System

Abstract

An object of a system according to an embodiment is to quickly identify a disaster area and a damage status at the time of a disaster and provide information for determining the next action.SOLUTION: A system includes an installation unit, a collection unit, an analysis unit, and a determination unit. The installation unit installs a camera. The collection unit collects an image captured by the camera installed by the installation unit. The analysis unit analyzes the video collected by the collection unit and specifies a disaster area or a damage situation. The determination unit determines a next action on the basis of the disaster area or the damage status specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is not possible to quickly identify the affected areas and damage situations during a disaster, and there is a lack of information to decide the next action to take.

[0005] The system according to the embodiment aims to quickly identify the affected location and damage situation in the event of a disaster and to provide information for determining the next action to be taken. [Means for solving the problem]

[0006] A system according to an embodiment includes an installation unit, a collection unit, an analysis unit, and a determination unit. The installation unit installs cameras. The collection unit collects video images captured by the cameras installed by the installation unit. The analysis unit analyzes the video images collected by the collection unit and identifies a disaster location or a damage situation. The determination unit determines a next action based on the disaster location or a damage situation identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly identify the affected location and damage situation in the event of a disaster and provide information for determining the next action to be taken. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A disaster information collection system according to an embodiment of the present invention is a system in which cameras are installed to capture images of disaster situations in real time, and a generation AI analyzes the images to identify affected areas and damage conditions, and then determines the next action. The disaster information collection system installs cameras to capture images of disaster situations in real time, and a generation AI analyzes the images to identify affected areas and damage conditions, and then determines the next action, thereby providing information that will lead to the next action in disaster response. For example, in a disaster information collection system, when a disaster such as an earthquake or typhoon occurs, cameras are automatically activated to capture video of the affected area. This video is transmitted to a generation AI. The generation AI then analyzes the transmitted video. The generation AI uses image analysis technology based on deep learning to identify affected areas and damage conditions in the video. For example, it analyzes the state of collapsed buildings and road disruption to obtain detailed information about the affected area. Furthermore, based on the information analyzed by the generation AI, the system identifies affected areas and aggregates the damage conditions. GPS data is used to identify affected areas, and specific evaluation criteria are set for the evaluation of the damage conditions. For example, the system identifies the locations of affected businesses and homes and evaluates the extent of the damage. It also collects information about the surrounding area and provides information that will lead to the next steps in disaster response. This allows the disaster information collection system to quickly collect information in the event of a disaster, enabling appropriate disaster response. This allows the disaster information collection system to quickly collect information in the event of a disaster, enabling appropriate disaster response. For example, it allows for the rapid decision-making of specific response measures, such as delivering relief supplies to affected areas and securing evacuation routes.

[0029] A disaster information collection system according to an embodiment includes an installation unit, a collection unit, an analysis unit, and a determination unit. The installation unit installs cameras. For example, the installation unit installs cameras at base stations, public facilities, and major transportation hubs. The installation unit can also analyze past disaster data to select camera installation locations. For example, the installation unit analyzes past earthquake data and installs cameras in areas that suffered severe earthquake damage. The collection unit collects video footage captured by the cameras installed by the installation unit. For example, the collection unit collects video footage captured by the cameras in real time. The collection unit can also customize collection methods when collecting video footage, taking into account environmental conditions of the collection target. For example, the collection unit collects video footage using highly waterproof cameras during rainy weather. The analysis unit analyzes the video footage collected by the collection unit to identify disaster locations and damage conditions. For example, the analysis unit can use image analysis technology using deep learning to identify disaster locations and damage conditions in the video footage. The analysis unit can also use GPS data to identify disaster locations and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit identifies the locations of affected businesses and homes and evaluates the extent of the damage. The determination unit determines the next action based on the affected locations and damage status identified by the analysis unit. The determination unit determines the next action, such as delivering relief supplies or securing evacuation routes, based on the affected locations and damage status. This enables the disaster information collection system according to the embodiment to efficiently collect information during a disaster and determine the next action. For example, the disaster information collection system can quickly determine specific response measures, such as delivering relief supplies to affected areas and securing evacuation routes.

[0030] The installation unit can install cameras at base stations, public facilities, and transportation hubs. Base stations include, for example, communication base stations and power base stations. Public facilities include, for example, schools, hospitals, and government offices. Transportation hubs include, for example, train stations, bus terminals, and airports. The installation unit, for example, installs cameras at base stations and collects video footage using communication infrastructure. The installation unit can also install cameras at public facilities to monitor the status of facilities that function as evacuation shelters during disasters. Furthermore, the installation unit can install cameras at transportation hubs to monitor traffic conditions during disasters. This allows for a variety of camera installation locations and allows disaster information to be collected over a wide area. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, to select camera installation locations, the installation unit can input past disaster data into the generation AI and have the generation AI select the optimal installation location.

[0031] The collection unit can collect video captured by the camera in real time. "Real time" refers to a state in which delay time is minimized, meaning, for example, that video is collected with a delay of within a few seconds. The collection unit, for example, collects video captured by the camera in real time to immediately grasp the disaster situation. The collection unit can also customize the collection method when collecting video, taking into account the environmental conditions of the collection target. For example, the collection unit collects video using a highly waterproof camera in rainy weather. The collection unit can also collect video using a wind-resistant camera in windy areas. Furthermore, the collection unit can collect video using a snow-resistant camera in snowy areas. This allows the disaster situation to be grasped in real time. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input video captured by the camera into a generation AI, which analyzes and collects the video in real time.

[0032] The analysis unit can identify damaged locations and damage conditions within the video using image analysis technology based on deep learning. Deep learning is a type of machine learning that uses neural networks, and by learning from large amounts of data, it enables highly accurate analysis. The analysis unit, for example, uses image analysis technology based on deep learning to identify damaged locations and damage conditions within the video. For example, the analysis unit analyzes the collapsed state of buildings and the disruption of roads to obtain detailed information about the affected area. The analysis unit can also use GPS data to identify damaged locations and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of the damage. This enables highly accurate identification of damaged locations and damage conditions. Some or all of the above-mentioned processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs video captured by a camera into the generative AI, and identifies damaged locations and damage conditions using image analysis technology based on deep learning.

[0033] The analysis unit can use GPS data to identify affected locations and set specific evaluation criteria for assessing the damage situation. GPS data is location information obtained using the Global Positioning System, allowing for highly accurate location identification. The analysis unit, for example, uses GPS data to identify affected locations and sets specific evaluation criteria for assessing the damage situation. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of damage. Evaluation criteria can include, for example, the degree of building collapse and the extent of road disruption. This enables accurate identification of affected locations and evaluation of the damage situation. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input GPS data into the generation AI and have the generation AI identify affected locations and assess the damage situation.

