System

Abstract

A system is provided.SOLUTION: A system comprising: sensor means for detecting traffic flow in real time; sensor means for detecting ambient brightness in real time; control means for receiving and analyzing data from the sensor means and controlling the state of lighting; and lighting means for automatically adjusting the lighting on and off by the control means.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] In modern cities, it is important that lighting on roads and public spaces is always appropriate. However, current lighting systems often have fixed controls, which are insufficient in terms of energy efficiency and cost. In particular, lights may remain on even when traffic volume is low or the environment is sufficiently bright, or conversely, lights may not be turned on when needed. This results in energy waste and reduced safety. The objective of this invention is to provide a system that efficiently and safely controls lighting on roads and public spaces, adapting to traffic volume and ambient brightness. [Means for solving the problem]

[0005] The lighting system of the present invention is composed of a sensor means that detects traffic volume in real time, a sensor means that detects ambient brightness in real time, a control means that receives and analyzes data from these sensors, and a lighting means that automatically adjusts the on / off of lights.Traffic volume sensors and brightness sensors are installed on roads and outdoor spaces, respectively, and the control means uses logic to turn on lights when traffic volume exceeds a certain threshold or brightness falls below a specific threshold, so that lights are turned on only when needed, improving energy efficiency and reducing costs.

[0006] "Sensor means for detecting traffic volume in real time" refers to all sensor devices that instantly measure and acquire traffic data such as the number and speed of vehicles traveling on roads.

[0007] "Sensor means for detecting ambient brightness in real time" refers to a light sensor device in general for instantly measuring and acquiring the intensity of ambient light.

[0008] "Control means for receiving and analyzing data from sensor means and controlling the lighting status" refers to a control system or device in general that receives and analyzes input data from sensors and determines and executes on / off control of lighting based on the analysis results.

[0009] "Lighting means" refers to a lighting device in general, including a lighting fixture for illuminating a predetermined area and its control system.

[0010] "Road-mounted" refers to a configuration in which traffic sensors are fixed to the road surface or surrounding structures.

[0011] "Installed in an outdoor space" refers to a configuration in which the light sensor is placed outside a building, particularly to cover a wide area.

[0012] "When traffic volume exceeds a certain threshold" refers to when the traffic data detected by the sensor is greater than a preset reference value.

[0013] "When the brightness is below a certain threshold" refers to when the intensity of the ambient light detected by the sensor is less than a preset reference value.

[0014] "Logic" refers to the set of instructions or algorithms that determine the state of a light based on the data received. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, and a lighting means for automatically adjusting the on / off of lighting using the control means. This system aims to improve energy efficiency, reduce costs, and improve safety.

[0037] Explanation of program processing

[0038] 1. System initialization:

[0039] The server first initializes the system and registers the lights, traffic sensors, and brightness sensors.

[0040] The server creates an instance of the SmartLightingSystem class, initializing the dictionary that stores the lighting state and the list of traffic and brightness sensors.

[0041] The server adds multiple lights to the system, and these lights are all initially off.

[0042] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0043] 2. Initial display:

[0044] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0045] 3. Lighting adjustment cycle:

[0046] The server retrieves data from traffic sensors and light sensors at regular intervals and updates the lighting status.

[0047] The adjust_lighting method aggregates data from all traffic sensors and uses the results to determine traffic volume, and aggregates data from all light sensors to evaluate the ambient light intensity.

[0048] If the traffic volume is above a certain threshold or the ambient light is below a certain threshold, the server will turn the lights on, otherwise it will turn them off. This process is performed every cycle.

[0049] Specific examples

[0050] Night road

[0051] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0052] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0053] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0054] The server evaluates the case where the traffic volume is 75 (30 + 25 + 20) and the average brightness is 150 ((200 + 150 + 100) / 3).

[0055] Under these conditions, the server turns on all lights because traffic volume is greater than 50 and brightness is less than or equal to 300.

[0056] Daytime intersection

[0057] During the day, if the traffic sensor detects high traffic volume and the light sensor indicates a high reading:

[0058] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0059] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0060] The server evaluates the case where the traffic volume is 155 (50 + 45 + 60) and the average brightness is 950.

[0061] Under these conditions, traffic volume is high but brightness is above 300, so the server turns off the lights.

[0062] This invention makes it possible to efficiently control lighting on roads and public spaces according to traffic volume and ambient brightness, which has the advantage of improving energy efficiency while also ensuring safety.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The server creates an instance of the SmartLightingSystem class, which initializes it with an empty dictionary to record lighting states, and a list of traffic and brightness sensors.

[0066] Step 2:

[0067] The server adds the lighting devices to the system. Specifically, it registers five lighting devices (e.g., light-0 to light-4) as off in the initial state.

[0068] Step 3:

[0069] The server adds traffic and light sensors to the system by registering three traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and three light sensors (e.g., light-sensor-0 to light-sensor-2).

[0070] Step 4:

[0071] The server calls the display_status method to display the initial state (on / off) of all lighting devices. Initially, all lights are on.

[0072] Step 5:

[0073] The server collects data from the traffic and light sensors at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0074] Step 6:

[0075] The server executes the adjust_lighting method to turn on and off lighting devices based on the data from the sensors. If the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the corresponding lighting device is turned on. Otherwise, it is turned off.

[0076] Step 7:

[0077] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0078] Step 8:

[0079] The server repeats the process from step 5 to step 7. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Conventional lighting control systems are unable to achieve efficient lighting control according to traffic volume and ambient brightness, making it difficult to improve energy efficiency and ensure safety. Furthermore, there are issues with operability due to insufficient functions such as system initialization, data analysis, and real-time display of lighting status.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off of the lighting by the control means, a means for the control means to read the initial settings of the system in an initialization step, register the sensors and lighting, and display the initial status, a means for acquiring and analyzing data from the sensors at regular time intervals, a means for adjusting the lighting status based on traffic volume and brightness data, and a means for displaying the lighting status, thereby enabling improved energy efficiency and ensuring safety.

[0085] "Sensor means for detecting traffic volume in real time" refers to a device that instantly measures the number of vehicles and pedestrians on the road and continuously collects that data.

[0086] "Sensor means for detecting ambient brightness in real time" is a device that instantly measures the external light environment and continuously collects that data.

[0087] "Control means for receiving and analyzing data from the sensor means and controlling the lighting status" refers to a system that receives and analyzes data sent from the sensor and manages the operation of the lighting device based on the results.

[0088] "Lighting means that automatically adjusts the on / off of lighting by the control means" refers to a device that automatically switches on and off lighting in accordance with instructions from the control means.

[0089] "Means for reading the initial settings of the system in the initialization step, registering sensors and lighting, and displaying the initial status" refers to a device that has the function of reading the setting information required when the system starts operating, registering each device in the system, and displaying the current status to the user.

[0090] "Means for acquiring and analyzing data from sensors at regular time intervals" refers to a device that collects data from sensors at a pre-set cycle and analyzes the data.

[0091] The "means for adjusting lighting conditions based on traffic volume and brightness data" refers to a system that automatically adjusts lighting conditions, such as turning the lights on and off, based on the acquired traffic volume and brightness data.

[0092] A "light status display means" is a device that visually displays the current light on / off status and other related information.

[0093] The lighting control system of the present invention detects traffic volume and ambient brightness in real time and automatically adjusts lighting based on that data. The purpose of this system is to improve energy efficiency and ensure safety. Specific components of the system include sensors that detect traffic volume, sensors that detect ambient brightness, control means that receive and analyze the data, and lighting means that turn lights on and off.

[0094] Hardware and software used

[0095] The system hardware includes the following elements:

[0096] Traffic sensors: These are installed on roads to detect traffic volume in real time. Specific product examples include ultrasonic sensors and camera-based traffic sensors.

[0097] Brightness sensor: Installed in outdoor spaces, it detects the intensity of ambient light in real time. Specific product examples include light sensors and lux meters.

[0098] Server: Receives and analyzes data and controls lighting. In addition to standard server hardware, a communication module and data analysis software are installed.

[0099] The software includes the following elements:

[0100] Sensor data collection module: A module that collects data from traffic sensors and brightness sensors.

[0101] Data analysis module: Analyzes collected data and evaluates traffic volume and ambient brightness.

[0102] Control program: Based on the evaluation results, issues instructions to turn the lights on or off.

[0103] Specific examples

[0104] Night road

[0105] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0106] 1. Data Acquisition:

[0107] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0108] The server also gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0109] 2. Data Analysis:

[0110] The server analyzes the acquired data and determines that the traffic volume is 75 (30 + 25 + 20).

[0111] Evaluate that the average ambient brightness is 150 ((200 + 150 + 100) / 3).

[0112] 3. Lighting control:

[0113] The server turns on all lights because traffic volume is above the threshold (50) and brightness is below the set threshold (300).

[0114] 4. Status display:

[0115] The server will display the state of the lights as they are turned on, for example, "Light 1: On, Light 2: On, Light 3: On".

[0116] Based on this example, this invention effectively controls lighting according to traffic volume and ambient brightness data, thereby improving energy efficiency and ensuring safety. This system can be used in a variety of environments, including roads and public spaces.

[0117] Example prompt:

[0118] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0119] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1: Initialize the system

[0122] The server loads the initial system configuration, which includes the locations and settings of lights, traffic sensors, and brightness sensors.

[0123] Input: Initial configuration data from a configuration file or database.

[0124] Output: System initialization state with configuration data.

[0125] The server creates an instance of the SmartLightingSystem class and initializes a dictionary to store the lighting state and a list of traffic and brightness sensors.

[0126] Specific operation: The server registers all lights in the system as off and sets the location information of each sensor.

[0127] Step 2: Viewing the initial state

[0128] The server calls the display_status method to ensure that all lights are initially off and display their status.

[0129] Input: State data after system initialization.

[0130] Output: Screen showing the initial state.

[0131] Specific operation: The server displays the status of the lights on the terminal or web interface.

[0132] Step 3: Start collecting data

[0133] The server collects data from traffic sensors and light sensors at regular intervals.

[0134] Input: Communication protocol or API call from the sensor.

[0135] Output: Traffic and lighting data collected in real time.

[0136] Specific operation: The server calls the sensor's API to receive traffic volume and brightness data.

[0137] Step 4: Data analysis

[0138] The server analyzes the acquired data and evaluates traffic volume and ambient brightness.

[0139] Input: Raw data obtained from traffic and light sensors.

[0140] Output: The total traffic volume and average brightness values ​​as the analysis results.

[0141] Specific operation: The values ​​sent from each sensor are aggregated to calculate the total and average traffic volume and brightness.

[0142] Step 5: Implementing lighting control

[0143] Based on the analysis results, the server uses the adjust_lighting method to turn the lights on or off.

[0144] Input: Analysis results (traffic volume and brightness evaluation values).

[0145] Output: The state of the light (on or off).

[0146] Specific behavior: Turn on lights if traffic volume exceeds threshold and brightness is below a certain threshold, otherwise turn off.

[0147] Step 6: Status Updates and Display

[0148] The server updates the light status and displays the current status with the display_status method.

[0149] Input: Latest lighting conditions.

[0150] Output: Updated lighting status display.

[0151] Specific operation: The server records when a light is turned on or off in a database or log file, and displays the current status on the screen.

[0152] Example prompt sentence:

[0153] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0154] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Current lighting control systems in industrial facilities often do not adequately consider energy efficiency or work environment safety. As a result, unnecessary energy consumption and reduced work efficiency due to inappropriate lighting conditions occur. In addition, the difficulty of real-time lighting adjustment according to traffic volume and ambient brightness means that they are forced to rely on fixed and inefficient lighting conditions.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off state of the lighting by the control means, and a means for optimizing lighting in work spaces using data from the sensor means installed in industrial facilities. This enables efficient and flexible lighting control based on real-time data, and also optimizes energy consumption and provides a safe and efficient work environment.

[0160] "Sensor means for detecting traffic volume in real time" refers to equipment for detecting the movement of people and objects within an industrial facility in real time and collecting that data.

[0161] "Sensor means for detecting ambient brightness in real time" refers to equipment for detecting the amount of light and illuminance within an industrial facility in real time and collecting that data.

[0162] "Control means" refers to a device or system that receives and analyzes data from the aforementioned sensor means and controls the lighting status.

[0163] "Lighting means for automatically adjusting the on / off state of lighting" refers to a device or system that automatically adjusts the on / off state of lighting based on instructions from a control means.

[0164] "Means for optimizing lighting in work spaces using data from sensor means installed in industrial facilities" refers to equipment or systems that utilize data from traffic sensors and brightness sensors installed in industrial facilities to optimize lighting conditions in work spaces.

[0165] The lighting control system of the present invention aims to improve energy efficiency and optimize the working environment in industrial facilities. This system includes sensor means for detecting traffic volume in real time, sensor means for detecting ambient brightness in real time, control means for receiving and analyzing data from these sensors, and lighting means for controlling the lighting status.

[0166] The server first initializes the system and registers various sensors and lighting devices. Specifically, the system collects and analyzes real-time data from traffic sensors and brightness sensors installed in industrial facilities, and automatically adjusts lighting on and off based on the collected data. This provides an optimal lighting environment and minimizes energy consumption.

[0167] The hardware used includes traffic and brightness sensors installed within industrial facilities, a server for collecting and analyzing data from these sensors, and lighting devices. The software uses Python scripts to collect data from the sensors in real time and analyzes the data to control the lighting status. In particular, the server calculates traffic volume from traffic sensor data and evaluates ambient brightness from brightness sensor data. Based on this data, it executes logic to control lighting on and off.

[0168] For example, if the traffic sensor in a particular area of ​​an industrial facility reads 70 and the light sensor reads 250, the server can determine that traffic is above a certain threshold and light is below a certain threshold and turn on the lights in that area, thus providing the right lighting only where it is needed and using energy efficiently.

[0169] An example of a prompt is:

[0170] We will demonstrate a system that automatically controls lighting based on traffic volume and ambient light within a factory, collecting data in real time to demonstrate how it can optimize energy efficiency while providing a safe working environment.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The server initializes the system. It registers traffic sensors, light sensors, and lighting devices. Specifically, it sets the initial state of lighting devices to off and records the sensor location information. The IDs and location information of the traffic sensors and light sensors are required as input, and the initial state of each sensor and lighting device in the system is set as output.

[0174] Step 2:

[0175] The server collects real-time data from traffic sensors and light sensors. The server periodically accesses these sensors to obtain the current traffic volume data and light volume data. As input, real-time data from the sensors is required, and as output, the latest data from each sensor is stored in the server.

[0176] Step 3:

[0177] The server analyzes the collected data and evaluates traffic volume and ambient brightness. It calculates the sum of traffic volume data and the average brightness data. As input, it requires traffic volume data and brightness data obtained from sensors, and as output, it obtains the sum of traffic volume and the average brightness.

[0178] Step 4:

[0179] The server executes the lighting control logic based on the pre-processed data. If the traffic volume is above a certain threshold and the brightness is below a certain threshold, it sets the light on; otherwise, it turns the light off. As input, it requires the total traffic volume and the average brightness, and as output, it determines the on / off state of the light.

[0180] Step 5:

[0181] The server sends an on or off command to the lighting device to actually change the state of the light. Specifically, it calls the lighting device's API to change the state. The input is the on / off state of the light, and the output is the change of the physical state of the light.

[0182] Step 6:

[0183] The server displays the current light status for the user or terminal to monitor the overall system status. The user can check the on / off status of the lights and manually change the settings if necessary. As input, it requires light status data from the server and as output, it provides user-visible status information.

[0184] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0185] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting lighting on / off, and an emotion engine for recognizing user emotions in real time. This system aims to achieve improved energy efficiency, cost reduction, improved safety, and user comfort.

[0186] Explanation of program processing

[0187] 1. System initialization:

[0188] The server first initializes the system and registers the lighting, traffic sensors, brightness sensors, and emotion engine.

[0189] The server creates an instance of the SmartLightingSystem class, which initializes the dictionary that stores the lighting status, the list of traffic sensors and the list of brightness sensors, and the emotion engine.

[0190] The server adds multiple lights to the system, and these lights are all initially off.

[0191] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0192] 2. Initial display:

[0193] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0194] 3. Lighting adjustment cycle:

[0195] The server obtains data from the traffic sensor, light sensor, and emotion engine at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0196] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, body movements, etc. to determine the user's emotional state.

