearby smartphones when it detects abnormal behavior. The collection unit can also collect heart rate data from nearby smart watches when it detects a traffic accident. Furthermore, the collection unit can collect audio data from nearby smart devices when it detects an abnormal sound. In this way, detailed information can be obtained by collecting additional data from nearby smart devices when it detects a specific abnormality. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of a specific abnormality to the generation AI and cause the generation AI to collect additional data from the smart devices.

[0045] The analysis unit can integrate video data and audio data to perform highly accurate anomaly detection. The analysis unit, for example, integrates video data and audio data to detect abnormal behavior with high accuracy. For example, the analysis unit can integrate video data and audio data to simultaneously detect a person falling and screaming. The analysis unit can also integrate video data and audio data to detect traffic accidents with high accuracy. For example, the analysis unit simultaneously detects vehicle collisions and collision sounds. The analysis unit can also integrate video data and audio data to detect abandoned bicycles with high accuracy. For example, the analysis unit simultaneously detects bicycles that have not moved for a certain period of time and surrounding sounds. In this way, by integrating video data and audio data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input video data and audio data to a generation AI and have the generation AI perform anomaly detection.

[0046] When an abnormality is detected, the analysis unit can compare the data with previous data to identify an abnormal pattern. For example, when abnormal behavior is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past abnormal behavior with current data to identify a common pattern. Furthermore, when a traffic accident is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past traffic accidents with current data to identify a common pattern. Furthermore, when an abandoned bicycle is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past abandoned bicycles with current data to identify a common pattern. This makes it easier to identify an abnormal pattern by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data and current data into a generation AI and cause the generation AI to identify an abnormal pattern.

[0047] When the analysis unit detects an abnormality, it can infer the cause of the abnormality and propose preventive measures. For example, when the analysis unit detects abnormal behavior, it can infer the cause of the abnormality and propose preventive measures. For example, the analysis unit can analyze the cause of the abnormal behavior and propose preventive measures. Furthermore, when the analysis unit detects a traffic accident, it can infer the cause of the accident and propose preventive measures. For example, the analysis unit can analyze the cause of the traffic accident and propose preventive measures. Furthermore, when the analysis unit detects an abandoned bicycle, it can infer the cause of the accident and propose preventive measures. For example, the analysis unit can analyze the cause of the abandoned bicycle and propose preventive measures. In this way, inferring the cause of the abnormality and proposing preventive measures helps prevent recurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the cause of the abnormality to the generation AI and have the generation AI execute the proposal of preventive measures.

[0048] When the analysis unit detects an abnormality, it can identify the range of the abnormality in cooperation with other street lights or a monitoring system. For example, when the analysis unit detects abnormal behavior, it can identify the range of the abnormality in cooperation with other street lights. For example, the analysis unit can identify the range of the abnormal behavior by coordinating with data from other street lights. Furthermore, when the analysis unit detects a traffic accident, it can identify the range of the abnormality in cooperation with other monitoring systems. For example, the analysis unit can identify the range of the traffic accident by coordinating with data from other monitoring systems. Furthermore, when the analysis unit detects an abandoned bicycle, it can identify the range of the abnormality in cooperation with other street lights. For example, the analysis unit can identify the range of the abandoned bicycle by coordinating with data from other street lights. This makes it easier to identify the range of the abnormality by coordinating with other street lights or monitoring systems. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other street lights or monitoring systems into the generation AI and cause the generation AI to identify the range of the abnormality.

[0049] When the analysis unit detects an abnormality, it can predict the extent of the abnormality's impact and propose a countermeasure. For example, when the analysis unit detects abnormal behavior, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of abnormal behavior and propose a countermeasure. Furthermore, when the analysis unit detects a traffic accident, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of a traffic accident and propose a countermeasure. Furthermore, when the analysis unit detects an abandoned bicycle, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of an abandoned bicycle and propose a countermeasure. This enables a rapid response by predicting the extent of the abnormality's impact and proposing a countermeasure. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the extent of the abnormality's impact into the generation AI and have the generation AI execute the proposed countermeasure.

[0050] When an abnormality is detected, the analysis unit can analyze the frequency of the abnormality and propose long-term measures. For example, when abnormal behavior is detected, the analysis unit can analyze the frequency of the abnormality and propose long-term measures. For example, the analysis unit can analyze the frequency of abnormal behavior and propose long-term measures. Furthermore, when a traffic accident is detected, the analysis unit can analyze the frequency of the accident and propose long-term measures. For example, the analysis unit can analyze the frequency of traffic accidents and propose long-term measures. Furthermore, when an abandoned bicycle is detected, the analysis unit can analyze the frequency of the accident and propose long-term measures. For example, the analysis unit can analyze the frequency of abandoned bicycles and propose long-term measures. In this way, by analyzing the frequency of abnormalities and proposing long-term measures, it is possible to ensure sustainable safety. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the frequency of abnormalities to the generation AI and have the generation AI propose long-term measures.

[0051] The reporting unit can change the priority of the report destination when it detects an abnormality. For example, when it detects abnormal behavior, the reporting unit can prioritize reporting to the police. Furthermore, when it detects a traffic accident, the reporting unit can also prioritize reporting to the fire department or ambulance. Furthermore, when it detects an abandoned bicycle, the reporting unit can also prioritize reporting to the administrator. This allows for a rapid response by changing the priority of the report destination depending on the type of abnormality. Some or all of the above-mentioned processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI change the priority of the report destination.

[0052] When the reporting unit detects an abnormality, it can include the analysis results in the report content. For example, when the reporting unit detects abnormal behavior, it can include detailed analysis results in the report content. Furthermore, when the reporting unit detects a traffic accident, it can also include detailed analysis results in the report content. Furthermore, when the reporting unit detects an abandoned bicycle, it can also include detailed analysis results in the report content. By including detailed analysis results in the report content, the reporting party can respond quickly. Some or all of the above-mentioned processing in the reporting unit can be performed using AI, for example, or can be performed without using AI. For example, the reporting unit can input data of the analysis results into a generation AI and have the generation AI generate the report content.