[0034] The decision unit can determine the next action, such as delivering relief supplies or securing an evacuation route, based on the location of the disaster or the damage situation. The decision unit determines the next action, such as delivering relief supplies or securing an evacuation route, based on, for example, the location of the disaster or the damage situation. Relief supplies include, for example, food, water, medicine, etc. Securing an evacuation route includes, for example, criteria for setting evacuation routes and methods for displaying evacuation routes. This enables a prompt and appropriate disaster response. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data on the location of the disaster and the damage situation into the generation AI and have the generation AI determine the next action.

[0035] The installation unit can analyze past disaster data and select optimal camera installation locations. Past disaster data includes, for example, disaster data such as earthquakes, typhoons, and floods. The installation unit can, for example, analyze past earthquake data and install cameras in areas that suffered severe earthquake damage. The installation unit can also analyze past typhoon data and install cameras in areas along the typhoon's path. Furthermore, the installation unit can analyze past flood data and install cameras in areas that suffered severe flood damage. This enables optimal camera installation based on past disaster data. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input past disaster data into the generation AI and have the generation AI select the optimal camera installation location.

[0036] When installing a camera, the installation unit can determine the optimal installation angle by taking into account the surrounding environment and obstacles. The surrounding environment includes, for example, the layout of buildings and the natural environment. Obstacles include, for example, buildings, trees, and vehicles. For example, if there is a tall building in the vicinity, the installation unit can adjust the installation angle of the camera to avoid it. In addition, if there are many trees in the vicinity, the installation unit can also adjust the installation angle of the camera to ensure visibility. Furthermore, if there is heavy traffic in the vicinity, the installation unit can adjust the installation angle of the camera to monitor traffic flow. This enables optimal camera installation taking into account the surrounding environment and obstacles. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input data on the surrounding environment and obstacles into the generation AI and have the generation AI determine the optimal installation angle.

[0037] When installing a camera, the installation unit can select a durable installation method taking into account the weather conditions of the installation location. Weather conditions include, for example, wind speed, rainfall, and temperature. For example, in a rainy region, the installation unit selects a camera with high waterproof performance and devise an installation method. Furthermore, in a windy region, the installation unit can select a wind-resistant installation method to strengthen the fixation of the camera. Furthermore, in a snowy region, the installation unit can select a snow-resistant installation method to strengthen the protection of the camera. This enables a durable camera installation according to the weather conditions. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input weather condition data into the generation AI and have the generation AI select a durable installation method.

[0038] When installing a camera, the installation unit can select the optimal installation method by taking into account the geographical characteristics of the installation location. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, in mountainous areas, the installation unit can install the camera at a high position to ensure visibility. In urban areas, the installation unit can also install the camera between buildings to monitor a wide area. Furthermore, in coastal areas, the installation unit can select an installation method that is resistant to sea breezes to ensure the durability of the camera. This enables optimal camera installation according to the geographical characteristics. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input data on geographical characteristics into the generation AI and have the generation AI select the optimal installation method.

[0039] When installing a camera, the installation unit can strengthen crime prevention measures by taking into account the crime rate of the installation location. Crime rates include, for example, police statistical data and the number of crimes. For example, in areas with high crime rates, the installation unit installs the camera so that it also functions as a security camera. The installation unit can also devise a way to install the camera in an inconspicuous location in areas with high crime rates. Furthermore, in areas with high crime rates, the installation unit can install cameras in multiple locations to expand the monitoring area. This makes it possible to strengthen crime prevention measures according to the crime rate. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input crime rate data into the generation AI and have the generation AI strengthen crime prevention measures.

[0040] When installing cameras, the installation unit can adjust the number of cameras to be installed taking into account the population density of the installation location. Population density includes, for example, census data and local demographic statistics. For example, in areas with high population density, the installation unit can increase the number of cameras to expand the monitoring area. The installation unit can also reduce the number of cameras to be installed in areas with low population density to reduce costs. Furthermore, in areas with medium population density, the installation unit can select an appropriate number of cameras to install and monitor efficiently. This makes it possible to adjust the number of cameras to be installed according to population density. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input population density data into the generation AI and have the generation AI adjust the number of cameras to be installed.

[0041] When collecting video, the collection unit can select the optimal collection method by referring to past collected data. The past collected data includes, for example, the type of collected data and the collection period. The collection unit, for example, analyzes the past collected data and selects the most effective collection method. The collection unit can also optimize the collection frequency and collection range by referring to the past collected data. Furthermore, the collection unit can customize the collection method based on the past collected data to efficiently collect video. This enables optimal video collection based on the past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the past collected data into the generation AI and have the generation AI select the optimal collection method.

[0042] When collecting video, the collection unit can customize the collection means by taking into account the environmental conditions of the collection target. Environmental conditions include, for example, temperature, humidity, and illuminance. For example, the collection unit collects video using a camera with high waterproof performance when it is raining. In addition, the collection unit can also collect video using a camera that is resistant to wind in windy areas. Furthermore, the collection unit can also collect video using a camera that is resistant to snow in snowy areas. This enables optimal video collection according to environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on environmental conditions into the generation AI and have the generation AI customize the collection means.

[0043] The collection unit can improve collection accuracy by detecting movements and changes in the collection target in real time when collecting video. Detection of movements and changes includes, for example, motion detection algorithms and change detection technology. For example, if the collection target is moving, the collection unit detects the movement in real time and collects video. The collection unit can also improve collection accuracy by detecting changes in the collection target in real time. Furthermore, the collection unit can analyze movements and changes in the collection target and select the optimal collection method. This improves collection accuracy by detecting movements and changes in real time. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input data on movements and changes in the collection target to the generation AI and have the generation AI improve collection accuracy.

[0044] When collecting video, the collection unit can adjust the collection range taking into account the geographical location information of the collection target. Geographical location information includes, for example, GPS data and map information. The collection unit can, for example, optimize the collection range based on the geographical location information of the collection target. The collection unit can also expand the collection range taking into account the geographical location information of the collection target. Furthermore, the collection unit can also refer to the geographical location information of the collection target and adjust the collection range to efficiently collect video. This makes it possible to adjust the optimal collection range based on the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and have the generation AI adjust the collection range.

[0045] When collecting video, the collection unit can optimize the collection method by referring to past data of the target to be collected. The past data includes, for example, the type of collected data and the collection period. The collection unit, for example, analyzes the past data of the target to be collected and selects the optimal collection method. The collection unit can also optimize the collection frequency and collection range by referring to the past data of the target to be collected. Furthermore, the collection unit can customize the collection method based on the past data of the target to collect and collect video efficiently. This makes it possible to select the optimal collection method based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input past data into the generation AI and have the generation AI optimize the collection method.

[0046] The collection unit can improve the accuracy of the information by simultaneously collecting peripheral information about the collection target when collecting video. The peripheral information includes, for example, information about surrounding buildings and traffic information. The collection unit can, for example, simultaneously collect peripheral information about the collection target to improve the accuracy of the information. The collection unit can also analyze the peripheral information about the collection target and select the optimal collection method. Furthermore, the collection unit can customize the collection method based on the peripheral information about the collection target to efficiently collect video. This improves the accuracy of the information by simultaneously collecting peripheral information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input peripheral information data into a generation AI and have the generation AI improve the accuracy of the information.

