system

Abstract

The system according to this embodiment aims to reduce false alarms and enable a rapid and effective fire response. [Solution] The system according to the embodiment comprises a detection unit, an identification unit, a notification unit, a guidance unit, and a lighting unit. The detection unit detects false alarms. The identification unit identifies the fire based on the information detected by the detection unit. The notification unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The notification unit notifies the administrator based on the information reported by the notification unit. The guidance unit automatically announces appropriate evacuation routes based on the information notified by the notification unit. The lighting unit turns on evacuation guidance lights based on the evacuation routes guided by the guidance unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there are many false alarms in fire detectors and the response in an emergency is not efficient.

[0005] The system according to the embodiment aims to reduce false alarms and realize a quick and effective fire response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, an identification unit, a reporting unit, a notification unit, a guidance unit, and a lighting unit. The detection unit detects false alarms. The identification unit identifies the fire based on the information detected by the detection unit. The reporting unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The notification unit notifies the administrator based on the information reported by the reporting unit. The guidance unit automatically announces appropriate evacuation routes based on the information notified by the notification unit. The lighting unit illuminates evacuation guidance lights based on the evacuation routes guided by the guidance unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce false alarms and enable a rapid and effective fire response. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Smart Fire Safe AI Agent System according to an embodiment of the present invention is a system that optimizes fire alarm and response in large commercial facilities. This system utilizes AI technology to optimize fire alarm and response, reduce false alarms, and achieve a rapid and effective fire response. The Smart Fire Safe AI Agent System comprises a detection unit for detecting false alarms, an identification unit for identifying fires, a notification unit for automatically notifying the fire department, a notification unit for notifying administrators, a guidance unit for automatically announcing optimal evacuation routes, and a lighting unit for illuminating evacuation guidance lights. For example, the Smart Fire Safe AI Agent System distinguishes between cigarette smoke, cooking smoke, and fires, suppressing the occurrence of false alarms. This reduces unnecessary evacuations and dispatches due to false alarms. Next, the Smart Fire Safe AI Agent System analyzes the location and scale of the fire and automatically notifies the fire department and administrators. This enables a rapid and appropriate response. Furthermore, the Smart Fire Safe AI Agent System automatically announces optimal evacuation routes at each location, supporting safe evacuation. This improves the efficiency of life-saving efforts in emergencies. Furthermore, the Smart FireSafe AI Agent System detects malfunctions and sensor degradation and directs appropriate maintenance. This maintains system reliability and enables long-term operation. Finally, the Smart FireSafe AI Agent System illuminates evacuation guidance lights to support evacuation. This provides visual guidance and improves evacuation safety. As a result, the Smart FireSafe AI Agent System reduces false alarms and enables a rapid and effective fire response.

[0029] The Smart Fire Safe AI Agent System according to this embodiment comprises a detection unit, an identification unit, a notification unit, a guidance unit, and a lighting unit. The detection unit detects false alarms. The detection unit distinguishes between cigarette or cooking smoke and fire, for example, using AI. For example, the detection unit can perform a component analysis of the smoke to distinguish between cigarette or cooking smoke and fire smoke. The detection unit can also measure the concentration and temperature of the smoke to detect the occurrence of a fire. Furthermore, the detection unit can analyze the movement of the smoke to understand the progress of the fire. The identification unit identifies the fire based on the information detected by the detection unit. The identification unit analyzes the location and scale of the fire, for example, using AI. For example, the identification unit can identify the location of the fire and evaluate its scale. Furthermore, the identification unit can analyze the rate of fire progression to predict the spread of the fire. Furthermore, the identification unit can identify the source of the fire and analyze its cause. The notification unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The reporting unit, for example, uses AI to analyze fire information and generate appropriate reporting content. For example, the reporting unit can generate reporting content that includes information such as the location, scale, and progression of the fire. The reporting unit can also update the reporting content in real time to provide the latest information. Furthermore, the reporting unit can customize the reporting content to provide reports tailored to the scale and progression of the fire. The notification unit notifies administrators based on the information reported by the reporting unit. The notification unit, for example, uses AI to analyze the reporting content and generate appropriate notification content. For example, the notification unit can generate notification content that includes information such as the location, scale, and progression of the fire. Furthermore, the notification unit can update the notification content in real time to provide the latest information. Furthermore, the notification unit can customize the notification content to provide notifications tailored to the administrator's needs. The guidance unit automatically announces the optimal evacuation route based on the information notified by the notification unit. The guidance unit, for example, uses AI to analyze fire information and generate appropriate evacuation routes. For example, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progression of the fire. Furthermore, the information desk can update evacuation routes in real time, providing the latest information.Furthermore, the guidance unit can customize evacuation routes and provide guidance tailored to the needs of evacuees. The lighting unit illuminates evacuation guidance lights based on the evacuation routes guided by the guidance unit. The lighting unit generates lighting patterns for evacuation guidance lights using AI, for example. For example, the lighting unit can generate an optimal lighting pattern based on information such as the location, scale, and progress of the fire. The lighting unit can also update the lighting pattern in real time to provide the latest information. In addition, the lighting unit can customize the lighting pattern and illuminate the lights according to the needs of evacuees. As a result, the Smart Fire Safe AI Agent System according to this embodiment can reduce false alarms and achieve a rapid and effective fire response.

[0030] The detection unit detects false alarms. For example, the detection unit uses AI to distinguish between cigarette or cooking smoke and fire smoke. Specifically, the AI ​​performs smoke component analysis to identify cigarette or cooking smoke from fire smoke. Smoke component analysis includes techniques that measure the chemical composition and particle size of the smoke using gas sensors and optical sensors. This allows for the detection of specific chemical substances in cigarette smoke and oils in cooking smoke, and their distinction from combustion products in fire smoke. The detection unit can also measure the smoke concentration and temperature to detect the occurrence of a fire. Smoke concentration is measured using light scattering or absorption methods, and temperature is measured using thermal cameras or infrared sensors. Furthermore, the detection unit can analyze the movement of smoke to understand the progression of a fire. Fluid dynamics models and image analysis techniques are used to analyze smoke movement and predict the diffusion speed and direction of the smoke. As a result, the detection unit enables early fire detection and reduction of false alarms, allowing for a rapid response.

[0031] The identification unit identifies the fire based on information detected by the detection unit. For example, the identification unit uses AI to analyze the location and scale of the fire. Specifically, the AI ​​integrates data on smoke composition, concentration, temperature, and movement provided by the detection unit to identify the location of the fire. To identify the location of the fire, an algorithm is used that utilizes building layout information and historical fire data to quickly identify the source of the fire. The identification unit can also evaluate the scale of the fire. Parameters such as the burning area, smoke concentration, and temperature rise rate are considered when evaluating the scale of the fire. Furthermore, the identification unit can analyze the rate of fire progression and predict the spread of the fire. Factors such as combustion characteristics, building structure, and wind direction are considered when analyzing the rate of fire progression, and a model is used to predict the extent and duration of fire spread. As a result, the identification unit can provide detailed information about the fire and support a quick and appropriate response.

[0032] The reporting unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The reporting unit can, for example, use AI to analyze fire information and generate appropriate reporting content. Specifically, the reporting unit can generate reporting content that includes information such as the location, scale, and progress of the fire. The reporting content includes detailed information such as the time the fire started, the building address, the floor on which the fire started, the scale of the fire, the rate of progression, and the need for evacuation. The reporting unit can also update the reporting content in real time to provide the latest information. For example, if the progress of the fire changes, the reporting unit will immediately incorporate the new information and update the reporting content. Furthermore, the reporting unit can customize the reporting content and make reports according to the scale and progress of the fire. This allows the reporting unit to provide the fire department with quick and accurate information and support effective fire response.

[0033] The notification unit notifies administrators based on information reported by the reporting unit. The notification unit can, for example, use AI to analyze the reported information and generate appropriate notifications. Specifically, the notification unit can generate notifications that include information such as the location, scale, and progress of a fire. These notifications may include detailed information such as the time the fire started, the building address, the floor on which the fire occurred, the scale of the fire, its rate of progression, and the need for evacuation. The notification unit can also update notifications in real time to provide the latest information. For example, if the progress of the fire changes, the notification unit immediately incorporates the new information and updates the notification. Furthermore, the notification unit can customize notifications to meet the administrator's needs. For example, if an administrator wants to receive specific information preferentially, that information can be highlighted in the notification. This allows the notification unit to provide administrators with quick and accurate information, supporting effective fire response.

[0034] The guidance unit automatically announces the optimal evacuation route based on information notified by the notification unit. For example, the guidance unit uses AI to analyze fire information and generate appropriate evacuation routes. Specifically, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progression of the fire. The generation of the evacuation route takes into account building layout information, information on obstacles in the evacuation route, and the location information of evacuees. The guidance unit can also update the evacuation route in real time and provide the latest information. For example, if the progression of the fire changes, the guidance unit will immediately take in the new information and update the evacuation route. Furthermore, the guidance unit can customize the evacuation route and provide guidance according to the needs of evacuees. For example, if an evacuee has physical limitations, it can provide an evacuation route that takes those limitations into account. In this way, the guidance unit can provide evacuees with a quick and accurate evacuation route and support safe evacuation.

[0035] The lighting unit illuminates evacuation guidance lights based on the evacuation routes guided by the guidance unit. The lighting unit generates illumination patterns for the evacuation guidance lights, for example, using AI. Specifically, the lighting unit can generate the optimal illumination pattern based on information such as the location, scale, and progression of a fire. The generation of the illumination pattern takes into account information such as obstacles in the evacuation route, the location of evacuees, and the length of the evacuation route. The lighting unit can also update the illumination pattern in real time to provide the latest information. For example, if the progression of the fire changes, the lighting unit will immediately take in the new information and update the illumination pattern. Furthermore, the lighting unit can customize the illumination pattern to meet the needs of evacuees. For example, if an evacuee has visual limitations, the lighting unit can provide an illumination pattern that takes those limitations into account. This allows the lighting unit to provide evacuees with quick and accurate evacuation guidance and support safe evacuation.

[0036] The detection unit can distinguish between cigarette or cooking smoke and fire smoke. For example, the detection unit uses AI to analyze the components of the smoke and identify cigarette or cooking smoke from fire smoke. For example, the detection unit can analyze the components of the smoke and detect specific chemical substances contained in cigarette or cooking smoke. The detection unit can also measure the concentration and temperature of the smoke to detect the occurrence of a fire. For example, the detection unit can detect the occurrence of a fire when the smoke concentration exceeds a certain threshold. Furthermore, the detection unit can analyze the movement of the smoke and understand the progression of the fire. For example, the detection unit can analyze the pattern of smoke movement and determine the direction and speed of fire progression. This can suppress the occurrence of false alarms. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the smoke component analysis into AI, and the AI ​​can analyze the components of the smoke and distinguish between cigarette or cooking smoke and fire smoke.