[0197] 4. Automatic lighting adjustment:

[0198] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and the emotion engine.

[0199] If the traffic sensor data is above a certain threshold (e.g. 50) or the light sensor data is below a certain threshold (e.g. 300), the server will turn the lights on, otherwise it will turn them off.

[0200] 5. Emotion-based regulation:

[0201] The server adjusts the brightness and color temperature of the lighting according to the user's emotion recognized by the emotion engine.

[0202] For example, if the user is determined to be anxious or stressed, the server may set the lighting to a brighter color temperature, whereas if the user is determined to be relaxed, the server may set the lighting to a warmer color temperature.

[0203] Specific examples

[0204] Night road

[0205] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0206] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0207] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0208] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0209] Under this condition, the server will turn on all lights because the traffic volume is above 50 and the brightness is below 300. It will also increase the brightness of the lights to reduce user anxiety.

[0210] Park during the day

[0211] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0212] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0213] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0214] The server obtains data indicating the user's relaxation state from the emotion engine.

[0215] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0216] These examples demonstrate how the lighting control system of the present invention can improve energy efficiency and ensure safety and comfort. The system can realize a sustainable urban environment by optimally controlling lighting based on traffic volume, ambient brightness, and user emotions.

[0217] The processing flow will be explained below.

[0218] Step 1:

[0219] The server creates an instance of the SmartLightingSystem class, which initializes an empty dictionary for storing lighting status, a list of traffic and brightness sensors, and an emotion engine for recognizing user emotions.

[0220] Step 2:

[0221] The server registers the lighting devices in the system. Specifically, it sets five lighting devices (e.g., light-0 to light-4) to off in the initial state.

[0222] Step 3:

[0223] The server adds traffic sensors and light sensors to the system by registering traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and light sensors (e.g., light-sensor-0 to light-sensor-2) according to their respective roles.

[0224] Step 4:

[0225] The server calls the display_status method to display the initial state of all lighting devices. Notice that initially, all of these lights are off.

[0226] Step 5:

[0227] The server collects data from traffic sensors, light sensors, and the emotion engine at regular intervals.

[0228] The server calls the check_traffic method to get data (traffic volume) from the traffic sensor.

[0229] The server calls the check_light_level method to get data from the light sensor (ambient light intensity).

[0230] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0231] Step 6:

[0232] The server executes the adjust_lighting method and controls the on / off of lighting means based on data from each sensor and emotion engine.

[0233] For example, if the traffic sensor data exceeds a certain threshold (e.g., 50), or if the light sensor data falls below a certain threshold (e.g., 300), the server will turn on the corresponding lighting means; otherwise, it will turn them off.

[0234] Step 7:

[0235] The server adjusts the brightness and color temperature of the lighting based on the user's emotions recognized by the emotion engine.

[0236] Specifically, if it is determined that the user is feeling anxious or stressed, the server sets the lighting to be brighter.

[0237] On the other hand, if it is determined that the user is relaxed, the server maintains the feeling of relaxation by changing the color temperature of the lighting to a warmer color.

[0238] Step 8:

[0239] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0240] Step 9:

[0241] The server repeats the process from step 5 to step 8 at regular intervals. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0242] Example 2

[0243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] Conventional lighting control systems mainly control lighting on / off based on physical data such as traffic volume and ambient brightness, but because they are unable to take the user's emotional state into account, they have the problem of being unable to fully address issues such as improving energy efficiency and user comfort. Furthermore, to improve safety and comfort at night, it is necessary to fine-tune lighting adjustments based on the user's emotional state in addition to traffic volume and brightness.

[0245] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a means for recognizing a user's emotion in real time, a control means for receiving and analyzing data from the sensor means and the emotion recognition means and controlling the lighting state, a lighting means for automatically adjusting the lighting on / off, and a means for adjusting the lighting brightness and color temperature based on the user's emotional state. This enables lighting control that takes into account the user's emotional state in addition to traffic volume and ambient brightness, thereby achieving higher energy efficiency and user comfort and safety.

[0246] "Sensor means for detecting traffic volume in real time" refers to a device for detecting the number and movement of vehicles and pedestrians on a road in real time.

[0247] The "sensor means for detecting ambient brightness in real time" is a device for detecting brightness in an outdoor space or a specific area in real time.

[0248] The "means for recognizing a user's emotions in real time" refers to a device or software that analyzes the user's facial expressions, tone of voice, body movements, etc., and recognizes the user's emotional state in real time.

[0249] The "control means" refers to hardware and software for receiving and analyzing data from the sensor means and emotion recognition means, and controlling the lighting status based on the results.

[0250] The "lighting means" is a device for turning on and off the lighting and adjusting the brightness and color temperature based on instructions from the control means.

[0251] The "means for adjusting the brightness and color temperature of lighting" refers to a device or software for appropriately adjusting the brightness and color temperature of lighting based on the emotional state of the user.

[0252] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and data from a means for recognizing user emotions in real time and controlling the lighting status, a lighting means for automatically adjusting the on / off of lighting, and a means for adjusting the brightness and color temperature of lighting based on the user's emotional state.

[0253] System initialization

[0254] The server first initializes the system. It creates an instance of SmartLightingSystem and initializes the dictionary that stores the lighting status and the sensor list. During this initialization process, the server adds multiple lights to the system, all of which are initially turned off. It adds traffic sensors and brightness sensors to the system and sets the locations of the sensors to roads and outdoor spaces. It also creates an instance of the emotion engine and registers it in the system.

[0255] Displaying the initial state

[0256] The server calls the display_status method to display the initial state of each light. You can see that all lights are initially off.

[0257] Lighting Adjustment Cycle

[0258] The server collects data from the traffic sensor, light sensor, and emotion engine at regular intervals. It calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor. It also uses the emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0259] Automatic lighting adjustment

[0260] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and emotion engines. For example, if the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the server turns on the lights; otherwise, it turns them off.

[0261] Emotion-Based Adjustment

[0262] The server adjusts the brightness and color temperature of the lighting according to the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling anxious or stressed, it will set the lighting brighter. On the other hand, if the user is determined to be relaxed, it will set the lighting to a warmer color temperature.

[0263] Specific examples

[0264] Night road

[0265] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0266] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0267] The server obtains the light sensor data (e.g., light-sensor-0 is 200, light-sensor-1 is 150, and light-sensor-2 is 100).

[0268] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0269] Under these conditions, the server turns on all the lights because the traffic volume is above 50 and the brightness is below 300. It also increases the brightness of the lights to reduce the user's anxiety.

[0270] Park during the day

[0271] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0272] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0273] The server obtains the light sensor data (e.g., light-sensor-0 is 900, light-sensor-1 is 950, and light-sensor-2 is 1000).

[0274] The server obtains data indicating the user's relaxation state from the emotion engine.

[0275] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0276] Example prompts to input to the generative AI model

[0277] "Please explain how a lighting control system with an emotion engine works on roads at night. Specifically, please explain in detail how it adjusts lighting based on traffic sensors, brightness sensors, and emotion recognition results, including specific data examples."

[0278] As described above, the lighting control system of the present invention controls lighting based on the user's emotional state in addition to traffic volume and ambient brightness, thereby improving energy efficiency and user comfort and safety.

[0279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0280] Step 1:

[0281] System initialization

[0282] Input: System startup command

[0283] Specific processing: The server creates an instance of SmartLightingSystem to initialize the system. At this time, it creates an internal dictionary, initializes the traffic sensor list and the light sensor list, and also creates an instance of the emotion engine.

[0284] Output: Initialization complete message

[0285] Specific operation: The server outputs a log message saying "Initialization is starting" and sets all lights in the system to off by default. It adds traffic sensors and light sensors to a list and records their positions. When initialization is complete, it outputs a log message saying "System initialization completed."

[0286] Step 2:

[0287] Displaying the initial state

[0288] Input: Initialization complete message

[0289] Specific processing: The server calls the display_status method to display the initial status of the lighting.

[0290] Output: Initial lighting state

[0291] Specific operation: The server outputs the light status to the console as "Light 1: Off, Light 2: Off, Light 3: Off", and leaves a log stating "The initial light settings have been displayed."

[0292] Step 3:

[0293] Data Acquisition Cycle

[0294] Input: Timer event

[0295] Specific processing: The server calls the check_traffic and check_light_level methods at regular intervals to obtain data from the traffic and light sensors, and uses the emotion engine to analyze the user's emotional state.

[0296] Output: Traffic data, brightness data, emotion data

[0297] Specific operation: The server logs the data from each sensor, such as "Data obtained from traffic sensor" and "Data obtained from brightness sensor." When the emotion engine outputs the analysis results, it leaves a log stating "User's emotional state obtained from emotion engine."

[0298] Step 4:

[0299] Automatic lighting adjustment

[0300] Input: Traffic data, brightness data

[0301] Specific processing: The server executes the adjust_lighting method to turn the lights on or off based on the data from the sensor.

[0302] Output: Lighting status

[0303] Specific operation: If the traffic volume exceeds 50 or the brightness is below 300, the server will log "Turn on all lights" and turn on the lights. In the opposite case, the server will log "Turn off all lights" and turn off the lights.

[0304] Step 5:

[0305] Emotion-Based Adjustment

[0306] Input: Emotion data

[0307] Specific processing: Based on the data from the emotion engine, the server adjusts the brightness and color temperature of the lighting according to the user's emotional state.

[0308] Output: Adjusted lighting conditions

[0309] Specific behavior: If the user is feeling anxious or stressed, the server will log "brighten the lighting to reduce the user's anxiety" and increase the brightness. If the user is relaxed, the server will log "change the lighting to warmer colors to maintain the user's sense of relaxation" and change the color temperature to warmer colors.

[0310] (Application example 2)

[0311] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] Current lighting control systems can only control lighting based on traffic volume and ambient brightness, and are unable to adjust lighting based on the emotional state of the user, resulting in insufficient improvements in comfort and safety. Furthermore, further improvements in energy efficiency are needed.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a detection means for detecting traffic volume in real time, a detection means for detecting ambient brightness in real time, an emotion recognition means for recognizing the emotional state of a user in real time, a control means for receiving and analyzing data from the detection means and the emotion recognition means and controlling the lighting state, and a lighting means for automatically turning the lighting on and off and adjusting the brightness by the control means. This makes it possible to provide an optimal lighting environment based on traffic volume, ambient brightness, and the emotional state of the user, improving safety and comfort while also increasing energy efficiency.

[0314] "Means for detecting traffic volume in real time" refers to sensors or devices for instantly grasping the volume of traffic flow.

[0315] The "detection means for detecting the ambient brightness in real time" is a sensor or device for instantly measuring the brightness of the surrounding environment.

[0316] "Emotion recognition means for recognizing the user's emotional state in real time" refers to sensors or engines that can instantly identify the user's emotions from their facial expressions and behavior.

[0317] The "control means for receiving and analyzing data from the detection means and emotion recognition means and controlling the lighting status" is a device that receives data on traffic volume, ambient brightness, and the user's emotional state, analyzes this data, and adjusts the lighting on / off and brightness.

[0318] "Lighting means for automatically adjusting the on / off and brightness of lighting by the control means" refers to a device for automatically adjusting the lighting state and brightness in accordance with instructions from the control means.

[0319] This invention is a lighting control system that includes multiple sensors that detect traffic volume, ambient brightness, and the user's emotional state in real time, and control means that analyzes this data to automatically turn lights on and off and adjust brightness.

[0320] System configuration

[0321] Hardware:

[0322] Traffic volume detection sensor: A sensor that detects the volume of traffic flow in real time, allowing you to instantly grasp how much traffic is present.

[0323] Brightness detection sensor: This sensor detects the brightness of the surrounding environment in real time, enabling the lighting to be adjusted instantly to match the brightness of the outside world.

[0324] Emotion recognition engine: A device that recognizes the user's emotional state in real time from their facial expressions and movements, enabling adjustments to improve the user's comfort.

[0325] software:

[0326] Control means (running on server): A software program that receives and analyzes traffic volume, lighting, and emotional state data. Implemented using Python and / or available APIs.

[0327] Lighting means: A device that turns lights on and off and adjusts brightness and color temperature according to instructions from the control means, thereby ensuring safety and comfort.

[0328] Data Acquisition and Calculation

[0329] The server receives real-time data from traffic sensors, brightness sensors, and emotion recognition engines, and analyzes this data to adjust lighting on / off, brightness, and color temperature based on the data.

[0330] Processing flow

[0331] 1. Data Acquisition:

[0332] Data is sent to the server from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. For example, if the traffic volume sensor detects 30, the ambient brightness sensor detects 200, and the user is in an anxious state, the server receives this data.

[0333] 2. Data Analysis:

[0334] Based on the received data, the system checks conditions such as whether traffic volume exceeds a threshold, whether the surrounding brightness is below a certain level, etc. At the same time, the user's emotional state is also analyzed.

[0335] 3. Adjust the lighting:

[0336] Based on set thresholds, for example, if there is heavy traffic and low brightness, or if the user feels uneasy, the server will turn on the lighting means and automatically adjust the brightness and color temperature as needed.

[0337] Examples and prompts

[0338] For example, if the traffic sensor detects a small number of vehicles in a park at night, the light sensor indicates a low reading, and the user is determined to be feeling anxious, the following process will occur:

[0339] The server receives traffic data (30), brightness data (200), and emotion data (anxiety).

[0340] Based on this data, the lights are turned on and further increased brightness is provided to reduce user anxiety.

[0341] Example prompt sentence:

[0342] "Design your lighting system to turn on lights when all traffic sensor readings are below 30 and light sensor readings are below 300, and brighten them further if there is a sense of unrest."

[0343] This allows the system to provide an optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, improving safety, ensuring user comfort, and increasing energy efficiency.

[0344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0345] Step 1: Get the data

[0346] The server receives real-time data from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. The input data includes traffic volume data, brightness data, and user emotion data. The data from these sensors is obtained through API and sent to the server.

[0347] Step 2: Data storage process

[0348] The server temporarily stores the received data and records it in a database for subsequent analysis. Specifically, it stores traffic volume data, brightness data, and emotion data in corresponding tables, allowing subsequent data analysis to be performed quickly.

[0349] Step 3: Analyze the data

[0350] The server analyzes the stored data to determine whether traffic volume is above a set threshold, whether lighting is below a set threshold, and the user's emotional state, using simple conditional and statistical methods.

[0351] Step 4: Conditional Decisions

[0352] The server determines the lighting operation based on the results of the data analysis. For example, if traffic volume exceeds a threshold and brightness is low, the lights will be turned on. If the emotion recognition engine determines that the user is in an anxious state, the lights will be increased in brightness. Processing at this stage is performed based on control logic.

[0353] Step 5: Lighting adjustment instructions

[0354] Based on the results of the processing, the server sends instructions to the lighting means to turn it on or off or adjust the brightness. Specifically, it generates a lighting control signal and sends that signal to the lighting device. This allows the lighting to be turned on or off appropriately and the brightness to be set according to the user's emotional state.

[0355] Step 6: Status check and feedback

[0356] The server checks the lighting status and receives feedback data from the lighting device. It verifies whether the lighting is operating correctly as instructed and sends control instructions again if there is a problem. This step also receives feedback from users and adjusts the system's operation.

[0357] Step 7: Logging and Reporting

[0358] The server stores all processing data and results in logs and generates periodic reports. This makes it easier to evaluate the system's operation history and performance. The logs include input data (traffic volume, brightness, emotional state), analysis results, control instructions, feedback data, etc. The log data is stored in a database and is backed up periodically.

[0359] Through these steps, the system can automatically provide the optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, thereby improving safety and optimizing energy efficiency.

[0360] 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.

[0361] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0362] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 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.

[0366] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0367] 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.

[0368] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0369] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0370] 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.

[0371] 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.

[0372] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0373] 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.

[0374] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0375] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0376] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, and a lighting means for automatically adjusting the on / off of lighting using the control means. This system aims to improve energy efficiency, reduce costs, and improve safety.

[0377] Explanation of program processing

[0378] 1. System initialization:

[0379] The server first initializes the system and registers the lights, traffic sensors, and brightness sensors.

