System

Abstract

To provide a system capable of providing proper behavioral instructions while precisely predicting individual risk of damage during a disaster.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a prediction unit, and an instruction unit. The collection unit collects real-time image and sensor data from vehicles, pedestrians, and residents. The analysis unit comprehends the surrounding area by analyzing data collected by the collection unit. The prediction unit predicts disaster risks locally and precisely by integrating the data analyzed by the analysis unit with publicly available information from a wide area. The instruction unit provides individualized behavioral instructions for each subject based on the risk predicted by the prediction 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately predicted individual damage risks in the event of a disaster and provided appropriate action instructions, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately predict individual damage risks in the event of a disaster and provide appropriate action instructions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and an instruction unit. The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. The prediction unit integrates the data analyzed by the analysis unit with public information over a wide area, and predicts disaster risk locally and precisely. The instruction unit provides behavioral instructions that are individualized for each target person based on the risk predicted by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can accurately predict individual risks of damage in the event of a disaster and provide appropriate instructions on what to do. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that collects individual environmental information from vehicles, pedestrians, and residents waiting indoors in real time in flood-prone areas and analyzes the surrounding conditions. This system first collects real-time video and sensor data from vehicles, pedestrians, and residents. Next, the collected data is analyzed, and the target's risk of damage is predicted locally and with high accuracy in conjunction with wide-area public information. Furthermore, if there is a road that is difficult to pass when flooded, an appropriate detour route is instructed and behavior instructions optimized for each target are provided. This allows people to act with peace of mind even in emergencies. For example, real-time video and sensor data are collected from vehicles, pedestrians, and residents. At this time, data is collected from cameras and sensors mounted on vehicles, sensors on smartphones held by pedestrians, household sensors installed by residents, and the like. For example, the data includes video captured by a vehicle's camera, location information acquired by a pedestrian's smartphone, and water level information detected by a resident's household sensor. Next, the collected data is analyzed. A generation AI is used for the analysis, and each data is integrated to grasp the surrounding conditions. For example, the system analyzes the flooding status of roads from vehicle camera footage and identifies safe evacuation routes from pedestrian location information. In addition, it predicts the progress of flooding based on water level information obtained from residents' home sensors. In addition, it predicts the target's risk of damage locally and with high accuracy in conjunction with wide-area public information. For example, by integrating public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments and combining it with individual environmental information, it makes more accurate risk predictions. Finally, if there are roads that are difficult to pass when flooded, it instructs appropriate detour routes. The generation AI calculates the optimal detour route based on the collected data and public information and provides it to the target. For example, it instructs vehicles to detour routes through voice guidance and navigation systems, and presents pedestrians with safe evacuation routes through smartphone apps. It also instructs residents to evacuate through home sensors. This mechanism allows people to act with peace of mind even in emergencies. For example, vehicles can move safely by avoiding flooded roads, and pedestrians can secure safe evacuation routes. In addition, residents can receive evacuation instructions early and respond quickly.This enables the system to collect individual environmental information from vehicles, pedestrians and residents in real time in areas at risk of flooding or other water damage, and analyze the surrounding situation.

[0029] The flood prediction system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and an instruction unit. The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. The collection unit collects data, for example, from cameras and sensors mounted on vehicles, sensors on smartphones carried by pedestrians, and home sensors installed by residents. For example, the collection unit collects video captured by a vehicle's camera. The collection unit can also collect location information acquired by the pedestrian's smartphone. The collection unit can also collect water level information detected by the resident's home sensor. For example, the collection unit collects vehicle camera video in real time and transmits it to the analysis unit. The collection unit can also acquire location information from the pedestrian's smartphone and transmit it to the analysis unit. The collection unit can also acquire water level information from the resident's home sensor and transmit it to the analysis unit. The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. The analysis unit analyzes the flooding situation of a road from, for example, a vehicle's camera video. The analysis unit can also identify a safe evacuation route from the pedestrian's location information. The analysis unit can also predict the progress of flooding based on water level information obtained from the residents' home sensors. For example, the analysis unit analyzes the vehicle's camera image to grasp the flooding status of the road. The analysis unit can also analyze the position information of pedestrians and identify safe evacuation routes. The analysis unit can also analyze the water level information obtained from the residents' home sensors to predict the progress of flooding. The prediction unit integrates the data analyzed by the analysis unit with wide-area public information to predict the disaster risk locally and with high accuracy. The prediction unit integrates public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments, and combines it with individual environmental information to perform risk prediction. For example, the prediction unit combines the flood forecasts from the Japan Meteorological Agency with individual environmental information to perform risk prediction. The prediction unit can also combine the evacuation information from local governments with individual environmental information to perform risk prediction. The instruction unit provides behavioral instructions optimized for each target person based on the risk predicted by the prediction unit. The instruction unit, for example, calculates an optimal detour route based on collected data and public information, and instructs the vehicle on the detour route via voice guidance or a navigation system.The instruction unit can also present a safe evacuation route to pedestrians through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors. For example, the instruction unit instructs vehicles to take a detour route through voice guidance. The instruction unit can also present pedestrians with a safe evacuation route through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors. As a result, the flood prediction system according to the embodiment can collect data from vehicles, pedestrians, and residents, analyze, predict, and give instructions, allowing residents to act with peace of mind even in an emergency. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can calculate an optimal detour route based on the collected data and public information, and instruct vehicles to take the detour route through voice guidance or a navigation system. The instruction unit can also present pedestrians with a safe evacuation route through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors.