[0037] The identification unit can analyze the location and scale of a fire. For example, the identification unit can use AI to pinpoint the location of a fire and evaluate its scale. For example, to pinpoint the location of a fire, the identification unit can analyze the distance and direction from the source of the fire. The identification unit can also analyze the rate of fire progression and the extent of burning to evaluate the scale of the fire. For example, the identification unit can monitor the rate of fire progression in real time and predict the spread of the fire. Furthermore, the identification unit can pinpoint the source of the fire and analyze its cause. For example, to pinpoint the source of the fire, the identification unit can analyze environmental data around the location of the fire. This allows for accurate pinpointing of the location and scale of the fire. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input data for pinpointing the location of a fire into an AI, which can then analyze the data to pinpoint the location of the fire.

[0038] The reporting unit can automatically notify the fire department. The reporting unit can, for example, use AI to analyze fire information and generate appropriate reporting content. For example, the reporting unit can generate reporting content that includes information such as the location, scale, and progress of the fire. The reporting unit can also update the reporting content in real time to provide the latest information. For example, the reporting unit can update the reporting content according to the progress of the fire and provide the fire department with the latest information. Furthermore, the reporting unit can customize the reporting content and make reports according to the scale and progress of the fire. For example, if the fire is large, the reporting unit can generate detailed reporting content to encourage a quick response. This enables prompt notification to the fire department. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire information into AI, and the AI ​​can analyze the information and generate reporting content.

[0039] The notification unit can notify administrators. The notification unit can, for example, use AI to analyze the reported content and generate appropriate notification content. For example, the notification unit can generate notification content that includes information such as the location, scale, and progress of the fire. The notification unit can also update the notification content in real time to provide the latest information. For example, the notification unit can update the notification content according to the progress of the fire to provide administrators with the latest information. Furthermore, the notification unit can customize the notification content to provide notifications that meet the administrator's needs. For example, the notification unit can prioritize notifying administrators of the information they need, encouraging a quick response. This enables rapid notification to administrators. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the reported content into AI, which can then analyze the content and generate notification content.

[0040] The guidance unit can automatically announce the optimal evacuation route. For example, the guidance unit can use AI to analyze fire information and generate an appropriate evacuation route. For example, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progress of the fire. The guidance unit can also update the evacuation route in real time and provide the latest information. For example, the guidance unit can update the evacuation route according to the progress of the fire and provide evacuees with the latest information. Furthermore, the guidance unit can customize the evacuation route and provide guidance according to the needs of evacuees. For example, the guidance unit can provide the optimal evacuation route based on the location information of evacuees and support rapid evacuation. This can support safe evacuation. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input fire information into AI, and the AI ​​can analyze the information and generate an evacuation route.

[0041] The lighting unit can turn on evacuation guidance lights. The lighting unit can generate lighting patterns for evacuation guidance lights using, for example, AI. For example, the lighting unit can generate an optimal lighting pattern based on information such as the location, scale, and progression of a fire. The lighting unit can also update the lighting pattern in real time to provide the latest information. For example, the lighting unit can update the lighting pattern according to the progression of the fire to provide evacuees with the latest information. Furthermore, the lighting unit can customize the lighting pattern to provide lighting according to the needs of evacuees. For example, the lighting unit can provide an optimal lighting pattern based on the location information of evacuees to support rapid evacuation. This enables visual guidance of evacuation. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input fire information into AI, and the AI ​​can analyze the information to generate a lighting pattern.

[0042] The detection unit can detect malfunctions and sensor degradation. For example, the detection unit can use AI to monitor the sensor's status and detect malfunctions or degradation. For example, the detection unit can analyze the sensor's output data and detect abnormal patterns. The detection unit can also monitor the sensor's operating status in real time and detect abnormalities early. For example, the detection unit can monitor the sensor's response time and output fluctuations and detect abnormalities. Furthermore, the detection unit can predict sensor degradation and instruct appropriate maintenance. For example, the detection unit can predict the progression of degradation based on the sensor's usage history and environmental conditions and notify the timing of maintenance. This helps maintain the reliability of the system. Some or all of the above-described processes in the detection unit may be performed using AI or not using AI. For example, the detection unit can input sensor output data into AI, which can analyze the data to detect malfunctions or degradation.

[0043] The notification unit can instruct appropriate maintenance. For example, the notification unit can use AI to analyze the sensor status and generate appropriate maintenance instructions. For example, the notification unit can determine the need for maintenance based on information about sensor failures or deterioration and notify specific maintenance instructions. The notification unit can also update maintenance timing in real time and provide the latest information. For example, the notification unit can adjust the maintenance timing according to the sensor status and instruct maintenance at the optimal time. Furthermore, the notification unit can customize maintenance instructions and instruct maintenance according to the type of sensor and usage conditions. For example, the notification unit can specifically instruct the necessary maintenance work for a particular sensor, enabling efficient maintenance. This can enhance the efficiency of regular maintenance. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input sensor status data into AI, and the AI ​​can analyze the data and generate maintenance instructions.

[0044] The detection unit can analyze ambient sounds to further suppress the occurrence of false alarms. For example, the detection unit can use AI to analyze ambient sounds and identify sounds that cause false alarms. For example, the detection unit can detect specific sounds indicating the occurrence of a fire from ambient sounds. The detection unit can also analyze patterns of ambient sounds to identify sounds that cause false alarms. For example, the detection unit can monitor changes in ambient sounds in real time to suppress the occurrence of false alarms. Furthermore, the detection unit can accumulate ambient sound data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past ambient sound data to predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be further suppressed by analyzing ambient sounds. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input ambient sound data into AI, and the AI ​​can analyze the data to identify sounds that cause false alarms.

[0045] The detection unit can suppress the occurrence of false alarms by considering environmental data such as temperature and humidity. For example, the detection unit can use AI to analyze temperature and humidity data and identify factors that cause false alarms. For example, the detection unit can monitor changes in temperature and humidity in real time and suppress the occurrence of false alarms. The detection unit can also identify factors that cause false alarms based on temperature and humidity data. For example, the detection unit can analyze temperature and humidity data to predict the occurrence of false alarms and take countermeasures. Furthermore, the detection unit can accumulate temperature and humidity data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past temperature and humidity data to predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be suppressed by considering environmental data such as temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input temperature and humidity data into AI, and the AI ​​can analyze the data to identify factors that cause false alarms.

[0046] The detection unit can suppress the occurrence of false alarms by considering the building's structural information. For example, the detection unit can use AI to analyze the building's structural information and identify factors that cause false alarms. For example, the detection unit can identify factors that cause false alarms based on the building's structural information. The detection unit can also analyze the building's structural information in real time and suppress the occurrence of false alarms. For example, the detection unit can predict the occurrence of false alarms and take countermeasures based on the building's structural information. Furthermore, the detection unit can accumulate building structural information and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past building structural information, predict the occurrence of false alarms, and take countermeasures. In this way, the occurrence of false alarms can be suppressed by considering the building's structural information. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input building structural information into AI, and the AI ​​can analyze the data and identify factors that cause false alarms.

[0047] The detection unit can suppress the occurrence of false alarms by referring to past fire data. The detection unit can, for example, use AI to analyze past fire data and identify factors that cause false alarms. For example, the detection unit can identify factors that cause false alarms based on past fire data. The detection unit can also suppress the occurrence of false alarms by analyzing past fire data in real time. For example, the detection unit can predict the occurrence of false alarms and take countermeasures based on past fire data. Furthermore, the detection unit can accumulate past fire data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past fire data, predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be suppressed by referring to past fire data. Some or all of the above processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input past fire data into AI, and the AI ​​can analyze the data and identify factors that cause false alarms.

[0048] The specific unit can analyze the rate of fire progression and improve specific accuracy. The specific unit can, for example, use AI to analyze the rate of fire progression. For example, the specific unit can monitor the rate of fire progression in real time and improve specific accuracy. The specific unit can also adjust specific accuracy based on the rate of fire progression. For example, the specific unit can analyze the rate of fire progression, predict specific accuracy, and take countermeasures. Furthermore, the specific unit can accumulate data on the rate of fire progression and improve specific accuracy based on past data. For example, the specific unit can analyze past fire progression rate data, predict specific accuracy, and take countermeasures. In this way, specific accuracy can be improved by analyzing the rate of fire progression. Some or all of the above processing in the specific unit may be performed using AI or not using AI. For example, the specific unit can input fire progression rate data into AI, and the AI ​​can analyze the data to improve specific accuracy.

[0049] The identification unit can utilize additional sensors to pinpoint the source of a fire. For example, the identification unit can use AI to analyze data from the additional sensors and identify the source of the fire. For example, the identification unit can install additional sensors and collect data to pinpoint the source of a fire. The identification unit can also identify the source of a fire based on the data from the additional sensors. For example, the identification unit can monitor data from the additional sensors in real time and identify the source of a fire. Furthermore, the identification unit can accumulate data from the additional sensors and identify the source of a fire based on past data. For example, the identification unit can analyze past data from the additional sensors and identify the source of a fire. In this way, by utilizing additional sensors, the source of a fire can be accurately identified. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input data from the additional sensors into the AI, and the AI ​​can analyze the data to identify the source of the fire.

[0050] The identification unit can improve accuracy by considering the time of fire occurrence. For example, the identification unit can use AI to analyze the time of fire occurrence and improve accuracy. For example, the identification unit can adjust accuracy based on the time of fire occurrence. The identification unit can also improve accuracy by monitoring the time of fire occurrence in real time. For example, the identification unit can analyze the time of fire occurrence, predict accuracy, and take countermeasures. Furthermore, the identification unit can accumulate data on the time of fire occurrence and improve accuracy based on past data. For example, the identification unit can analyze past fire occurrence time data, predict accuracy, and take countermeasures. In this way, accuracy can be improved by considering the time of fire occurrence. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input fire occurrence time data into AI, and the AI ​​can analyze the data to improve accuracy.

[0051] The identification unit can improve its accuracy by referring to surrounding information of the fire's location. For example, the identification unit can use AI to analyze surrounding information of the fire's location and improve its accuracy. For example, the identification unit can adjust its accuracy based on surrounding information of the fire's location. Furthermore, the identification unit can improve its accuracy by monitoring surrounding information of the fire's location in real time. For example, the identification unit can analyze surrounding information of the fire's location, predict its accuracy, and take countermeasures. In addition, the identification unit can accumulate surrounding information of the fire's location and improve its accuracy based on past data. For example, the identification unit can analyze surrounding information of past fires, predict its accuracy, and take countermeasures. This allows for improved accuracy by referring to surrounding information of the fire's location. Some or all of the above-described processes in the identification unit may be performed using AI or without AI. For example, the identification unit can input surrounding information of the fire's location into AI, which can then analyze the data to improve accuracy.