[0380] The server creates an instance of the SmartLightingSystem class, initializing the dictionary that stores the lighting state and the list of traffic and brightness sensors.

[0381] The server adds multiple lights to the system, and these lights are all initially off.

[0382] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0383] 2. Initial display:

[0384] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0385] 3. Lighting adjustment cycle:

[0386] The server retrieves data from traffic sensors and light sensors at regular intervals and updates the lighting status.

[0387] The adjust_lighting method aggregates data from all traffic sensors and uses the results to determine traffic volume, and aggregates data from all light sensors to evaluate the ambient light intensity.

[0388] If the traffic volume is above a certain threshold or the ambient light is below a certain threshold, the server will turn the lights on, otherwise it will turn them off. This process is performed every cycle.

[0389] Specific examples

[0390] Night road

[0391] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0392] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0393] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0394] The server evaluates the case where the traffic volume is 75 (30 + 25 + 20) and the average brightness is 150 ((200 + 150 + 100) / 3).

[0395] Under these conditions, the server turns on all lights because traffic volume is greater than 50 and brightness is less than or equal to 300.

[0396] Daytime intersection

[0397] During the day, if the traffic sensor detects high traffic volume and the light sensor indicates a high reading:

[0398] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0399] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0400] The server evaluates the case where the traffic volume is 155 (50 + 45 + 60) and the average brightness is 950.

[0401] Under these conditions, traffic volume is high but brightness is above 300, so the server turns off the lights.

[0402] This invention makes it possible to efficiently control lighting on roads and public spaces according to traffic volume and ambient brightness, which has the advantage of improving energy efficiency while also ensuring safety.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] The server creates an instance of the SmartLightingSystem class, which initializes it with an empty dictionary to record lighting states, and a list of traffic and brightness sensors.

[0406] Step 2:

[0407] The server adds the lighting devices to the system. Specifically, it registers five lighting devices (e.g., light-0 to light-4) as off in the initial state.

[0408] Step 3:

[0409] The server adds traffic and light sensors to the system by registering three traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and three light sensors (e.g., light-sensor-0 to light-sensor-2).

[0410] Step 4:

[0411] The server calls the display_status method to display the initial state (on / off) of all lighting devices. Initially, all lights are on.

[0412] Step 5:

[0413] The server collects data from the traffic and light sensors at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0414] Step 6:

[0415] The server executes the adjust_lighting method to turn on and off lighting devices based on the data from the sensors. If the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the corresponding lighting device is turned on. Otherwise, it is turned off.

[0416] Step 7:

[0417] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0418] Step 8:

[0419] The server repeats the process from step 5 to step 7. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0420] Example 1

[0421] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0422] Conventional lighting control systems are unable to achieve efficient lighting control according to traffic volume and ambient brightness, making it difficult to improve energy efficiency and ensure safety. Furthermore, there are issues with operability due to insufficient functions such as system initialization, data analysis, and real-time display of lighting status.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0424] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off of the lighting by the control means, a means for the control means to read the initial settings of the system in an initialization step, register the sensors and lighting, and display the initial status, a means for acquiring and analyzing data from the sensors at regular time intervals, a means for adjusting the lighting status based on traffic volume and brightness data, and a means for displaying the lighting status, thereby enabling improved energy efficiency and ensuring safety.

[0425] "Sensor means for detecting traffic volume in real time" refers to a device that instantly measures the number of vehicles and pedestrians on the road and continuously collects that data.

[0426] "Sensor means for detecting ambient brightness in real time" is a device that instantly measures the external light environment and continuously collects that data.

[0427] "Control means for receiving and analyzing data from the sensor means and controlling the lighting status" refers to a system that receives and analyzes data sent from the sensor and manages the operation of the lighting device based on the results.

[0428] "Lighting means that automatically adjusts the on / off of lighting by the control means" refers to a device that automatically switches on and off lighting in accordance with instructions from the control means.

[0429] "Means for reading the initial settings of the system in the initialization step, registering sensors and lighting, and displaying the initial status" refers to a device that has the function of reading the setting information required when the system starts operating, registering each device in the system, and displaying the current status to the user.

[0430] "Means for acquiring and analyzing data from sensors at regular time intervals" refers to a device that collects data from sensors at a pre-set cycle and analyzes the data.

[0431] The "means for adjusting lighting conditions based on traffic volume and brightness data" refers to a system that automatically adjusts lighting conditions, such as turning the lights on and off, based on the acquired traffic volume and brightness data.

[0432] A "light status display means" is a device that visually displays the current light on / off status and other related information.

[0433] The lighting control system of the present invention detects traffic volume and ambient brightness in real time and automatically adjusts lighting based on that data. The purpose of this system is to improve energy efficiency and ensure safety. Specific components of the system include sensors that detect traffic volume, sensors that detect ambient brightness, control means that receive and analyze the data, and lighting means that turn lights on and off.

[0434] Hardware and software used

[0435] The system hardware includes the following elements:

[0436] Traffic sensors: These are installed on roads to detect traffic volume in real time. Specific product examples include ultrasonic sensors and camera-based traffic sensors.

[0437] Brightness sensor: Installed in outdoor spaces, it detects the intensity of ambient light in real time. Specific product examples include light sensors and lux meters.

[0438] Server: Receives and analyzes data and controls lighting. In addition to standard server hardware, a communication module and data analysis software are installed.

[0439] The software includes the following elements:

[0440] Sensor data collection module: A module that collects data from traffic sensors and brightness sensors.

[0441] Data analysis module: Analyzes collected data and evaluates traffic volume and ambient brightness.

[0442] Control program: Based on the evaluation results, issues instructions to turn the lights on or off.

[0443] Specific examples

[0444] Night road

[0445] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0446] 1. Data Acquisition:

[0447] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0448] The server also gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0449] 2. Data Analysis:

[0450] The server analyzes the acquired data and determines that the traffic volume is 75 (30 + 25 + 20).

[0451] Evaluate that the average ambient brightness is 150 ((200 + 150 + 100) / 3).

[0452] 3. Lighting control:

[0453] The server turns on all lights because traffic volume is above the threshold (50) and brightness is below the set threshold (300).

[0454] 4. Status display:

[0455] The server will display the state of the lights as they are turned on, for example, "Light 1: On, Light 2: On, Light 3: On".

[0456] Based on this example, this invention effectively controls lighting according to traffic volume and ambient brightness data, thereby improving energy efficiency and ensuring safety. This system can be used in a variety of environments, including roads and public spaces.

[0457] Example prompt:

[0458] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0459] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0461] Step 1: Initialize the system

[0462] The server loads the initial system configuration, which includes the locations and settings of lights, traffic sensors, and brightness sensors.

[0463] Input: Initial configuration data from a configuration file or database.

[0464] Output: System initialization state with configuration data.

[0465] The server creates an instance of the SmartLightingSystem class and initializes a dictionary to store the lighting state and a list of traffic and brightness sensors.

[0466] Specific operation: The server registers all lights in the system as off and sets the location information of each sensor.

[0467] Step 2: Viewing the initial state

[0468] The server calls the display_status method to ensure that all lights are initially off and display their status.

[0469] Input: State data after system initialization.

[0470] Output: Screen showing the initial state.

[0471] Specific operation: The server displays the status of the lights on the terminal or web interface.

[0472] Step 3: Start collecting data

[0473] The server collects data from traffic sensors and light sensors at regular intervals.

[0474] Input: Communication protocol or API call from the sensor.

[0475] Output: Traffic and lighting data collected in real time.

[0476] Specific operation: The server calls the sensor's API to receive traffic volume and brightness data.

[0477] Step 4: Data analysis

[0478] The server analyzes the acquired data and evaluates traffic volume and ambient brightness.

[0479] Input: Raw data obtained from traffic and light sensors.

[0480] Output: The total traffic volume and average brightness values ​​as the analysis results.

[0481] Specific operation: The values ​​sent from each sensor are aggregated to calculate the total and average traffic volume and brightness.

[0482] Step 5: Implementing lighting control

[0483] Based on the analysis results, the server uses the adjust_lighting method to turn the lights on or off.

[0484] Input: Analysis results (traffic volume and brightness evaluation values).

[0485] Output: The state of the light (on or off).

[0486] Specific behavior: Turn on lights if traffic volume exceeds threshold and brightness is below a certain threshold, otherwise turn off.

[0487] Step 6: Status Updates and Display

[0488] The server updates the light status and displays the current status with the display_status method.

[0489] Input: Latest lighting conditions.

[0490] Output: Updated lighting status display.

[0491] Specific operation: The server records when a light is turned on or off in a database or log file, and displays the current status on the screen.

[0492] Example prompt sentence:

[0493] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0494] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0495] (Application example 1)

[0496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] Current lighting control systems in industrial facilities often do not adequately consider energy efficiency or work environment safety. As a result, unnecessary energy consumption and reduced work efficiency due to inappropriate lighting conditions occur. In addition, the difficulty of real-time lighting adjustment according to traffic volume and ambient brightness means that they are forced to rely on fixed and inefficient lighting conditions.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0499] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off state of the lighting by the control means, and a means for optimizing lighting in work spaces using data from the sensor means installed in industrial facilities. This enables efficient and flexible lighting control based on real-time data, and also optimizes energy consumption and provides a safe and efficient work environment.

[0500] "Sensor means for detecting traffic volume in real time" refers to equipment for detecting the movement of people and objects within an industrial facility in real time and collecting that data.

[0501] "Sensor means for detecting ambient brightness in real time" refers to equipment for detecting the amount of light and illuminance within an industrial facility in real time and collecting that data.

[0502] "Control means" refers to a device or system that receives and analyzes data from the aforementioned sensor means and controls the lighting status.

[0503] "Lighting means for automatically adjusting the on / off state of lighting" refers to a device or system that automatically adjusts the on / off state of lighting based on instructions from a control means.

[0504] "Means for optimizing lighting in work spaces using data from sensor means installed in industrial facilities" refers to equipment or systems that utilize data from traffic sensors and brightness sensors installed in industrial facilities to optimize lighting conditions in work spaces.

[0505] The lighting control system of the present invention aims to improve energy efficiency and optimize the working environment in industrial facilities. This system includes sensor means for detecting traffic volume in real time, sensor means for detecting ambient brightness in real time, control means for receiving and analyzing data from these sensors, and lighting means for controlling the lighting status.

[0506] The server first initializes the system and registers various sensors and lighting devices. Specifically, the system collects and analyzes real-time data from traffic sensors and brightness sensors installed in industrial facilities, and automatically adjusts lighting on and off based on the collected data. This provides an optimal lighting environment and minimizes energy consumption.

[0507] The hardware used includes traffic and brightness sensors installed within industrial facilities, a server for collecting and analyzing data from these sensors, and lighting devices. The software uses Python scripts to collect data from the sensors in real time and analyzes the data to control the lighting status. In particular, the server calculates traffic volume from traffic sensor data and evaluates ambient brightness from brightness sensor data. Based on this data, it executes logic to control lighting on and off.

[0508] For example, if the traffic sensor in a particular area of ​​an industrial facility reads 70 and the light sensor reads 250, the server can determine that traffic is above a certain threshold and light is below a certain threshold and turn on the lights in that area, thus providing the right lighting only where it is needed and using energy efficiently.

[0509] An example of a prompt is:

[0510] We will demonstrate a system that automatically controls lighting based on traffic volume and ambient light within a factory, collecting data in real time to demonstrate how it can optimize energy efficiency while providing a safe working environment.

[0511] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0512] Step 1:

[0513] The server initializes the system. It registers traffic sensors, light sensors, and lighting devices. Specifically, it sets the initial state of lighting devices to off and records the sensor location information. The IDs and location information of the traffic sensors and light sensors are required as input, and the initial state of each sensor and lighting device in the system is set as output.

[0514] Step 2:

[0515] The server collects real-time data from traffic sensors and light sensors. The server periodically accesses these sensors to obtain the current traffic volume data and light volume data. As input, real-time data from the sensors is required, and as output, the latest data from each sensor is stored in the server.

[0516] Step 3:

[0517] The server analyzes the collected data and evaluates traffic volume and ambient brightness. It calculates the sum of traffic volume data and the average brightness data. As input, it requires traffic volume data and brightness data obtained from sensors, and as output, it obtains the sum of traffic volume and the average brightness.

[0518] Step 4:

[0519] The server executes the lighting control logic based on the pre-processed data. If the traffic volume is above a certain threshold and the brightness is below a certain threshold, it sets the light on; otherwise, it turns the light off. As input, it requires the total traffic volume and the average brightness, and as output, it determines the on / off state of the light.

[0520] Step 5:

[0521] The server sends an on or off command to the lighting device to actually change the state of the light. Specifically, it calls the lighting device's API to change the state. The input is the on / off state of the light, and the output is the change of the physical state of the light.

[0522] Step 6:

[0523] The server displays the current light status for the user or terminal to monitor the overall system status. The user can check the on / off status of the lights and manually change the settings if necessary. As input, it requires light status data from the server and as output, it provides user-visible status information.

[0524] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0525] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting lighting on / off, and an emotion engine for recognizing user emotions in real time. This system aims to achieve improved energy efficiency, cost reduction, improved safety, and user comfort.

[0526] Explanation of program processing

[0527] 1. System initialization:

[0528] The server first initializes the system and registers the lighting, traffic sensors, brightness sensors, and emotion engine.

[0529] The server creates an instance of the SmartLightingSystem class, which initializes the dictionary that stores the lighting status, the list of traffic sensors and the list of brightness sensors, and the emotion engine.

[0530] The server adds multiple lights to the system, and these lights are all initially off.

[0531] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0532] 2. Initial display:

[0533] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0534] 3. Lighting adjustment cycle:

[0535] The server obtains data from the traffic sensor, light sensor, and emotion engine at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0536] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, body movements, etc. to determine the user's emotional state.

[0537] 4. Automatic lighting adjustment:

[0538] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and the emotion engine.

[0539] If the traffic sensor data is above a certain threshold (e.g. 50) or the light sensor data is below a certain threshold (e.g. 300), the server will turn the lights on, otherwise it will turn them off.

[0540] 5. Emotion-based regulation:

[0541] The server adjusts the brightness and color temperature of the lighting according to the user's emotion recognized by the emotion engine.

[0542] For example, if the user is determined to be anxious or stressed, the server may set the lighting to a brighter color temperature, whereas if the user is determined to be relaxed, the server may set the lighting to a warmer color temperature.

[0543] Specific examples

[0544] Night road

[0545] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0546] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0547] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0548] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0549] Under this condition, the server will turn on all lights because the traffic volume is above 50 and the brightness is below 300. It will also increase the brightness of the lights to reduce user anxiety.

[0550] Park during the day

[0551] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0552] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0553] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0554] The server obtains data indicating the user's relaxation state from the emotion engine.

[0555] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0556] These examples demonstrate how the lighting control system of the present invention can improve energy efficiency and ensure safety and comfort. The system can realize a sustainable urban environment by optimally controlling lighting based on traffic volume, ambient brightness, and user emotions.

[0557] The processing flow will be explained below.

[0558] Step 1:

[0559] The server creates an instance of the SmartLightingSystem class, which initializes an empty dictionary for storing lighting status, a list of traffic and brightness sensors, and an emotion engine for recognizing user emotions.

[0560] Step 2:

[0561] The server registers the lighting devices in the system. Specifically, it sets five lighting devices (e.g., light-0 to light-4) to off in the initial state.

[0562] Step 3:

[0563] The server adds traffic sensors and light sensors to the system by registering traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and light sensors (e.g., light-sensor-0 to light-sensor-2) according to their respective roles.

[0564] Step 4:

[0565] The server calls the display_status method to display the initial state of all lighting devices. Notice that initially, all of these lights are off.

[0566] Step 5:

[0567] The server collects data from traffic sensors, light sensors, and the emotion engine at regular intervals.

[0568] The server calls the check_traffic method to get data (traffic volume) from the traffic sensor.

[0569] The server calls the check_light_level method to get data from the light sensor (ambient light intensity).

[0570] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0571] Step 6:

[0572] The server executes the adjust_lighting method and controls the on / off of lighting means based on data from each sensor and emotion engine.

[0573] For example, if the traffic sensor data exceeds a certain threshold (e.g., 50), or if the light sensor data falls below a certain threshold (e.g., 300), the server will turn on the corresponding lighting means; otherwise, it will turn them off.