[0047] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. The past analysis data includes, for example, the type of analysis data and the analysis period. The analysis unit, for example, analyzes the past analysis data and selects the most effective analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to the past analysis data. Furthermore, the analysis unit can customize the analysis algorithm based on the past analysis data to perform efficient analysis. This makes it possible to select the optimal analysis algorithm based on the past analysis data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0048] During analysis, the analysis unit can detect specific patterns in the video to improve the accuracy of the analysis. Detecting specific patterns includes, for example, pattern recognition algorithms and feature extraction techniques. The analysis unit can, for example, detect patterns of collapsed buildings in the video to improve the accuracy of the analysis. The analysis unit can also detect patterns of road disruptions in the video to improve the accuracy of the analysis. Furthermore, the analysis unit can detect patterns of people's evacuation behavior in the video to improve the accuracy of the analysis. Detecting specific patterns thereby improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input specific patterns in the video into the generation AI and have the generation AI improve the accuracy of the analysis.

[0049] During analysis, the analysis unit can select an analysis method taking into consideration the quality and resolution of the video. Video quality includes, for example, resolution, frame rate, and noise level. The analysis unit, for example, performs detailed analysis using high-resolution video. The analysis unit can also perform quick analysis using low-resolution video. Furthermore, the analysis unit can select the optimal analysis method depending on the video quality. This makes it possible to select the optimal analysis method depending on the video quality and resolution. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input data on the video quality and resolution into the generation AI and have the generation AI select the analysis method.

[0050] During analysis, the analysis unit can adjust the analysis priority based on the time when the video was shot. The time when the video was shot includes, for example, the date and time of the video, the season, and the time of day. The analysis unit, for example, prioritizes analyzing the most recent video and provides information in real time. The analysis unit can also adjust the analysis priority by referring to past video. Furthermore, the analysis unit can select the optimal analysis method based on the time when the video was shot. This makes it possible to optimally adjust the analysis priority based on the time when the video was shot. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input data on the time when the video was shot into the generation AI and have the generation AI adjust the analysis priority.

[0051] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the related data of the video. Related data includes, for example, related video data and sensor data. The analysis unit can improve the accuracy of the analysis by referring to, for example, the related data of the video. The analysis unit can also select the optimal analysis method based on the related data of the video. Furthermore, the analysis unit can analyze the related data of the video and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the related data. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the related data of the video into the generation AI and have the generation AI improve the accuracy of the analysis.

[0052] During analysis, the analysis unit can select an analysis method taking into account the geographical characteristics of the location where the video was shot. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, when analyzing video of a mountainous area, the analysis unit selects an analysis method taking into account the characteristics of the topography. Furthermore, when analyzing video of an urban area, the analysis unit can select an analysis method taking into account the density of buildings. Furthermore, when analyzing video of a coastal area, the analysis unit can select an analysis method taking into account the tides. This makes it possible to select an optimal analysis method based on geographical characteristics. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data on the geographical characteristics of the location where the video was shot into the generation AI and have the generation AI select an analysis method.

[0053] When determining the next action, the decision unit can select the optimal action by referring to past action data. The past action data includes, for example, the type of action and the timing of its implementation. The decision unit, for example, analyzes the past action data and selects the most effective action. The decision unit can also adjust the priority of actions by referring to the past action data. Furthermore, the decision unit can customize and execute the optimal action based on the past action data. This makes it possible to select the optimal action based on the past action data. Some or all of the above-described processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input past action data into a generation AI and have the generation AI select the optimal action.

[0054] When determining the next action, the decision unit can monitor the situation at the disaster site in real time and adjust the action. "Real time" refers to a state in which delay time is minimized, meaning, for example, that information is updated with a delay of within a few seconds. The decision unit, for example, monitors the situation at the disaster site in real time and selects the optimal action. The decision unit can also adjust the priority of actions according to the situation at the disaster site. Furthermore, the decision unit can customize and execute actions based on the situation at the disaster site. This makes it possible to adjust the optimal action based on real-time situation monitoring. Some or all of the above-mentioned processing in the decision unit may be performed using AI or without AI. For example, the decision unit can input real-time data at the disaster site to the generation AI and have the generation AI adjust the action.

[0055] When determining the next action, the decision unit can optimize the action by taking into account information about the surrounding area of ​​the disaster site. The surrounding information includes, for example, information about surrounding buildings and traffic information. The decision unit selects the optimal action by taking into account, for example, information about the surrounding area of ​​the disaster site. The decision unit can also adjust the priority of actions according to the information about the surrounding area of ​​the disaster site. Furthermore, the decision unit can customize and execute actions based on the information about the surrounding area of ​​the disaster site. This makes it possible to select the optimal action based on the information about the surrounding area. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data about the surrounding area to a generation AI and cause the generation AI to optimize the action.

[0056] When determining the next action, the decision unit can select an action taking into consideration the geographical characteristics of the disaster site. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, the decision unit selects an action taking into consideration the topographical characteristics for a disaster site in a mountainous area. The decision unit can also select an action taking into consideration the density of buildings for a disaster site in an urban area. Furthermore, the decision unit can select an action taking into consideration the tides for a disaster site in a coastal area. This makes it possible to select an optimal action based on geographical characteristics. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data on the geographical characteristics of the disaster site into the generation AI and have the generation AI select an action.

[0057] When determining the next action, the decision unit can optimize the action by referring to past data of the disaster-stricken location. Past data includes, for example, disaster data and response history. The decision unit, for example, analyzes past data of the disaster-stricken location and selects the most effective action. The decision unit can also adjust the priority of actions by referring to past data of the disaster-stricken location. Furthermore, the decision unit can customize and execute actions based on past data of the disaster-stricken location. This makes it possible to select the optimal action based on past data. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input past data of the disaster-stricken location into a generation AI and cause the generation AI to optimize the action.

[0058] When determining the next action, the decision unit can adjust the action taking into account the population density of the disaster-stricken area. Population density includes, for example, census data and local demographic statistics. For example, the decision unit prioritizes the rapid delivery of relief supplies to disaster-stricken areas with high population densities. The decision unit can also prioritize ensuring efficient evacuation routes for disaster-stricken areas with low population densities. Furthermore, the decision unit can select and execute appropriate actions for disaster-stricken areas with medium population densities. This makes it possible to select optimal actions based on population density. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input population density data of the disaster-stricken area to the generation AI and have the generation AI adjust the action.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The installation department can take into account local socioeconomic data when selecting camera installation locations. For example, the installation department can install cameras in low-income areas to prioritize monitoring areas that need assistance in the event of a disaster. The installation department can also install cameras in densely populated commercial areas to collect information to minimize the economic impact of a disaster. Furthermore, the installation department can install cameras in tourist areas to provide information to ensure the safety of tourists in the event of a disaster. This allows for optimal camera installation based on socioeconomic data.