[0030] The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. Vehicles are equipped with cameras and sensors, and collect video and sensor data from these devices. Specifically, the vehicle's camera captures road conditions and the surrounding environment, and the sensor detects environmental data such as temperature, humidity, and vibration. These data are collected in real time and sent to a central database. Pedestrians carry smartphones and collect location information and environmental sensor data from the smartphone. The smartphone's GPS function is used to accurately determine the pedestrian's current location, and acceleration and gyro sensors are used to detect the pedestrian's movement and the surrounding environment. Residents install household sensors and collect water level information and environmental data from these sensors. The household sensors are installed, for example, in basements and gardens, and the water level sensors detect the presence or absence of flooding and rising water levels, while the temperature and humidity sensors monitor changes in the environment. This allows the collection unit to collect a wide range of data from various devices and grasp the situation in real time. Furthermore, the collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and prediction units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. For the analysis, AI is used to process the data in real time and grasp the surrounding situation. Specifically, it detects the flooding of roads from the vehicle's camera footage, identifies safe evacuation routes from pedestrian location information, and predicts the progress of the flooding based on water level information obtained from residents' home sensors. AI analyzes the camera footage using image recognition technology to identify flooded areas and obstacles on the road. It also analyzes pedestrian location information and calculates the optimal evacuation route. It also analyzes water level information from home sensors and predicts the progress speed and range of the flooding. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, it predicts fluctuations in risk in specific areas and time periods based on past flooding data and plans future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The prediction unit predicts the risk of damage by integrating the data analyzed by the analysis unit with public information from a wide area. Specifically, by utilizing public information from a wide area, such as rainfall information and river water level information provided by the Japan Meteorological Agency, and integrating it with the collected data, more accurate risk prediction is performed. For example, based on the rainfall information from the Japan Meteorological Agency, future rainfall is predicted, and based on the river water level information, the risk of river flooding is evaluated. By integrating these pieces of information, it is possible to predict the risk of flooding and the need for evacuation in a specific area. The prediction unit uses AI to analyze these data and simulate multiple scenarios to identify the most likely risk. This allows the prediction unit to predict the risk of damage with high accuracy and provide information for taking appropriate measures. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the amount of rainfall or the water level of a river changes suddenly, the prediction unit immediately takes in new data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessment by taking into account the characteristics of each area and past disaster history. This enables the prediction unit to always provide highly accurate risk predictions based on the latest information, and to support rapid and appropriate responses.

[0033] The instruction unit provides appropriate action instructions based on the risk predicted by the prediction unit. Specifically, the vehicle displays a detour route on the navigation system, pedestrians are notified of a safe evacuation route via their smartphone, and residents are provided with evacuation instructions via home sensors. The vehicle's navigation system calculates the optimal detour route based on map information updated in real time and displays it to the driver. Pedestrians' smartphones are notified of the optimal evacuation route based on their current location and evacuation destination, and are alerted by voice guidance, vibration notifications, etc. Residents are provided with information on the need for evacuation and evacuation destinations via home sensors. This allows the instruction unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the instruction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, the evacuation route is reviewed and the instructions are improved based on feedback from users who have received evacuation instructions. The instruction unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the instruction unit to provide the user with prompt and reliable instructions on what to do, thereby minimizing the risk of damage.

[0034] The collection unit can collect data from a camera or sensor mounted on a vehicle, a sensor of a smartphone carried by a pedestrian, and a home sensor installed by a resident. The collection unit, for example, collects data from a camera or sensor mounted on a vehicle. For example, the collection unit can collect video data from a dash cam or an in-car camera of the vehicle. The collection unit can also collect location data from a GPS sensor of the vehicle. The collection unit can also collect data from a sensor of a smartphone carried by a pedestrian. For example, the collection unit can collect data from an acceleration sensor or a gyro sensor of the smartphone. The collection unit can also collect location data from a GPS sensor of the smartphone. The collection unit can also collect data from a home sensor installed by a resident. For example, the collection unit can collect data from a home temperature sensor or a humidity sensor. The collection unit can also collect water level data from a home water level sensor. In this way, the collection unit can obtain more detailed environmental information by collecting data from various devices. A part 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 data obtained from a vehicle's cameras and sensors into the generation AI and have the generation AI analyze the data.

[0035] The analysis unit can analyze the flooding status of the road from the vehicle's camera image, identify a safe evacuation route from the pedestrian's position information, and predict the progress of the flooding based on the water level information obtained from the resident's home sensor. The analysis unit, for example, analyzes the vehicle's camera image to grasp the flooding status of the road. For example, the analysis unit can use an image analysis algorithm to evaluate the depth of the flooding from the vehicle's camera image. The analysis unit can also analyze the pedestrian's position information and identify a safe evacuation route. For example, the analysis unit can identify a safe route according to the evacuation route selection criteria based on the pedestrian's position information. The analysis unit can also analyze the water level information obtained from the resident's home sensor and predict the progress of the flooding. For example, the analysis unit can evaluate the progress speed of the flooding using a water level change prediction model. This allows the analysis unit to analyze various data and grasp the surrounding situation more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input vehicle camera footage to the generation AI and have the generation AI analyze the flooding situation. The analysis unit can also input pedestrian location information to the generation AI and have the generation AI identify safe evacuation routes. The analysis unit can also input water level information obtained from residents' home sensors to the generation AI and have the generation AI predict the progress of the flooding.