[0052] The reporting unit can customize the content of its reports according to the scale of the fire. For example, the reporting unit can use AI to analyze the scale of the fire and customize the content of the report. For example, the reporting unit can adjust the content of the report based on the scale of the fire. The reporting unit can also monitor the scale of the fire in real time and customize the content of the report. For example, the reporting unit can analyze the scale of the fire, predict the content of the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the scale of fires and customize the content of the report based on past data. For example, the reporting unit can analyze past fire scale data, predict the content of the report, and take countermeasures. This makes it possible to provide appropriate report content according to the scale of the fire. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire scale data into AI, and the AI ​​can analyze the data and customize the content of the report.

[0053] The reporting unit can update the progress of the fire in real time and modify the report content as needed. For example, the reporting unit can use AI to analyze the progress of the fire and modify the report content. For example, the reporting unit can monitor the progress of the fire in real time and modify the report content. The reporting unit can also modify the report content based on the progress of the fire. For example, the reporting unit can analyze the progress of the fire, predict the content of the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the progress of the fire and modify the report content based on past data. For example, the reporting unit can analyze past fire progress data, predict the content of the report, and take countermeasures. This makes it possible to provide appropriate report content according to the progress of the fire. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire progress data into AI, and the AI ​​can analyze the data and modify the report content.

[0054] The reporting unit can optimize the content of its reports by considering the geographical information of the fire's location. For example, the reporting unit can use AI to analyze the geographical information of the fire's location and optimize the content of its reports. For example, the reporting unit can adjust the content of its reports based on the geographical information of the fire's location. The reporting unit can also monitor the geographical information of the fire's location in real time and optimize the content of its reports. For example, the reporting unit can analyze the geographical information of the fire's location, predict the content of its reports, and take appropriate measures. Furthermore, the reporting unit can accumulate geographical information of the fire's location and optimize the content of its reports based on past data. For example, the reporting unit can analyze geographical information of past fires, predict the content of its reports, and take appropriate measures. In this way, the content of reports can be optimized by considering the geographical information of the fire's location. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input geographical information of the fire's location into AI, and the AI ​​can analyze the data and optimize the content of its reports.

[0055] The reporting unit can predict the extent of a fire's impact and reflect this in the report. For example, the reporting unit can use AI to analyze the extent of the fire's impact and revise the report. For example, the reporting unit can monitor the extent of the fire's impact in real time and revise the report. The reporting unit can also revise the report based on the extent of the fire's impact. For example, the reporting unit can analyze the extent of the fire's impact, predict the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the extent of the fire's impact and revise the report based on past data. For example, the reporting unit can analyze past fire impact data, predict the report, and take countermeasures. This allows the report to be appropriately reflected by predicting the extent of the fire's impact. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire impact data into AI, which can analyze the data and revise the report.

[0056] The notification unit can update the progress of the fire in real time and modify the notification content as needed. For example, the notification unit can use AI to analyze the progress of the fire and modify the notification content. For example, the notification unit can monitor the progress of the fire in real time and modify the notification content. The notification unit can also modify the notification content based on the progress of the fire. For example, the notification unit can analyze the progress of the fire, predict the content of the notification, and take countermeasures. Furthermore, the notification unit can accumulate data on the progress of the fire and modify the notification content based on past data. For example, the notification unit can analyze past fire progress data, predict the content of the notification, and take countermeasures. This makes it possible to provide appropriate notification content according to the progress of the fire. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input fire progress data into AI, and the AI ​​can analyze the data and modify the notification content.

[0057] The notification unit can optimize notification content by considering the geographical information of the fire's location. For example, the notification unit can use AI to analyze the geographical information of the fire's location and optimize the notification content. For example, the notification unit can adjust the notification content based on the geographical information of the fire's location. The notification unit can also monitor the geographical information of the fire's location in real time and optimize the notification content. For example, the notification unit can analyze the geographical information of the fire's location, predict the notification content, and take countermeasures. Furthermore, the notification unit can accumulate geographical information of the fire's location and optimize notification content based on past data. For example, the notification unit can analyze geographical information of past fire locations, predict the notification content, and take countermeasures. This allows for the optimization of notification content by considering the geographical information of the fire's location. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input geographical information of the fire's location into AI, which can then analyze the data and optimize the notification content.

[0058] The guidance unit can update the progress of the fire in real time and modify evacuation routes as needed. For example, the guidance unit can use AI to analyze the progress of the fire and modify evacuation routes. For example, the guidance unit can monitor the progress of the fire in real time and modify evacuation routes. The guidance unit can also modify evacuation routes based on the progress of the fire. For example, the guidance unit can analyze the progress of the fire, predict evacuation routes, and take countermeasures. Furthermore, the guidance unit can accumulate data on the progress of the fire and modify evacuation routes based on past data. For example, the guidance unit can analyze past fire progress data, predict evacuation routes, and take countermeasures. This makes it possible to provide appropriate evacuation routes according to the progress of the fire. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input fire progress data into AI, and the AI ​​can analyze the data and modify evacuation routes.

[0059] The guidance unit can provide the optimal evacuation route by considering the location information of evacuees. For example, the guidance unit can use AI to analyze the location information of evacuees and provide the optimal evacuation route. For example, the guidance unit can monitor the location information of evacuees in real time and provide the optimal evacuation route. The guidance unit can also adjust the evacuation route based on the location information of evacuees. For example, the guidance unit can analyze the location information of evacuees and predict and provide the optimal evacuation route. Furthermore, the guidance unit can accumulate the location information of evacuees and provide the optimal evacuation route based on past data. For example, the guidance unit can analyze past evacuation location data and predict and provide the optimal evacuation route. This makes it possible to provide the optimal evacuation route based on the location information of evacuees. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the location data of evacuees into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0060] The guidance system can provide the optimal evacuation route by considering the building's structural information. For example, the guidance system can use AI to analyze the building's structural information and provide the optimal evacuation route. For example, the guidance system can provide the optimal evacuation route based on the building's structural information. The guidance system can also monitor the building's structural information in real time and adjust the evacuation route. For example, the guidance system can analyze the building's structural information and predict and provide the optimal evacuation route. Furthermore, the guidance system can accumulate building structural information and provide the optimal evacuation route based on past data. For example, the guidance system can analyze past building structural information data and predict and provide the optimal evacuation route. This allows the guidance system to provide the optimal evacuation route based on the building's structural information. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can input building structural information data into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0061] The guidance unit can provide the optimal evacuation route by referring to the evacuee's past evacuation history. The guidance unit can, for example, use AI to analyze the evacuee's past evacuation history and provide the optimal evacuation route. For example, the guidance unit can provide the optimal evacuation route based on the evacuee's past evacuation history. The guidance unit can also monitor the evacuee's past evacuation history in real time and adjust the evacuation route. For example, the guidance unit can analyze the evacuee's past evacuation history and predict and provide the optimal evacuation route. Furthermore, the guidance unit can accumulate the evacuee's past evacuation history and provide the optimal evacuation route based on past data. For example, the guidance unit can analyze the past evacuation history data of evacuees and predict and provide the optimal evacuation route. This makes it possible to provide the optimal evacuation route based on the evacuee's past evacuation history. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the evacuee's past evacuation history data into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0062] The lighting unit can update the fire's progress in real time and adjust the lighting pattern of the evacuation guidance lights as needed. For example, the lighting unit can use AI to analyze the fire's progress and adjust the lighting pattern. For example, the lighting unit can monitor the fire's progress in real time and adjust the lighting pattern. The lighting unit can also adjust the lighting pattern based on the fire's progress. For example, the lighting unit can analyze the fire's progress, predict the lighting pattern, and take countermeasures. Furthermore, the lighting unit can accumulate fire progress data and adjust the lighting pattern based on past data. For example, the lighting unit can analyze past fire progress data, predict the lighting pattern, and take countermeasures. This allows for the provision of an appropriate lighting pattern according to the fire's progress. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input fire progress data into AI, which can analyze the data and adjust the lighting pattern.

[0063] The lighting unit can provide an optimal lighting pattern considering the location information of evacuees. For example, the lighting unit can use AI to analyze the location information of evacuees and provide an optimal lighting pattern. For example, the lighting unit can monitor the location information of evacuees in real time and provide an optimal lighting pattern. The lighting unit can also adjust the lighting pattern based on the location information of evacuees. For example, the lighting unit can analyze the location information of evacuees and predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate the location information of evacuees and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past evacuee location data and predict and provide an optimal lighting pattern. This makes it possible to provide an optimal lighting pattern based on the location information of evacuees. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input evacuee location data into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0064] The lighting unit can provide an optimal lighting pattern by considering the building's structural information. For example, the lighting unit can use AI to analyze the building's structural information and provide an optimal lighting pattern. For example, the lighting unit can provide an optimal lighting pattern based on the building's structural information. The lighting unit can also monitor the building's structural information in real time and adjust the lighting pattern. For example, the lighting unit can analyze the building's structural information, predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate building structural information and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past building structural information data, predict and provide an optimal lighting pattern. This allows the lighting unit to provide an optimal lighting pattern based on the building's structural information. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input building structural information data into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0065] The lighting unit can provide an optimal lighting pattern by referring to the past evacuation history of evacuees. For example, the lighting unit can use AI to analyze the past evacuation history of evacuees and provide an optimal lighting pattern. For example, the lighting unit can provide an optimal lighting pattern based on the past evacuation history of evacuees. The lighting unit can also monitor the past evacuation history of evacuees in real time and adjust the lighting pattern. For example, the lighting unit can analyze the past evacuation history of evacuees and predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate the past evacuation history of evacuees and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past evacuation history data of evacuees and predict and provide an optimal lighting pattern. This makes it possible to provide an optimal lighting pattern based on the past evacuation history of evacuees. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input the past evacuation history data of evacuees into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The detection unit can analyze ambient sounds to further suppress the occurrence of false alarms. For example, the detection unit can detect specific sounds indicating the occurrence of a fire from ambient sounds. It can also analyze patterns of ambient sounds to identify sounds that cause false alarms. Furthermore, the detection unit can accumulate ambient sound data and predict the occurrence of false alarms based on past data. In this way, the occurrence of false alarms can be further suppressed by analyzing ambient sounds.

[0068] The identification unit can utilize additional sensors to pinpoint the source of a fire. For example, the identification unit can install additional sensors and collect data to identify the source of a fire. Furthermore, the identification unit can identify the source of a fire based on the data from the additional sensors. In addition, the identification unit can accumulate the data from the additional sensors and identify the source of a fire based on past data. This allows for accurate identification of the source of a fire by utilizing additional sensors.

[0069] The reporting unit can predict the extent of a fire's impact and reflect this in the report. For example, the reporting unit can monitor the extent of the fire's impact in real time and modify the report accordingly. It can also modify the report based on the fire's impact. Furthermore, the reporting unit can accumulate data on the fire's impact and modify the report based on past data. This allows for accurate reflection of the report by predicting the fire's impact.