[0061] When collecting video footage captured by a camera, the collection unit can simultaneously collect metadata for the video. For example, the collection unit collects information on the date and time the video was taken and the location where it was taken, thereby streamlining video management. The collection unit can also collect technical information such as the resolution and frame rate of the video to optimize processing in the analysis unit. Furthermore, the collection unit can collect information on the conditions under which the video was taken (for example, weather and illuminance) to help evaluate the quality of the video. This enables efficient information collection using video metadata.

[0062] The analysis unit analyzes the audio data in the video to gain a more detailed understanding of the disaster situation. For example, the analysis unit can detect the sounds of buildings collapsing or people screaming, and assess the severity of the damage. The analysis unit can also detect voices calling for rescue and identify locations where a rapid response is required. Furthermore, the analysis unit can analyze environmental sounds (for example, the sounds of wind and rain) to understand the progression of the disaster. This makes it possible to accurately grasp the disaster situation using audio data.

[0063] The analysis unit can analyze text data in the video and collect information during a disaster. For example, the analysis unit can analyze the text on signs and road signs to identify the location of the disaster area. The analysis unit can also analyze the text on message boards held up by disaster victims to understand the content of rescue requests. Furthermore, the analysis unit can analyze subtitles in news footage to collect the latest information about the disaster. This makes it possible to efficiently collect information using text data.

[0064] The decision unit can determine the next action to take taking into account the infrastructure status of the affected area. For example, the decision unit can check the power supply status of the affected area and send generators to areas without power. The decision unit can also check the communication infrastructure status of the affected area and provide satellite phones to areas where communication has been cut off. Furthermore, the decision unit can check the water infrastructure status of the affected area and deliver drinking water to areas where the water supply has been cut off. This enables appropriate disaster response based on the infrastructure status.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The installation department installs cameras. For example, the installation department installs cameras at base stations, public facilities, and major transportation hubs. The installation department can also analyze past disaster data to select camera installation locations. For example, the installation department analyzes past earthquake data and installs cameras in areas that suffered major earthquake damage. Step 2: The collection unit collects the video captured by the camera installed by the installation unit. For example, the collection unit collects the video captured by the camera in real time. The collection unit can also customize the collection method when collecting the video, taking into account the environmental conditions of the collection target. For example, the collection unit collects video using a camera with high waterproof performance when it is raining. Step 3: The analysis unit analyzes the video collected by the collection unit and identifies the affected areas and damage conditions. For example, the analysis unit uses image analysis technology that uses deep learning to identify the affected areas and damage conditions in the video. The analysis unit can also use GPS data to identify the affected areas and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of the damage. Step 4: The determination unit determines the next action based on the disaster location and damage situation identified by the analysis unit. The determination unit determines the next action, such as delivering relief supplies or securing evacuation routes, based on the disaster location and damage situation. This allows the disaster information collection system according to the embodiment to efficiently collect information during a disaster and determine the next action. For example, the disaster information collection system can quickly determine specific countermeasures, such as delivering relief supplies to disaster-stricken areas and securing evacuation routes.

[0067] (Example 2) A disaster information collection system according to an embodiment of the present invention is a system in which cameras are installed to capture images of disaster situations in real time, and a generation AI analyzes the images to identify affected areas and damage conditions, and then determines the next action. The disaster information collection system installs cameras to capture images of disaster situations in real time, and a generation AI analyzes the images to identify affected areas and damage conditions, and then determines the next action, thereby providing information that will lead to the next action in disaster response. For example, in a disaster information collection system, when a disaster such as an earthquake or typhoon occurs, cameras are automatically activated to capture video of the affected area. This video is transmitted to a generation AI. The generation AI then analyzes the transmitted video. The generation AI uses image analysis technology based on deep learning to identify affected areas and damage conditions in the video. For example, it analyzes the state of collapsed buildings and road disruption to obtain detailed information about the affected area. Furthermore, based on the information analyzed by the generation AI, the system identifies affected areas and aggregates the damage conditions. GPS data is used to identify affected areas, and specific evaluation criteria are set for the evaluation of the damage conditions. For example, the system identifies the locations of affected businesses and homes and evaluates the extent of the damage. It also collects information about the surrounding area and provides information that will lead to the next steps in disaster response. This allows the disaster information collection system to quickly collect information in the event of a disaster, enabling appropriate disaster response. This allows the disaster information collection system to quickly collect information in the event of a disaster, enabling appropriate disaster response. For example, it allows for the rapid decision-making of specific response measures, such as delivering relief supplies to affected areas and securing evacuation routes.

[0068] A disaster information collection system according to an embodiment includes an installation unit, a collection unit, an analysis unit, and a determination unit. The installation unit installs cameras. For example, the installation unit installs cameras at base stations, public facilities, and major transportation hubs. The installation unit can also analyze past disaster data to select camera installation locations. For example, the installation unit analyzes past earthquake data and installs cameras in areas that suffered severe earthquake damage. The collection unit collects video footage captured by the cameras installed by the installation unit. For example, the collection unit collects video footage captured by the cameras in real time. The collection unit can also customize collection methods when collecting video footage, taking into account environmental conditions of the collection target. For example, the collection unit collects video footage using highly waterproof cameras during rainy weather. The analysis unit analyzes the video footage collected by the collection unit to identify disaster locations and damage conditions. For example, the analysis unit can use image analysis technology using deep learning to identify disaster locations and damage conditions in the video footage. The analysis unit can also use GPS data to identify disaster locations and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit identifies the locations of affected businesses and homes and evaluates the extent of the damage. The determination unit determines the next action based on the affected locations and damage status identified by the analysis unit. The determination unit determines the next action, such as delivering relief supplies or securing evacuation routes, based on the affected locations and damage status. This enables the disaster information collection system according to the embodiment to efficiently collect information during a disaster and determine the next action. For example, the disaster information collection system can quickly determine specific response measures, such as delivering relief supplies to affected areas and securing evacuation routes.

[0069] The installation unit can install cameras at base stations, public facilities, and transportation hubs. Base stations include, for example, communication base stations and power base stations. Public facilities include, for example, schools, hospitals, and government offices. Transportation hubs include, for example, train stations, bus terminals, and airports. The installation unit, for example, installs cameras at base stations and collects video footage using communication infrastructure. The installation unit can also install cameras at public facilities to monitor the status of facilities that function as evacuation shelters during disasters. Furthermore, the installation unit can install cameras at transportation hubs to monitor traffic conditions during disasters. This allows for a variety of camera installation locations and allows disaster information to be collected over a wide area. Some or all of the above-described processing in the installation unit may be performed using, for example, AI, or may be performed without using AI. For example, to select camera installation locations, the installation unit can input past disaster data into the generation AI and have the generation AI select the optimal installation location.