[0574] Step 7:

[0575] The server adjusts the brightness and color temperature of the lighting based on the user's emotions recognized by the emotion engine.

[0576] Specifically, if it is determined that the user is feeling anxious or stressed, the server sets the lighting to be brighter.

[0577] On the other hand, if it is determined that the user is relaxed, the server maintains the feeling of relaxation by changing the color temperature of the lighting to a warmer color.

[0578] Step 8:

[0579] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0580] Step 9:

[0581] The server repeats the process from step 5 to step 8 at regular intervals. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0582] Example 2

[0583] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0584] Conventional lighting control systems mainly control lighting on / off based on physical data such as traffic volume and ambient brightness, but because they are unable to take the user's emotional state into account, they have the problem of being unable to fully address issues such as improving energy efficiency and user comfort. Furthermore, to improve safety and comfort at night, it is necessary to fine-tune lighting adjustments based on the user's emotional state in addition to traffic volume and brightness.

[0585] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a means for recognizing a user's emotion in real time, a control means for receiving and analyzing data from the sensor means and the emotion recognition means and controlling the lighting state, a lighting means for automatically adjusting the lighting on / off, and a means for adjusting the lighting brightness and color temperature based on the user's emotional state. This enables lighting control that takes into account the user's emotional state in addition to traffic volume and ambient brightness, thereby achieving higher energy efficiency and user comfort and safety.

[0586] "Sensor means for detecting traffic volume in real time" refers to a device for detecting the number and movement of vehicles and pedestrians on a road in real time.

[0587] The "sensor means for detecting ambient brightness in real time" is a device for detecting brightness in an outdoor space or a specific area in real time.

[0588] The "means for recognizing a user's emotions in real time" refers to a device or software that analyzes the user's facial expressions, tone of voice, body movements, etc., and recognizes the user's emotional state in real time.

[0589] The "control means" refers to hardware and software for receiving and analyzing data from the sensor means and emotion recognition means, and controlling the lighting status based on the results.

[0590] The "lighting means" is a device for turning on and off the lighting and adjusting the brightness and color temperature based on instructions from the control means.

[0591] The "means for adjusting the brightness and color temperature of lighting" refers to a device or software for appropriately adjusting the brightness and color temperature of lighting based on the emotional state of the user.

[0592] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and data from a means for recognizing user emotions in real time and controlling the lighting status, a lighting means for automatically adjusting the on / off of lighting, and a means for adjusting the brightness and color temperature of lighting based on the user's emotional state.

[0593] System initialization

[0594] The server first initializes the system. It creates an instance of SmartLightingSystem and initializes the dictionary that stores the lighting status and the sensor list. During this initialization process, the server adds multiple lights to the system, all of which are initially turned off. It adds traffic sensors and brightness sensors to the system and sets the locations of the sensors to roads and outdoor spaces. It also creates an instance of the emotion engine and registers it in the system.

[0595] Displaying the initial state

[0596] The server calls the display_status method to display the initial state of each light. You can see that all lights are initially off.

[0597] Lighting Adjustment Cycle

[0598] The server collects data from the traffic sensor, light sensor, and emotion engine at regular intervals. It calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor. It also uses the emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0599] Automatic lighting adjustment

[0600] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and emotion engines. For example, if the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the server turns on the lights; otherwise, it turns them off.

[0601] Emotion-Based Adjustment

[0602] The server adjusts the brightness and color temperature of the lighting according to the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling anxious or stressed, it will set the lighting brighter. On the other hand, if the user is determined to be relaxed, it will set the lighting to a warmer color temperature.

[0603] Specific examples

[0604] Night road

[0605] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0606] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0607] The server obtains the light sensor data (e.g., light-sensor-0 is 200, light-sensor-1 is 150, and light-sensor-2 is 100).

[0608] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0609] Under these conditions, the server turns on all the lights because the traffic volume is above 50 and the brightness is below 300. It also increases the brightness of the lights to reduce the user's anxiety.

[0610] Park during the day

[0611] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0612] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0613] The server obtains the light sensor data (e.g., light-sensor-0 is 900, light-sensor-1 is 950, and light-sensor-2 is 1000).

[0614] The server obtains data indicating the user's relaxation state from the emotion engine.

[0615] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0616] Example prompts to input to the generative AI model

[0617] "Please explain how a lighting control system with an emotion engine works on roads at night. Specifically, please explain in detail how it adjusts lighting based on traffic sensors, brightness sensors, and emotion recognition results, including specific data examples."

[0618] As described above, the lighting control system of the present invention controls lighting based on the user's emotional state in addition to traffic volume and ambient brightness, thereby improving energy efficiency and user comfort and safety.

[0619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] System initialization

[0622] Input: System startup command

[0623] Specific processing: The server creates an instance of SmartLightingSystem to initialize the system. At this time, it creates an internal dictionary, initializes the traffic sensor list and the light sensor list, and also creates an instance of the emotion engine.

[0624] Output: Initialization complete message

[0625] Specific operation: The server outputs a log message saying "Initialization is starting" and sets all lights in the system to off by default. It adds traffic sensors and light sensors to a list and records their positions. When initialization is complete, it outputs a log message saying "System initialization completed."

[0626] Step 2:

[0627] Displaying the initial state

[0628] Input: Initialization complete message

[0629] Specific processing: The server calls the display_status method to display the initial status of the lighting.

[0630] Output: Initial lighting state

[0631] Specific operation: The server outputs the light status to the console as "Light 1: Off, Light 2: Off, Light 3: Off", and leaves a log stating "The initial light settings have been displayed."

[0632] Step 3:

[0633] Data Acquisition Cycle

[0634] Input: Timer event

[0635] Specific processing: The server calls the check_traffic and check_light_level methods at regular intervals to obtain data from the traffic and light sensors, and uses the emotion engine to analyze the user's emotional state.

[0636] Output: Traffic data, brightness data, emotion data

[0637] Specific operation: The server logs the data from each sensor, such as "Data obtained from traffic sensor" and "Data obtained from brightness sensor." When the emotion engine outputs the analysis results, it leaves a log stating "User's emotional state obtained from emotion engine."

[0638] Step 4:

[0639] Automatic lighting adjustment

[0640] Input: Traffic data, brightness data

[0641] Specific processing: The server executes the adjust_lighting method to turn the lights on or off based on the data from the sensor.

[0642] Output: Lighting status

[0643] Specific operation: If the traffic volume exceeds 50 or the brightness is below 300, the server will log "Turn on all lights" and turn on the lights. In the opposite case, the server will log "Turn off all lights" and turn off the lights.

[0644] Step 5:

[0645] Emotion-Based Adjustment

[0646] Input: Emotion data

[0647] Specific processing: Based on the data from the emotion engine, the server adjusts the brightness and color temperature of the lighting according to the user's emotional state.

[0648] Output: Adjusted lighting conditions

[0649] Specific behavior: If the user is feeling anxious or stressed, the server will log "brighten the lighting to reduce the user's anxiety" and increase the brightness. If the user is relaxed, the server will log "change the lighting to warmer colors to maintain the user's sense of relaxation" and change the color temperature to warmer colors.

[0650] (Application example 2)

[0651] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] Current lighting control systems can only control lighting based on traffic volume and ambient brightness, and are unable to adjust lighting based on the emotional state of the user, resulting in insufficient improvements in comfort and safety. Furthermore, further improvements in energy efficiency are needed.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a detection means for detecting traffic volume in real time, a detection means for detecting ambient brightness in real time, an emotion recognition means for recognizing the emotional state of a user in real time, a control means for receiving and analyzing data from the detection means and the emotion recognition means and controlling the lighting state, and a lighting means for automatically turning the lighting on and off and adjusting the brightness by the control means. This makes it possible to provide an optimal lighting environment based on traffic volume, ambient brightness, and the emotional state of the user, improving safety and comfort while also increasing energy efficiency.

[0654] "Means for detecting traffic volume in real time" refers to sensors or devices for instantly grasping the volume of traffic flow.

[0655] The "detection means for detecting the ambient brightness in real time" is a sensor or device for instantly measuring the brightness of the surrounding environment.

[0656] "Emotion recognition means for recognizing the user's emotional state in real time" refers to sensors or engines that can instantly identify the user's emotions from their facial expressions and behavior.

[0657] The "control means for receiving and analyzing data from the detection means and emotion recognition means and controlling the lighting status" is a device that receives data on traffic volume, ambient brightness, and the user's emotional state, analyzes this data, and adjusts the lighting on / off and brightness.

[0658] "Lighting means for automatically adjusting the on / off and brightness of lighting by the control means" refers to a device for automatically adjusting the lighting state and brightness in accordance with instructions from the control means.

[0659] This invention is a lighting control system that includes multiple sensors that detect traffic volume, ambient brightness, and the user's emotional state in real time, and control means that analyzes this data to automatically turn lights on and off and adjust brightness.

[0660] System configuration

[0661] Hardware:

[0662] Traffic volume detection sensor: A sensor that detects the volume of traffic flow in real time, allowing you to instantly grasp how much traffic is present.

[0663] Brightness detection sensor: This sensor detects the brightness of the surrounding environment in real time, enabling the lighting to be adjusted instantly to match the brightness of the outside world.

[0664] Emotion recognition engine: A device that recognizes the user's emotional state in real time from their facial expressions and movements, enabling adjustments to improve the user's comfort.

[0665] software:

[0666] Control means (running on server): A software program that receives and analyzes traffic volume, lighting, and emotional state data. Implemented using Python and / or available APIs.

[0667] Lighting means: A device that turns lights on and off and adjusts brightness and color temperature according to instructions from the control means, thereby ensuring safety and comfort.

[0668] Data Acquisition and Calculation

[0669] The server receives real-time data from traffic sensors, brightness sensors, and emotion recognition engines, and analyzes this data to adjust lighting on / off, brightness, and color temperature based on the data.

[0670] Processing flow

[0671] 1. Data Acquisition:

[0672] Data is sent to the server from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. For example, if the traffic volume sensor detects 30, the ambient brightness sensor detects 200, and the user is in an anxious state, the server receives this data.

[0673] 2. Data Analysis:

[0674] Based on the received data, the system checks conditions such as whether traffic volume exceeds a threshold, whether the surrounding brightness is below a certain level, etc. At the same time, the user's emotional state is also analyzed.

[0675] 3. Adjust the lighting:

[0676] Based on set thresholds, for example, if there is heavy traffic and low brightness, or if the user feels uneasy, the server will turn on the lighting means and automatically adjust the brightness and color temperature as needed.

[0677] Examples and prompts

[0678] For example, if the traffic sensor detects a small number of vehicles in a park at night, the light sensor indicates a low reading, and the user is determined to be feeling anxious, the following process will occur:

[0679] The server receives traffic data (30), brightness data (200), and emotion data (anxiety).

[0680] Based on this data, the lights are turned on and further increased brightness is provided to reduce user anxiety.

[0681] Example prompt sentence:

[0682] "Design your lighting system to turn on lights when all traffic sensor readings are below 30 and light sensor readings are below 300, and brighten them further if there is a sense of unrest."

[0683] This allows the system to provide an optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, improving safety, ensuring user comfort, and increasing energy efficiency.

[0684] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0685] Step 1: Get the data

[0686] The server receives real-time data from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. The input data includes traffic volume data, brightness data, and user emotion data. The data from these sensors is obtained through API and sent to the server.

[0687] Step 2: Data storage process

[0688] The server temporarily stores the received data and records it in a database for subsequent analysis. Specifically, it stores traffic volume data, brightness data, and emotion data in corresponding tables, allowing subsequent data analysis to be performed quickly.

[0689] Step 3: Analyze the data

[0690] The server analyzes the stored data to determine whether traffic volume is above a set threshold, whether lighting is below a set threshold, and the user's emotional state, using simple conditional and statistical methods.

[0691] Step 4: Conditional Decisions

[0692] The server determines the lighting operation based on the results of the data analysis. For example, if traffic volume exceeds a threshold and brightness is low, the lights will be turned on. If the emotion recognition engine determines that the user is in an anxious state, the lights will be increased in brightness. Processing at this stage is performed based on control logic.

[0693] Step 5: Lighting adjustment instructions

[0694] Based on the results of the processing, the server sends instructions to the lighting means to turn it on or off or adjust the brightness. Specifically, it generates a lighting control signal and sends that signal to the lighting device. This allows the lighting to be turned on or off appropriately and the brightness to be set according to the user's emotional state.

[0695] Step 6: Status check and feedback

[0696] The server checks the lighting status and receives feedback data from the lighting device. It verifies whether the lighting is operating correctly as instructed and sends control instructions again if there is a problem. This step also receives feedback from users and adjusts the system's operation.

[0697] Step 7: Logging and Reporting

[0698] The server stores all processing data and results in logs and generates periodic reports. This makes it easier to evaluate the system's operation history and performance. The logs include input data (traffic volume, brightness, emotional state), analysis results, control instructions, feedback data, etc. The log data is stored in a database and is backed up periodically.

[0699] Through these steps, the system can automatically provide the optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, thereby improving safety and optimizing energy efficiency.

[0700] 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.

[0701] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0702] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0703] [Third embodiment]

[0704] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0705] 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.

[0706] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0707] 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.

[0708] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0709] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0710] 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.

[0711] 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.

[0712] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0713] 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.

[0714] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0715] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0716] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, and a lighting means for automatically adjusting the on / off of lighting using the control means. This system aims to improve energy efficiency, reduce costs, and improve safety.

[0717] Explanation of program processing

[0718] 1. System initialization:

[0719] The server first initializes the system and registers the lights, traffic sensors, and brightness sensors.

[0720] The server creates an instance of the SmartLightingSystem class, initializing the dictionary that stores the lighting state and the list of traffic and brightness sensors.

[0721] The server adds multiple lights to the system, and these lights are all initially off.

[0722] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0723] 2. Initial display:

[0724] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0725] 3. Lighting adjustment cycle:

[0726] The server retrieves data from traffic sensors and light sensors at regular intervals and updates the lighting status.

[0727] The adjust_lighting method aggregates data from all traffic sensors and uses the results to determine traffic volume, and aggregates data from all light sensors to evaluate the ambient light intensity.

[0728] If the traffic volume is above a certain threshold or the ambient light is below a certain threshold, the server will turn the lights on, otherwise it will turn them off. This process is performed every cycle.

[0729] Specific examples

[0730] Night road

[0731] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0732] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0733] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0734] The server evaluates the case where the traffic volume is 75 (30 + 25 + 20) and the average brightness is 150 ((200 + 150 + 100) / 3).

[0735] Under these conditions, the server turns on all lights because traffic volume is greater than 50 and brightness is less than or equal to 300.

[0736] Daytime intersection

[0737] During the day, if the traffic sensor detects high traffic volume and the light sensor indicates a high reading:

[0738] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0739] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0740] The server evaluates the case where the traffic volume is 155 (50 + 45 + 60) and the average brightness is 950.

[0741] Under these conditions, traffic volume is high but brightness is above 300, so the server turns off the lights.

[0742] This invention makes it possible to efficiently control lighting on roads and public spaces according to traffic volume and ambient brightness, which has the advantage of improving energy efficiency while also ensuring safety.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The server creates an instance of the SmartLightingSystem class, which initializes it with an empty dictionary to record lighting states, and a list of traffic and brightness sensors.

[0746] Step 2:

[0747] The server adds the lighting devices to the system. Specifically, it registers five lighting devices (e.g., light-0 to light-4) as off in the initial state.

[0748] Step 3:

[0749] The server adds traffic and light sensors to the system by registering three traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and three light sensors (e.g., light-sensor-0 to light-sensor-2).

[0750] Step 4:

[0751] The server calls the display_status method to display the initial state (on / off) of all lighting devices. Initially, all lights are on.

[0752] Step 5:

[0753] The server collects data from the traffic and light sensors at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0754] Step 6:

[0755] The server executes the adjust_lighting method to turn on and off lighting devices based on the data from the sensors. If the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the corresponding lighting device is turned on. Otherwise, it is turned off.