[0036] The prediction unit can integrate public information such as a flood forecast from the Japan Meteorological Agency or evacuation information from a local government, and perform risk prediction by combining it with individual environmental information. The prediction unit performs risk prediction by combining, for example, a flood forecast from the Japan Meteorological Agency with individual environmental information. For example, the prediction unit can acquire a flood forecast from the Japan Meteorological Agency, and perform risk prediction by integrating it with vehicle camera images, pedestrian position information, and data obtained from residents' home sensors. The prediction unit can also perform risk prediction by combining it with individual environmental information based on evacuation information from a local government. For example, the prediction unit can acquire evacuation information from a local government, and perform risk prediction by integrating it with vehicle camera images, pedestrian position information, and data obtained from residents' home sensors. As a result, the prediction unit can perform risk prediction with higher accuracy by combining public information and individual environmental information. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input a flood forecast from the Japan Meteorological Agency and evacuation information from a local government into the generation AI, and cause the generation AI to execute risk prediction.

[0037] The instruction unit can calculate an appropriate detour route based on the collected data and public information, instruct the detour route to the vehicle through voice guidance or a navigation system, present a safe evacuation route to the pedestrian through a smartphone app, and give evacuation instructions to the residents through a home sensor. The instruction unit, for example, calculates an optimal detour route based on the collected data and public information, and instructs the vehicle to the detour route through voice guidance or a navigation system. For example, the instruction unit can display the detour route on the vehicle's navigation system and give instructions to the driver through voice guidance. The instruction unit can also present a safe evacuation route to the pedestrian through a smartphone app. For example, the instruction unit can display the evacuation route on the pedestrian's smartphone and guide the pedestrian to the safe route through a notification. The instruction unit can also give evacuation instructions to the residents through a home sensor. For example, the instruction unit can display evacuation instructions on the home sensor and give instructions to the residents through voice guidance. As a result, the instruction unit can provide each target with an optimized action instruction, allowing them to act with peace of mind even in an emergency. A part or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can calculate the optimal detour route based on collected data and public information, and instruct vehicles on the detour route through voice guidance or navigation systems. The instruction unit can also present safe evacuation routes to pedestrians through smartphone apps. The instruction unit can also instruct residents to evacuate through home sensors.

[0038] The collection unit can dynamically change the timing of data collection according to the speed of the vehicle and the moving speed of the pedestrian. The collection unit dynamically changes the timing of data collection according to, for example, the speed of the vehicle and the moving speed of the pedestrian. For example, when the vehicle is moving at high speed, the collection unit can shorten the timing of data collection to update information in real time. In addition, when the pedestrian is moving slowly, the collection unit can also lengthen the timing of data collection to reduce battery consumption. In addition, when the vehicle is stopped, the collection unit can appropriately set the timing of data collection and provide necessary information. Thereby, the collection unit can provide appropriate information in real time by adjusting the timing of data collection according to the moving speed. Part 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 the vehicle speed data and the pedestrian moving speed data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0039] The collection unit can simultaneously collect additional information such as surrounding environmental sounds and temperature when collecting data. For example, the collection unit collects surrounding environmental sounds and analyzes the noise level when collecting data. For example, the collection unit can collect surrounding environmental sounds using a microphone and analyze the noise level according to the sound evaluation criteria. The collection unit can also collect the surrounding temperature and analyze the change in temperature when collecting data. For example, the collection unit can collect the surrounding temperature using a temperature sensor and analyze the change in temperature according to the temperature evaluation criteria. The collection unit can also collect humidity information and analyze the change in humidity when collecting data. For example, the collection unit can collect the surrounding humidity using a humidity sensor and analyze the change in humidity according to the humidity evaluation criteria. Thereby, the collection unit can grasp the environmental situation in more detail by collecting the additional information. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the surrounding environmental sound data and temperature data to the generation AI and cause the generation AI to execute the analysis of the additional information.

[0040] The collection unit can prioritize data collection under a specific situation by referring to the user's past behavior history when collecting data. The collection unit, for example, can prioritize data collection under a specific situation by referring to the user's past behavior history when collecting data. For example, the collection unit can prioritize data collection in a place where the user has frequently visited in the past. The collection unit can also prioritize data collection in a place where the user has encountered a dangerous situation in the past. The collection unit can also prioritize data collection in a place where the user has evacuated in the past. Thereby, the collection unit can prioritize data collection under a specific situation by referring to the past behavior history. Part 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 the user's past behavior history data to the generation AI and cause the generation AI to execute the priority of data collection under a specific situation.