[0070] The collection unit can collect video captured by the camera in real time. "Real time" refers to a state in which delay time is minimized, meaning, for example, that video is collected with a delay of within a few seconds. The collection unit, for example, collects video captured by the camera in real time to immediately grasp the disaster situation. The collection unit can also customize the collection method when collecting video, taking into account the environmental conditions of the collection target. For example, the collection unit collects video using a highly waterproof camera in rainy weather. The collection unit can also collect video using a wind-resistant camera in windy areas. Furthermore, the collection unit can collect video using a snow-resistant camera in snowy areas. This allows the disaster situation to be grasped in real time. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input video captured by the camera into a generation AI, which analyzes and collects the video in real time.

[0071] The analysis unit can identify damaged locations and damage conditions within the video using image analysis technology based on deep learning. Deep learning is a type of machine learning that uses neural networks, and by learning from large amounts of data, it enables highly accurate analysis. The analysis unit, for example, uses image analysis technology based on deep learning to identify damaged locations and damage conditions within the video. For example, the analysis unit analyzes the collapsed state of buildings and the disruption of roads to obtain detailed information about the affected area. The analysis unit can also use GPS data to identify damaged locations and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of the damage. This enables highly accurate identification of damaged locations and damage conditions. Some or all of the above-mentioned processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs video captured by a camera into the generative AI, and identifies damaged locations and damage conditions using image analysis technology based on deep learning.

[0072] The analysis unit can use GPS data to identify affected locations and set specific evaluation criteria for assessing the damage situation. GPS data is location information obtained using the Global Positioning System, allowing for highly accurate location identification. The analysis unit, for example, uses GPS data to identify affected locations and sets specific evaluation criteria for assessing the damage situation. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of damage. Evaluation criteria can include, for example, the degree of building collapse and the extent of road disruption. This enables accurate identification of affected locations and evaluation of the damage situation. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input GPS data into the generation AI and have the generation AI identify affected locations and assess the damage situation.

[0073] The decision unit can determine the next action, such as delivering relief supplies or securing an evacuation route, based on the location of the disaster or the damage situation. The decision unit determines the next action, such as delivering relief supplies or securing an evacuation route, based on, for example, the location of the disaster or the damage situation. Relief supplies include, for example, food, water, medicine, etc. Securing an evacuation route includes, for example, criteria for setting evacuation routes and methods for displaying evacuation routes. This enables a prompt and appropriate disaster response. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data on the location of the disaster and the damage situation into the generation AI and have the generation AI determine the next action.

[0074] The installation unit can estimate the user's state and adjust the installation location of the camera based on the estimated user's state. For example, if the user is feeling anxious, the installation unit installs the camera in a location where it is easy to see to provide a sense of security. Furthermore, if the user is relaxed, the installation unit can install the camera in an inconspicuous location to protect privacy. Furthermore, if the user is facing an emergency, the installation unit can quickly install the camera to monitor the situation in real time. This enables camera installation according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without AI. For example, the installation unit can input the user's emotion data into the generation AI and have the generation AI adjust the installation location of the camera.

[0075] The installation unit can analyze past disaster data and select optimal camera installation locations. Past disaster data includes, for example, disaster data such as earthquakes, typhoons, and floods. The installation unit can, for example, analyze past earthquake data and install cameras in areas that suffered severe earthquake damage. The installation unit can also analyze past typhoon data and install cameras in areas along the typhoon's path. Furthermore, the installation unit can analyze past flood data and install cameras in areas that suffered severe flood damage. This enables optimal camera installation based on past disaster data. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input past disaster data into the generation AI and have the generation AI select the optimal camera installation location.

[0076] When installing a camera, the installation unit can determine the optimal installation angle by taking into account the surrounding environment and obstacles. The surrounding environment includes, for example, the layout of buildings and the natural environment. Obstacles include, for example, buildings, trees, and vehicles. For example, if there is a tall building in the vicinity, the installation unit can adjust the installation angle of the camera to avoid it. In addition, if there are many trees in the vicinity, the installation unit can also adjust the installation angle of the camera to ensure visibility. Furthermore, if there is heavy traffic in the vicinity, the installation unit can adjust the installation angle of the camera to monitor traffic flow. This enables optimal camera installation taking into account the surrounding environment and obstacles. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input data on the surrounding environment and obstacles into the generation AI and have the generation AI determine the optimal installation angle.

[0077] When installing a camera, the installation unit can select a durable installation method taking into account the weather conditions of the installation location. Weather conditions include, for example, wind speed, rainfall, and temperature. For example, in a rainy region, the installation unit selects a camera with high waterproof performance and devise an installation method. Furthermore, in a windy region, the installation unit can select a wind-resistant installation method to strengthen the fixation of the camera. Furthermore, in a snowy region, the installation unit can select a snow-resistant installation method to strengthen the protection of the camera. This enables a durable camera installation according to the weather conditions. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input weather condition data into the generation AI and have the generation AI select a durable installation method.

[0078] The installation unit can estimate the user's state and adjust the timing of camera installation based on the estimated user's state. For example, if the user is feeling anxious, the installation unit can quickly install a camera to provide a sense of security. Furthermore, if the user is relaxed, the installation unit can adjust the installation timing to protect privacy. Furthermore, if the user is facing an emergency, the installation unit can instantly install a camera to monitor the situation in real time. This enables the adjustment of camera installation timing according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-described processing in the installation unit may be performed using AI, or may be performed without AI. For example, the installation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of camera installation.

[0079] When installing a camera, the installation unit can select the optimal installation method by taking into account the geographical characteristics of the installation location. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, in mountainous areas, the installation unit can install the camera at a high position to ensure visibility. In urban areas, the installation unit can also install the camera between buildings to monitor a wide area. Furthermore, in coastal areas, the installation unit can select an installation method that is resistant to sea breezes to ensure the durability of the camera. This enables optimal camera installation according to the geographical characteristics. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input data on geographical characteristics into the generation AI and have the generation AI select the optimal installation method.

[0080] When installing a camera, the installation unit can strengthen crime prevention measures by taking into account the crime rate of the installation location. Crime rates include, for example, police statistical data and the number of crimes. For example, in areas with high crime rates, the installation unit installs the camera so that it also functions as a security camera. The installation unit can also devise a way to install the camera in an inconspicuous location in areas with high crime rates. Furthermore, in areas with high crime rates, the installation unit can install cameras in multiple locations to expand the monitoring area. This makes it possible to strengthen crime prevention measures according to the crime rate. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input crime rate data into the generation AI and have the generation AI strengthen crime prevention measures.

[0081] When installing cameras, the installation unit can adjust the number of cameras to be installed taking into account the population density of the installation location. Population density includes, for example, census data and local demographic statistics. For example, in areas with high population density, the installation unit can increase the number of cameras to expand the monitoring area. The installation unit can also reduce the number of cameras to be installed in areas with low population density to reduce costs. Furthermore, in areas with medium population density, the installation unit can select an appropriate number of cameras to install and monitor efficiently. This makes it possible to adjust the number of cameras to be installed according to population density. Some or all of the above-mentioned processing in the installation unit may be performed using AI, or may be performed without using AI. For example, the installation unit can input population density data into the generation AI and have the generation AI adjust the number of cameras to be installed.