[0070] The notification unit can optimize notification content by considering the geographical information of the fire's location. For example, the notification unit can adjust notification content based on the geographical information of the fire's location. Furthermore, the notification unit can monitor the geographical information of the fire's location in real time and optimize notification content accordingly. In addition, the notification unit can accumulate geographical information of the fire's location and optimize notification content based on past data. This allows for the optimization of notification content by considering the geographical information of the fire's location.

[0071] The guidance system can provide the optimal evacuation route by referring to the evacuee's past evacuation history. For example, the guidance system can provide the optimal evacuation route based on the evacuee's past evacuation history. Furthermore, the guidance system can monitor the evacuee's past evacuation history in real time and adjust the evacuation route accordingly. In addition, the guidance system can accumulate the evacuee's past evacuation history and provide the optimal evacuation route based on this historical data. This allows the system to provide the optimal evacuation route based on the evacuee's past evacuation history.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The detection unit detects false alarms. For example, it uses AI to distinguish between cigarette or cooking smoke and fire smoke. It can perform smoke component analysis to identify cigarette or cooking smoke from fire smoke. It can also measure smoke concentration and temperature to detect the occurrence of a fire. Furthermore, it can analyze the movement of smoke to understand the progression of the fire. Step 2: The identification unit identifies the fire based on the information detected by the detection unit. For example, it uses AI to analyze the location and scale of the fire. It can identify the location of the fire and assess its scale. It can also analyze the rate of fire progression and predict its spread. Furthermore, it can identify the source of the fire and analyze its cause. Step 3: The reporting unit analyzes the location and scale of the fire identified by the specific unit and automatically notifies the fire department. For example, it can use AI to analyze fire information and generate appropriate reporting content. It can generate reporting content that includes information such as the location, scale, and progress of the fire. It can also update the reporting content in real time to provide the latest information. Furthermore, it can customize the reporting content to provide reports tailored to the scale and progress of the fire. Step 4: The notification unit notifies the administrator based on the information reported by the reporting unit. For example, it can use AI to analyze the reported content and generate appropriate notification content. It can generate notification content that includes information such as the location, scale, and progress of the fire. It can also update the notification content in real time to provide the latest information. Furthermore, it can customize the notification content to provide notifications tailored to the administrator's needs. Step 5: The guidance unit automatically announces the optimal evacuation route based on the information notified by the notification unit. For example, it uses AI to analyze fire information and generate appropriate evacuation routes. Based on information such as the location, scale, and progress of the fire, it can generate the optimal evacuation route. It can also update the evacuation route in real time to provide the latest information. Furthermore, it can customize the evacuation route to provide guidance tailored to the needs of evacuees. Step 6: The lighting unit illuminates the evacuation guidance lights based on the evacuation routes indicated by the guidance unit. For example, AI can be used to generate lighting patterns for the evacuation guidance lights. Based on information such as the location, scale, and progress of the fire, the optimal lighting pattern can be generated. It is also possible to update the lighting pattern in real time to provide the latest information. Furthermore, the lighting pattern can be customized to illuminate according to the needs of evacuees.

[0074] (Example of form 2) The Smart Fire Safe AI Agent System according to an embodiment of the present invention is a system that optimizes fire alarm and response in large commercial facilities. This system utilizes AI technology to optimize fire alarm and response, reduce false alarms, and achieve a rapid and effective fire response. The Smart Fire Safe AI Agent System comprises a detection unit for detecting false alarms, an identification unit for identifying fires, a notification unit for automatically notifying the fire department, a notification unit for notifying administrators, a guidance unit for automatically announcing optimal evacuation routes, and a lighting unit for illuminating evacuation guidance lights. For example, the Smart Fire Safe AI Agent System distinguishes between cigarette smoke, cooking smoke, and fires, suppressing the occurrence of false alarms. This reduces unnecessary evacuations and dispatches due to false alarms. Next, the Smart Fire Safe AI Agent System analyzes the location and scale of the fire and automatically notifies the fire department and administrators. This enables a rapid and appropriate response. Furthermore, the Smart Fire Safe AI Agent System automatically announces optimal evacuation routes at each location, supporting safe evacuation. This improves the efficiency of life-saving efforts in emergencies. Furthermore, the Smart FireSafe AI Agent System detects malfunctions and sensor degradation and directs appropriate maintenance. This maintains system reliability and enables long-term operation. Finally, the Smart FireSafe AI Agent System illuminates evacuation guidance lights to support evacuation. This provides visual guidance and improves evacuation safety. As a result, the Smart FireSafe AI Agent System reduces false alarms and enables a rapid and effective fire response.

[0075] The Smart Fire Safe AI Agent System according to this embodiment comprises a detection unit, an identification unit, a notification unit, a guidance unit, and a lighting unit. The detection unit detects false alarms. The detection unit distinguishes between cigarette or cooking smoke and fire, for example, using AI. For example, the detection unit can perform a component analysis of the smoke to distinguish between cigarette or cooking smoke and fire smoke. The detection unit can also measure the concentration and temperature of the smoke to detect the occurrence of a fire. Furthermore, the detection unit can analyze the movement of the smoke to understand the progress of the fire. The identification unit identifies the fire based on the information detected by the detection unit. The identification unit analyzes the location and scale of the fire, for example, using AI. For example, the identification unit can identify the location of the fire and evaluate its scale. Furthermore, the identification unit can analyze the rate of fire progression to predict the spread of the fire. Furthermore, the identification unit can identify the source of the fire and analyze its cause. The notification unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The reporting unit, for example, uses AI to analyze fire information and generate appropriate reporting content. For example, the reporting unit can generate reporting content that includes information such as the location, scale, and progression of the fire. The reporting unit can also update the reporting content in real time to provide the latest information. Furthermore, the reporting unit can customize the reporting content to provide reports tailored to the scale and progression of the fire. The notification unit notifies administrators based on the information reported by the reporting unit. The notification unit, for example, uses AI to analyze the reporting content and generate appropriate notification content. For example, the notification unit can generate notification content that includes information such as the location, scale, and progression of the fire. Furthermore, the notification unit can update the notification content in real time to provide the latest information. Furthermore, the notification unit can customize the notification content to provide notifications tailored to the administrator's needs. The guidance unit automatically announces the optimal evacuation route based on the information notified by the notification unit. The guidance unit, for example, uses AI to analyze fire information and generate appropriate evacuation routes. For example, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progression of the fire. Furthermore, the information desk can update evacuation routes in real time, providing the latest information.Furthermore, the guidance unit can customize evacuation routes and provide guidance tailored to the needs of evacuees. The lighting unit illuminates evacuation guidance lights based on the evacuation routes guided by the guidance unit. The lighting unit generates lighting patterns for evacuation guidance lights using AI, for example. For example, the lighting unit can generate an optimal lighting pattern based on information such as the location, scale, and progress of the fire. The lighting unit can also update the lighting pattern in real time to provide the latest information. In addition, the lighting unit can customize the lighting pattern and illuminate the lights according to the needs of evacuees. As a result, the Smart Fire Safe AI Agent System according to this embodiment can reduce false alarms and achieve a rapid and effective fire response.

[0076] The detection unit detects false alarms. For example, the detection unit uses AI to distinguish between cigarette or cooking smoke and fire smoke. Specifically, the AI ​​performs smoke component analysis to identify cigarette or cooking smoke from fire smoke. Smoke component analysis includes techniques that measure the chemical composition and particle size of the smoke using gas sensors and optical sensors. This allows for the detection of specific chemical substances in cigarette smoke and oils in cooking smoke, and their distinction from combustion products in fire smoke. The detection unit can also measure the smoke concentration and temperature to detect the occurrence of a fire. Smoke concentration is measured using light scattering or absorption methods, and temperature is measured using thermal cameras or infrared sensors. Furthermore, the detection unit can analyze the movement of smoke to understand the progression of a fire. Fluid dynamics models and image analysis techniques are used to analyze smoke movement and predict the diffusion speed and direction of the smoke. As a result, the detection unit enables early fire detection and reduction of false alarms, allowing for a rapid response.

[0077] The identification unit identifies the fire based on information detected by the detection unit. For example, the identification unit uses AI to analyze the location and scale of the fire. Specifically, the AI ​​integrates data on smoke composition, concentration, temperature, and movement provided by the detection unit to identify the location of the fire. To identify the location of the fire, an algorithm is used that utilizes building layout information and historical fire data to quickly identify the source of the fire. The identification unit can also evaluate the scale of the fire. Parameters such as the burning area, smoke concentration, and temperature rise rate are considered when evaluating the scale of the fire. Furthermore, the identification unit can analyze the rate of fire progression and predict the spread of the fire. Factors such as combustion characteristics, building structure, and wind direction are considered when analyzing the rate of fire progression, and a model is used to predict the extent and duration of fire spread. As a result, the identification unit can provide detailed information about the fire and support a quick and appropriate response.

[0078] The reporting unit analyzes the location and scale of the fire identified by the identification unit and automatically notifies the fire department. The reporting unit can, for example, use AI to analyze fire information and generate appropriate reporting content. Specifically, the reporting unit can generate reporting content that includes information such as the location, scale, and progress of the fire. The reporting content includes detailed information such as the time the fire started, the building address, the floor on which the fire started, the scale of the fire, the rate of progression, and the need for evacuation. The reporting unit can also update the reporting content in real time to provide the latest information. For example, if the progress of the fire changes, the reporting unit will immediately incorporate the new information and update the reporting content. Furthermore, the reporting unit can customize the reporting content and make reports according to the scale and progress of the fire. This allows the reporting unit to provide the fire department with quick and accurate information and support effective fire response.

[0079] The notification unit notifies administrators based on information reported by the reporting unit. The notification unit can, for example, use AI to analyze the reported information and generate appropriate notifications. Specifically, the notification unit can generate notifications that include information such as the location, scale, and progress of a fire. These notifications may include detailed information such as the time the fire started, the building address, the floor on which the fire occurred, the scale of the fire, its rate of progression, and the need for evacuation. The notification unit can also update notifications in real time to provide the latest information. For example, if the progress of the fire changes, the notification unit immediately incorporates the new information and updates the notification. Furthermore, the notification unit can customize notifications to meet the administrator's needs. For example, if an administrator wants to receive specific information preferentially, that information can be highlighted in the notification. This allows the notification unit to provide administrators with quick and accurate information, supporting effective fire response.

[0080] The guidance unit automatically announces the optimal evacuation route based on information notified by the notification unit. For example, the guidance unit uses AI to analyze fire information and generate appropriate evacuation routes. Specifically, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progression of the fire. The generation of the evacuation route takes into account building layout information, information on obstacles in the evacuation route, and the location information of evacuees. The guidance unit can also update the evacuation route in real time and provide the latest information. For example, if the progression of the fire changes, the guidance unit will immediately take in the new information and update the evacuation route. Furthermore, the guidance unit can customize the evacuation route and provide guidance according to the needs of evacuees. For example, if an evacuee has physical limitations, it can provide an evacuation route that takes those limitations into account. In this way, the guidance unit can provide evacuees with a quick and accurate evacuation route and support safe evacuation.