[0041] The collection unit can adjust the collection frequency in consideration of the remaining battery level of the user's device when collecting data. For example, the collection unit adjusts the collection frequency in consideration of the remaining battery level of the user's device when collecting data. For example, when the remaining battery level of the user's device is low, the collection unit can reduce the frequency of data collection to suppress battery consumption. In addition, when the remaining battery level of the user's device is sufficient, the collection unit can increase the frequency of data collection to provide detailed information. In addition, when the remaining battery level of the user's device is medium, the collection unit can set the frequency of data collection to an appropriate level and provide balanced information. As a result, the collection unit can provide appropriate information while suppressing battery consumption of the device by considering the remaining battery level. Part 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 the remaining battery level data of the user's device to the generation AI and cause the generation AI to adjust the collection frequency.

[0042] The analysis unit can evaluate the reliability of data during analysis and exclude low-reliability data. The analysis unit, for example, evaluates the reliability of data during analysis and excludes low-reliability data. For example, the analysis unit can exclude data when the source of data collection is unclear. The analysis unit can also exclude data when the collection time of data is unclear. The analysis unit can also exclude data when the content of data is unclear. In this way, the analysis unit improves the accuracy of the analysis result by excluding low-reliability data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to perform a reliability evaluation of data and exclude low-reliability data.

[0043] The analysis unit can detect abnormal values ​​by comparing with past data during analysis and perform special processing on the abnormal values. The analysis unit can, for example, detect abnormal values ​​by comparing with past data during analysis and perform special processing on the abnormal values. For example, the analysis unit can detect an abnormally high water level by comparing with past data and issue a warning. The analysis unit can also detect an abnormally low temperature by comparing with past data and issue a warning. The analysis unit can also detect an abnormally high noise level by comparing with past data and issue a warning. This allows the analysis unit to quickly respond to abnormal situations by detecting abnormal values ​​and performing special processing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to perform a process of detecting abnormal values ​​by comparing with past data, and cause the generation AI to perform special processing on the abnormal values.

[0044] The analysis unit can apply different analysis algorithms for each data collection source during analysis. The analysis unit, for example, applies different analysis algorithms for each data collection source during analysis. For example, the analysis unit can apply a vehicle-specific analysis algorithm to data collected from vehicles. The analysis unit can also apply a pedestrian-specific analysis algorithm to data collected from pedestrians. The analysis unit can also apply a resident-specific analysis algorithm to data collected from residents. In this way, the analysis unit applies an appropriate analysis algorithm for each data collection source, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input vehicle data, pedestrian data, and resident data to the generation AI and cause the generation AI to execute an analysis algorithm suitable for each data.

[0045] The analysis unit can change the analysis method based on the collection time period of the data during analysis. The analysis unit, for example, changes the analysis method based on the collection time period of the data during analysis. For example, the analysis unit can apply a daytime analysis method to data collected during the day. The analysis unit can also apply a nighttime analysis method to data collected at night. The analysis unit can also apply an early morning analysis method to data collected in the early morning. Thereby, the analysis unit applies an analysis method according to the collection time period, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collection time period data to the generation AI and cause the generation AI to execute a change in the analysis method based on the collection time period.

[0046] The prediction unit can improve prediction accuracy by referring to past disaster data at the time of prediction. The prediction unit can improve prediction accuracy by referring to past disaster data at the time of prediction, for example. For example, the prediction unit can predict a current risk by referring to past flood data. The prediction unit can also predict a current risk by referring to past typhoon data. The prediction unit can also predict a current risk by referring to past earthquake data. As a result, the prediction unit improves prediction accuracy by referring to past disaster data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input past disaster data to the generation AI and cause the generation AI to improve prediction accuracy.

[0047] The prediction unit can simulate risks under different weather conditions at the time of prediction and present a plurality of scenarios. The prediction unit, for example, simulates risks under different weather conditions at the time of prediction and presents a plurality of scenarios. For example, the prediction unit can simulate risks in rainy weather and present a risk scenario. The prediction unit can also simulate risks in sunny weather and present a risk scenario. The prediction unit can also simulate risks on snowy days and present a risk scenario. Thereby, the prediction unit can present a plurality of scenarios by simulating risks under different weather conditions. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input different weather condition data to the generation AI and cause the generation AI to execute a risk simulation.

[0048] The prediction unit can perform risk prediction taking into account the user's current location information at the time of prediction. The prediction unit performs risk prediction taking into account the user's current location information, for example, at the time of prediction. For example, the prediction unit can predict the risk of the user's current location with priority. In addition, the prediction unit can predict the risk of the destination when the user is moving. In addition, the prediction unit can predict the risk of the location when the user is staying in a specific location. Thereby, the prediction unit can perform more accurate risk prediction by taking into account the current location information. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the user's current location information data to the generation AI and cause the generation AI to execute risk prediction.

[0049] The prediction unit can customize the risk prediction by referring to the user's past evacuation history at the time of prediction. The prediction unit, for example, customizes the risk prediction by referring to the user's past evacuation history at the time of prediction. For example, the prediction unit can predict the risk of a place where the user has evacuated in the past with priority. The prediction unit can also predict the risk of a time period when the user evacuated in the past. The prediction unit can also predict the risk of a route where the user has evacuated in the past. Thereby, the prediction unit can customize the risk prediction by referring to the past evacuation history. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the user's past evacuation history data to the generation AI and cause the generation AI to customize the risk prediction.