[0082] The collection unit can estimate the user's state and adjust the frequency of video collection based on the estimated user's state. For example, if the user feels anxious, the collection unit can increase the frequency of video collection to provide a sense of security. Furthermore, if the user feels relaxed, the collection unit can also reduce the frequency of video collection to protect privacy. Furthermore, if the user is facing an emergency, the collection unit can maximize the frequency of video collection to monitor the situation in real time. This enables adjustment of the frequency of video collection according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the frequency of video collection.

[0083] When collecting video, the collection unit can select the optimal collection method by referring to past collected data. The past collected data includes, for example, the type of collected data and the collection period. The collection unit, for example, analyzes the past collected data and selects the most effective collection method. The collection unit can also optimize the collection frequency and collection range by referring to the past collected data. Furthermore, the collection unit can customize the collection method based on the past collected data to efficiently collect video. This enables optimal video collection based on the past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the past collected data into the generation AI and have the generation AI select the optimal collection method.

[0084] When collecting video, the collection unit can customize the collection means by taking into account the environmental conditions of the collection target. Environmental conditions include, for example, temperature, humidity, and illuminance. For example, the collection unit collects video using a camera with high waterproof performance when it is raining. In addition, the collection unit can also collect video using a camera that is resistant to wind in windy areas. Furthermore, the collection unit can also collect video using a camera that is resistant to snow in snowy areas. This enables optimal video collection according to environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data on environmental conditions into the generation AI and have the generation AI customize the collection means.

[0085] The collection unit can improve collection accuracy by detecting movements and changes in the collection target in real time when collecting video. Detection of movements and changes includes, for example, motion detection algorithms and change detection technology. For example, if the collection target is moving, the collection unit detects the movement in real time and collects video. The collection unit can also improve collection accuracy by detecting changes in the collection target in real time. Furthermore, the collection unit can analyze movements and changes in the collection target and select the optimal collection method. This improves collection accuracy by detecting movements and changes in real time. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can input data on movements and changes in the collection target to the generation AI and have the generation AI improve collection accuracy.

[0086] The collection unit can estimate the user's state and determine the priority of the videos to be collected based on the estimated user's state. For example, if the user is feeling anxious, the collection unit can prioritize collecting important videos. Furthermore, if the user is relaxed, the collection unit can also adjust the priority of videos to protect privacy. Furthermore, if the user is facing an emergency, the collection unit can prioritize collecting videos with high urgency. This enables the priority of video collection to be determined according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the videos to be collected.

[0087] When collecting video, the collection unit can adjust the collection range taking into account the geographical location information of the collection target. Geographical location information includes, for example, GPS data and map information. The collection unit can, for example, optimize the collection range based on the geographical location information of the collection target. The collection unit can also expand the collection range taking into account the geographical location information of the collection target. Furthermore, the collection unit can also refer to the geographical location information of the collection target and adjust the collection range to efficiently collect video. This makes it possible to adjust the optimal collection range based on the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and have the generation AI adjust the collection range.

[0088] When collecting video, the collection unit can optimize the collection method by referring to past data of the target to be collected. The past data includes, for example, the type of collected data and the collection period. The collection unit, for example, analyzes the past data of the target to be collected and selects the optimal collection method. The collection unit can also optimize the collection frequency and collection range by referring to the past data of the target to be collected. Furthermore, the collection unit can customize the collection method based on the past data of the target to collect and collect video efficiently. This makes it possible to select the optimal collection method based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input past data into the generation AI and have the generation AI optimize the collection method.

[0089] The collection unit can improve the accuracy of the information by simultaneously collecting peripheral information about the collection target when collecting video. The peripheral information includes, for example, information about surrounding buildings and traffic information. The collection unit can, for example, simultaneously collect peripheral information about the collection target to improve the accuracy of the information. The collection unit can also analyze the peripheral information about the collection target and select the optimal collection method. Furthermore, the collection unit can customize the collection method based on the peripheral information about the collection target to efficiently collect video. This improves the accuracy of the information by simultaneously collecting peripheral information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input peripheral information data into a generation AI and have the generation AI improve the accuracy of the information.

[0090] The analysis unit can estimate the user's state and adjust the display method of the analysis results based on the estimated user's state. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0091] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis data. The past analysis data includes, for example, the type of analysis data and the analysis period. The analysis unit, for example, analyzes the past analysis data and selects the most effective analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to the past analysis data. Furthermore, the analysis unit can customize the analysis algorithm based on the past analysis data to perform efficient analysis. This makes it possible to select the optimal analysis algorithm based on the past analysis data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0092] During analysis, the analysis unit can detect specific patterns in the video to improve the accuracy of the analysis. Detecting specific patterns includes, for example, pattern recognition algorithms and feature extraction techniques. The analysis unit can, for example, detect patterns of collapsed buildings in the video to improve the accuracy of the analysis. The analysis unit can also detect patterns of road disruptions in the video to improve the accuracy of the analysis. Furthermore, the analysis unit can detect patterns of people's evacuation behavior in the video to improve the accuracy of the analysis. Detecting specific patterns thereby improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input specific patterns in the video into the generation AI and have the generation AI improve the accuracy of the analysis.

[0093] During analysis, the analysis unit can select an analysis method taking into consideration the quality and resolution of the video. Video quality includes, for example, resolution, frame rate, and noise level. The analysis unit, for example, performs detailed analysis using high-resolution video. The analysis unit can also perform quick analysis using low-resolution video. Furthermore, the analysis unit can select the optimal analysis method depending on the video quality. This makes it possible to select the optimal analysis method depending on the video quality and resolution. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input data on the video quality and resolution into the generation AI and have the generation AI select the analysis method.

[0094] The analysis unit can estimate the user's state and prioritize the analysis results based on the estimated user's state. For example, if the user is feeling anxious, the analysis unit can prioritize displaying important analysis results. The analysis unit can also display detailed analysis results if the user is relaxed. Furthermore, if the user is facing an emergency, the analysis unit can prioritize displaying analysis results with high urgency. This makes it possible to prioritize analysis results according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0095] During analysis, the analysis unit can adjust the analysis priority based on the time when the video was shot. The time when the video was shot includes, for example, the date and time of the video, the season, and the time of day. The analysis unit, for example, prioritizes analyzing the most recent video and provides information in real time. The analysis unit can also adjust the analysis priority by referring to past video. Furthermore, the analysis unit can select the optimal analysis method based on the time when the video was shot. This makes it possible to optimally adjust the analysis priority based on the time when the video was shot. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input data on the time when the video was shot into the generation AI and have the generation AI adjust the analysis priority.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the related data of the video. Related data includes, for example, related video data and sensor data. The analysis unit can improve the accuracy of the analysis by referring to, for example, the related data of the video. The analysis unit can also select the optimal analysis method based on the related data of the video. Furthermore, the analysis unit can analyze the related data of the video and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the related data. Some or all of the above-mentioned processing in the analysis unit can be performed using a generation AI. For example, the analysis unit can input the related data of the video into the generation AI and have the generation AI improve the accuracy of the analysis.