[0081] The lighting unit illuminates evacuation guidance lights based on the evacuation routes guided by the guidance unit. The lighting unit generates illumination patterns for the evacuation guidance lights, for example, using AI. Specifically, the lighting unit can generate the optimal illumination pattern based on information such as the location, scale, and progression of a fire. The generation of the illumination pattern takes into account information such as obstacles in the evacuation route, the location of evacuees, and the length of the evacuation route. The lighting unit can also update the illumination pattern in real time to provide the latest information. For example, if the progression of the fire changes, the lighting unit will immediately take in the new information and update the illumination pattern. Furthermore, the lighting unit can customize the illumination pattern to meet the needs of evacuees. For example, if an evacuee has visual limitations, the lighting unit can provide an illumination pattern that takes those limitations into account. This allows the lighting unit to provide evacuees with quick and accurate evacuation guidance and support safe evacuation.

[0082] The detection unit can distinguish between cigarette or cooking smoke and fire smoke. For example, the detection unit uses AI to analyze the components of the smoke and identify cigarette or cooking smoke from fire smoke. For example, the detection unit can analyze the components of the smoke and detect specific chemical substances contained in cigarette or cooking smoke. The detection unit can also measure the concentration and temperature of the smoke to detect the occurrence of a fire. For example, the detection unit can detect the occurrence of a fire when the smoke concentration exceeds a certain threshold. Furthermore, the detection unit can analyze the movement of the smoke and understand the progression of the fire. For example, the detection unit can analyze the pattern of smoke movement and determine the direction and speed of fire progression. This can suppress the occurrence of false alarms. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the smoke component analysis into AI, and the AI ​​can analyze the components of the smoke and distinguish between cigarette or cooking smoke and fire smoke.

[0083] The identification unit can analyze the location and scale of a fire. For example, the identification unit can use AI to pinpoint the location of a fire and evaluate its scale. For example, to pinpoint the location of a fire, the identification unit can analyze the distance and direction from the source of the fire. The identification unit can also analyze the rate of fire progression and the extent of burning to evaluate the scale of the fire. For example, the identification unit can monitor the rate of fire progression in real time and predict the spread of the fire. Furthermore, the identification unit can pinpoint the source of the fire and analyze its cause. For example, to pinpoint the source of the fire, the identification unit can analyze environmental data around the location of the fire. This allows for accurate pinpointing of the location and scale of the fire. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input data for pinpointing the location of a fire into an AI, which can then analyze the data to pinpoint the location of the fire.

[0084] The reporting unit can automatically notify the fire department. The reporting unit can, for example, use AI to analyze fire information and generate appropriate reporting content. For example, the reporting unit can generate reporting content that includes information such as the location, scale, and progress of the fire. The reporting unit can also update the reporting content in real time to provide the latest information. For example, the reporting unit can update the reporting content according to the progress of the fire and provide the fire department with the latest information. Furthermore, the reporting unit can customize the reporting content and make reports according to the scale and progress of the fire. For example, if the fire is large, the reporting unit can generate detailed reporting content to encourage a quick response. This enables prompt notification to the fire department. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire information into AI, and the AI ​​can analyze the information and generate reporting content.

[0085] The notification unit can notify administrators. The notification unit can, for example, use AI to analyze the reported content and generate appropriate notification content. For example, the notification unit can generate notification content that includes information such as the location, scale, and progress of the fire. The notification unit can also update the notification content in real time to provide the latest information. For example, the notification unit can update the notification content according to the progress of the fire to provide administrators with the latest information. Furthermore, the notification unit can customize the notification content to provide notifications that meet the administrator's needs. For example, the notification unit can prioritize notifying administrators of the information they need, encouraging a quick response. This enables rapid notification to administrators. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the reported content into AI, which can then analyze the content and generate notification content.

[0086] The guidance unit can automatically announce the optimal evacuation route. For example, the guidance unit can use AI to analyze fire information and generate an appropriate evacuation route. For example, the guidance unit can generate the optimal evacuation route based on information such as the location, scale, and progress of the fire. The guidance unit can also update the evacuation route in real time and provide the latest information. For example, the guidance unit can update the evacuation route according to the progress of the fire and provide evacuees with the latest information. Furthermore, the guidance unit can customize the evacuation route and provide guidance according to the needs of evacuees. For example, the guidance unit can provide the optimal evacuation route based on the location information of evacuees and support rapid evacuation. This can support safe evacuation. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input fire information into AI, and the AI ​​can analyze the information and generate an evacuation route.

[0087] The lighting unit can turn on evacuation guidance lights. The lighting unit can generate lighting patterns for evacuation guidance lights using, for example, AI. For example, the lighting unit can generate an optimal lighting pattern based on information such as the location, scale, and progression of a fire. The lighting unit can also update the lighting pattern in real time to provide the latest information. For example, the lighting unit can update the lighting pattern according to the progression of the fire to provide evacuees with the latest information. Furthermore, the lighting unit can customize the lighting pattern to provide lighting according to the needs of evacuees. For example, the lighting unit can provide an optimal lighting pattern based on the location information of evacuees to support rapid evacuation. This enables visual guidance of evacuation. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input fire information into AI, and the AI ​​can analyze the information to generate a lighting pattern.

[0088] The detection unit can detect malfunctions and sensor degradation. For example, the detection unit can use AI to monitor the sensor's status and detect malfunctions or degradation. For example, the detection unit can analyze the sensor's output data and detect abnormal patterns. The detection unit can also monitor the sensor's operating status in real time and detect abnormalities early. For example, the detection unit can monitor the sensor's response time and output fluctuations and detect abnormalities. Furthermore, the detection unit can predict sensor degradation and instruct appropriate maintenance. For example, the detection unit can predict the progression of degradation based on the sensor's usage history and environmental conditions and notify the timing of maintenance. This helps maintain the reliability of the system. Some or all of the above-described processes in the detection unit may be performed using AI or not using AI. For example, the detection unit can input sensor output data into AI, which can analyze the data to detect malfunctions or degradation.

[0089] The notification unit can instruct appropriate maintenance. For example, the notification unit can use AI to analyze the sensor status and generate appropriate maintenance instructions. For example, the notification unit can determine the need for maintenance based on information about sensor failures or deterioration and notify specific maintenance instructions. The notification unit can also update maintenance timing in real time and provide the latest information. For example, the notification unit can adjust the maintenance timing according to the sensor status and instruct maintenance at the optimal time. Furthermore, the notification unit can customize maintenance instructions and instruct maintenance according to the type of sensor and usage conditions. For example, the notification unit can specifically instruct the necessary maintenance work for a particular sensor, enabling efficient maintenance. This can enhance the efficiency of regular maintenance. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input sensor status data into AI, and the AI ​​can analyze the data and generate maintenance instructions.

[0090] The detection unit can estimate the user's emotions and adjust the accuracy of false alarm detection based on the estimated emotions. The detection unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the detection unit can analyze the user's facial expressions and voice to estimate emotions. The detection unit can also adjust the accuracy of false alarm detection based on the user's emotions. For example, if the user is tense, the detection unit can apply stricter criteria to improve the accuracy of false alarm detection. Furthermore, if the user is relaxed, the detection unit can apply more flexible criteria to loosen the accuracy of false alarm detection. This allows for more appropriate detection by adjusting the accuracy of false alarm detection according to the user's emotions. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into AI, which can analyze the data to estimate emotions and adjust the accuracy of false alarm detection.

[0091] The detection unit can analyze ambient sounds to further suppress the occurrence of false alarms. For example, the detection unit can use AI to analyze ambient sounds and identify sounds that cause false alarms. For example, the detection unit can detect specific sounds indicating the occurrence of a fire from ambient sounds. The detection unit can also analyze patterns of ambient sounds to identify sounds that cause false alarms. For example, the detection unit can monitor changes in ambient sounds in real time to suppress the occurrence of false alarms. Furthermore, the detection unit can accumulate ambient sound data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past ambient sound data to predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be further suppressed by analyzing ambient sounds. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input ambient sound data into AI, and the AI ​​can analyze the data to identify sounds that cause false alarms.

[0092] The detection unit can suppress the occurrence of false alarms by considering environmental data such as temperature and humidity. For example, the detection unit can use AI to analyze temperature and humidity data and identify factors that cause false alarms. For example, the detection unit can monitor changes in temperature and humidity in real time and suppress the occurrence of false alarms. The detection unit can also identify factors that cause false alarms based on temperature and humidity data. For example, the detection unit can analyze temperature and humidity data to predict the occurrence of false alarms and take countermeasures. Furthermore, the detection unit can accumulate temperature and humidity data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past temperature and humidity data to predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be suppressed by considering environmental data such as temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input temperature and humidity data into AI, and the AI ​​can analyze the data to identify factors that cause false alarms.

[0093] The detection unit can estimate the user's emotions and determine the priority of detection results based on the estimated emotions. The detection unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the detection unit can analyze the user's facial expressions and voice to estimate emotions. The detection unit can also determine the priority of detection results based on the user's emotions. For example, if the user is tense, the detection unit can prioritize displaying important detection results. Furthermore, if the user is relaxed, the detection unit can adjust the display order of detection results and display them according to their importance. In this way, by determining the priority of detection results according to the user's emotions, important information can be provided preferentially. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into AI, which can analyze the data to estimate emotions and determine the priority of detection results.

[0094] The detection unit can suppress the occurrence of false alarms by considering the building's structural information. For example, the detection unit can use AI to analyze the building's structural information and identify factors that cause false alarms. For example, the detection unit can identify factors that cause false alarms based on the building's structural information. The detection unit can also analyze the building's structural information in real time and suppress the occurrence of false alarms. For example, the detection unit can predict the occurrence of false alarms and take countermeasures based on the building's structural information. Furthermore, the detection unit can accumulate building structural information and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past building structural information, predict the occurrence of false alarms, and take countermeasures. In this way, the occurrence of false alarms can be suppressed by considering the building's structural information. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input building structural information into AI, and the AI ​​can analyze the data and identify factors that cause false alarms.

[0095] The detection unit can suppress the occurrence of false alarms by referring to past fire data. The detection unit can, for example, use AI to analyze past fire data and identify factors that cause false alarms. For example, the detection unit can identify factors that cause false alarms based on past fire data. The detection unit can also suppress the occurrence of false alarms by analyzing past fire data in real time. For example, the detection unit can predict the occurrence of false alarms and take countermeasures based on past fire data. Furthermore, the detection unit can accumulate past fire data and predict the occurrence of false alarms based on past data. For example, the detection unit can analyze past fire data, predict the occurrence of false alarms and take countermeasures. In this way, the occurrence of false alarms can be suppressed by referring to past fire data. Some or all of the above processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input past fire data into AI, and the AI ​​can analyze the data and identify factors that cause false alarms.