[0050] The instruction unit can propose an optimal evacuation route in consideration of the user's current physical condition when instructing. The instruction unit, for example, proposes an optimal evacuation route in consideration of the user's current physical condition when instructing. For example, the instruction unit can propose the shortest route when the user is tired. In addition, the instruction unit can also propose a slightly detour route when the user is seeking healthy exercise. In addition, the instruction unit can also propose a route including a rest point when the user is in poor health. In this way, the instruction unit can propose a more appropriate evacuation route by considering the user's physical condition. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's physical condition data to the generation AI and cause the generation AI to execute the proposal of an optimal evacuation route.

[0051] The instruction unit can change the instruction method in consideration of the communication status of the user's device when giving an instruction. The instruction unit, for example, changes the instruction method in consideration of the communication status of the user's device when giving an instruction. For example, when the user's device is out of communication range, the instruction unit can provide an instruction method that can be used offline. In addition, when the user's device is in low-speed communication, the instruction unit can also provide a text-based instruction method. In addition, when the user's device is in high-speed communication, the instruction unit can also provide an instruction method including video and audio. Thereby, the instruction unit can provide more appropriate information by changing the instruction method according to the communication status. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input communication status data of the user's device to the generation AI and cause the generation AI to execute the change in the instruction method.

[0052] The instruction unit can propose an evacuation route by taking into consideration the location information of the user's family and friends when instructing. The instruction unit, for example, proposes an evacuation route by taking into consideration the location information of the user's family and friends when instructing. For example, the instruction unit can propose a route by which the user can evacuate together when the user's family is nearby. In addition, the instruction unit can also propose a route by which the user can evacuate together when the user's friends are nearby. In addition, the instruction unit can also propose the safest evacuation route when the user's family and friends are far away. Thereby, the instruction unit can propose a more appropriate evacuation route by taking into consideration the location information of the family and friends. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input location information data of the user's family and friends to the generation AI and cause the generation AI to execute the proposal of an evacuation route.

[0053] The instruction unit can select an optimal instruction method by referring to the user's past evacuation behavior at the time of instruction. The instruction unit, for example, selects an optimal instruction method by referring to the user's past evacuation behavior at the time of instruction. For example, the instruction unit can provide an optimal instruction method based on an evacuation route used by the user in the past. The instruction unit can also provide an optimal instruction method based on a time period in which the user evacuated in the past. The instruction unit can also provide an optimal instruction method based on a place in which the user evacuated in the past. Thereby, the instruction unit can select a more appropriate instruction method by referring to the past evacuation behavior. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's past evacuation behavior data to the generation AI and cause the generation AI to select an optimal instruction method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can dynamically change the timing of data collection according to the speed of the vehicle and the moving speed of the pedestrian. For example, when the vehicle is moving at high speed, the timing of data collection can be shortened to update the information in real time. Also, when the pedestrian is moving slowly, the timing of data collection can be lengthened to reduce battery consumption. Furthermore, when the vehicle is stopped, the timing of data collection can be set appropriately to provide necessary information. As a result, the collection unit can provide appropriate information in real time by adjusting the timing of data collection according to the moving speed.

[0056] The collection unit can simultaneously collect additional information such as surrounding environmental sounds and temperature when collecting data. For example, surrounding environmental sounds can be collected and the noise level can be analyzed when collecting data. The surrounding temperature can also be collected and changes in temperature can be analyzed. Furthermore, humidity information can be collected and changes in humidity can be analyzed. In this way, the collection unit can grasp the environmental situation in more detail by collecting the additional information.

[0057] When collecting data, the collection unit can prioritize data collection under specific circumstances by referring to the user's past behavior history. For example, data collection can be prioritized in places that the user has frequently visited in the past. Data collection can also be prioritized in places where the user has encountered dangerous situations in the past. Furthermore, data collection can be prioritized in places where the user has evacuated in the past. In this way, the collection unit can prioritize data collection under specific circumstances by referring to the user's past behavior history.

[0058] During analysis, the analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, if the source of data collection is unclear, the data can be excluded. Also, if the time of data collection is unclear, the data can be excluded. Furthermore, if the content of the data is unclear, the data can be excluded. In this way, the analysis unit can improve the accuracy of the analysis results by excluding unreliable data.

[0059] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and perform special processing for the abnormal values. For example, it can detect abnormally high water levels by comparing with past data and issue a warning. It can also detect abnormally low air temperatures by comparing with past data and issue a warning. It can also detect abnormally high noise levels by comparing with past data and issue a warning. In this way, the analysis unit can quickly respond to abnormal situations by detecting abnormal values ​​and performing special processing.

[0060] The prediction unit can improve prediction accuracy by referring to past disaster data when making predictions. For example, the current risk can be predicted by referring to past flood data. The current risk can also be predicted by referring to past typhoon data. Furthermore, the current risk can also be predicted by referring to past earthquake data. In this way, the prediction unit can improve prediction accuracy by referring to past disaster data.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. For example, data is collected from cameras and sensors mounted on vehicles, sensors on pedestrians' smartphones, and home sensors installed by residents. The collection unit collects vehicle camera video, location information from pedestrians' smartphones, and water level information from residents' home sensors in real time, and sends it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. For example, it analyzes the flooding status of roads from vehicle camera footage, identifies safe evacuation routes from pedestrian location information, and predicts the progress of flooding based on water level information obtained from residents' home sensors. Step 3: The prediction unit combines the data analyzed by the analysis unit with publicly available information over a wide area to predict disaster risks locally and with a high degree of accuracy. For example, publicly available information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments can be combined with individual environmental information to make risk predictions. Step 4: The instruction unit provides behavioral instructions optimized for each target person based on the risks predicted by the prediction unit. For example, the unit calculates the optimal detour route based on collected data and public information, and instructs vehicles on the detour route through voice guidance and navigation systems, presents safe evacuation routes to pedestrians through smartphone apps, and instructs residents to evacuate through home sensors.