[0097] During analysis, the analysis unit can select an analysis method taking into account the geographical characteristics of the location where the video was shot. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, when analyzing video of a mountainous area, the analysis unit selects an analysis method taking into account the characteristics of the topography. Furthermore, when analyzing video of an urban area, the analysis unit can select an analysis method taking into account the density of buildings. Furthermore, when analyzing video of a coastal area, the analysis unit can select an analysis method taking into account the tides. This makes it possible to select an optimal analysis method based on geographical characteristics. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data on the geographical characteristics of the location where the video was shot into the generation AI and have the generation AI select an analysis method.

[0098] The determination unit can estimate the user's state and determine the priority of the next action based on the estimated user's state. For example, if the user is feeling anxious, the determination unit can prioritize important actions. The determination unit can also suggest detailed actions if the user is relaxed. Furthermore, if the user is facing an emergency, the determination unit can prioritize actions with high urgency. This makes it possible to determine the priority of the next action according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-mentioned processing in the determination unit may be performed using AI or without AI. For example, the determination unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the next action.

[0099] When determining the next action, the decision unit can select the optimal action by referring to past action data. The past action data includes, for example, the type of action and the timing of its implementation. The decision unit, for example, analyzes the past action data and selects the most effective action. The decision unit can also adjust the priority of actions by referring to the past action data. Furthermore, the decision unit can customize and execute the optimal action based on the past action data. This makes it possible to select the optimal action based on the past action data. Some or all of the above-described processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input past action data into a generation AI and have the generation AI select the optimal action.

[0100] When determining the next action, the decision unit can monitor the situation at the disaster site in real time and adjust the action. "Real time" refers to a state in which delay time is minimized, meaning, for example, that information is updated with a delay of within a few seconds. The decision unit, for example, monitors the situation at the disaster site in real time and selects the optimal action. The decision unit can also adjust the priority of actions according to the situation at the disaster site. Furthermore, the decision unit can customize and execute actions based on the situation at the disaster site. This makes it possible to adjust the optimal action based on real-time situation monitoring. Some or all of the above-mentioned processing in the decision unit may be performed using AI or without AI. For example, the decision unit can input real-time data at the disaster site to the generation AI and have the generation AI adjust the action.

[0101] When determining the next action, the decision unit can optimize the action by taking into account information about the surrounding area of ​​the disaster site. The surrounding information includes, for example, information about surrounding buildings and traffic information. The decision unit selects the optimal action by taking into account, for example, information about the surrounding area of ​​the disaster site. The decision unit can also adjust the priority of actions according to the information about the surrounding area of ​​the disaster site. Furthermore, the decision unit can customize and execute actions based on the information about the surrounding area of ​​the disaster site. This makes it possible to select the optimal action based on the information about the surrounding area. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data about the surrounding area to a generation AI and cause the generation AI to optimize the action.

[0102] The determination unit can estimate the user's state and adjust the display method of the next action based on the estimated user's state. For example, if the user is feeling anxious, the determination unit can provide a simple, highly visible display method. If the user is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, if the user is facing an emergency, the determination unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the next action according to the user's emotions. The user's state is estimated using, for example, emotion recognition technology or a behavioral data analysis method. Some or all of the above-mentioned processing in the determination unit may be performed using AI or without AI. For example, the determination unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the display method of the next action.

[0103] When determining the next action, the decision unit can select an action taking into consideration the geographical characteristics of the disaster site. Geographical characteristics include, for example, topography, land use, and traffic conditions. For example, the decision unit selects an action taking into consideration the topographical characteristics for a disaster site in a mountainous area. The decision unit can also select an action taking into consideration the density of buildings for a disaster site in an urban area. Furthermore, the decision unit can select an action taking into consideration the tides for a disaster site in a coastal area. This makes it possible to select an optimal action based on geographical characteristics. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input data on the geographical characteristics of the disaster site into the generation AI and have the generation AI select an action.

[0104] When determining the next action, the decision unit can optimize the action by referring to past data of the disaster-stricken location. Past data includes, for example, disaster data and response history. The decision unit, for example, analyzes past data of the disaster-stricken location and selects the most effective action. The decision unit can also adjust the priority of actions by referring to past data of the disaster-stricken location. Furthermore, the decision unit can customize and execute actions based on past data of the disaster-stricken location. This makes it possible to select the optimal action based on past data. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input past data of the disaster-stricken location into a generation AI and cause the generation AI to optimize the action.

[0105] When determining the next action, the decision unit can adjust the action taking into account the population density of the disaster-stricken area. Population density includes, for example, census data and local demographic statistics. For example, the decision unit prioritizes the rapid delivery of relief supplies to disaster-stricken areas with high population densities. The decision unit can also prioritize ensuring efficient evacuation routes for disaster-stricken areas with low population densities. Furthermore, the decision unit can select and execute appropriate actions for disaster-stricken areas with medium population densities. This makes it possible to select optimal actions based on population density. Some or all of the above-mentioned processing in the decision unit may be performed using AI, or may be performed without using AI. For example, the decision unit can input population density data of the disaster-stricken area to the generation AI and have the generation AI adjust the action. === Hard Collateral 1-1 === Each of the multiple elements, including the installation unit, collection unit, analysis unit, and determination unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the installation unit selects a location for installing a camera using the control unit 46A of the smart device 14 and installs the camera. The collection unit collects video using the camera 42 of the smart device 14 and processes the collected video using the identification processing unit 290 of the data processing device 12. The analysis unit performs image analysis using deep learning using the identification processing unit 290 of the data processing device 12 to identify the location and damage situation of the disaster. The determination unit determines the next action using the identification processing unit 290 of the data processing device 12 and provides the information through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the installation unit, collection unit, analysis unit, and decision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the installation unit selects a camera installation location and installs the camera using the control unit 46A of the smart glasses 214. The collection unit collects video using the camera 42 of the smart glasses 214 and processes the collected video using the identification processing unit 290 of the data processing device 12. The analysis unit performs image analysis using deep learning using the identification processing unit 290 of the data processing device 12 to identify the disaster location and damage situation. The decision unit determines the next action using the identification processing unit 290 of the data processing device 12 and provides information through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the installation unit, collection unit, analysis unit, and decision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the installation unit selects a location for installing a camera using the control unit 46A of the headset terminal 314 and installs the camera. The collection unit collects video using the camera 42 of the headset terminal 314 and processes the collected video using the identification processing unit 290 of the data processing device 12. The analysis unit performs image analysis using deep learning using the identification processing unit 290 of the data processing device 12 to identify the location and damage situation of the disaster. The decision unit determines the next action using the identification processing unit 290 of the data processing device 12 and provides information through the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the installation unit, collection unit, analysis unit, and decision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the installation unit selects a location for installing a camera using the control unit 46A of the robot 414 and installs the camera. The collection unit collects video using the camera 42 of the robot 414 and processes the collected video using the identification processing unit 290 of the data processing device 12. The analysis unit performs image analysis using deep learning using the identification processing unit 290 of the data processing device 12 to identify the disaster location and damage situation. The decision unit determines the next action using the identification processing unit 290 of the data processing device 12 and provides information through the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The installation department can take into account local socioeconomic data when selecting camera installation locations. For example, the installation department can install cameras in low-income areas to prioritize monitoring areas that need assistance in the event of a disaster. The installation department can also install cameras in densely populated commercial areas to collect information to minimize the economic impact of a disaster. Furthermore, the installation department can install cameras in tourist areas to provide information to ensure the safety of tourists in the event of a disaster. This allows for optimal camera installation based on socioeconomic data.