[0096] The identification unit can estimate the user's emotions and adjust the accuracy of fire identification based on the estimated emotions. The identification unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the identification unit can analyze the user's facial expressions and voice to estimate emotions. The identification unit can also adjust the accuracy of fire identification based on the user's emotions. For example, if the user is tense, the identification unit can apply stricter criteria to improve the accuracy of fire identification. Furthermore, if the user is relaxed, the identification unit can apply more flexible criteria to loosen the accuracy of fire identification. This allows for more appropriate identification by adjusting the accuracy of fire identification according to the user's emotions. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into AI, which can analyze the data to estimate emotions and adjust the accuracy of fire identification.

[0097] The specific unit can analyze the rate of fire progression and improve specific accuracy. The specific unit can, for example, use AI to analyze the rate of fire progression. For example, the specific unit can monitor the rate of fire progression in real time and improve specific accuracy. The specific unit can also adjust specific accuracy based on the rate of fire progression. For example, the specific unit can analyze the rate of fire progression, predict specific accuracy, and take countermeasures. Furthermore, the specific unit can accumulate data on the rate of fire progression and improve specific accuracy based on past data. For example, the specific unit can analyze past fire progression rate data, predict specific accuracy, and take countermeasures. In this way, specific accuracy can be improved by analyzing the rate of fire progression. Some or all of the above processing in the specific unit may be performed using AI or not using AI. For example, the specific unit can input fire progression rate data into AI, and the AI ​​can analyze the data to improve specific accuracy.

[0098] The identification unit can utilize additional sensors to pinpoint the source of a fire. For example, the identification unit can use AI to analyze data from the additional sensors and identify the source of the fire. For example, the identification unit can install additional sensors and collect data to pinpoint the source of a fire. The identification unit can also identify the source of a fire based on the data from the additional sensors. For example, the identification unit can monitor data from the additional sensors in real time and identify the source of a fire. Furthermore, the identification unit can accumulate data from the additional sensors and identify the source of a fire based on past data. For example, the identification unit can analyze past data from the additional sensors and identify the source of a fire. In this way, by utilizing additional sensors, the source of a fire can be accurately identified. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, the identification unit can input data from the additional sensors into the AI, and the AI ​​can analyze the data to identify the source of the fire.

[0099] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. The identification unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, the identification unit can analyze the user's facial expressions and voice to estimate emotions. The identification unit can also adjust the display method of the identification results based on the user's emotions. For example, if the user is tense, the identification unit can provide the identification results in a highly visible display method. Furthermore, if the user is relaxed, the identification unit can display the identification results in detail. By adjusting the display method of the identification results according to the user's emotions, a more appropriate display becomes possible. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input user emotion data into an AI, the AI ​​can analyze the data to estimate emotions, and adjust the display method of the identification results.

[0100] The identification unit can improve accuracy by considering the time of fire occurrence. For example, the identification unit can use AI to analyze the time of fire occurrence and improve accuracy. For example, the identification unit can adjust accuracy based on the time of fire occurrence. The identification unit can also improve accuracy by monitoring the time of fire occurrence in real time. For example, the identification unit can analyze the time of fire occurrence, predict accuracy, and take countermeasures. Furthermore, the identification unit can accumulate data on the time of fire occurrence and improve accuracy based on past data. For example, the identification unit can analyze past fire occurrence time data, predict accuracy, and take countermeasures. In this way, accuracy can be improved by considering the time of fire occurrence. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input fire occurrence time data into AI, and the AI ​​can analyze the data to improve accuracy.

[0101] The identification unit can improve its accuracy by referring to surrounding information of the fire's location. For example, the identification unit can use AI to analyze surrounding information of the fire's location and improve its accuracy. For example, the identification unit can adjust its accuracy based on surrounding information of the fire's location. Furthermore, the identification unit can improve its accuracy by monitoring surrounding information of the fire's location in real time. For example, the identification unit can analyze surrounding information of the fire's location, predict its accuracy, and take countermeasures. In addition, the identification unit can accumulate surrounding information of the fire's location and improve its accuracy based on past data. For example, the identification unit can analyze surrounding information of past fires, predict its accuracy, and take countermeasures. This allows for improved accuracy by referring to surrounding information of the fire's location. Some or all of the above-described processes in the identification unit may be performed using AI or without AI. For example, the identification unit can input surrounding information of the fire's location into AI, which can then analyze the data to improve accuracy.

[0102] The reporting unit can estimate the user's emotions and adjust the urgency of the report based on the estimated emotions. The reporting unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reporting unit can analyze the user's facial expressions and voice to estimate emotions. The reporting unit can also adjust the urgency of the report based on the user's emotions. For example, if the reporting unit is tense, it can encourage a quick response to increase the urgency of the report. Furthermore, if the reporting unit is relaxed, it can encourage a flexible response to reduce the urgency of the report. This allows for more appropriate reporting by adjusting the urgency of the report according to the user's emotions. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into an AI, which can analyze the data to estimate emotions and adjust the urgency of the report.

[0103] The reporting unit can customize the content of its reports according to the scale of the fire. For example, the reporting unit can use AI to analyze the scale of the fire and customize the content of the report. For example, the reporting unit can adjust the content of the report based on the scale of the fire. The reporting unit can also monitor the scale of the fire in real time and customize the content of the report. For example, the reporting unit can analyze the scale of the fire, predict the content of the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the scale of fires and customize the content of the report based on past data. For example, the reporting unit can analyze past fire scale data, predict the content of the report, and take countermeasures. This makes it possible to provide appropriate report content according to the scale of the fire. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire scale data into AI, and the AI ​​can analyze the data and customize the content of the report.

[0104] The reporting unit can update the progress of the fire in real time and modify the report content as needed. For example, the reporting unit can use AI to analyze the progress of the fire and modify the report content. For example, the reporting unit can monitor the progress of the fire in real time and modify the report content. The reporting unit can also modify the report content based on the progress of the fire. For example, the reporting unit can analyze the progress of the fire, predict the content of the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the progress of the fire and modify the report content based on past data. For example, the reporting unit can analyze past fire progress data, predict the content of the report, and take countermeasures. This makes it possible to provide appropriate report content according to the progress of the fire. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire progress data into AI, and the AI ​​can analyze the data and modify the report content.

[0105] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. The notification unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the notification unit can estimate emotions by analyzing the user's facial expressions and voice. The notification unit can also determine the priority of notifications based on the user's emotions. For example, if the user is stressed, the notification unit can prioritize displaying important notifications. Furthermore, if the user is relaxed, the notification unit can adjust the display order of notifications and display them according to their importance. In this way, important notifications can be provided preferentially by determining the priority of notifications according to the user's emotions. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into AI, which can analyze the data to estimate emotions and determine the priority of notifications.

[0106] The reporting unit can optimize the content of its reports by considering the geographical information of the fire's location. For example, the reporting unit can use AI to analyze the geographical information of the fire's location and optimize the content of its reports. For example, the reporting unit can adjust the content of its reports based on the geographical information of the fire's location. The reporting unit can also monitor the geographical information of the fire's location in real time and optimize the content of its reports. For example, the reporting unit can analyze the geographical information of the fire's location, predict the content of its reports, and take appropriate measures. Furthermore, the reporting unit can accumulate geographical information of the fire's location and optimize the content of its reports based on past data. For example, the reporting unit can analyze geographical information of past fires, predict the content of its reports, and take appropriate measures. In this way, the content of reports can be optimized by considering the geographical information of the fire's location. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input geographical information of the fire's location into AI, and the AI ​​can analyze the data and optimize the content of its reports.

[0107] The reporting unit can predict the extent of a fire's impact and reflect this in the report. For example, the reporting unit can use AI to analyze the extent of the fire's impact and revise the report. For example, the reporting unit can monitor the extent of the fire's impact in real time and revise the report. The reporting unit can also revise the report based on the extent of the fire's impact. For example, the reporting unit can analyze the extent of the fire's impact, predict the report, and take countermeasures. Furthermore, the reporting unit can accumulate data on the extent of the fire's impact and revise the report based on past data. For example, the reporting unit can analyze past fire impact data, predict the report, and take countermeasures. This allows the report to be appropriately reflected by predicting the extent of the fire's impact. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input fire impact data into AI, which can analyze the data and revise the report.

[0108] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. The notification unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the notification unit can analyze the user's facial expressions and voice to estimate emotions. The notification unit can also adjust the notification content based on the user's emotions. For example, if the user is tense, the notification unit can provide a concise and clear notification. Furthermore, if the user is relaxed, the notification unit can provide a detailed notification. This allows for more appropriate notifications by adjusting the notification content according to the user's emotions. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into an AI, which can analyze the data to estimate emotions and adjust the notification content.

[0109] The notification unit can update the progress of the fire in real time and modify the notification content as needed. For example, the notification unit can use AI to analyze the progress of the fire and modify the notification content. For example, the notification unit can monitor the progress of the fire in real time and modify the notification content. The notification unit can also modify the notification content based on the progress of the fire. For example, the notification unit can analyze the progress of the fire, predict the content of the notification, and take countermeasures. Furthermore, the notification unit can accumulate data on the progress of the fire and modify the notification content based on past data. For example, the notification unit can analyze past fire progress data, predict the content of the notification, and take countermeasures. This makes it possible to provide appropriate notification content according to the progress of the fire. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input fire progress data into AI, and the AI ​​can analyze the data and modify the notification content.

[0110] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. The notification unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the notification unit can estimate emotions by analyzing the user's facial expressions and voice. The notification unit can also determine the priority of notifications based on the user's emotions. For example, if the user is stressed, the notification unit can prioritize displaying important notifications. Furthermore, if the user is relaxed, the notification unit can adjust the display order of notifications and display them according to their importance. In this way, important notifications can be provided preferentially by determining the priority of notifications according to the user's emotions. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into AI, which can analyze the data to estimate emotions and determine the priority of notifications.

[0111] The notification unit can optimize notification content by considering the geographical information of the fire's location. For example, the notification unit can use AI to analyze the geographical information of the fire's location and optimize the notification content. For example, the notification unit can adjust the notification content based on the geographical information of the fire's location. The notification unit can also monitor the geographical information of the fire's location in real time and optimize the notification content. For example, the notification unit can analyze the geographical information of the fire's location, predict the notification content, and take countermeasures. Furthermore, the notification unit can accumulate geographical information of the fire's location and optimize notification content based on past data. For example, the notification unit can analyze geographical information of past fire locations, predict the notification content, and take countermeasures. This allows for the optimization of notification content by considering the geographical information of the fire's location. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input geographical information of the fire's location into AI, which can then analyze the data and optimize the notification content.