[0063] (Example 2) The system according to the embodiment of the present invention is a system that collects individual environmental information from vehicles, pedestrians, and residents waiting indoors in real time in flood-prone areas and analyzes the surrounding conditions. This system first collects real-time video and sensor data from vehicles, pedestrians, and residents. Next, the collected data is analyzed, and the target's risk of damage is predicted locally and with high accuracy in conjunction with wide-area public information. Furthermore, if there is a road that is difficult to pass when flooded, an appropriate detour route is instructed and behavior instructions optimized for each target are provided. This allows people to act with peace of mind even in emergencies. For example, real-time video and sensor data are collected from vehicles, pedestrians, and residents. At this time, data is collected from cameras and sensors mounted on vehicles, sensors on smartphones held by pedestrians, household sensors installed by residents, and the like. For example, the data includes video captured by a vehicle's camera, location information acquired by a pedestrian's smartphone, and water level information detected by a resident's household sensor. Next, the collected data is analyzed. A generation AI is used for the analysis, and each data is integrated to grasp the surrounding conditions. For example, the system analyzes the flooding status of roads from vehicle camera footage and identifies safe evacuation routes from pedestrian location information. In addition, it predicts the progress of flooding based on water level information obtained from residents' home sensors. In addition, it predicts the target's risk of damage locally and with high accuracy in conjunction with wide-area public information. For example, by integrating public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments and combining it with individual environmental information, it makes more accurate risk predictions. Finally, if there are roads that are difficult to pass when flooded, it instructs appropriate detour routes. The generation AI calculates the optimal detour route based on the collected data and public information and provides it to the target. For example, it instructs vehicles to detour routes through voice guidance and navigation systems, and presents pedestrians with safe evacuation routes through smartphone apps. It also instructs residents to evacuate through home sensors. This mechanism allows people to act with peace of mind even in emergencies. For example, vehicles can move safely by avoiding flooded roads, and pedestrians can secure safe evacuation routes. In addition, residents can receive evacuation instructions early and respond quickly.This enables the system to collect individual environmental information from vehicles, pedestrians and residents in real time in areas at risk of flooding or other water damage, and analyze the surrounding situation.

[0064] The flood prediction system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and an instruction unit. The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. The collection unit collects data, for example, from cameras and sensors mounted on vehicles, sensors on smartphones carried by pedestrians, and home sensors installed by residents. For example, the collection unit collects video captured by a vehicle's camera. The collection unit can also collect location information acquired by the pedestrian's smartphone. The collection unit can also collect water level information detected by the resident's home sensor. For example, the collection unit collects vehicle camera video in real time and transmits it to the analysis unit. The collection unit can also acquire location information from the pedestrian's smartphone and transmit it to the analysis unit. The collection unit can also acquire water level information from the resident's home sensor and transmit it to the analysis unit. The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. The analysis unit analyzes the flooding situation of a road from, for example, a vehicle's camera video. The analysis unit can also identify a safe evacuation route from the pedestrian's location information. The analysis unit can also predict the progress of flooding based on water level information obtained from the residents' home sensors. For example, the analysis unit analyzes the vehicle's camera image to grasp the flooding status of the road. The analysis unit can also analyze the position information of pedestrians and identify safe evacuation routes. The analysis unit can also analyze the water level information obtained from the residents' home sensors to predict the progress of flooding. The prediction unit integrates the data analyzed by the analysis unit with wide-area public information to predict the disaster risk locally and with high accuracy. The prediction unit integrates public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments, and combines it with individual environmental information to perform risk prediction. For example, the prediction unit combines the flood forecasts from the Japan Meteorological Agency with individual environmental information to perform risk prediction. The prediction unit can also combine the evacuation information from local governments with individual environmental information to perform risk prediction. The instruction unit provides behavioral instructions optimized for each target person based on the risk predicted by the prediction unit. The instruction unit, for example, calculates an optimal detour route based on collected data and public information, and instructs the vehicle on the detour route via voice guidance or a navigation system.The instruction unit can also present a safe evacuation route to pedestrians through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors. For example, the instruction unit instructs vehicles to take a detour route through voice guidance. The instruction unit can also present pedestrians with a safe evacuation route through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors. As a result, the flood prediction system according to the embodiment can collect data from vehicles, pedestrians, and residents, analyze, predict, and give instructions, allowing residents to act with peace of mind even in an emergency. Some or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can calculate an optimal detour route based on the collected data and public information, and instruct vehicles to take the detour route through voice guidance or a navigation system. The instruction unit can also present pedestrians with a safe evacuation route through a smartphone app. The instruction unit can also give evacuation instructions to residents through home sensors.