[0053] When an abnormality is detected, the reporting unit can select the contact method for the reporting destination. For example, when abnormal behavior is detected, the reporting unit can notify the police by phone. Furthermore, when a traffic accident is detected, the reporting unit can also notify the fire department or emergency services by email. Furthermore, when an abandoned bicycle is detected, the reporting unit can also notify the administrator by SMS. This enables prompt reporting by selecting the optimal contact method depending on the type of abnormality. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI select the contact method.

[0054] The reporting unit can also send a notification to nearby residents or related parties when it detects an abnormality. For example, the reporting unit can send a notification to nearby residents when it detects abnormal behavior. The reporting unit can also send a notification to related parties when it detects a traffic accident. Furthermore, the reporting unit can also send a notification to nearby residents or related parties when it detects an abandoned bicycle. This allows for a rapid response by sending notifications to nearby residents and related parties when an abnormality is detected. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI send a notification.

[0055] The reporting unit can provide the report content in multiple languages ​​when it detects an abnormality. For example, when it detects abnormal behavior, the reporting unit provides the report content in multiple languages. The reporting unit can also provide the report content in multiple languages ​​when it detects a traffic accident. Furthermore, the reporting unit can also provide the report content in multiple languages ​​when it detects an abandoned bicycle. By providing the report content in multiple languages, information can be quickly conveyed to people who speak different languages. Some or all of the above-mentioned processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input the data of the report content into a generation AI and cause the generation AI to generate the report content in multiple languages.

[0056] When the reporting unit detects an abnormality, it can include map information or a photo of the scene in the report. For example, when the reporting unit detects abnormal behavior, it can include map information in the report. Furthermore, when the reporting unit detects a traffic accident, it can also include a photo of the scene in the report. Furthermore, when the reporting unit detects an abandoned bicycle, it can also include map information and a photo of the scene in the report. By including map information and a photo of the scene in the report, the person to be notified can quickly identify the scene and take action. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input data such as map information and a photo of the scene into a generation AI and have the generation AI generate the report.

[0057] The lighting adjustment unit can monitor pedestrian traffic or traffic volume in real time and dynamically change the lighting directionality or illumination range. For example, the lighting adjustment unit widens the lighting directionality during times of heavy pedestrian traffic. The lighting adjustment unit can also narrow the illumination range during times of light traffic. Furthermore, the lighting adjustment unit can adjust the lighting directionality and illumination range in real time when the pedestrian traffic or traffic volume changes. This allows for energy savings and improved safety by dynamically changing the lighting directionality and illumination range according to the pedestrian traffic or traffic volume. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input pedestrian traffic or traffic volume data into the generation AI and have the generation AI adjust the lighting directionality and illumination range.

[0058] The lighting adjustment unit can improve visibility by increasing the surrounding lighting when an abnormality is detected. For example, the lighting adjustment unit temporarily increases the surrounding lighting when abnormal behavior is detected. The lighting adjustment unit can also temporarily increase the surrounding lighting when a traffic accident is detected. Furthermore, the lighting adjustment unit can temporarily increase the surrounding lighting when an abandoned bicycle is detected. In this way, increasing the surrounding lighting when an abnormality is detected improves visibility and enables a prompt response. Some or all of the above-described processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to increase the lighting.

[0059] The lighting adjustment unit can change the color temperature of the lighting and issue a warning signal when an abnormality is detected. For example, when abnormal behavior is detected, the lighting adjustment unit changes the color temperature of the lighting to red and issues a warning. Furthermore, when a traffic accident is detected, the lighting adjustment unit can change the color temperature of the lighting to blue and issue a warning. Furthermore, when an abandoned bicycle is detected, the lighting adjustment unit can change the color temperature of the lighting to yellow and issue a warning. In this way, by changing the color temperature of the lighting when an abnormality is detected, a visual warning can be issued. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to change the color temperature.

[0060] The lighting adjustment unit can change the lighting pattern in accordance with the identified event. For example, when an emergency occurs, the lighting adjustment unit changes the lighting pattern to flashing. The lighting adjustment unit can also change the lighting pattern to colorful lights during a festival. Furthermore, after the specific event ends, the lighting pattern can be returned to its normal state. In this way, by changing the lighting pattern in accordance with the specific event, a lighting environment suitable for the event can be provided. Some or all of the above-described processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input event data into the generation AI and cause the generation AI to change the lighting pattern.

[0061] When an abnormality is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights or a monitoring system. For example, when abnormal behavior is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. In addition, when a traffic accident is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. Furthermore, when an abandoned bicycle is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. In this way, by unifying the lighting pattern in cooperation with other street lights when an abnormality is detected, the extent of the abnormality can be visually indicated. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to execute coordination with other street lights.

[0062] The lighting adjustment unit can change the brightness of the lighting to attract attention when it detects an abnormality. For example, the lighting adjustment unit can gradually change the brightness of the lighting to attract attention when it detects abnormal behavior. The lighting adjustment unit can also gradually change the brightness of the lighting to attract attention when it detects a traffic accident. Furthermore, the lighting adjustment unit can also gradually change the brightness of the lighting to attract attention when it detects an abandoned bicycle. In this way, by gradually changing the brightness of the lighting when an abnormality is detected, it is possible to visually attract attention. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to change the brightness.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The street lighting system can further include a temperature sensor. The temperature sensor can, for example, monitor the ambient temperature in real time and detect abnormal temperature changes. For example, it can detect an abnormal temperature rise in the early stages of a fire and immediately notify the fire department. The temperature sensor can also issue a warning when it detects a low temperature to prevent freezing in cold regions. Furthermore, the temperature sensor can also issue a warning when the ambient temperature exceeds a certain level to reduce the risk of heatstroke during high temperatures in summer. Thus, adding a temperature sensor can reduce the risks of fire, freezing, heatstroke, and the like.

[0065] The street lighting system can further include a vibration sensor. The vibration sensor can, for example, detect earthquakes and shocks in real time and detect abnormal vibrations. For example, it can detect vibrations in the early stages of an earthquake and immediately issue an alarm. The vibration sensor can also detect shocks during traffic accidents and quickly report them. Furthermore, the vibration sensor can detect vibrations caused by building deterioration and identify areas that require early repairs. Thus, adding a vibration sensor can reduce the risks of earthquakes, traffic accidents, building deterioration, and the like.