[0756] Step 7:

[0757] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0758] Step 8:

[0759] The server repeats the process from step 5 to step 7. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0760] Example 1

[0761] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0762] Conventional lighting control systems are unable to achieve efficient lighting control according to traffic volume and ambient brightness, making it difficult to improve energy efficiency and ensure safety. Furthermore, there are issues with operability due to insufficient functions such as system initialization, data analysis, and real-time display of lighting status.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0764] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off of the lighting by the control means, a means for the control means to read the initial settings of the system in an initialization step, register the sensors and lighting, and display the initial status, a means for acquiring and analyzing data from the sensors at regular time intervals, a means for adjusting the lighting status based on traffic volume and brightness data, and a means for displaying the lighting status, thereby enabling improved energy efficiency and ensuring safety.

[0765] "Sensor means for detecting traffic volume in real time" refers to a device that instantly measures the number of vehicles and pedestrians on the road and continuously collects that data.

[0766] "Sensor means for detecting ambient brightness in real time" is a device that instantly measures the external light environment and continuously collects that data.

[0767] "Control means for receiving and analyzing data from the sensor means and controlling the lighting status" refers to a system that receives and analyzes data sent from the sensor and manages the operation of the lighting device based on the results.

[0768] "Lighting means that automatically adjusts the on / off of lighting by the control means" refers to a device that automatically switches on and off lighting in accordance with instructions from the control means.

[0769] "Means for reading the initial settings of the system in the initialization step, registering sensors and lighting, and displaying the initial status" refers to a device that has the function of reading the setting information required when the system starts operating, registering each device in the system, and displaying the current status to the user.

[0770] "Means for acquiring and analyzing data from sensors at regular time intervals" refers to a device that collects data from sensors at a pre-set cycle and analyzes the data.

[0771] The "means for adjusting lighting conditions based on traffic volume and brightness data" refers to a system that automatically adjusts lighting conditions, such as turning the lights on and off, based on the acquired traffic volume and brightness data.

[0772] A "light status display means" is a device that visually displays the current light on / off status and other related information.

[0773] The lighting control system of the present invention detects traffic volume and ambient brightness in real time and automatically adjusts lighting based on that data. The purpose of this system is to improve energy efficiency and ensure safety. Specific components of the system include sensors that detect traffic volume, sensors that detect ambient brightness, control means that receive and analyze the data, and lighting means that turn lights on and off.

[0774] Hardware and software used

[0775] The system hardware includes the following elements:

[0776] Traffic sensors: These are installed on roads to detect traffic volume in real time. Specific product examples include ultrasonic sensors and camera-based traffic sensors.

[0777] Brightness sensor: Installed in outdoor spaces, it detects the intensity of ambient light in real time. Specific product examples include light sensors and lux meters.

[0778] Server: Receives and analyzes data and controls lighting. In addition to standard server hardware, a communication module and data analysis software are installed.

[0779] The software includes the following elements:

[0780] Sensor data collection module: A module that collects data from traffic sensors and brightness sensors.

[0781] Data analysis module: Analyzes collected data and evaluates traffic volume and ambient brightness.

[0782] Control program: Based on the evaluation results, issues instructions to turn the lights on or off.

[0783] Specific examples

[0784] Night road

[0785] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[0786] 1. Data Acquisition:

[0787] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0788] The server also gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0789] 2. Data Analysis:

[0790] The server analyzes the acquired data and determines that the traffic volume is 75 (30 + 25 + 20).

[0791] Evaluate that the average ambient brightness is 150 ((200 + 150 + 100) / 3).

[0792] 3. Lighting control:

[0793] The server turns on all lights because traffic volume is above the threshold (50) and brightness is below the set threshold (300).

[0794] 4. Status display:

[0795] The server will display the state of the lights as they are turned on, for example, "Light 1: On, Light 2: On, Light 3: On".

[0796] Based on this example, this invention effectively controls lighting according to traffic volume and ambient brightness data, thereby improving energy efficiency and ensuring safety. This system can be used in a variety of environments, including roads and public spaces.

[0797] Example prompt:

[0798] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0799] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0800] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0801] Step 1: Initialize the system

[0802] The server loads the initial system configuration, which includes the locations and settings of lights, traffic sensors, and brightness sensors.

[0803] Input: Initial configuration data from a configuration file or database.

[0804] Output: System initialization state with configuration data.

[0805] The server creates an instance of the SmartLightingSystem class and initializes a dictionary to store the lighting state and a list of traffic and brightness sensors.

[0806] Specific operation: The server registers all lights in the system as off and sets the location information of each sensor.

[0807] Step 2: Viewing the initial state

[0808] The server calls the display_status method to ensure that all lights are initially off and display their status.

[0809] Input: State data after system initialization.

[0810] Output: Screen showing the initial state.

[0811] Specific operation: The server displays the status of the lights on the terminal or web interface.

[0812] Step 3: Start collecting data

[0813] The server collects data from traffic sensors and light sensors at regular intervals.

[0814] Input: Communication protocol or API call from the sensor.

[0815] Output: Traffic and lighting data collected in real time.

[0816] Specific operation: The server calls the sensor's API to receive traffic volume and brightness data.

[0817] Step 4: Data analysis

[0818] The server analyzes the acquired data and evaluates traffic volume and ambient brightness.

[0819] Input: Raw data obtained from traffic and light sensors.

[0820] Output: The total traffic volume and average brightness values ​​as the analysis results.

[0821] Specific operation: The values ​​sent from each sensor are aggregated to calculate the total and average traffic volume and brightness.

[0822] Step 5: Implementing lighting control

[0823] Based on the analysis results, the server uses the adjust_lighting method to turn the lights on or off.

[0824] Input: Analysis results (traffic volume and brightness evaluation values).

[0825] Output: The state of the light (on or off).

[0826] Specific behavior: Turn on lights if traffic volume exceeds threshold and brightness is below a certain threshold, otherwise turn off.

[0827] Step 6: Status Updates and Display

[0828] The server updates the light status and displays the current status with the display_status method.

[0829] Input: Latest lighting conditions.

[0830] Output: Updated lighting status display.

[0831] Specific operation: The server records when a light is turned on or off in a database or log file, and displays the current status on the screen.

[0832] Example prompt sentence:

[0833] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[0834] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[0835] (Application example 1)

[0836] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0837] Current lighting control systems in industrial facilities often do not adequately consider energy efficiency or work environment safety. As a result, unnecessary energy consumption and reduced work efficiency due to inappropriate lighting conditions occur. In addition, the difficulty of real-time lighting adjustment according to traffic volume and ambient brightness means that they are forced to rely on fixed and inefficient lighting conditions.

[0838] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0839] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off state of the lighting by the control means, and a means for optimizing lighting in work spaces using data from the sensor means installed in industrial facilities. This enables efficient and flexible lighting control based on real-time data, and also optimizes energy consumption and provides a safe and efficient work environment.

[0840] "Sensor means for detecting traffic volume in real time" refers to equipment for detecting the movement of people and objects within an industrial facility in real time and collecting that data.

[0841] "Sensor means for detecting ambient brightness in real time" refers to equipment for detecting the amount of light and illuminance within an industrial facility in real time and collecting that data.

[0842] "Control means" refers to a device or system that receives and analyzes data from the aforementioned sensor means and controls the lighting status.

[0843] "Lighting means for automatically adjusting the on / off state of lighting" refers to a device or system that automatically adjusts the on / off state of lighting based on instructions from a control means.

[0844] "Means for optimizing lighting in work spaces using data from sensor means installed in industrial facilities" refers to equipment or systems that utilize data from traffic sensors and brightness sensors installed in industrial facilities to optimize lighting conditions in work spaces.

[0845] The lighting control system of the present invention aims to improve energy efficiency and optimize the working environment in industrial facilities. This system includes sensor means for detecting traffic volume in real time, sensor means for detecting ambient brightness in real time, control means for receiving and analyzing data from these sensors, and lighting means for controlling the lighting status.

[0846] The server first initializes the system and registers various sensors and lighting devices. Specifically, the system collects and analyzes real-time data from traffic sensors and brightness sensors installed in industrial facilities, and automatically adjusts lighting on and off based on the collected data. This provides an optimal lighting environment and minimizes energy consumption.

[0847] The hardware used includes traffic and brightness sensors installed within industrial facilities, a server for collecting and analyzing data from these sensors, and lighting devices. The software uses Python scripts to collect data from the sensors in real time and analyzes the data to control the lighting status. In particular, the server calculates traffic volume from traffic sensor data and evaluates ambient brightness from brightness sensor data. Based on this data, it executes logic to control lighting on and off.

[0848] For example, if the traffic sensor in a particular area of ​​an industrial facility reads 70 and the light sensor reads 250, the server can determine that traffic is above a certain threshold and light is below a certain threshold and turn on the lights in that area, thus providing the right lighting only where it is needed and using energy efficiently.

[0849] An example of a prompt is:

[0850] We will demonstrate a system that automatically controls lighting based on traffic volume and ambient light within a factory, collecting data in real time to demonstrate how it can optimize energy efficiency while providing a safe working environment.

[0851] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0852] Step 1:

[0853] The server initializes the system. It registers traffic sensors, light sensors, and lighting devices. Specifically, it sets the initial state of lighting devices to off and records the sensor location information. The IDs and location information of the traffic sensors and light sensors are required as input, and the initial state of each sensor and lighting device in the system is set as output.

[0854] Step 2:

[0855] The server collects real-time data from traffic sensors and light sensors. The server periodically accesses these sensors to obtain the current traffic volume data and light volume data. As input, real-time data from the sensors is required, and as output, the latest data from each sensor is stored in the server.

[0856] Step 3:

[0857] The server analyzes the collected data and evaluates traffic volume and ambient brightness. It calculates the sum of traffic volume data and the average brightness data. As input, it requires traffic volume data and brightness data obtained from sensors, and as output, it obtains the sum of traffic volume and the average brightness.

[0858] Step 4:

[0859] The server executes the lighting control logic based on the pre-processed data. If the traffic volume is above a certain threshold and the brightness is below a certain threshold, it sets the light on; otherwise, it turns the light off. As input, it requires the total traffic volume and the average brightness, and as output, it determines the on / off state of the light.

[0860] Step 5:

[0861] The server sends an on or off command to the lighting device to actually change the state of the light. Specifically, it calls the lighting device's API to change the state. The input is the on / off state of the light, and the output is the change of the physical state of the light.

[0862] Step 6:

[0863] The server displays the current light status for the user or terminal to monitor the overall system status. The user can check the on / off status of the lights and manually change the settings if necessary. As input, it requires light status data from the server and as output, it provides user-visible status information.

[0864] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0865] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting lighting on / off, and an emotion engine for recognizing user emotions in real time. This system aims to achieve improved energy efficiency, cost reduction, improved safety, and user comfort.

[0866] Explanation of program processing

[0867] 1. System initialization:

[0868] The server first initializes the system and registers the lighting, traffic sensors, brightness sensors, and emotion engine.

[0869] The server creates an instance of the SmartLightingSystem class, which initializes the dictionary that stores the lighting status, the list of traffic sensors and the list of brightness sensors, and the emotion engine.

[0870] The server adds multiple lights to the system, and these lights are all initially off.

[0871] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[0872] 2. Initial display:

[0873] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[0874] 3. Lighting adjustment cycle:

[0875] The server obtains data from the traffic sensor, light sensor, and emotion engine at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[0876] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, body movements, etc. to determine the user's emotional state.

[0877] 4. Automatic lighting adjustment:

[0878] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and the emotion engine.

[0879] If the traffic sensor data is above a certain threshold (e.g. 50) or the light sensor data is below a certain threshold (e.g. 300), the server will turn the lights on, otherwise it will turn them off.

[0880] 5. Emotion-based regulation:

[0881] The server adjusts the brightness and color temperature of the lighting according to the user's emotion recognized by the emotion engine.

[0882] For example, if the user is determined to be anxious or stressed, the server may set the lighting to a brighter color temperature, whereas if the user is determined to be relaxed, the server may set the lighting to a warmer color temperature.

[0883] Specific examples

[0884] Night road

[0885] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0886] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0887] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[0888] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0889] Under this condition, the server will turn on all lights because the traffic volume is above 50 and the brightness is below 300. It will also increase the brightness of the lights to reduce user anxiety.

[0890] Park during the day

[0891] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0892] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0893] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[0894] The server obtains data indicating the user's relaxation state from the emotion engine.

[0895] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0896] These examples demonstrate how the lighting control system of the present invention can improve energy efficiency and ensure safety and comfort. The system can realize a sustainable urban environment by optimally controlling lighting based on traffic volume, ambient brightness, and user emotions.

[0897] The processing flow will be explained below.

[0898] Step 1:

[0899] The server creates an instance of the SmartLightingSystem class, which initializes an empty dictionary for storing lighting status, a list of traffic and brightness sensors, and an emotion engine for recognizing user emotions.

[0900] Step 2:

[0901] The server registers the lighting devices in the system. Specifically, it sets five lighting devices (e.g., light-0 to light-4) to off in the initial state.

[0902] Step 3:

[0903] The server adds traffic sensors and light sensors to the system by registering traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and light sensors (e.g., light-sensor-0 to light-sensor-2) according to their respective roles.

[0904] Step 4:

[0905] The server calls the display_status method to display the initial state of all lighting devices. Notice that initially, all of these lights are off.

[0906] Step 5:

[0907] The server collects data from traffic sensors, light sensors, and the emotion engine at regular intervals.

[0908] The server calls the check_traffic method to get data (traffic volume) from the traffic sensor.

[0909] The server calls the check_light_level method to get data from the light sensor (ambient light intensity).

[0910] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0911] Step 6:

[0912] The server executes the adjust_lighting method and controls the on / off of lighting means based on data from each sensor and emotion engine.

[0913] For example, if the traffic sensor data exceeds a certain threshold (e.g., 50), or if the light sensor data falls below a certain threshold (e.g., 300), the server will turn on the corresponding lighting means; otherwise, it will turn them off.

[0914] Step 7:

[0915] The server adjusts the brightness and color temperature of the lighting based on the user's emotions recognized by the emotion engine.

[0916] Specifically, if it is determined that the user is feeling anxious or stressed, the server sets the lighting to be brighter.

[0917] On the other hand, if it is determined that the user is relaxed, the server maintains the feeling of relaxation by changing the color temperature of the lighting to a warmer color.

[0918] Step 8:

[0919] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[0920] Step 9:

[0921] The server repeats the process from step 5 to step 8 at regular intervals. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[0922] Example 2

[0923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0924] Conventional lighting control systems mainly control lighting on / off based on physical data such as traffic volume and ambient brightness, but because they are unable to take the user's emotional state into account, they have the problem of being unable to fully address issues such as improving energy efficiency and user comfort. Furthermore, to improve safety and comfort at night, it is necessary to fine-tune lighting adjustments based on the user's emotional state in addition to traffic volume and brightness.

[0925] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a means for recognizing a user's emotion in real time, a control means for receiving and analyzing data from the sensor means and the emotion recognition means and controlling the lighting state, a lighting means for automatically adjusting the lighting on / off, and a means for adjusting the lighting brightness and color temperature based on the user's emotional state. This enables lighting control that takes into account the user's emotional state in addition to traffic volume and ambient brightness, thereby achieving higher energy efficiency and user comfort and safety.

[0926] "Sensor means for detecting traffic volume in real time" refers to a device for detecting the number and movement of vehicles and pedestrians on a road in real time.

[0927] The "sensor means for detecting ambient brightness in real time" is a device for detecting brightness in an outdoor space or a specific area in real time.

[0928] The "means for recognizing a user's emotions in real time" refers to a device or software that analyzes the user's facial expressions, tone of voice, body movements, etc., and recognizes the user's emotional state in real time.

[0929] The "control means" refers to hardware and software for receiving and analyzing data from the sensor means and emotion recognition means, and controlling the lighting status based on the results.

[0930] The "lighting means" is a device for turning on and off the lighting and adjusting the brightness and color temperature based on instructions from the control means.

[0931] The "means for adjusting the brightness and color temperature of lighting" refers to a device or software for appropriately adjusting the brightness and color temperature of lighting based on the emotional state of the user.

[0932] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and data from a means for recognizing user emotions in real time and controlling the lighting status, a lighting means for automatically adjusting the on / off of lighting, and a means for adjusting the brightness and color temperature of lighting based on the user's emotional state.