[0108] When collecting video footage captured by a camera, the collection unit can simultaneously collect metadata for the video. For example, the collection unit collects information on the date and time the video was taken and the location where it was taken, thereby streamlining video management. The collection unit can also collect technical information such as the resolution and frame rate of the video to optimize processing in the analysis unit. Furthermore, the collection unit can collect information on the conditions under which the video was taken (for example, weather and illuminance) to help evaluate the quality of the video. This enables efficient information collection using video metadata.

[0109] The analysis unit analyzes the audio data in the video to gain a more detailed understanding of the disaster situation. For example, the analysis unit can detect the sounds of buildings collapsing or people screaming, and assess the severity of the damage. The analysis unit can also detect voices calling for rescue and identify locations where a rapid response is required. Furthermore, the analysis unit can analyze environmental sounds (for example, the sounds of wind and rain) to understand the progression of the disaster. This makes it possible to accurately grasp the disaster situation using audio data.

[0110] The analysis unit can analyze text data in the video and collect information during a disaster. For example, the analysis unit can analyze the text on signs and road signs to identify the location of the disaster area. The analysis unit can also analyze the text on message boards held up by disaster victims to understand the content of rescue requests. Furthermore, the analysis unit can analyze subtitles in news footage to collect the latest information about the disaster. This makes it possible to efficiently collect information using text data.

[0111] The decision unit can determine the next action to take taking into account the infrastructure status of the affected area. For example, the decision unit can check the power supply status of the affected area and send generators to areas without power. The decision unit can also check the communication infrastructure status of the affected area and provide satellite phones to areas where communication has been cut off. Furthermore, the decision unit can check the water infrastructure status of the affected area and deliver drinking water to areas where the water supply has been cut off. This enables appropriate disaster response based on the infrastructure status.

[0112] The installation unit can estimate the user's emotions and adjust the installation location of the camera based on the estimated emotions. For example, if the user is feeling anxious, the installation unit installs the camera in a location where it is easy to see in order to give the user a sense of security. Also, if the user is relaxed, the installation unit can install the camera in an inconspicuous location to protect the user's privacy. Furthermore, if the user is facing an emergency, the installation unit can quickly install the camera to monitor the situation in real time. This makes it possible to install cameras according to the user's emotions.

[0113] The collection unit can estimate the user's emotions and adjust the frequency of video collection based on the estimated emotions. For example, if the user feels anxious, the collection unit can increase the frequency of video collection to provide a sense of security. Also, if the user feels relaxed, the collection unit can reduce the frequency of video collection to protect privacy. Furthermore, if the user is facing an emergency, the collection unit can maximize the frequency of video collection to monitor the situation in real time. This makes it possible to adjust the frequency of video collection according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is facing an emergency, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0115] The determination unit can estimate the user's emotions and determine the priority of the next action based on the estimated emotions. For example, if the user feels anxious, the determination unit can prioritize the execution of important actions. Also, if the user feels relaxed, the determination unit can suggest detailed actions. Furthermore, if the user is facing an emergency, the determination unit can prioritize the execution of actions with high urgency. This makes it possible to determine the priority of the next action according to the user's emotions.

[0116] The determination unit can estimate the user's emotion and adjust the display method of the next action based on the estimated emotion. For example, if the user is feeling anxious, the determination unit can provide a simple, highly visible display method. If the user is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, if the user is facing an emergency, the determination unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the next action according to the user's emotion.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The installation department installs cameras. For example, the installation department installs cameras at base stations, public facilities, and major transportation hubs. The installation department can also analyze past disaster data to select camera installation locations. For example, the installation department analyzes past earthquake data and installs cameras in areas that suffered major earthquake damage. Step 2: The collection unit collects the video captured by the camera installed by the installation unit. For example, the collection unit collects the video captured by the camera in real time. The collection unit can also customize the collection method when collecting the video, taking into account the environmental conditions of the collection target. For example, the collection unit collects video using a camera with high waterproof performance when it is raining. Step 3: The analysis unit analyzes the video collected by the collection unit and identifies the affected areas and damage conditions. For example, the analysis unit uses image analysis technology that uses deep learning to identify the affected areas and damage conditions in the video. The analysis unit can also use GPS data to identify the affected areas and set specific evaluation criteria for assessing the damage conditions. For example, the analysis unit can identify the locations of affected businesses and homes and evaluate the extent of the damage. Step 4: The determination unit determines the next action based on the disaster location and damage situation identified by the analysis unit. The determination unit determines the next action, such as delivering relief supplies or securing evacuation routes, based on the disaster location and damage situation. This allows the disaster information collection system according to the embodiment to efficiently collect information during a disaster and determine the next action. For example, the disaster information collection system can quickly determine specific countermeasures, such as delivering relief supplies to disaster-stricken areas and securing evacuation routes.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an installation unit for installing the camera; a collection unit that collects images captured by the cameras installed by the installation unit; an analysis unit that analyzes the video collected by the collection unit and identifies the location or state of the damage; a decision unit that decides a next action based on the disaster location or damage situation identified by the analysis unit. A system characterized by:

2. The installation unit includes: Install cameras at base stations, public facilities, and transportation hubs 2. The system of claim 1.

3. The collecting unit Collecting camera footage in real time 2. The system of claim 1.

4. The analysis unit Using image analysis technology based on deep learning, the affected areas and damage conditions can be identified in the video.

2. The system of claim 1.

5. The analysis unit Use GPS data to identify affected areas and set specific criteria for damage assessment 2. The system of claim 1.

6. The determination unit Determine the next action to take, such as delivering relief supplies or securing evacuation routes, based on the location or damage situation of the disaster.

2. The system of claim 1.

7. The installation unit includes: Estimate the user's state and adjust the camera location based on the estimated user state.

2. The system of claim 1.

8. The installation unit includes: Analyzing past disaster data and selecting optimal camera installation locations 2. The system of claim 1.

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