[0066] The street lighting system can further include gas sensors. Gas sensors can detect, for example, harmful and flammable gases in real time and detect abnormal gas concentrations. For example, they can detect gas leaks and immediately issue an alarm. Gas sensors can also detect harmful gas leaks in industrial areas and quickly report them. Furthermore, gas sensors can monitor carbon monoxide concentrations in underground parking lots and issue an alarm if they reach dangerous levels. Thus, adding gas sensors can reduce the risk of gas leaks and harmful gases.

[0067] The street lighting system can further include a humidity sensor. The humidity sensor can, for example, monitor the ambient humidity in real time and detect abnormal humidity changes. For example, because high humidity increases the risk of mold growth, the humidity sensor can issue an alert when it detects high humidity. The humidity sensor can also issue an alert when it detects low humidity to reduce the risk of fire in dry environments. Furthermore, the humidity sensor can provide humidity data to assist in proper humidity management in agricultural areas. Thus, adding a humidity sensor can reduce the risk of mold growth and fire and assist in humidity management in agricultural areas.

[0068] The street lighting system can further include a wind speed sensor. The wind speed sensor can, for example, monitor the surrounding wind speed in real time and detect abnormal changes in wind speed. For example, during strong winds or typhoons, the wind speed sensor can detect high wind speeds and immediately issue an alert. The wind speed sensor can also provide wind speed data to support safety management at construction sites. Furthermore, the wind speed sensor can also be used to collect wind speed data at wind power plants and optimize power generation efficiency. Thus, adding a wind speed sensor can reduce the risk of strong winds and typhoons and support safety management at construction sites and wind power plants.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit uses cameras or microphones to collect data. The cameras capture high-resolution images day and night, and the microphones pick up surrounding sounds. For example, the cameras monitor the area around streetlights, and the microphones collect the sounds of people talking and cars. Step 2: The analysis unit analyzes the data collected by the collection unit. Using multimodal AI, the analysis unit detects abnormal behavior, abandoned bicycles, traffic accidents, etc. from video data, and abnormal sounds and late-night behavior from audio data. For example, it can detect a person falling from video data and screams and collision sounds from audio data. Step 3: The reporting unit reports abnormalities based on the data analyzed by the analysis unit. For example, if abnormal behavior is detected, the reporting unit immediately notifies the police, and if a traffic accident is detected, the reporting unit notifies the fire department or emergency services. Step 4: The lighting adjustment unit adjusts the lighting based on the data analyzed by the analysis unit. For example, it widens the directionality of the lighting during busy times and narrows the illumination range during times of low traffic. It also adjusts the light intensity as needed, reducing the light intensity late at night to reduce energy consumption.

[0071] (Example 2) A street lighting system according to an embodiment of the present invention is equipped with a multimodal AI that also analyzes audio data, ensuring public safety regardless of changes in day and night, visibility, or pedestrian traffic. The street lighting system uses cameras and microphones to collect data, and multimodal AI analyzes this data to detect abnormal behavior, late-night behavior, abandoned bicycles, traffic accidents, and other issues, and then reports them to the police, fire department, and administrator. The street lighting system also adjusts the directionality and illumination range of lighting according to pedestrian and traffic volume, adjusting light intensity as needed to prevent crime and conserve energy. For example, the street lighting system uses cameras to capture high-resolution video images day and night, and microphones to collect surrounding audio. The street lighting system then uses multimodal AI to analyze the collected video and audio data. For example, the multimodal AI detects abnormal behavior, abandoned bicycles, and traffic accidents from the video data, and abnormal sounds and late-night behavior from the audio data. The street lighting system then reports the detected anomalies to the police, fire department, and administrator. For example, if abnormal behavior is detected, the police will be notified immediately, and if a traffic accident is detected, the fire department or emergency services will be notified. Furthermore, street lighting systems adjust the directionality and illumination range of lighting according to the number of people and traffic volumes. For example, the directionality of lighting is widened during times of heavy foot traffic, and the illumination range is narrowed during times of light traffic. Energy can also be saved by adjusting the intensity of the light as needed. For example, energy consumption can be reduced by reducing the intensity of the light late at night. This allows street lighting systems to ensure public safety, day and night. For example, detecting abnormal behavior or traffic accidents enables a quick response, which is expected to prevent crime and reduce accidents. Energy can also be saved by adjusting the directionality, illumination range, and intensity of lighting. For example, energy consumption can be reduced by reducing the intensity of the light late at night.

[0072] A street lighting system according to an embodiment includes a collection unit, an analysis unit, a reporting unit, and a lighting adjustment unit. The collection unit collects data using a camera or a microphone. The camera, for example, captures high-resolution video day or night. The collection unit also includes a microphone that picks up surrounding sounds. For example, the camera monitors the area around the street light, and the microphone collects people's conversations and the sounds of cars. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses multimodal AI to detect abnormal behavior, abandoned bicycles, traffic accidents, and the like from the video data. The analysis unit also detects abnormal sounds and late-night behavior from the audio data. For example, the multimodal AI detects a person falling from the video data, and detects screams and collision sounds from the audio data. The reporting unit reports an abnormality based on the data analyzed by the analysis unit. For example, the reporting unit immediately reports to the police if it detects abnormal behavior, and to the fire department or emergency services if it detects a traffic accident. This enables a rapid response. The lighting adjustment unit adjusts the lighting based on the data analyzed by the analysis unit. For example, the lighting adjustment unit widens the directionality of the lighting during busy hours and narrows the illumination range during light traffic hours. The lighting adjustment unit also adjusts the intensity of the light as needed. For example, by reducing the intensity of the light late at night, energy consumption can be reduced. As a result, the street lighting system according to the embodiment can ensure public safety and save energy.

[0073] The collection unit can be equipped with a camera that captures high-definition images day and night. The camera can, for example, capture high-resolution images during the day and capture images in the dark at night using an infrared camera. The camera can also monitor a wide area using a wide-angle lens. Furthermore, the camera has a zoom function and can capture distant objects in detail. This improves the accuracy of detecting anomalies by capturing high-resolution images day and night. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by the camera to a generation AI and have the generation AI detect anomalies from the video data.