[0933] System initialization

[0934] The server first initializes the system. It creates an instance of SmartLightingSystem and initializes the dictionary that stores the lighting status and the sensor list. During this initialization process, the server adds multiple lights to the system, all of which are initially turned off. It adds traffic sensors and brightness sensors to the system and sets the locations of the sensors to roads and outdoor spaces. It also creates an instance of the emotion engine and registers it in the system.

[0935] Displaying the initial state

[0936] The server calls the display_status method to display the initial state of each light. You can see that all lights are initially off.

[0937] Lighting Adjustment Cycle

[0938] The server collects data from the traffic sensor, light sensor, and emotion engine at regular intervals. It calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor. It also uses the emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[0939] Automatic lighting adjustment

[0940] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and emotion engines. For example, if the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the server turns on the lights; otherwise, it turns them off.

[0941] Emotion-Based Adjustment

[0942] The server adjusts the brightness and color temperature of the lighting according to the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling anxious or stressed, it will set the lighting brighter. On the other hand, if the user is determined to be relaxed, it will set the lighting to a warmer color temperature.

[0943] Specific examples

[0944] Night road

[0945] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[0946] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[0947] The server obtains the light sensor data (e.g., light-sensor-0 is 200, light-sensor-1 is 150, and light-sensor-2 is 100).

[0948] The server obtains data indicative of the user's anxiety state from the emotion engine.

[0949] Under these conditions, the server turns on all the lights because the traffic volume is above 50 and the brightness is below 300. It also increases the brightness of the lights to reduce the user's anxiety.

[0950] Park during the day

[0951] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[0952] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[0953] The server obtains the light sensor data (e.g., light-sensor-0 is 900, light-sensor-1 is 950, and light-sensor-2 is 1000).

[0954] The server obtains data indicating the user's relaxation state from the emotion engine.

[0955] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[0956] Example prompts to input to the generative AI model

[0957] "Please explain how a lighting control system with an emotion engine works on roads at night. Specifically, please explain in detail how it adjusts lighting based on traffic sensors, brightness sensors, and emotion recognition results, including specific data examples."

[0958] As described above, the lighting control system of the present invention controls lighting based on the user's emotional state in addition to traffic volume and ambient brightness, thereby improving energy efficiency and user comfort and safety.

[0959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0960] Step 1:

[0961] System initialization

[0962] Input: System startup command

[0963] Specific processing: The server creates an instance of SmartLightingSystem to initialize the system. At this time, it creates an internal dictionary, initializes the traffic sensor list and the light sensor list, and also creates an instance of the emotion engine.

[0964] Output: Initialization complete message

[0965] Specific operation: The server outputs a log message saying "Initialization is starting" and sets all lights in the system to off by default. It adds traffic sensors and light sensors to a list and records their positions. When initialization is complete, it outputs a log message saying "System initialization completed."

[0966] Step 2:

[0967] Displaying the initial state

[0968] Input: Initialization complete message

[0969] Specific processing: The server calls the display_status method to display the initial status of the lighting.

[0970] Output: Initial lighting state

[0971] Specific operation: The server outputs the light status to the console as "Light 1: Off, Light 2: Off, Light 3: Off", and leaves a log stating "The initial light settings have been displayed."

[0972] Step 3:

[0973] Data Acquisition Cycle

[0974] Input: Timer event

[0975] Specific processing: The server calls the check_traffic and check_light_level methods at regular intervals to obtain data from the traffic and light sensors, and uses the emotion engine to analyze the user's emotional state.

[0976] Output: Traffic data, brightness data, emotion data

[0977] Specific operation: The server logs the data from each sensor, such as "Data obtained from traffic sensor" and "Data obtained from brightness sensor." When the emotion engine outputs the analysis results, it leaves a log stating "User's emotional state obtained from emotion engine."

[0978] Step 4:

[0979] Automatic lighting adjustment

[0980] Input: Traffic data, brightness data

[0981] Specific processing: The server executes the adjust_lighting method to turn the lights on or off based on the data from the sensor.

[0982] Output: Lighting status

[0983] Specific operation: If the traffic volume exceeds 50 or the brightness is below 300, the server will log "Turn on all lights" and turn on the lights. In the opposite case, the server will log "Turn off all lights" and turn off the lights.

[0984] Step 5:

[0985] Emotion-Based Adjustment

[0986] Input: Emotion data

[0987] Specific processing: Based on the data from the emotion engine, the server adjusts the brightness and color temperature of the lighting according to the user's emotional state.

[0988] Output: Adjusted lighting conditions

[0989] Specific behavior: If the user is feeling anxious or stressed, the server will log "brighten the lighting to reduce the user's anxiety" and increase the brightness. If the user is relaxed, the server will log "change the lighting to warmer colors to maintain the user's sense of relaxation" and change the color temperature to warmer colors.

[0990] (Application example 2)

[0991] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0992] Current lighting control systems can only control lighting based on traffic volume and ambient brightness, and are unable to adjust lighting based on the emotional state of the user, resulting in insufficient improvements in comfort and safety. Furthermore, further improvements in energy efficiency are needed.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a detection means for detecting traffic volume in real time, a detection means for detecting ambient brightness in real time, an emotion recognition means for recognizing the emotional state of a user in real time, a control means for receiving and analyzing data from the detection means and the emotion recognition means and controlling the lighting state, and a lighting means for automatically turning the lighting on and off and adjusting the brightness by the control means. This makes it possible to provide an optimal lighting environment based on traffic volume, ambient brightness, and the emotional state of the user, improving safety and comfort while also increasing energy efficiency.

[0994] "Means for detecting traffic volume in real time" refers to sensors or devices for instantly grasping the volume of traffic flow.

[0995] The "detection means for detecting the ambient brightness in real time" is a sensor or device for instantly measuring the brightness of the surrounding environment.

[0996] "Emotion recognition means for recognizing the user's emotional state in real time" refers to sensors or engines that can instantly identify the user's emotions from their facial expressions and behavior.

[0997] The "control means for receiving and analyzing data from the detection means and emotion recognition means and controlling the lighting status" is a device that receives data on traffic volume, ambient brightness, and the user's emotional state, analyzes this data, and adjusts the lighting on / off and brightness.

[0998] "Lighting means for automatically adjusting the on / off and brightness of lighting by the control means" refers to a device for automatically adjusting the lighting state and brightness in accordance with instructions from the control means.

[0999] This invention is a lighting control system that includes multiple sensors that detect traffic volume, ambient brightness, and the user's emotional state in real time, and control means that analyzes this data to automatically turn lights on and off and adjust brightness.

[1000] System configuration

[1001] Hardware:

[1002] Traffic volume detection sensor: A sensor that detects the volume of traffic flow in real time, allowing you to instantly grasp how much traffic is present.

[1003] Brightness detection sensor: This sensor detects the brightness of the surrounding environment in real time, enabling the lighting to be adjusted instantly to match the brightness of the outside world.

[1004] Emotion recognition engine: A device that recognizes the user's emotional state in real time from their facial expressions and movements, enabling adjustments to improve the user's comfort.

[1005] software:

[1006] Control means (running on server): A software program that receives and analyzes traffic volume, lighting, and emotional state data. Implemented using Python and / or available APIs.

[1007] Lighting means: A device that turns lights on and off and adjusts brightness and color temperature according to instructions from the control means, thereby ensuring safety and comfort.

[1008] Data Acquisition and Calculation

[1009] The server receives real-time data from traffic sensors, brightness sensors, and emotion recognition engines, and analyzes this data to adjust lighting on / off, brightness, and color temperature based on the data.

[1010] Processing flow

[1011] 1. Data Acquisition:

[1012] Data is sent to the server from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. For example, if the traffic volume sensor detects 30, the ambient brightness sensor detects 200, and the user is in an anxious state, the server receives this data.

[1013] 2. Data Analysis:

[1014] Based on the received data, the system checks conditions such as whether traffic volume exceeds a threshold, whether the surrounding brightness is below a certain level, etc. At the same time, the user's emotional state is also analyzed.

[1015] 3. Adjust the lighting:

[1016] Based on set thresholds, for example, if there is heavy traffic and low brightness, or if the user feels uneasy, the server will turn on the lighting means and automatically adjust the brightness and color temperature as needed.

[1017] Examples and prompts

[1018] For example, if the traffic sensor detects a small number of vehicles in a park at night, the light sensor indicates a low reading, and the user is determined to be feeling anxious, the following process will occur:

[1019] The server receives traffic data (30), brightness data (200), and emotion data (anxiety).

[1020] Based on this data, the lights are turned on and further increased brightness is provided to reduce user anxiety.

[1021] Example prompt sentence:

[1022] "Design your lighting system to turn on lights when all traffic sensor readings are below 30 and light sensor readings are below 300, and brighten them further if there is a sense of unrest."

[1023] This allows the system to provide an optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, improving safety, ensuring user comfort, and increasing energy efficiency.

[1024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1025] Step 1: Get the data

[1026] The server receives real-time data from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. The input data includes traffic volume data, brightness data, and user emotion data. The data from these sensors is obtained through API and sent to the server.

[1027] Step 2: Data storage process

[1028] The server temporarily stores the received data and records it in a database for subsequent analysis. Specifically, it stores traffic volume data, brightness data, and emotion data in corresponding tables, allowing subsequent data analysis to be performed quickly.

[1029] Step 3: Analyze the data

[1030] The server analyzes the stored data to determine whether traffic volume is above a set threshold, whether lighting is below a set threshold, and the user's emotional state, using simple conditional and statistical methods.

[1031] Step 4: Conditional Decisions

[1032] The server determines the lighting operation based on the results of the data analysis. For example, if traffic volume exceeds a threshold and brightness is low, the lights will be turned on. If the emotion recognition engine determines that the user is in an anxious state, the lights will be increased in brightness. Processing at this stage is performed based on control logic.

[1033] Step 5: Lighting adjustment instructions

[1034] Based on the results of the processing, the server sends instructions to the lighting means to turn it on or off or adjust the brightness. Specifically, it generates a lighting control signal and sends that signal to the lighting device. This allows the lighting to be turned on or off appropriately and the brightness to be set according to the user's emotional state.

[1035] Step 6: Status check and feedback

[1036] The server checks the lighting status and receives feedback data from the lighting device. It verifies whether the lighting is operating correctly as instructed and sends control instructions again if there is a problem. This step also receives feedback from users and adjusts the system's operation.

[1037] Step 7: Logging and Reporting

[1038] The server stores all processing data and results in logs and generates periodic reports. This makes it easier to evaluate the system's operation history and performance. The logs include input data (traffic volume, brightness, emotional state), analysis results, control instructions, feedback data, etc. The log data is stored in a database and is backed up periodically.

[1039] Through these steps, the system can automatically provide the optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, thereby improving safety and optimizing energy efficiency.

[1040] 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.

[1041] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1042] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1043] [Fourth embodiment]

[1044] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1045] 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.

[1046] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1047] 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.

[1048] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1049] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1050] 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.

[1051] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1052] 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.

[1053] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1054] 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.

[1055] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1056] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1057] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, and a lighting means for automatically adjusting the on / off of lighting using the control means. This system aims to improve energy efficiency, reduce costs, and improve safety.

[1058] Explanation of program processing

[1059] 1. System initialization:

[1060] The server first initializes the system and registers the lights, traffic sensors, and brightness sensors.

[1061] The server creates an instance of the SmartLightingSystem class, initializing the dictionary that stores the lighting state and the list of traffic and brightness sensors.

[1062] The server adds multiple lights to the system, and these lights are all initially off.

[1063] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[1064] 2. Initial display:

[1065] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[1066] 3. Lighting adjustment cycle:

[1067] The server retrieves data from traffic sensors and light sensors at regular intervals and updates the lighting status.

[1068] The adjust_lighting method aggregates data from all traffic sensors and uses the results to determine traffic volume, and aggregates data from all light sensors to evaluate the ambient light intensity.

[1069] If the traffic volume is above a certain threshold or the ambient light is below a certain threshold, the server will turn the lights on, otherwise it will turn them off. This process is performed every cycle.

[1070] Specific examples

[1071] Night road

[1072] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[1073] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[1074] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[1075] The server evaluates the case where the traffic volume is 75 (30 + 25 + 20) and the average brightness is 150 ((200 + 150 + 100) / 3).

[1076] Under these conditions, the server turns on all lights because traffic volume is greater than 50 and brightness is less than or equal to 300.

[1077] Daytime intersection

[1078] During the day, if the traffic sensor detects high traffic volume and the light sensor indicates a high reading:

[1079] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[1080] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[1081] The server evaluates the case where the traffic volume is 155 (50 + 45 + 60) and the average brightness is 950.

[1082] Under these conditions, traffic volume is high but brightness is above 300, so the server turns off the lights.

[1083] This invention makes it possible to efficiently control lighting on roads and public spaces according to traffic volume and ambient brightness, which has the advantage of improving energy efficiency while also ensuring safety.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The server creates an instance of the SmartLightingSystem class, which initializes it with an empty dictionary to record lighting states, and a list of traffic and brightness sensors.

[1087] Step 2:

[1088] The server adds the lighting devices to the system. Specifically, it registers five lighting devices (e.g., light-0 to light-4) as off in the initial state.

[1089] Step 3:

[1090] The server adds traffic and light sensors to the system by registering three traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and three light sensors (e.g., light-sensor-0 to light-sensor-2).

[1091] Step 4:

[1092] The server calls the display_status method to display the initial state (on / off) of all lighting devices. Initially, all lights are on.

[1093] Step 5:

[1094] The server collects data from the traffic and light sensors at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[1095] Step 6:

[1096] The server executes the adjust_lighting method to turn on and off lighting devices based on the data from the sensors. If the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the corresponding lighting device is turned on. Otherwise, it is turned off.

[1097] Step 7:

[1098] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[1099] Step 8:

[1100] The server repeats the process from step 5 to step 7. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[1101] Example 1

[1102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1103] Conventional lighting control systems are unable to achieve efficient lighting control according to traffic volume and ambient brightness, making it difficult to improve energy efficiency and ensure safety. Furthermore, there are issues with operability due to insufficient functions such as system initialization, data analysis, and real-time display of lighting status.

[1104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1105] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off of the lighting by the control means, a means for the control means to read the initial settings of the system in an initialization step, register the sensors and lighting, and display the initial status, a means for acquiring and analyzing data from the sensors at regular time intervals, a means for adjusting the lighting status based on traffic volume and brightness data, and a means for displaying the lighting status, thereby enabling improved energy efficiency and ensuring safety.

[1106] "Sensor means for detecting traffic volume in real time" refers to a device that instantly measures the number of vehicles and pedestrians on the road and continuously collects that data.

[1107] "Sensor means for detecting ambient brightness in real time" is a device that instantly measures the external light environment and continuously collects that data.

[1108] "Control means for receiving and analyzing data from the sensor means and controlling the lighting status" refers to a system that receives and analyzes data sent from the sensor and manages the operation of the lighting device based on the results.

[1109] "Lighting means that automatically adjusts the on / off of lighting by the control means" refers to a device that automatically switches on and off lighting in accordance with instructions from the control means.

[1110] "Means for reading the initial settings of the system in the initialization step, registering sensors and lighting, and displaying the initial status" refers to a device that has the function of reading the setting information required when the system starts operating, registering each device in the system, and displaying the current status to the user.

[1111] "Means for acquiring and analyzing data from sensors at regular time intervals" refers to a device that collects data from sensors at a pre-set cycle and analyzes the data.

[1112] The "means for adjusting lighting conditions based on traffic volume and brightness data" refers to a system that automatically adjusts lighting conditions, such as turning the lights on and off, based on the acquired traffic volume and brightness data.

[1113] A "light status display means" is a device that visually displays the current light on / off status and other related information.

[1114] The lighting control system of the present invention detects traffic volume and ambient brightness in real time and automatically adjusts lighting based on that data. The purpose of this system is to improve energy efficiency and ensure safety. Specific components of the system include sensors that detect traffic volume, sensors that detect ambient brightness, control means that receive and analyze the data, and lighting means that turn lights on and off.

[1115] Hardware and software used

[1116] The system hardware includes the following elements:

[1117] Traffic sensors: These are installed on roads to detect traffic volume in real time. Specific product examples include ultrasonic sensors and camera-based traffic sensors.