[0065] The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. Vehicles are equipped with cameras and sensors, and collect video and sensor data from these devices. Specifically, the vehicle's camera captures road conditions and the surrounding environment, and the sensor detects environmental data such as temperature, humidity, and vibration. These data are collected in real time and sent to a central database. Pedestrians carry smartphones and collect location information and environmental sensor data from the smartphone. The smartphone's GPS function is used to accurately determine the pedestrian's current location, and acceleration and gyro sensors are used to detect the pedestrian's movement and the surrounding environment. Residents install household sensors and collect water level information and environmental data from these sensors. The household sensors are installed, for example, in basements and gardens, and the water level sensors detect the presence or absence of flooding and rising water levels, while the temperature and humidity sensors monitor changes in the environment. This allows the collection unit to collect a wide range of data from various devices and grasp the situation in real time. Furthermore, the collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and prediction units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. For the analysis, AI is used to process the data in real time and grasp the surrounding situation. Specifically, it detects the flooding of roads from the vehicle's camera footage, identifies safe evacuation routes from pedestrian location information, and predicts the progress of the flooding based on water level information obtained from residents' home sensors. AI analyzes the camera footage using image recognition technology to identify flooded areas and obstacles on the road. It also analyzes pedestrian location information and calculates the optimal evacuation route. It also analyzes water level information from home sensors and predicts the progress speed and range of the flooding. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the surrounding risk situation in real time. In addition, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, it predicts fluctuations in risk in specific areas and time periods based on past flooding data and plans future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The prediction unit predicts the risk of damage by integrating the data analyzed by the analysis unit with public information from a wide area. Specifically, by utilizing public information from a wide area, such as rainfall information and river water level information provided by the Japan Meteorological Agency, and integrating it with the collected data, more accurate risk prediction is performed. For example, based on the rainfall information from the Japan Meteorological Agency, future rainfall is predicted, and based on the river water level information, the risk of river flooding is evaluated. By integrating these pieces of information, it is possible to predict the risk of flooding and the need for evacuation in a specific area. The prediction unit uses AI to analyze these data and simulate multiple scenarios to identify the most likely risk. This allows the prediction unit to predict the risk of damage with high accuracy and provide information for taking appropriate measures. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the amount of rainfall or the water level of a river changes suddenly, the prediction unit immediately takes in new data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessment by taking into account the characteristics of each area and past disaster history. This enables the prediction unit to always provide highly accurate risk predictions based on the latest information, and to support rapid and appropriate responses.

[0068] The instruction unit provides appropriate action instructions based on the risk predicted by the prediction unit. Specifically, the vehicle displays a detour route on the navigation system, pedestrians are notified of a safe evacuation route via their smartphone, and residents are provided with evacuation instructions via home sensors. The vehicle's navigation system calculates the optimal detour route based on map information updated in real time and displays it to the driver. Pedestrians' smartphones are notified of the optimal evacuation route based on their current location and evacuation destination, and are alerted by voice guidance, vibration notifications, etc. Residents are provided with information on the need for evacuation and evacuation destinations via home sensors. This allows the instruction unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the instruction unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, the evacuation route is reviewed and the instructions are improved based on feedback from users who have received evacuation instructions. The instruction unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the instruction unit to provide the user with prompt and reliable instructions on what to do, thereby minimizing the risk of damage.

[0069] The collection unit can collect data from a camera or sensor mounted on a vehicle, a sensor of a smartphone carried by a pedestrian, and a home sensor installed by a resident. The collection unit, for example, collects data from a camera or sensor mounted on a vehicle. For example, the collection unit can collect video data from a dash cam or an in-car camera of the vehicle. The collection unit can also collect location data from a GPS sensor of the vehicle. The collection unit can also collect data from a sensor of a smartphone carried by a pedestrian. For example, the collection unit can collect data from an acceleration sensor or a gyro sensor of the smartphone. The collection unit can also collect location data from a GPS sensor of the smartphone. The collection unit can also collect data from a home sensor installed by a resident. For example, the collection unit can collect data from a home temperature sensor or a humidity sensor. The collection unit can also collect water level data from a home water level sensor. In this way, the collection unit can obtain more detailed environmental information by collecting data from various devices. A part 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 data obtained from a vehicle's cameras and sensors into the generation AI and have the generation AI analyze the data.

[0070] The analysis unit can analyze the flooding status of the road from the vehicle's camera image, identify a safe evacuation route from the pedestrian's position information, and predict the progress of the flooding based on the water level information obtained from the resident's home sensor. The analysis unit, for example, analyzes the vehicle's camera image to grasp the flooding status of the road. For example, the analysis unit can use an image analysis algorithm to evaluate the depth of the flooding from the vehicle's camera image. The analysis unit can also analyze the pedestrian's position information and identify a safe evacuation route. For example, the analysis unit can identify a safe route according to the evacuation route selection criteria based on the pedestrian's position information. The analysis unit can also analyze the water level information obtained from the resident's home sensor and predict the progress of the flooding. For example, the analysis unit can evaluate the progress speed of the flooding using a water level change prediction model. This allows the analysis unit to analyze various data and grasp the surrounding situation more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input vehicle camera footage to the generation AI and have the generation AI analyze the flooding situation. The analysis unit can also input pedestrian location information to the generation AI and have the generation AI identify safe evacuation routes. The analysis unit can also input water level information obtained from residents' home sensors to the generation AI and have the generation AI predict the progress of the flooding.