[0074] The collection unit may include a microphone that picks up environmental sounds. The microphone may, for example, be capable of picking up ambient sounds with high sensitivity. The microphone may also have a noise-canceling function, which can eliminate unnecessary noise. Furthermore, the microphone is directional and can preferentially collect sounds from a specific direction. This allows the collection unit to analyze the audio data and detect anomalies by picking up ambient sounds. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input audio data acquired by the microphone to a generation AI and cause the generation AI to detect anomalies from the audio data.

[0075] The analysis unit can detect abnormal behavior, abandoned bicycles, or traffic accidents from the video data. The analysis unit detects abnormal behavior from the video data, for example, using multimodal AI. For example, the analysis unit can detect a person lying down or suspicious behavior. The analysis unit can also detect abandoned bicycles. For example, the analysis unit can detect a bicycle that has not moved for a certain period of time and recognize it as an abandoned bicycle. The analysis unit can also detect traffic accidents. For example, the analysis unit can detect a vehicle collision or fall and recognize it as a traffic accident. This makes it possible to detect abnormal behavior, abandoned bicycles, traffic accidents, etc. from the video data with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input video data to a generation AI and have the generation AI detect abnormal behavior, abandoned bicycles, and traffic accidents.

[0076] The analysis unit can detect abnormal sounds or late-night behavior from the audio data. The analysis unit detects abnormal sounds from the audio data, for example, using multimodal AI. For example, the analysis unit can detect screams or crashing sounds. The analysis unit can also detect late-night behavior. For example, the analysis unit detects suspicious sounds or movements occurring late at night and recognizes them as abnormal. This allows for highly accurate detection of abnormal sounds and late-night behavior from the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI detect abnormal sounds or late-night behavior.

[0077] The reporting unit can notify the police when it detects abnormal behavior. For example, when the analysis unit detects abnormal behavior, the reporting unit immediately notifies the police. For example, the reporting unit provides detailed information about the abnormal behavior to the police to encourage a prompt response. The reporting unit can also include location information and video data in the report content. This makes it easier for the police to grasp the situation at the scene. This makes it possible to quickly notify the police when abnormal behavior is detected. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input information about the abnormal behavior into a generation AI and have the generation AI generate the report content.

[0078] The reporting unit can notify the fire department or emergency services when it detects a traffic accident. For example, when the analysis unit detects a traffic accident, the reporting unit immediately notifies the fire department or emergency services. For example, the reporting unit provides detailed information about the traffic accident to the fire department or emergency services to encourage a prompt response. The reporting unit can also include location information and video data in the report content. This makes it easier for the fire department or emergency services to understand the situation at the scene. This makes it possible to quickly notify the fire department or emergency services when a traffic accident is detected. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input information about the traffic accident into a generation AI and have the generation AI generate the report content.

[0079] The lighting adjustment unit can widen the directionality of lighting during times when there is a lot of foot traffic. For example, the lighting adjustment unit widens the directionality of lighting during times when there is a lot of foot traffic. For example, the lighting adjustment unit improves visibility by illuminating a wide area. The lighting adjustment unit can also dynamically change the directionality of lighting. For example, it automatically widens the directionality when the number of foot traffic increases and narrows the directionality when the number of foot traffic decreases. In this way, visibility can be improved by widening the directionality of lighting during times when there is a lot of foot traffic. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, or without, AI, for example. For example, the lighting adjustment unit can input pedestrian traffic data into the generation AI and have the generation AI adjust the directionality of the lighting.

[0080] The lighting adjustment unit can narrow the illumination range during times when traffic volume is low. For example, the lighting adjustment unit narrows the illumination range during times when traffic volume is low. For example, the lighting adjustment unit reduces unnecessary energy consumption by narrowing the illumination range in order to save energy. The lighting adjustment unit can also dynamically change the illumination range. For example, the lighting adjustment unit automatically narrows the illumination range when traffic volume decreases and widens the illumination range when traffic volume increases. In this way, energy can be saved by narrowing the illumination range during times when traffic volume is low. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input traffic volume data into the generation AI and have the generation AI adjust the illumination range.

[0081] The lighting adjustment unit can adjust the light intensity as needed. For example, the lighting adjustment unit adjusts the light intensity as needed. For example, the lighting adjustment unit reduces energy consumption by dimming the light intensity late at night. The lighting adjustment unit can also dynamically change the light intensity. For example, the lighting adjustment unit automatically adjusts the light intensity according to the surrounding brightness and the number of people passing by. This allows energy to be saved by adjusting the light intensity as needed. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input data on the surrounding brightness and the number of people passing by into the generation AI and have the generation AI adjust the light intensity.

[0082] The collection unit can predict the user's emotions and adjust the timing of data collection based on the predicted user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is nervous, the collection unit increases the frequency of data collection to collect more detailed data. Alternatively, if the user is relaxed, the collection unit can reduce the frequency of data collection to collect the minimum amount of data necessary. Furthermore, if the user is in a hurry, the collection unit can speed up the timing of data collection to collect data immediately. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0083] The collection unit can automatically adjust the camera exposure or microphone sensitivity in response to changes in the daytime and nighttime environment. The collection unit automatically adjusts the camera exposure in response to changes in the daytime and nighttime environment. For example, the collection unit can lower the camera exposure during the day and increase it at night to capture high-resolution video. The collection unit can also automatically adjust the microphone sensitivity in response to changes in the daytime and nighttime environment. For example, the collection unit can lower the microphone sensitivity during the day and increase it at night to pick up ambient sounds. The collection unit can also adjust the camera exposure and microphone sensitivity to a medium level during dim light hours such as evening and early morning. This allows optimal data to be collected at all times by automatically adjusting the camera exposure and microphone sensitivity in response to changes in the daytime and nighttime environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input daytime and nighttime environmental data into the generation AI and cause the generation AI to adjust the camera exposure and microphone sensitivity.

[0084] The collection unit can dynamically change the type or amount of collected data depending on a specific event. The collection unit dynamically changes the type and amount of collected data depending on the specific event. For example, when there is a large gathering, the camera resolution can be increased to collect detailed video. The collection unit can also increase the microphone sensitivity during a festival to collect detailed audio data. Furthermore, after the specific event ends, the amount of collected data can be returned to normal. This allows for efficient collection of necessary data by dynamically changing the type and amount of collected data depending on the specific event. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input event data into a generation AI and have the generation AI change the type and amount of collected data.