[1118] Brightness sensor: Installed in outdoor spaces, it detects the intensity of ambient light in real time. Specific product examples include light sensors and lux meters.

[1119] Server: Receives and analyzes data and controls lighting. In addition to standard server hardware, a communication module and data analysis software are installed.

[1120] The software includes the following elements:

[1121] Sensor data collection module: A module that collects data from traffic sensors and brightness sensors.

[1122] Data analysis module: Analyzes collected data and evaluates traffic volume and ambient brightness.

[1123] Control program: Based on the evaluation results, issues instructions to turn the lights on or off.

[1124] Specific examples

[1125] Night road

[1126] At night, if the traffic sensor detects a small number of vehicles and the light sensor indicates a low reading:

[1127] 1. Data Acquisition:

[1128] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[1129] The server also gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[1130] 2. Data Analysis:

[1131] The server analyzes the acquired data and determines that the traffic volume is 75 (30 + 25 + 20).

[1132] Evaluate that the average ambient brightness is 150 ((200 + 150 + 100) / 3).

[1133] 3. Lighting control:

[1134] The server turns on all lights because traffic volume is above the threshold (50) and brightness is below the set threshold (300).

[1135] 4. Status display:

[1136] The server will display the state of the lights as they are turned on, for example, "Light 1: On, Light 2: On, Light 3: On".

[1137] Based on this example, this invention effectively controls lighting according to traffic volume and ambient brightness data, thereby improving energy efficiency and ensuring safety. This system can be used in a variety of environments, including roads and public spaces.

[1138] Example prompt:

[1139] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[1140] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[1141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1142] Step 1: Initialize the system

[1143] The server loads the initial system configuration, which includes the locations and settings of lights, traffic sensors, and brightness sensors.

[1144] Input: Initial configuration data from a configuration file or database.

[1145] Output: System initialization state with configuration data.

[1146] The server creates an instance of the SmartLightingSystem class and initializes a dictionary to store the lighting state and a list of traffic and brightness sensors.

[1147] Specific operation: The server registers all lights in the system as off and sets the location information of each sensor.

[1148] Step 2: Viewing the initial state

[1149] The server calls the display_status method to ensure that all lights are initially off and display their status.

[1150] Input: State data after system initialization.

[1151] Output: Screen showing the initial state.

[1152] Specific operation: The server displays the status of the lights on the terminal or web interface.

[1153] Step 3: Start collecting data

[1154] The server collects data from traffic sensors and light sensors at regular intervals.

[1155] Input: Communication protocol or API call from the sensor.

[1156] Output: Traffic and lighting data collected in real time.

[1157] Specific operation: The server calls the sensor's API to receive traffic volume and brightness data.

[1158] Step 4: Data analysis

[1159] The server analyzes the acquired data and evaluates traffic volume and ambient brightness.

[1160] Input: Raw data obtained from traffic and light sensors.

[1161] Output: The total traffic volume and average brightness values ​​as the analysis results.

[1162] Specific operation: The values ​​sent from each sensor are aggregated to calculate the total and average traffic volume and brightness.

[1163] Step 5: Implementing lighting control

[1164] Based on the analysis results, the server uses the adjust_lighting method to turn the lights on or off.

[1165] Input: Analysis results (traffic volume and brightness evaluation values).

[1166] Output: The state of the light (on or off).

[1167] Specific behavior: Turn on lights if traffic volume exceeds threshold and brightness is below a certain threshold, otherwise turn off.

[1168] Step 6: Status Updates and Display

[1169] The server updates the light status and displays the current status with the display_status method.

[1170] Input: Latest lighting conditions.

[1171] Output: Updated lighting status display.

[1172] Specific operation: The server records when a light is turned on or off in a database or log file, and displays the current status on the screen.

[1173] Example prompt sentence:

[1174] "Prompt sentence for simulating lighting control at an intersection with few vehicles at night"

[1175] "Prompt sentence for simulating lighting control at a busy intersection during the day"

[1176] (Application example 1)

[1177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1178] Current lighting control systems in industrial facilities often do not adequately consider energy efficiency or work environment safety. As a result, unnecessary energy consumption and reduced work efficiency due to inappropriate lighting conditions occur. In addition, the difficulty of real-time lighting adjustment according to traffic volume and ambient brightness means that they are forced to rely on fixed and inefficient lighting conditions.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1180] In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting the on / off state of the lighting by the control means, and a means for optimizing lighting in work spaces using data from the sensor means installed in industrial facilities. This enables efficient and flexible lighting control based on real-time data, and also optimizes energy consumption and provides a safe and efficient work environment.

[1181] "Sensor means for detecting traffic volume in real time" refers to equipment for detecting the movement of people and objects within an industrial facility in real time and collecting that data.

[1182] "Sensor means for detecting ambient brightness in real time" refers to equipment for detecting the amount of light and illuminance within an industrial facility in real time and collecting that data.

[1183] "Control means" refers to a device or system that receives and analyzes data from the aforementioned sensor means and controls the lighting status.

[1184] "Lighting means for automatically adjusting the on / off state of lighting" refers to a device or system that automatically adjusts the on / off state of lighting based on instructions from a control means.

[1185] "Means for optimizing lighting in work spaces using data from sensor means installed in industrial facilities" refers to equipment or systems that utilize data from traffic sensors and brightness sensors installed in industrial facilities to optimize lighting conditions in work spaces.

[1186] The lighting control system of the present invention aims to improve energy efficiency and optimize the working environment in industrial facilities. This system includes sensor means for detecting traffic volume in real time, sensor means for detecting ambient brightness in real time, control means for receiving and analyzing data from these sensors, and lighting means for controlling the lighting status.

[1187] The server first initializes the system and registers various sensors and lighting devices. Specifically, the system collects and analyzes real-time data from traffic sensors and brightness sensors installed in industrial facilities, and automatically adjusts lighting on and off based on the collected data. This provides an optimal lighting environment and minimizes energy consumption.

[1188] The hardware used includes traffic and brightness sensors installed within industrial facilities, a server for collecting and analyzing data from these sensors, and lighting devices. The software uses Python scripts to collect data from the sensors in real time and analyzes the data to control the lighting status. In particular, the server calculates traffic volume from traffic sensor data and evaluates ambient brightness from brightness sensor data. Based on this data, it executes logic to control lighting on and off.

[1189] For example, if the traffic sensor in a particular area of ​​an industrial facility reads 70 and the light sensor reads 250, the server can determine that traffic is above a certain threshold and light is below a certain threshold and turn on the lights in that area, thus providing the right lighting only where it is needed and using energy efficiently.

[1190] An example of a prompt is:

[1191] We will demonstrate a system that automatically controls lighting based on traffic volume and ambient light within a factory, collecting data in real time to demonstrate how it can optimize energy efficiency while providing a safe working environment.

[1192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1193] Step 1:

[1194] The server initializes the system. It registers traffic sensors, light sensors, and lighting devices. Specifically, it sets the initial state of lighting devices to off and records the sensor location information. The IDs and location information of the traffic sensors and light sensors are required as input, and the initial state of each sensor and lighting device in the system is set as output.

[1195] Step 2:

[1196] The server collects real-time data from traffic sensors and light sensors. The server periodically accesses these sensors to obtain the current traffic volume data and light volume data. As input, real-time data from the sensors is required, and as output, the latest data from each sensor is stored in the server.

[1197] Step 3:

[1198] The server analyzes the collected data and evaluates traffic volume and ambient brightness. It calculates the sum of traffic volume data and the average brightness data. As input, it requires traffic volume data and brightness data obtained from sensors, and as output, it obtains the sum of traffic volume and the average brightness.

[1199] Step 4:

[1200] The server executes the lighting control logic based on the pre-processed data. If the traffic volume is above a certain threshold and the brightness is below a certain threshold, it sets the light on; otherwise, it turns the light off. As input, it requires the total traffic volume and the average brightness, and as output, it determines the on / off state of the light.

[1201] Step 5:

[1202] The server sends an on or off command to the lighting device to actually change the state of the light. Specifically, it calls the lighting device's API to change the state. The input is the on / off state of the light, and the output is the change of the physical state of the light.

[1203] Step 6:

[1204] The server displays the current light status for the user or terminal to monitor the overall system status. The user can check the on / off status of the lights and manually change the settings if necessary. As input, it requires light status data from the server and as output, it provides user-visible status information.

[1205] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1206] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and controlling the lighting status, a lighting means for automatically adjusting lighting on / off, and an emotion engine for recognizing user emotions in real time. This system aims to achieve improved energy efficiency, cost reduction, improved safety, and user comfort.

[1207] Explanation of program processing

[1208] 1. System initialization:

[1209] The server first initializes the system and registers the lighting, traffic sensors, brightness sensors, and emotion engine.

[1210] The server creates an instance of the SmartLightingSystem class, which initializes the dictionary that stores the lighting status, the list of traffic sensors and the list of brightness sensors, and the emotion engine.

[1211] The server adds multiple lights to the system, and these lights are all initially off.

[1212] Traffic sensors and brightness sensors are added to the system, and the locations where each sensor is installed are set on the road and in the outdoor space.

[1213] 2. Initial display:

[1214] The server calls the display_status method to display the current state of each light (on or off). Initially, all lights are off.

[1215] 3. Lighting adjustment cycle:

[1216] The server obtains data from the traffic sensor, light sensor, and emotion engine at regular intervals. At this time, the server calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor.

[1217] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, body movements, etc. to determine the user's emotional state.

[1218] 4. Automatic lighting adjustment:

[1219] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and the emotion engine.

[1220] If the traffic sensor data is above a certain threshold (e.g. 50) or the light sensor data is below a certain threshold (e.g. 300), the server will turn the lights on, otherwise it will turn them off.

[1221] 5. Emotion-based regulation:

[1222] The server adjusts the brightness and color temperature of the lighting according to the user's emotion recognized by the emotion engine.

[1223] For example, if the user is determined to be anxious or stressed, the server may set the lighting to a brighter color temperature, whereas if the user is determined to be relaxed, the server may set the lighting to a warmer color temperature.

[1224] Specific examples

[1225] Night road

[1226] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[1227] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[1228] The server gets the light sensor data (e.g. light-sensor-0 is 200, light-sensor-1 is 150, light-sensor-2 is 100).

[1229] The server obtains data indicative of the user's anxiety state from the emotion engine.

[1230] Under this condition, the server will turn on all lights because the traffic volume is above 50 and the brightness is below 300. It will also increase the brightness of the lights to reduce user anxiety.

[1231] Park during the day

[1232] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[1233] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[1234] The server gets the light sensor data (e.g. light-sensor-0 is 900, light-sensor-1 is 950, light-sensor-2 is 1000).

[1235] The server obtains data indicating the user's relaxation state from the emotion engine.

[1236] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[1237] These examples demonstrate how the lighting control system of the present invention can improve energy efficiency and ensure safety and comfort. The system can realize a sustainable urban environment by optimally controlling lighting based on traffic volume, ambient brightness, and user emotions.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The server creates an instance of the SmartLightingSystem class, which initializes an empty dictionary for storing lighting status, a list of traffic and brightness sensors, and an emotion engine for recognizing user emotions.

[1241] Step 2:

[1242] The server registers the lighting devices in the system. Specifically, it sets five lighting devices (e.g., light-0 to light-4) to off in the initial state.

[1243] Step 3:

[1244] The server adds traffic sensors and light sensors to the system by registering traffic sensors (e.g., traffic-sensor-0 to traffic-sensor-2) and light sensors (e.g., light-sensor-0 to light-sensor-2) according to their respective roles.

[1245] Step 4:

[1246] The server calls the display_status method to display the initial state of all lighting devices. Notice that initially, all of these lights are off.

[1247] Step 5:

[1248] The server collects data from traffic sensors, light sensors, and the emotion engine at regular intervals.

[1249] The server calls the check_traffic method to get data (traffic volume) from the traffic sensor.

[1250] The server calls the check_light_level method to get data from the light sensor (ambient light intensity).

[1251] The server uses an emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[1252] Step 6:

[1253] The server executes the adjust_lighting method and controls the on / off of lighting means based on data from each sensor and emotion engine.

[1254] For example, if the traffic sensor data exceeds a certain threshold (e.g., 50), or if the light sensor data falls below a certain threshold (e.g., 300), the server will turn on the corresponding lighting means; otherwise, it will turn them off.

[1255] Step 7:

[1256] The server adjusts the brightness and color temperature of the lighting based on the user's emotions recognized by the emotion engine.

[1257] Specifically, if it is determined that the user is feeling anxious or stressed, the server sets the lighting to be brighter.

[1258] On the other hand, if it is determined that the user is relaxed, the server maintains the feeling of relaxation by changing the color temperature of the lighting to a warmer color.

[1259] Step 8:

[1260] The server will call the display_status method again to display the new status of the lighting device, allowing you to check the latest lighting status.

[1261] Step 9:

[1262] The server repeats the process from step 5 to step 8 at regular intervals. This iterative process appropriately controls the lighting of roads and public spaces in real time.

[1263] Example 2

[1264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1265] Conventional lighting control systems mainly control lighting on / off based on physical data such as traffic volume and ambient brightness, but because they are unable to take the user's emotional state into account, they have the problem of being unable to fully address issues such as improving energy efficiency and user comfort. Furthermore, to improve safety and comfort at night, it is necessary to fine-tune lighting adjustments based on the user's emotional state in addition to traffic volume and brightness.

[1266] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a means for recognizing a user's emotion in real time, a control means for receiving and analyzing data from the sensor means and the emotion recognition means and controlling the lighting state, a lighting means for automatically adjusting the lighting on / off, and a means for adjusting the lighting brightness and color temperature based on the user's emotional state. This enables lighting control that takes into account the user's emotional state in addition to traffic volume and ambient brightness, thereby achieving higher energy efficiency and user comfort and safety.

[1267] "Sensor means for detecting traffic volume in real time" refers to a device for detecting the number and movement of vehicles and pedestrians on a road in real time.

[1268] The "sensor means for detecting ambient brightness in real time" is a device for detecting brightness in an outdoor space or a specific area in real time.

[1269] The "means for recognizing a user's emotions in real time" refers to a device or software that analyzes the user's facial expressions, tone of voice, body movements, etc., and recognizes the user's emotional state in real time.

[1270] The "control means" refers to hardware and software for receiving and analyzing data from the sensor means and emotion recognition means, and controlling the lighting status based on the results.

[1271] The "lighting means" is a device for turning on and off the lighting and adjusting the brightness and color temperature based on instructions from the control means.

[1272] The "means for adjusting the brightness and color temperature of lighting" refers to a device or software for appropriately adjusting the brightness and color temperature of lighting based on the emotional state of the user.

[1273] The lighting control system of the present invention comprises a sensor means for detecting traffic volume in real time, a sensor means for detecting ambient brightness in real time, a control means for receiving and analyzing data from the sensor means and data from a means for recognizing user emotions in real time and controlling the lighting status, a lighting means for automatically adjusting the on / off of lighting, and a means for adjusting the brightness and color temperature of lighting based on the user's emotional state.

[1274] System initialization

[1275] The server first initializes the system. It creates an instance of SmartLightingSystem and initializes the dictionary that stores the lighting status and the sensor list. During this initialization process, the server adds multiple lights to the system, all of which are initially turned off. It adds traffic sensors and brightness sensors to the system and sets the locations of the sensors to roads and outdoor spaces. It also creates an instance of the emotion engine and registers it in the system.

[1276] Displaying the initial state

[1277] The server calls the display_status method to display the initial state of each light. You can see that all lights are initially off.

[1278] Lighting Adjustment Cycle

[1279] The server collects data from the traffic sensor, light sensor, and emotion engine at regular intervals. It calls the check_traffic and check_light_level methods to obtain random values ​​from each sensor. It also uses the emotion engine to analyze the user's facial expressions, tone of voice, and body movements to determine the user's emotional state.

[1280] Automatic lighting adjustment

[1281] The server executes the adjust_lighting method to control the on / off of lighting means based on data from sensors and emotion engines. For example, if the traffic sensor data exceeds a certain threshold (e.g., 50) or the brightness sensor data falls below a certain threshold (e.g., 300), the server turns on the lights; otherwise, it turns them off.