[0112] The guidance unit can estimate the user's emotions and adjust the evacuation route guidance method based on the estimated emotions. The guidance unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the guidance unit can analyze the user's facial expressions and voice to estimate emotions. The guidance unit can also adjust the evacuation route guidance method based on the user's emotions. For example, if the user is tense, the guidance unit can provide concise and clear guidance. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance. By adjusting the evacuation route guidance method according to the user's emotions, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into AI, which can analyze the data to estimate emotions and adjust the evacuation route guidance method.

[0113] The guidance unit can update the progress of the fire in real time and modify evacuation routes as needed. For example, the guidance unit can use AI to analyze the progress of the fire and modify evacuation routes. For example, the guidance unit can monitor the progress of the fire in real time and modify evacuation routes. The guidance unit can also modify evacuation routes based on the progress of the fire. For example, the guidance unit can analyze the progress of the fire, predict evacuation routes, and take countermeasures. Furthermore, the guidance unit can accumulate data on the progress of the fire and modify evacuation routes based on past data. For example, the guidance unit can analyze past fire progress data, predict evacuation routes, and take countermeasures. This makes it possible to provide appropriate evacuation routes according to the progress of the fire. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input fire progress data into AI, and the AI ​​can analyze the data and modify evacuation routes.

[0114] The guidance unit can provide the optimal evacuation route by considering the location information of evacuees. For example, the guidance unit can use AI to analyze the location information of evacuees and provide the optimal evacuation route. For example, the guidance unit can monitor the location information of evacuees in real time and provide the optimal evacuation route. The guidance unit can also adjust the evacuation route based on the location information of evacuees. For example, the guidance unit can analyze the location information of evacuees and predict and provide the optimal evacuation route. Furthermore, the guidance unit can accumulate the location information of evacuees and provide the optimal evacuation route based on past data. For example, the guidance unit can analyze past evacuation location data and predict and provide the optimal evacuation route. This makes it possible to provide the optimal evacuation route based on the location information of evacuees. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the location data of evacuees into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0115] The guidance unit can estimate the user's emotions and determine the priority of evacuation routes based on the estimated emotions. The guidance unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the guidance unit can analyze the user's facial expressions and voice to estimate emotions. The guidance unit can also determine the priority of evacuation routes based on the user's emotions. For example, if the user is tense, the guidance unit can prioritize displaying important evacuation routes. Furthermore, if the user is relaxed, the guidance unit can adjust the display order of evacuation routes and display them according to their importance. In this way, by determining the priority of evacuation routes according to the user's emotions, important evacuation routes can be provided preferentially. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into AI, which can analyze the data to estimate emotions and determine the priority of evacuation routes.

[0116] The guidance system can provide the optimal evacuation route by considering the building's structural information. For example, the guidance system can use AI to analyze the building's structural information and provide the optimal evacuation route. For example, the guidance system can provide the optimal evacuation route based on the building's structural information. The guidance system can also monitor the building's structural information in real time and adjust the evacuation route. For example, the guidance system can analyze the building's structural information and predict and provide the optimal evacuation route. Furthermore, the guidance system can accumulate building structural information and provide the optimal evacuation route based on past data. For example, the guidance system can analyze past building structural information data and predict and provide the optimal evacuation route. This allows the guidance system to provide the optimal evacuation route based on the building's structural information. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can input building structural information data into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0117] The guidance unit can provide the optimal evacuation route by referring to the evacuee's past evacuation history. The guidance unit can, for example, use AI to analyze the evacuee's past evacuation history and provide the optimal evacuation route. For example, the guidance unit can provide the optimal evacuation route based on the evacuee's past evacuation history. The guidance unit can also monitor the evacuee's past evacuation history in real time and adjust the evacuation route. For example, the guidance unit can analyze the evacuee's past evacuation history and predict and provide the optimal evacuation route. Furthermore, the guidance unit can accumulate the evacuee's past evacuation history and provide the optimal evacuation route based on past data. For example, the guidance unit can analyze the past evacuation history data of evacuees and predict and provide the optimal evacuation route. This makes it possible to provide the optimal evacuation route based on the evacuee's past evacuation history. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input the evacuee's past evacuation history data into AI, and the AI ​​can analyze the data and provide the optimal evacuation route.

[0118] The lighting unit can estimate the user's emotions and adjust the way the evacuation guidance lights illuminate based on the estimated emotions. The lighting unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the lighting unit can analyze the user's facial expressions and voice to estimate emotions. The lighting unit can also adjust the way the evacuation guidance lights illuminate based on the user's emotions. For example, if the user is tense, the lighting unit can provide a bright and highly visible illumination method. Furthermore, if the user is relaxed, the lighting unit can illuminate with a soft light. This allows for more appropriate illumination by adjusting the illumination method of the evacuation guidance lights according to the user's emotions. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input user emotion data into AI, which can analyze the data to estimate emotions and adjust the illumination method of the evacuation guidance lights.

[0119] The lighting unit can update the fire's progress in real time and adjust the lighting pattern of the evacuation guidance lights as needed. For example, the lighting unit can use AI to analyze the fire's progress and adjust the lighting pattern. For example, the lighting unit can monitor the fire's progress in real time and adjust the lighting pattern. The lighting unit can also adjust the lighting pattern based on the fire's progress. For example, the lighting unit can analyze the fire's progress, predict the lighting pattern, and take countermeasures. Furthermore, the lighting unit can accumulate fire progress data and adjust the lighting pattern based on past data. For example, the lighting unit can analyze past fire progress data, predict the lighting pattern, and take countermeasures. This allows for the provision of an appropriate lighting pattern according to the fire's progress. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input fire progress data into AI, which can analyze the data and adjust the lighting pattern.

[0120] The lighting unit can provide an optimal lighting pattern considering the location information of evacuees. For example, the lighting unit can use AI to analyze the location information of evacuees and provide an optimal lighting pattern. For example, the lighting unit can monitor the location information of evacuees in real time and provide an optimal lighting pattern. The lighting unit can also adjust the lighting pattern based on the location information of evacuees. For example, the lighting unit can analyze the location information of evacuees and predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate the location information of evacuees and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past evacuee location data and predict and provide an optimal lighting pattern. This makes it possible to provide an optimal lighting pattern based on the location information of evacuees. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input evacuee location data into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0121] The lighting unit can estimate the user's emotions and determine the priority order for turning on evacuation guidance lights based on the estimated emotions. The lighting unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the lighting unit can analyze the user's facial expressions and voice to estimate emotions. The lighting unit can also determine the priority order for turning on evacuation guidance lights based on the user's emotions. For example, if the user is tense, the lighting unit can prioritize turning on important evacuation guidance lights. Furthermore, if the user is relaxed, the lighting unit can adjust the lighting order and turn on lights according to their importance. This allows important lights to be turned on preferentially by determining the priority order for turning on evacuation guidance lights according to the user's emotions. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input user emotion data into an AI, which can analyze the data to estimate emotions and determine the priority order for turning on evacuation guidance lights.

[0122] The lighting unit can provide an optimal lighting pattern by considering the building's structural information. For example, the lighting unit can use AI to analyze the building's structural information and provide an optimal lighting pattern. For example, the lighting unit can provide an optimal lighting pattern based on the building's structural information. The lighting unit can also monitor the building's structural information in real time and adjust the lighting pattern. For example, the lighting unit can analyze the building's structural information, predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate building structural information and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past building structural information data, predict and provide an optimal lighting pattern. This allows the lighting unit to provide an optimal lighting pattern based on the building's structural information. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input building structural information data into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0123] The lighting unit can provide an optimal lighting pattern by referring to the past evacuation history of evacuees. For example, the lighting unit can use AI to analyze the past evacuation history of evacuees and provide an optimal lighting pattern. For example, the lighting unit can provide an optimal lighting pattern based on the past evacuation history of evacuees. The lighting unit can also monitor the past evacuation history of evacuees in real time and adjust the lighting pattern. For example, the lighting unit can analyze the past evacuation history of evacuees and predict and provide an optimal lighting pattern. Furthermore, the lighting unit can accumulate the past evacuation history of evacuees and provide an optimal lighting pattern based on past data. For example, the lighting unit can analyze past evacuation history data of evacuees and predict and provide an optimal lighting pattern. This makes it possible to provide an optimal lighting pattern based on the past evacuation history of evacuees. Some or all of the above processing in the lighting unit may be performed using AI or not. For example, the lighting unit can input the past evacuation history data of evacuees into AI, and the AI ​​can analyze the data and provide an optimal lighting pattern.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The detection unit can analyze ambient sounds to further suppress the occurrence of false alarms. For example, the detection unit can detect specific sounds indicating the occurrence of a fire from ambient sounds. It can also analyze patterns of ambient sounds to identify sounds that cause false alarms. Furthermore, the detection unit can accumulate ambient sound data and predict the occurrence of false alarms based on past data. In this way, the occurrence of false alarms can be further suppressed by analyzing ambient sounds.

[0126] The identification unit can utilize additional sensors to pinpoint the source of a fire. For example, the identification unit can install additional sensors and collect data to identify the source of a fire. Furthermore, the identification unit can identify the source of a fire based on the data from the additional sensors. In addition, the identification unit can accumulate the data from the additional sensors and identify the source of a fire based on past data. This allows for accurate identification of the source of a fire by utilizing additional sensors.

[0127] The reporting unit can predict the extent of a fire's impact and reflect this in the report. For example, the reporting unit can monitor the extent of the fire's impact in real time and modify the report accordingly. It can also modify the report based on the fire's impact. Furthermore, the reporting unit can accumulate data on the fire's impact and modify the report based on past data. This allows for accurate reflection of the report by predicting the fire's impact.

[0128] The notification unit can optimize notification content by considering the geographical information of the fire's location. For example, the notification unit can adjust notification content based on the geographical information of the fire's location. Furthermore, the notification unit can monitor the geographical information of the fire's location in real time and optimize notification content accordingly. In addition, the notification unit can accumulate geographical information of the fire's location and optimize notification content based on past data. This allows for the optimization of notification content by considering the geographical information of the fire's location.

[0129] The guidance system can provide the optimal evacuation route by referring to the evacuee's past evacuation history. For example, the guidance system can provide the optimal evacuation route based on the evacuee's past evacuation history. Furthermore, the guidance system can monitor the evacuee's past evacuation history in real time and adjust the evacuation route accordingly. In addition, the guidance system can accumulate the evacuee's past evacuation history and provide the optimal evacuation route based on this historical data. This allows the system to provide the optimal evacuation route based on the evacuee's past evacuation history.

[0130] The detection unit can estimate the user's emotions and adjust the accuracy of false alarm detection based on the estimated emotions. For example, the detection unit can analyze the user's facial expressions and voice to estimate their emotions. Furthermore, the detection unit can adjust the accuracy of false alarm detection based on the user's emotions. For instance, if the user is tense, a stricter standard can be applied to increase the accuracy of false alarm detection. Conversely, if the user is relaxed, a more flexible standard can be applied to loosen the false alarm detection accuracy. This allows for more appropriate detection by adjusting the false alarm detection accuracy according to the user's emotions.