[0085] The collection unit can activate additional sensors to collect detailed data when it detects an abnormal sound or movement. For example, when it detects an abnormal sound, the collection unit activates additional sensors to collect detailed data. For example, when it detects an abnormal sound, the collection unit can activate a temperature sensor to measure the ambient temperature. Furthermore, when it detects abnormal movement, the collection unit can activate a vibration sensor to measure ground vibrations. Furthermore, when it detects an abnormal sound or movement, the collection unit can simultaneously activate multiple sensors to collect detailed data. In this way, detailed data can be collected by activating additional sensors when it detects an abnormal sound or movement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on abnormal sounds or movements to the generation AI and cause the generation AI to activate additional sensors.

[0086] The collection unit can predict the user's emotions and determine the priority of data to be collected based on the predicted user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, if the user is nervous, the collection of audio data can be prioritized. Also, if the user is relaxed, the collection of video data can be prioritized. Furthermore, if the user is in a hurry, the detection of abnormal behavior can be prioritized. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data to be collected.

[0087] When the collection unit detects a specified sound pattern, it can collect data in cooperation with other surrounding streetlights. For example, when the collection unit detects a specified sound pattern, it collects data in cooperation with other surrounding streetlights. For example, when the collection unit detects a shout, it simultaneously collects video from the cameras of surrounding streetlights. Furthermore, when the collection unit detects a collision sound, it can simultaneously collect audio from the microphones of surrounding streetlights. Furthermore, when the collection unit detects a specified sound pattern, it can also collect data in cooperation with surrounding streetlights to identify the extent of the abnormality. In this way, by collecting data in cooperation with other surrounding streetlights when a specified sound pattern is detected, it is possible to identify the extent of the abnormality. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of a specified sound pattern to a generation AI and cause the generation AI to execute coordination with other streetlights.

[0088] When the collection unit detects a specified movement, it can activate a drone to collect video data from the sky. For example, when the collection unit detects a specific movement, it activates a drone to collect video data from the sky. For example, when the collection unit detects abnormal movement, it activates a drone to collect video from the sky. Furthermore, when the collection unit detects a specific movement, it can activate a drone to collect video over a wide area. Furthermore, when the collection unit detects abnormal movement, it can activate a drone to collect detailed video and identify the range of the abnormality. In this way, by activating a drone when a specific movement is detected, it is possible to collect video data over a wide area. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of a specific movement to a generation AI and have the generation AI activate a drone.

[0089] The collection unit can collect additional data from nearby smart devices when it detects a specified abnormality. For example, the collection unit collects additional data from nearby smart devices when it detects a specific abnormality. For example, the collection unit collects location information from nearby smartphones when it detects abnormal behavior. The collection unit can also collect heart rate data from nearby smart watches when it detects a traffic accident. Furthermore, the collection unit can collect audio data from nearby smart devices when it detects an abnormal sound. In this way, detailed information can be obtained by collecting additional data from nearby smart devices when it detects a specific abnormality. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of a specific abnormality to the generation AI and cause the generation AI to collect additional data from the smart devices.

[0090] The analysis unit can predict the user's emotions and adjust the display method of the analysis results based on the predicted user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate information provision by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0091] The analysis unit can integrate video data and audio data to perform highly accurate anomaly detection. The analysis unit, for example, integrates video data and audio data to detect abnormal behavior with high accuracy. For example, the analysis unit can integrate video data and audio data to simultaneously detect a person falling and screaming. The analysis unit can also integrate video data and audio data to detect traffic accidents with high accuracy. For example, the analysis unit simultaneously detects vehicle collisions and collision sounds. The analysis unit can also integrate video data and audio data to detect abandoned bicycles with high accuracy. For example, the analysis unit simultaneously detects bicycles that have not moved for a certain period of time and surrounding sounds. In this way, by integrating video data and audio data, the accuracy of anomaly detection is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input video data and audio data to a generation AI and have the generation AI perform anomaly detection.

[0092] When an abnormality is detected, the analysis unit can compare the data with previous data to identify an abnormal pattern. For example, when abnormal behavior is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past abnormal behavior with current data to identify a common pattern. Furthermore, when a traffic accident is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past traffic accidents with current data to identify a common pattern. Furthermore, when an abandoned bicycle is detected, the analysis unit can compare the data with past data to identify an abnormal pattern. For example, the analysis unit can compare data of past abandoned bicycles with current data to identify a common pattern. This makes it easier to identify an abnormal pattern by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data and current data into a generation AI and cause the generation AI to identify an abnormal pattern.

[0093] When the analysis unit detects an abnormality, it can infer the cause of the abnormality and propose preventive measures. For example, when the analysis unit detects abnormal behavior, it can infer the cause of the abnormality and propose preventive measures. For example, the analysis unit can analyze the cause of the abnormal behavior and propose preventive measures. Furthermore, when the analysis unit detects a traffic accident, it can infer the cause of the accident and propose preventive measures. For example, the analysis unit can analyze the cause of the traffic accident and propose preventive measures. Furthermore, when the analysis unit detects an abandoned bicycle, it can infer the cause of the accident and propose preventive measures. For example, the analysis unit can analyze the cause of the abandoned bicycle and propose preventive measures. In this way, inferring the cause of the abnormality and proposing preventive measures helps prevent recurrence. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the cause of the abnormality to the generation AI and have the generation AI execute the proposal of preventive measures.

[0094] The analysis unit can predict the user's emotions and prioritize the analysis results based on the predicted user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis results of abnormal behavior can be prioritized. Also, if the user is relaxed, the analysis results of traffic accidents can be prioritized. Furthermore, if the user is in a hurry, the analysis results of abandoned bicycles can be prioritized. By prioritizing the analysis results according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0095] When the analysis unit detects an abnormality, it can identify the range of the abnormality in cooperation with other street lights or a monitoring system. For example, when the analysis unit detects abnormal behavior, it can identify the range of the abnormality in cooperation with other street lights. For example, the analysis unit can identify the range of the abnormal behavior by coordinating with data from other street lights. Furthermore, when the analysis unit detects a traffic accident, it can identify the range of the abnormality in cooperation with other monitoring systems. For example, the analysis unit can identify the range of the traffic accident by coordinating with data from other monitoring systems. Furthermore, when the analysis unit detects an abandoned bicycle, it can identify the range of the abnormality in cooperation with other street lights. For example, the analysis unit can identify the range of the abandoned bicycle by coordinating with data from other street lights. This makes it easier to identify the range of the abnormality by coordinating with other street lights or monitoring systems. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from other street lights or monitoring systems into the generation AI and cause the generation AI to identify the range of the abnormality.