[1282] Emotion-Based Adjustment

[1283] The server adjusts the brightness and color temperature of the lighting according to the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling anxious or stressed, it will set the lighting brighter. On the other hand, if the user is determined to be relaxed, it will set the lighting to a warmer color temperature.

[1284] Specific examples

[1285] Night road

[1286] At night, if the traffic sensor detects a small number of vehicles, the light sensor indicates a low reading, and the emotion engine determines that the user is feeling anxious:

[1287] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 30, traffic-sensor-1 is 25, traffic-sensor-2 is 20).

[1288] The server obtains the light sensor data (e.g., light-sensor-0 is 200, light-sensor-1 is 150, and light-sensor-2 is 100).

[1289] The server obtains data indicative of the user's anxiety state from the emotion engine.

[1290] Under these conditions, the server turns on all the lights because the traffic volume is above 50 and the brightness is below 300. It also increases the brightness of the lights to reduce the user's anxiety.

[1291] Park during the day

[1292] During the day, if the traffic sensor detects high traffic volume, the light sensor indicates a high reading, and the emotion engine determines that the user is relaxed:

[1293] The server gets the traffic sensor data (e.g. traffic-sensor-0 is 50, traffic-sensor-1 is 45, traffic-sensor-2 is 60).

[1294] The server obtains the light sensor data (e.g., light-sensor-0 is 900, light-sensor-1 is 950, and light-sensor-2 is 1000).

[1295] The server obtains data indicating the user's relaxation state from the emotion engine.

[1296] Under these conditions, the server will turn off the lights because traffic volume is high but the brightness is above 300. It will also adjust the color temperature of the lights as needed to maintain a relaxed feeling for the user.

[1297] Example prompts to input to the generative AI model

[1298] "Please explain how a lighting control system with an emotion engine works on roads at night. Specifically, please explain in detail how it adjusts lighting based on traffic sensors, brightness sensors, and emotion recognition results, including specific data examples."

[1299] As described above, the lighting control system of the present invention controls lighting based on the user's emotional state in addition to traffic volume and ambient brightness, thereby improving energy efficiency and user comfort and safety.

[1300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1301] Step 1:

[1302] System initialization

[1303] Input: System startup command

[1304] Specific processing: The server creates an instance of SmartLightingSystem to initialize the system. At this time, it creates an internal dictionary, initializes the traffic sensor list and the light sensor list, and also creates an instance of the emotion engine.

[1305] Output: Initialization complete message

[1306] Specific operation: The server outputs a log message saying "Initialization is starting" and sets all lights in the system to off by default. It adds traffic sensors and light sensors to a list and records their positions. When initialization is complete, it outputs a log message saying "System initialization completed."

[1307] Step 2:

[1308] Displaying the initial state

[1309] Input: Initialization complete message

[1310] Specific processing: The server calls the display_status method to display the initial status of the lighting.

[1311] Output: Initial lighting state

[1312] Specific operation: The server outputs the light status to the console as "Light 1: Off, Light 2: Off, Light 3: Off", and leaves a log stating "The initial light settings have been displayed."

[1313] Step 3:

[1314] Data Acquisition Cycle

[1315] Input: Timer event

[1316] Specific processing: The server calls the check_traffic and check_light_level methods at regular intervals to obtain data from the traffic and light sensors, and uses the emotion engine to analyze the user's emotional state.

[1317] Output: Traffic data, brightness data, emotion data

[1318] Specific operation: The server logs the data from each sensor, such as "Data obtained from traffic sensor" and "Data obtained from brightness sensor." When the emotion engine outputs the analysis results, it leaves a log stating "User's emotional state obtained from emotion engine."

[1319] Step 4:

[1320] Automatic lighting adjustment

[1321] Input: Traffic data, brightness data

[1322] Specific processing: The server executes the adjust_lighting method to turn the lights on or off based on the data from the sensor.

[1323] Output: Lighting status

[1324] Specific operation: If the traffic volume exceeds 50 or the brightness is below 300, the server will log "Turn on all lights" and turn on the lights. In the opposite case, the server will log "Turn off all lights" and turn off the lights.

[1325] Step 5:

[1326] Emotion-Based Adjustment

[1327] Input: Emotion data

[1328] Specific processing: Based on the data from the emotion engine, the server adjusts the brightness and color temperature of the lighting according to the user's emotional state.

[1329] Output: Adjusted lighting conditions

[1330] Specific behavior: If the user is feeling anxious or stressed, the server will log "brighten the lighting to reduce the user's anxiety" and increase the brightness. If the user is relaxed, the server will log "change the lighting to warmer colors to maintain the user's sense of relaxation" and change the color temperature to warmer colors.

[1331] (Application example 2)

[1332] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] Current lighting control systems can only control lighting based on traffic volume and ambient brightness, and are unable to adjust lighting based on the emotional state of the user, resulting in insufficient improvements in comfort and safety. Furthermore, further improvements in energy efficiency are needed.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a detection means for detecting traffic volume in real time, a detection means for detecting ambient brightness in real time, an emotion recognition means for recognizing the emotional state of a user in real time, a control means for receiving and analyzing data from the detection means and the emotion recognition means and controlling the lighting state, and a lighting means for automatically turning the lighting on and off and adjusting the brightness by the control means. This makes it possible to provide an optimal lighting environment based on traffic volume, ambient brightness, and the emotional state of the user, improving safety and comfort while also increasing energy efficiency.

[1335] "Means for detecting traffic volume in real time" refers to sensors or devices for instantly grasping the volume of traffic flow.

[1336] The "detection means for detecting the ambient brightness in real time" is a sensor or device for instantly measuring the brightness of the surrounding environment.

[1337] "Emotion recognition means for recognizing the user's emotional state in real time" refers to sensors or engines that can instantly identify the user's emotions from their facial expressions and behavior.

[1338] The "control means for receiving and analyzing data from the detection means and emotion recognition means and controlling the lighting status" is a device that receives data on traffic volume, ambient brightness, and the user's emotional state, analyzes this data, and adjusts the lighting on / off and brightness.

[1339] "Lighting means for automatically adjusting the on / off and brightness of lighting by the control means" refers to a device for automatically adjusting the lighting state and brightness in accordance with instructions from the control means.

[1340] This invention is a lighting control system that includes multiple sensors that detect traffic volume, ambient brightness, and the user's emotional state in real time, and control means that analyzes this data to automatically turn lights on and off and adjust brightness.

[1341] System configuration

[1342] Hardware:

[1343] Traffic volume detection sensor: A sensor that detects the volume of traffic flow in real time, allowing you to instantly grasp how much traffic is present.

[1344] Brightness detection sensor: This sensor detects the brightness of the surrounding environment in real time, enabling the lighting to be adjusted instantly to match the brightness of the outside world.

[1345] Emotion recognition engine: A device that recognizes the user's emotional state in real time from their facial expressions and movements, enabling adjustments to improve the user's comfort.

[1346] software:

[1347] Control means (running on server): A software program that receives and analyzes traffic volume, lighting, and emotional state data. Implemented using Python and / or available APIs.

[1348] Lighting means: A device that turns lights on and off and adjusts brightness and color temperature according to instructions from the control means, thereby ensuring safety and comfort.

[1349] Data Acquisition and Calculation

[1350] The server receives real-time data from traffic sensors, brightness sensors, and emotion recognition engines, and analyzes this data to adjust lighting on / off, brightness, and color temperature based on the data.

[1351] Processing flow

[1352] 1. Data Acquisition:

[1353] Data is sent to the server from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. For example, if the traffic volume sensor detects 30, the ambient brightness sensor detects 200, and the user is in an anxious state, the server receives this data.

[1354] 2. Data Analysis:

[1355] Based on the received data, the system checks conditions such as whether traffic volume exceeds a threshold, whether the surrounding brightness is below a certain level, etc. At the same time, the user's emotional state is also analyzed.

[1356] 3. Adjust the lighting:

[1357] Based on set thresholds, for example, if there is heavy traffic and low brightness, or if the user feels uneasy, the server will turn on the lighting means and automatically adjust the brightness and color temperature as needed.

[1358] Examples and prompts

[1359] For example, if the traffic sensor detects a small number of vehicles in a park at night, the light sensor indicates a low reading, and the user is determined to be feeling anxious, the following process will occur:

[1360] The server receives traffic data (30), brightness data (200), and emotion data (anxiety).

[1361] Based on this data, the lights are turned on and further increased brightness is provided to reduce user anxiety.

[1362] Example prompt sentence:

[1363] "Design your lighting system to turn on lights when all traffic sensor readings are below 30 and light sensor readings are below 300, and brighten them further if there is a sense of unrest."

[1364] This allows the system to provide an optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, improving safety, ensuring user comfort, and increasing energy efficiency.

[1365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1366] Step 1: Get the data

[1367] The server receives real-time data from the traffic volume detection sensor, the brightness detection sensor, and the emotion recognition engine. The input data includes traffic volume data, brightness data, and user emotion data. The data from these sensors is obtained through API and sent to the server.

[1368] Step 2: Data storage process

[1369] The server temporarily stores the received data and records it in a database for subsequent analysis. Specifically, it stores traffic volume data, brightness data, and emotion data in corresponding tables, allowing subsequent data analysis to be performed quickly.

[1370] Step 3: Analyze the data

[1371] The server analyzes the stored data to determine whether traffic volume is above a set threshold, whether lighting is below a set threshold, and the user's emotional state, using simple conditional and statistical methods.

[1372] Step 4: Conditional Decisions

[1373] The server determines the lighting operation based on the results of the data analysis. For example, if traffic volume exceeds a threshold and brightness is low, the lights will be turned on. If the emotion recognition engine determines that the user is in an anxious state, the lights will be increased in brightness. Processing at this stage is performed based on control logic.

[1374] Step 5: Lighting adjustment instructions

[1375] Based on the results of the processing, the server sends instructions to the lighting means to turn it on or off or adjust the brightness. Specifically, it generates a lighting control signal and sends that signal to the lighting device. This allows the lighting to be turned on or off appropriately and the brightness to be set according to the user's emotional state.

[1376] Step 6: Status check and feedback

[1377] The server checks the lighting status and receives feedback data from the lighting device. It verifies whether the lighting is operating correctly as instructed and sends control instructions again if there is a problem. This step also receives feedback from users and adjusts the system's operation.

[1378] Step 7: Logging and Reporting

[1379] The server stores all processing data and results in logs and generates periodic reports. This makes it easier to evaluate the system's operation history and performance. The logs include input data (traffic volume, brightness, emotional state), analysis results, control instructions, feedback data, etc. The log data is stored in a database and is backed up periodically.

[1380] Through these steps, the system can automatically provide the optimal lighting environment based on traffic volume, ambient brightness, and the user's emotional state, thereby improving safety and optimizing energy efficiency.

[1381] 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.

[1382] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1383] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1384] 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.

[1385] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1386] 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.

[1387] 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).

[1388] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1389] 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."

[1390] 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.

[1391] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1392] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1397] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] The following is further disclosed regarding the above embodiment.

[1403] (Claim 1)

[1404] a sensor means for detecting traffic volume in real time;

[1405] a sensor means for detecting ambient brightness in real time;

[1406] a control means for receiving and analyzing data from the sensor means and controlling the state of the lighting;

[1407] a lighting means for automatically adjusting the on / off of the lighting by the control means;

[1408] A system including:

[1409] (Claim 2)

[1410] a sensor means for detecting the traffic volume in real time is installed on the road;

[1411] A sensor means for detecting the ambient brightness in real time is installed in an outdoor space.

[1412] 10. The system of claim 1.

[1413] (Claim 3)

[1414] the control means includes logic for turning on lighting means when traffic volume exceeds a certain threshold and when ambient light falls below a specific threshold;

[1415] 10. The system of claim 1.

[1416] "Example 1"

[1417] (Claim 1)

[1418] a sensor means for detecting traffic volume in real time;

[1419] a sensor means for detecting ambient brightness in real time;

[1420] a control means for receiving and analyzing data from the sensor means and controlling the state of the lighting;

[1421] a lighting means for automatically adjusting the on / off of the lighting by the control means;

[1422] a means for reading an initial setting of the system, registering sensors and lighting, and displaying an initial state in an initialization step by the control means;

[1423] A means for acquiring and analyzing data from the sensor at regular time intervals;

[1424] a means for adjusting lighting conditions based on traffic and brightness data;

[1425] means for indicating the status of the lighting;

[1426] A system including:

[1427] (Claim 2)

[1428] a sensor means for detecting the traffic volume in real time is installed on the road;

[1429] 2. The system according to claim 1, wherein the sensor means for detecting the ambient brightness in real time is installed in an outdoor space.

[1430] (Claim 3)

[1431] 10. The system of claim 1, wherein the control means includes logic to turn on the lighting means when traffic volume exceeds a certain threshold and when ambient light falls below a particular threshold.

[1432] "Application Example 1"

[1433] (Claim 1)

[1434] a sensor means for detecting traffic volume in real time;

[1435] a sensor means for detecting ambient brightness in real time;

[1436] a control means for receiving and analyzing data from the sensor means and controlling the state of the lighting;

[1437] a lighting means for automatically adjusting the on / off of the lighting by the control means;

[1438] and means for optimizing lighting of work spaces using data from said sensor means installed in the industrial facility.

[1439] (Claim 2)

[1440] a sensor means for detecting the traffic volume in real time is installed in the industrial facility;

[1441] 2. The system according to claim 1, wherein a sensor means for detecting the ambient brightness in real time is installed in the industrial facility.

[1442] (Claim 3)

[1443] 10. The system of claim 1, wherein the control means includes logic to turn on lighting means when traffic volume exceeds a certain threshold and when ambient lighting falls below a particular threshold.

[1444] "Example 2: Combining Emotion Engines"

[1445] (Claim 1)

[1446] a sensor means for detecting traffic volume in real time;

[1447] a sensor means for detecting ambient brightness in real time;

[1448] a control means for receiving and analyzing data from the sensor means and data from the means for recognizing user emotions in real time, and controlling the state of the lighting;

[1449] a lighting means for automatically adjusting the on / off of the lighting by the control means;

[1450] means for adjusting the brightness and color temperature of the lighting based on the emotional state of the user;

[1451] A system including:

[1452] (Claim 2)

[1453] 2. The system according to claim 1, wherein the sensor means for detecting the traffic volume in real time is installed on a road, and the sensor means for detecting the ambient brightness in real time is installed in an outdoor space.

[1454] (Claim 3)

[1455] 10. The system of claim 1, wherein the control means includes logic to turn on the lighting means when traffic volume exceeds a certain threshold and when ambient light falls below a certain threshold, and further includes logic to adjust the brightness and color temperature of the lighting depending on the emotional state of the user.

[1456] "Application example 2 when combining emotion engines"

[1457] (Claim 1)

[1458] a detection means for detecting traffic volume in real time;

[1459] a detection means for detecting the ambient brightness in real time;

[1460] an emotion recognition means for recognizing the user's emotional state in real time;

[1461] a control means for receiving and analyzing data from the detection means and the emotion recognition means, and controlling the state of lighting;

[1462] a lighting means for automatically turning on and off the lighting and adjusting the brightness by the control means;

[1463] A system including:

[1464] (Claim 2)

[1465] a detection means for detecting the traffic volume in real time is installed in a public space;

[1466] The detection means for detecting the ambient brightness in real time is installed in an outdoor space.

[1467] 10. The system of claim 1.

[1468] (Claim 3)

[1469] the control means turns on the lighting means when traffic volume exceeds a certain threshold and when ambient brightness falls below a specific threshold;

[1470] comprising logic for adjusting the brightness and color temperature of the lighting means based on the emotional state of the user;

[1471] 10. The system of claim 1. [Explanation of symbols]

[1472] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a sensor means for detecting traffic volume in real time; a sensor means for detecting ambient brightness in real time; a control means for receiving and analyzing data from the sensor means and controlling the state of the lighting; a lighting means for automatically adjusting the on / off of the lighting by the control means; A system including:

2. a sensor means for detecting the traffic volume in real time is installed on the road; A sensor means for detecting the ambient brightness in real time is installed in an outdoor space. The system of claim 1 .

3. the control means includes logic for turning on lighting means when traffic volume exceeds a certain threshold and when ambient light falls below a specific threshold; The system of claim 1 .

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