[0131] The identification unit can estimate the user's emotions and adjust the accuracy of fire identification based on those emotions. For example, the identification unit can analyze the user's facial expressions and voice to estimate their emotions. It can also adjust the accuracy of fire identification based on the user's emotions. For instance, if the user is tense, a stricter standard can be applied to improve the accuracy of fire identification. Conversely, if the user is relaxed, a more flexible standard can be applied to loosen the accuracy of fire identification. By adjusting the accuracy of fire identification according to the user's emotions, more accurate identification becomes possible.

[0132] The reporting system can estimate the user's emotions and adjust the urgency of the report based on those emotions. For example, the reporting system can analyze the user's facial expressions and voice to estimate their emotions. It can also adjust the urgency of the report based on the user's emotions. For instance, if the user is tense, the system can encourage a quick response to increase the urgency of the report. Conversely, if the user is relaxed, the system can encourage a more flexible response to reduce the urgency of the report. By adjusting the urgency of the report according to the user's emotions, more appropriate reports can be made.

[0133] The notification unit can estimate the user's emotions and adjust the notification content based on those emotions. For example, the notification unit can analyze the user's facial expressions and voice to estimate their emotions. It can also adjust the notification content based on the user's emotions. For instance, if the user is tense, it can provide a concise and clear notification. Furthermore, if the user is relaxed, it can provide a more detailed notification. This allows for more appropriate notifications by adjusting the content according to the user's emotions.

[0134] The lighting unit can estimate the user's emotions and adjust the way the evacuation guidance lights illuminate based on those emotions. For example, the lighting unit can analyze the user's facial expressions and voice to estimate their emotions. It can also adjust the way the evacuation guidance lights illuminate based on the user's emotions. For instance, if the user is tense, it can provide a bright, highly visible illumination. Furthermore, if the user is relaxed, it can illuminate with a softer light. This allows for more appropriate illumination by adjusting the way the evacuation guidance lights illuminate according to the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The detection unit detects false alarms. For example, it uses AI to distinguish between cigarette or cooking smoke and fire smoke. It can perform smoke component analysis to identify cigarette or cooking smoke from fire smoke. It can also measure smoke concentration and temperature to detect the occurrence of a fire. Furthermore, it can analyze the movement of smoke to understand the progression of the fire. Step 2: The identification unit identifies the fire based on the information detected by the detection unit. For example, it uses AI to analyze the location and scale of the fire. It can identify the location of the fire and assess its scale. It can also analyze the rate of fire progression and predict its spread. Furthermore, it can identify the source of the fire and analyze its cause. Step 3: The reporting unit analyzes the location and scale of the fire identified by the specific unit and automatically notifies the fire department. For example, it can use AI to analyze fire information and generate appropriate reporting content. It can generate reporting content that includes information such as the location, scale, and progress of the fire. It can also update the reporting content in real time to provide the latest information. Furthermore, it can customize the reporting content to provide reports tailored to the scale and progress of the fire. Step 4: The notification unit notifies the administrator based on the information reported by the reporting unit. For example, it can use AI to analyze the reported content and generate appropriate notification content. It can generate notification content that includes information such as the location, scale, and progress of the fire. It can also update the notification content in real time to provide the latest information. Furthermore, it can customize the notification content to provide notifications tailored to the administrator's needs. Step 5: The guidance unit automatically announces the optimal evacuation route based on the information notified by the notification unit. For example, it uses AI to analyze fire information and generate appropriate evacuation routes. Based on information such as the location, scale, and progress of the fire, it can generate the optimal evacuation route. It can also update the evacuation route in real time to provide the latest information. Furthermore, it can customize the evacuation route to provide guidance tailored to the needs of evacuees. Step 6: The lighting unit illuminates the evacuation guidance lights based on the evacuation routes indicated by the guidance unit. For example, AI can be used to generate lighting patterns for the evacuation guidance lights. Based on information such as the location, scale, and progress of the fire, the optimal lighting pattern can be generated. It is also possible to update the lighting pattern in real time to provide the latest information. Furthermore, the lighting pattern can be customized to illuminate according to the needs of evacuees.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the detection unit, identification unit, notification unit, alert unit, guidance unit, and lighting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the smart device 14 to analyze the components of smoke and distinguish between cigarette smoke, cooking smoke, and fire smoke. The identification unit analyzes the location and scale of the fire using, for example, the identification processing unit 290 of the data processing unit 12. The notification unit automatically notifies the fire department using, for example, the identification processing unit 290 of the data processing unit 12. The alert unit notifies the administrator using, for example, the control unit 46A of the smart device 14. The guidance unit automatically announces the optimal evacuation route using, for example, the control unit 46A of the smart device 14. The lighting unit turns on evacuation guidance lights using, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the detection unit, identification unit, notification unit, alert unit, guidance unit, and lighting unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the smart glasses 214 to analyze the components of smoke and distinguish between cigarette smoke, cooking smoke, and fire smoke. The identification unit analyzes the location and scale of the fire, for example, by the identification processing unit 290 of the data processing unit 12. The notification unit automatically notifies the fire department, for example, by the identification processing unit 290 of the data processing unit 12. The alert unit notifies the administrator, for example, by the control unit 46A of the smart glasses 214. The guidance unit automatically announces the optimal evacuation route, for example, by the control unit 46A of the smart glasses 214. The lighting unit turns on evacuation guidance lights, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the detection unit, identification unit, notification unit, alert unit, guidance unit, and lighting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the headset terminal 314 to analyze the components of smoke and distinguish between cigarette smoke, cooking smoke, and fire smoke. The identification unit analyzes the location and scale of the fire using, for example, the identification processing unit 290 of the data processing unit 12. The notification unit automatically notifies the fire department using, for example, the identification processing unit 290 of the data processing unit 12. The alert unit notifies the administrator using, for example, the control unit 46A of the headset terminal 314. The guidance unit automatically announces the optimal evacuation route using, for example, the control unit 46A of the headset terminal 314. The lighting unit turns on evacuation guidance lights using, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the detection unit, identification unit, notification unit, alert unit, guidance unit, and lighting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the camera 42 and sensors of the robot 414 to analyze the components of smoke and distinguish between cigarette smoke, cooking smoke, and fire smoke. The identification unit analyzes the location and scale of the fire, for example, by the identification processing unit 290 of the data processing unit 12. The notification unit automatically notifies the fire department, for example, by the identification processing unit 290 of the data processing unit 12. The alert unit notifies the administrator, for example, by the control unit 46A of the robot 414. The guidance unit automatically announces the optimal evacuation route, for example, by the control unit 46A of the robot 414. The lighting unit turns on evacuation guidance lights, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] 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.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A system comprising: a detection unit for detecting false alarms; an identification unit for identifying a fire based on information detected by the detection unit; a reporting unit for analyzing the location and scale of the fire identified by the identification unit and automatically notifying the fire department; a notification unit for notifying the administrator based on the information reported by the reporting unit; a guidance unit for automatically announcing appropriate evacuation routes based on the information notified by the notification unit; and a lighting unit for illuminating evacuation guidance lights based on the evacuation routes guided by the guidance unit. (Note 2) The detection unit is Distinguish between cigarette smoke, cooking smoke, and fire. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, Analyze the location and scale of the fire. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, Automatically notifies the fire department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify the administrator The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guide section is Automatically announces the optimal evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned lighting unit is Turn on the evacuation guidance lights. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is Detects malfunctions and sensor degradation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned notification unit, Instruct on appropriate maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is The system estimates the user's emotions and adjusts the accuracy of false alarm detection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is By analyzing ambient sounds, the occurrence of false alarms is further reduced. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is By considering environmental data such as temperature and humidity, false alarms are suppressed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is The system estimates the user's emotions and prioritizes the detection results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is Suppressing the occurrence of false alarms by taking into account building structure information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is By referring to past fire data, false alarms are suppressed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, It estimates the user's emotions and adjusts the accuracy of fire detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, Analyze the rate of fire progression and improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, Utilize additional sensors to pinpoint the source of the fire. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, Improve specific accuracy by taking into account the time the fire started. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, Referencing surrounding information about the location of the fire improves the accuracy of specific actions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the urgency of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, Customize the notification content according to the scale of the fire. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The fire's progress will be updated in real time, and the report will be modified as needed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, Optimize the content of the fire report by taking into account the geographical information of the fire's location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, Predict the extent of the fire's impact and reflect this in the report. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, The fire's progress will be updated in real time, and the notification content will be revised as needed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, Optimize notification content by considering geographical information of the fire's location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guide section is The system estimates the user's emotions and adjusts the evacuation route guidance method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned guide section is The fire's progress will be updated in real time, and evacuation routes will be adjusted as needed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guide section is Provides the optimal evacuation route considering the location information of evacuees. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guide section is The system estimates the user's emotions and prioritizes evacuation routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guide section is Providing the optimal evacuation route considering the building's structural information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned guide section is Provides the optimal evacuation route by referring to the evacuee's past evacuation history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned lighting unit is The system estimates the user's emotions and adjusts the way the evacuation guidance lights illuminate based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned lighting unit is The system updates the progress of the fire in real time and adjusts the lighting patterns of evacuation guidance lights as needed. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned lighting unit is The system provides an optimal lighting pattern that takes into account the location information of evacuees. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned lighting unit is The system estimates the user's emotions and determines the priority for turning on evacuation guidance lights based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned lighting unit is Provides the optimal lighting pattern considering the building's structural information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned lighting unit is The system provides the optimal lighting pattern by referencing the evacuee's past evacuation history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that detects false alarms, An identification unit that identifies a fire based on the information detected by the aforementioned detection unit, The aforementioned identification unit analyzes the location and scale of the fire and automatically notifies the fire department, A notification unit that notifies the administrator based on the information reported by the aforementioned reporting unit, A guidance unit that automatically announces the appropriate evacuation route based on the information notified by the aforementioned notification unit, The system includes a lighting unit that illuminates evacuation guidance lights based on the evacuation route guided by the aforementioned guidance unit. A system characterized by the following features.

2. The detection unit, Distinguish between cigarette smoke, cooking smoke, and fire. The system according to feature 1.

3. The specified part is, Analyze the location and scale of the fire. The system according to feature 1.

4. The aforementioned reporting unit, Automatically notifies the fire department. The system according to feature 1.

5. The aforementioned notification unit, Notify the administrator The system according to feature 1.

6. The aforementioned guide section is Automatically announces the optimal evacuation route. The system according to feature 1.

7. The aforementioned lighting unit is Turn on the evacuation guidance lights. The system according to feature 1.

8. The detection unit, Detects malfunctions and sensor degradation. The system according to feature 1.

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