[0096] When the analysis unit detects an abnormality, it can predict the extent of the abnormality's impact and propose a countermeasure. For example, when the analysis unit detects abnormal behavior, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of abnormal behavior and propose a countermeasure. Furthermore, when the analysis unit detects a traffic accident, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of a traffic accident and propose a countermeasure. Furthermore, when the analysis unit detects an abandoned bicycle, it can predict the extent of the impact and propose a countermeasure. For example, the analysis unit can predict the extent of the impact of an abandoned bicycle and propose a countermeasure. This enables a rapid response by predicting the extent of the abnormality's impact and proposing a countermeasure. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the extent of the abnormality's impact into the generation AI and have the generation AI execute the proposed countermeasure.

[0097] When an abnormality is detected, the analysis unit can analyze the frequency of the abnormality and propose long-term measures. For example, when abnormal behavior is detected, the analysis unit can analyze the frequency of the abnormality and propose long-term measures. For example, the analysis unit can analyze the frequency of abnormal behavior and propose long-term measures. Furthermore, when a traffic accident is detected, the analysis unit can analyze the frequency of the accident and propose long-term measures. For example, the analysis unit can analyze the frequency of traffic accidents and propose long-term measures. Furthermore, when an abandoned bicycle is detected, the analysis unit can analyze the frequency of the accident and propose long-term measures. For example, the analysis unit can analyze the frequency of abandoned bicycles and propose long-term measures. In this way, by analyzing the frequency of abnormalities and proposing long-term measures, it is possible to ensure sustainable safety. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the frequency of abnormalities to the generation AI and have the generation AI propose long-term measures.

[0098] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, the notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide concise and clear content of the notification. Also, if the user is relaxed, the notification unit can provide detailed content of the notification. Furthermore, if the user is in a hurry, the notification unit can provide quick content of the notification. This allows for more appropriate notification by adjusting the content of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using an AI, for example, or without an AI. For example, the notification unit can input user emotion data into the generation AI and have the generation AI adjust the content of the notification.

[0099] The reporting unit can change the priority of the report destination when it detects an abnormality. For example, when it detects abnormal behavior, the reporting unit can prioritize reporting to the police. Furthermore, when it detects a traffic accident, the reporting unit can also prioritize reporting to the fire department or ambulance. Furthermore, when it detects an abandoned bicycle, the reporting unit can also prioritize reporting to the administrator. This allows for a rapid response by changing the priority of the report destination depending on the type of abnormality. Some or all of the above-mentioned processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI change the priority of the report destination.

[0100] When the reporting unit detects an abnormality, it can include the analysis results in the report content. For example, when the reporting unit detects abnormal behavior, it can include detailed analysis results in the report content. Furthermore, when the reporting unit detects a traffic accident, it can also include detailed analysis results in the report content. Furthermore, when the reporting unit detects an abandoned bicycle, it can also include detailed analysis results in the report content. By including detailed analysis results in the report content, the reporting party can respond quickly. Some or all of the above-mentioned processing in the reporting unit can be performed using AI, for example, or can be performed without using AI. For example, the reporting unit can input data of the analysis results into a generation AI and have the generation AI generate the report content.

[0101] When an abnormality is detected, the reporting unit can select the contact method for the reporting destination. For example, when abnormal behavior is detected, the reporting unit can notify the police by phone. Furthermore, when a traffic accident is detected, the reporting unit can also notify the fire department or emergency services by email. Furthermore, when an abandoned bicycle is detected, the reporting unit can also notify the administrator by SMS. This enables prompt reporting by selecting the optimal contact method depending on the type of abnormality. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI select the contact method.

[0102] The notification unit can estimate the user's emotions and adjust the urgency of the notification based on the estimated user's emotions. The notification unit, for example, estimates the user's emotions and adjusts the urgency of the notification based on the estimated user's emotions. For example, if the user is nervous, the urgency of the notification can be set high. Also, if the user is relaxed, the urgency of the notification can be set low. Furthermore, if the user is in a hurry, the urgency of the notification can be set to the highest. This allows for a more appropriate response by adjusting the urgency of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, an AI. For example, the notification unit can input user's emotion data into the generation AI and cause the generation AI to adjust the urgency of the notification.

[0103] The reporting unit can also send a notification to nearby residents or related parties when it detects an abnormality. For example, the reporting unit can send a notification to nearby residents when it detects abnormal behavior. The reporting unit can also send a notification to related parties when it detects a traffic accident. Furthermore, the reporting unit can also send a notification to nearby residents or related parties when it detects an abandoned bicycle. This allows for a rapid response by sending notifications to nearby residents and related parties when an abnormality is detected. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input abnormality data into the generation AI and have the generation AI send a notification.

[0104] The reporting unit can provide the report content in multiple languages ​​when it detects an abnormality. For example, when it detects abnormal behavior, the reporting unit provides the report content in multiple languages. The reporting unit can also provide the report content in multiple languages ​​when it detects a traffic accident. Furthermore, the reporting unit can also provide the report content in multiple languages ​​when it detects an abandoned bicycle. By providing the report content in multiple languages, information can be quickly conveyed to people who speak different languages. Some or all of the above-mentioned processing in the reporting unit may be performed using AI, for example, or may be performed without using AI. For example, the reporting unit can input the data of the report content into a generation AI and cause the generation AI to generate the report content in multiple languages.

[0105] When the reporting unit detects an abnormality, it can include map information or a photo of the scene in the report. For example, when the reporting unit detects abnormal behavior, it can include map information in the report. Furthermore, when the reporting unit detects a traffic accident, it can also include a photo of the scene in the report. Furthermore, when the reporting unit detects an abandoned bicycle, it can also include map information and a photo of the scene in the report. By including map information and a photo of the scene in the report, the person to be notified can quickly identify the scene and take action. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, AI, or can be performed without using AI. For example, the reporting unit can input data such as map information and a photo of the scene into a generation AI and have the generation AI generate the report.

[0106] The lighting adjustment unit can estimate the user's emotion and adjust the brightness of the lighting based on the estimated user's emotion. For example, the lighting adjustment unit can estimate the user's emotion and adjust the brightness of the lighting based on the estimated user's emotion. For example, if the user is nervous, the lighting brightness can be increased. Also, if the user is relaxed, the lighting brightness can be decreased. Furthermore, if the user is in a hurry, the lighting brightness can be maximized. This allows for adjusting the brightness of the lighting according to the user's emotion, providing a more comfortable environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the lighting adjustment unit can be performed using, for example, AI, or without AI. For example, the lighting adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the brightness of the lighting.

[0107] The lighting adjustment unit can monitor pedestrian traffic or traffic volume in real time and dynamically change the lighting directionality or illumination range. For example, the lighting adjustment unit widens the lighting directionality during times of heavy pedestrian traffic. The lighting adjustment unit can also narrow the illumination range during times of light traffic. Furthermore, the lighting adjustment unit can adjust the lighting directionality and illumination range in real time when the pedestrian traffic or traffic volume changes. This allows for energy savings and improved safety by dynamically changing the lighting directionality and illumination range according to the pedestrian traffic or traffic volume. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input pedestrian traffic or traffic volume data into the generation AI and have the generation AI adjust the lighting directionality and illumination range.

[0108] The lighting adjustment unit can improve visibility by increasing the surrounding lighting when an abnormality is detected. For example, the lighting adjustment unit temporarily increases the surrounding lighting when abnormal behavior is detected. The lighting adjustment unit can also temporarily increase the surrounding lighting when a traffic accident is detected. Furthermore, the lighting adjustment unit can temporarily increase the surrounding lighting when an abandoned bicycle is detected. In this way, increasing the surrounding lighting when an abnormality is detected improves visibility and enables a prompt response. Some or all of the above-described processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to increase the lighting.

[0109] The lighting adjustment unit can change the color temperature of the lighting and issue a warning signal when an abnormality is detected. For example, when abnormal behavior is detected, the lighting adjustment unit changes the color temperature of the lighting to red and issues a warning. Furthermore, when a traffic accident is detected, the lighting adjustment unit can change the color temperature of the lighting to blue and issue a warning. Furthermore, when an abandoned bicycle is detected, the lighting adjustment unit can change the color temperature of the lighting to yellow and issue a warning. In this way, by changing the color temperature of the lighting when an abnormality is detected, a visual warning can be issued. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to change the color temperature.

[0110] The lighting adjustment unit can predict the user's emotions and adjust the color of the lighting based on the predicted user's emotions. For example, the lighting adjustment unit estimates the user's emotions and adjusts the color of the lighting based on the estimated user's emotions. For example, if the user is nervous, the lighting color can be changed to blue. If the user is relaxed, the lighting color can be changed to green. Furthermore, if the user is in a hurry, the lighting color can be changed to red. This allows for adjusting the lighting color according to the user's emotions, providing a more comfortable environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the lighting adjustment unit can be performed using AI, for example, or without AI. For example, the lighting adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the lighting color.

[0111] The lighting adjustment unit can change the lighting pattern in accordance with the identified event. For example, when an emergency occurs, the lighting adjustment unit changes the lighting pattern to flashing. The lighting adjustment unit can also change the lighting pattern to colorful lights during a festival. Furthermore, after the specific event ends, the lighting pattern can be returned to its normal state. In this way, by changing the lighting pattern in accordance with the specific event, a lighting environment suitable for the event can be provided. Some or all of the above-described processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input event data into the generation AI and cause the generation AI to change the lighting pattern.

[0112] When an abnormality is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights or a monitoring system. For example, when abnormal behavior is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. In addition, when a traffic accident is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. Furthermore, when an abandoned bicycle is detected, the lighting adjustment unit can unify the lighting pattern in cooperation with other street lights. In this way, by unifying the lighting pattern in cooperation with other street lights when an abnormality is detected, the extent of the abnormality can be visually indicated. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to execute coordination with other street lights.

[0113] The lighting adjustment unit can change the brightness of the lighting to attract attention when it detects an abnormality. For example, the lighting adjustment unit can gradually change the brightness of the lighting to attract attention when it detects abnormal behavior. The lighting adjustment unit can also gradually change the brightness of the lighting to attract attention when it detects a traffic accident. Furthermore, the lighting adjustment unit can also gradually change the brightness of the lighting to attract attention when it detects an abandoned bicycle. In this way, by gradually changing the brightness of the lighting when an abnormality is detected, it is possible to visually attract attention. Some or all of the above-mentioned processing in the lighting adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the lighting adjustment unit can input abnormality data to the generation AI and cause the generation AI to change the brightness. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and lighting adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the data is processed by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reports an abnormality based on the analysis result. The lighting adjustment unit is realized, for example, by the control unit 46A of the smart device 14, and adjusts the directionality and illumination range of the lighting. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, notification unit, and illumination adjustment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the data is processed by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reports an abnormality based on the analysis result. The illumination adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214, and adjusts the directionality and illumination range of the illumination. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reporting unit, and lighting adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314, and the data is processed by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reports an abnormality based on the analysis result. The lighting adjustment unit is realized, for example, by the control unit 46A of the headset type terminal 314, and adjusts the directionality and illumination range of the lighting. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reporting unit, and lighting adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the data is processed by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reports an abnormality based on the analysis result. The lighting adjustment unit is realized, for example, by the control unit 46A of the robot 414, and adjusts the directionality and illumination range of the lighting.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The street lighting system can further include a temperature sensor. The temperature sensor can, for example, monitor the ambient temperature in real time and detect abnormal temperature changes. For example, it can detect an abnormal temperature rise in the early stages of a fire and immediately notify the fire department. The temperature sensor can also issue a warning when it detects a low temperature to prevent freezing in cold regions. Furthermore, the temperature sensor can also issue a warning when the ambient temperature exceeds a certain level to reduce the risk of heatstroke during high temperatures in summer. Thus, adding a temperature sensor can reduce the risks of fire, freezing, heatstroke, and the like.

[0116] The street lighting system can further include a vibration sensor. The vibration sensor can, for example, detect earthquakes and shocks in real time and detect abnormal vibrations. For example, it can detect vibrations in the early stages of an earthquake and immediately issue an alarm. The vibration sensor can also detect shocks during traffic accidents and quickly report them. Furthermore, the vibration sensor can detect vibrations caused by building deterioration and identify areas that require early repairs. Thus, adding a vibration sensor can reduce the risks of earthquakes, traffic accidents, building deterioration, and the like.

[0117] The street lighting system can further include gas sensors. Gas sensors can detect, for example, harmful and flammable gases in real time and detect abnormal gas concentrations. For example, they can detect gas leaks and immediately issue an alarm. Gas sensors can also detect harmful gas leaks in industrial areas and quickly report them. Furthermore, gas sensors can monitor carbon monoxide concentrations in underground parking lots and issue an alarm if they reach dangerous levels. Thus, adding gas sensors can reduce the risk of gas leaks and harmful gases.

[0118] The street lighting system can further include a humidity sensor. The humidity sensor can, for example, monitor the ambient humidity in real time and detect abnormal humidity changes. For example, because high humidity increases the risk of mold growth, the humidity sensor can issue an alert when it detects high humidity. The humidity sensor can also issue an alert when it detects low humidity to reduce the risk of fire in dry environments. Furthermore, the humidity sensor can provide humidity data to assist in proper humidity management in agricultural areas. Thus, adding a humidity sensor can reduce the risk of mold growth and fire and assist in humidity management in agricultural areas.

[0119] The street lighting system can further include a wind speed sensor. The wind speed sensor can, for example, monitor the surrounding wind speed in real time and detect abnormal changes in wind speed. For example, during strong winds or typhoons, the wind speed sensor can detect high wind speeds and immediately issue an alert. The wind speed sensor can also provide wind speed data to support safety management at construction sites. Furthermore, the wind speed sensor can also be used to collect wind speed data at wind power plants and optimize power generation efficiency. Thus, adding a wind speed sensor can reduce the risk of strong winds and typhoons and support safety management at construction sites and wind power plants.

[0120] The streetlight system can further estimate the user's emotions and adjust the content of the audio alert based on the estimated user's emotions. For example, if the user is nervous, the audio alert can be concise and clear. If the user is relaxed, the audio alert can include detailed information. Furthermore, if the user is in a hurry, the audio alert can quickly convey important information. By adjusting the content of the audio alert according to the user's emotions, more appropriate information can be provided.

[0121] The street lighting system can further estimate the user's emotions and adjust the color temperature of the lighting based on the estimated user's emotions. For example, if the user is nervous, the color temperature of the lighting can be changed to a warm color. If the user is relaxed, the color temperature of the lighting can be changed to a cool color. Furthermore, if the user is in a hurry, the color temperature of the lighting can be changed to a neutral color. In this way, a more comfortable environment can be provided by adjusting the color temperature of the lighting according to the user's emotions.

[0122] The streetlight system can further estimate the user's emotions and play music based on the estimated user's emotions. For example, if the user is nervous, it can play relaxing music. If the user is relaxed, it can play lively music. If the user is in a hurry, it can play fast-paced music. This allows the system to provide a more comfortable environment by playing music according to the user's emotions.

[0123] The streetlight system can further estimate the user's emotions and adjust the content of the advertisement display based on the estimated user's emotions. For example, if the user is nervous, an advertisement with relaxing content can be displayed. Alternatively, if the user is relaxed, an advertisement with lively content can be displayed. Furthermore, if the user is in a hurry, an advertisement that conveys information in a short amount of time can be displayed. In this way, by adjusting the content of the advertisement display according to the user's emotions, more effective advertisements can be provided.

[0124] The streetlight system can further estimate the user's emotions and adjust the emergency response method based on the estimated user's emotions. For example, if the user is nervous, a quick and concise response method can be provided. If the user is relaxed, a response method with detailed explanations can be provided. Furthermore, if the user is in a hurry, a response method that prioritizes the most important information can be provided. This allows for more appropriate responses by adjusting the emergency response method according to the user's emotions.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit uses cameras or microphones to collect data. The cameras capture high-resolution images day and night, and the microphones pick up surrounding sounds. For example, the cameras monitor the area around streetlights, and the microphones collect the sounds of people talking and cars. Step 2: The analysis unit analyzes the data collected by the collection unit. Using multimodal AI, the analysis unit detects abnormal behavior, abandoned bicycles, traffic accidents, etc. from video data, and abnormal sounds and late-night behavior from audio data. For example, it can detect a person falling from video data and screams and collision sounds from audio data. Step 3: The reporting unit reports abnormalities based on the data analyzed by the analysis unit. For example, if abnormal behavior is detected, the reporting unit immediately notifies the police, and if a traffic accident is detected, the reporting unit notifies the fire department or emergency services. Step 4: The lighting adjustment unit adjusts the lighting based on the data analyzed by the analysis unit. For example, it widens the directionality of the lighting during busy times and narrows the illumination range during times of low traffic. It also adjusts the light intensity as needed, reducing the light intensity late at night to reduce energy consumption.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 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 collection unit where a camera or microphone collects data; an analysis unit that analyzes the data collected by the collection unit; a reporting unit that reports an abnormality based on the data analyzed by the analysis unit; an illumination adjustment unit that adjusts illumination based on the data analyzed by the analysis unit; A system characterized by:

2. The collecting unit Equipped with a camera that captures high-definition images day and night 2. The system of claim 1.

3. The collecting unit Equipped with a microphone to pick up ambient sounds 2. The system of claim 1.

4. The analysis unit Detecting abnormal behavior, abandoned bicycles, or traffic accidents from video data 2. The system of claim 1.

5. The analysis unit Detecting abnormal sounds or late-night activity from audio data 2. The system of claim 1.

6. The reporting unit Notify the police if any unusual behavior is detected 2. The system of claim 1.

7. The reporting unit Notify the fire department or emergency services if a traffic accident is detected 2. The system of claim 1.

8. The illumination adjustment unit Widen the directionality of lighting during times of heavy foot traffic 2. The system of claim 1.

9. The illumination adjustment unit Narrow the illumination range during times of low traffic 2. The system of claim 1.

Images