[0071] The prediction unit can integrate public information such as a flood forecast from the Japan Meteorological Agency or evacuation information from a local government, and perform risk prediction by combining it with individual environmental information. The prediction unit performs risk prediction by combining, for example, a flood forecast from the Japan Meteorological Agency with individual environmental information. For example, the prediction unit can acquire a flood forecast from the Japan Meteorological Agency, and perform risk prediction by integrating it with vehicle camera images, pedestrian position information, and data obtained from residents' home sensors. The prediction unit can also perform risk prediction by combining it with individual environmental information based on evacuation information from a local government. For example, the prediction unit can acquire evacuation information from a local government, and perform risk prediction by integrating it with vehicle camera images, pedestrian position information, and data obtained from residents' home sensors. As a result, the prediction unit can perform risk prediction with higher accuracy by combining public information and individual environmental information. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input a flood forecast from the Japan Meteorological Agency and evacuation information from a local government into the generation AI, and cause the generation AI to execute risk prediction.

[0072] The instruction unit can calculate an appropriate detour route based on the collected data and public information, instruct the detour route to the vehicle through voice guidance or a navigation system, present a safe evacuation route to the pedestrian through a smartphone app, and give evacuation instructions to the residents through a home sensor. The instruction unit, for example, calculates an optimal detour route based on the collected data and public information, and instructs the vehicle to the detour route through voice guidance or a navigation system. For example, the instruction unit can display the detour route on the vehicle's navigation system and give instructions to the driver through voice guidance. The instruction unit can also present a safe evacuation route to the pedestrian through a smartphone app. For example, the instruction unit can display the evacuation route on the pedestrian's smartphone and guide the pedestrian to the safe route through a notification. The instruction unit can also give evacuation instructions to the residents through a home sensor. For example, the instruction unit can display evacuation instructions on the home sensor and give instructions to the residents through voice guidance. As a result, the instruction unit can provide each target with an optimized action instruction, allowing them to act with peace of mind even in an emergency. A part or all of the above-mentioned processing in the instruction unit may be performed using AI, for example, or may be performed without using AI. For example, the instruction unit can calculate the optimal detour route based on collected data and public information, and instruct vehicles on the detour route through voice guidance or navigation systems. The instruction unit can also present safe evacuation routes to pedestrians through smartphone apps. The instruction unit can also instruct residents to evacuate through home sensors.

[0073] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the frequency of data collection based on the estimated user's emotions. For example, when the user is nervous, the collection unit can increase the frequency of data collection to provide detailed information in real time. In addition, when the user is relaxed, the collection unit can also reduce the frequency of data collection to provide the minimum necessary information. In addition, when the user is in a hurry, the collection unit can set the frequency of data collection to a medium level to provide prompt and appropriate information. Thereby, the collection unit can provide more appropriate information by adjusting the frequency of data collection according to the user's emotions. Part 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 the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of data collection.

[0074] The collection unit can dynamically change the timing of data collection according to the speed of the vehicle and the moving speed of the pedestrian. The collection unit dynamically changes the timing of data collection according to, for example, the speed of the vehicle and the moving speed of the pedestrian. For example, when the vehicle is moving at high speed, the collection unit can shorten the timing of data collection to update information in real time. In addition, when the pedestrian is moving slowly, the collection unit can also lengthen the timing of data collection to reduce battery consumption. In addition, when the vehicle is stopped, the collection unit can appropriately set the timing of data collection and provide necessary information. Thereby, the collection unit can provide appropriate information in real time by adjusting the timing of data collection according to the moving speed. Part 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 the vehicle speed data and the pedestrian moving speed data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0075] The collection unit can simultaneously collect additional information such as surrounding environmental sounds and temperature when collecting data. For example, the collection unit collects surrounding environmental sounds and analyzes the noise level when collecting data. For example, the collection unit can collect surrounding environmental sounds using a microphone and analyze the noise level according to the sound evaluation criteria. The collection unit can also collect the surrounding temperature and analyze the change in temperature when collecting data. For example, the collection unit can collect the surrounding temperature using a temperature sensor and analyze the change in temperature according to the temperature evaluation criteria. The collection unit can also collect humidity information and analyze the change in humidity when collecting data. For example, the collection unit can collect the surrounding humidity using a humidity sensor and analyze the change in humidity according to the humidity evaluation criteria. Thereby, the collection unit can grasp the environmental situation in more detail by collecting the additional information. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the surrounding environmental sound data and temperature data to the generation AI and cause the generation AI to execute the analysis of the additional information.

[0076] The collection unit can estimate the user's emotions and select the type of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and selects the type of data to be collected based on the estimated user's emotions. For example, when the user is nervous, the collection unit can collect detailed environmental information to provide a sense of security. In addition, when the user is relaxed, the collection unit can collect only basic environmental information to avoid providing excessive information. In addition, when the user is in a hurry, the collection unit can preferentially collect important environmental information to enable a quick response. This allows the collection unit to provide more appropriate information by selecting the type of data to be collected according to the user's emotions. Part 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 the user's emotion data to the generation AI and cause the generation AI to select the type of data to be collected.

[0077] The collection unit can prioritize data collection under a specific situation by referring to the user's past behavior history when collecting data. The collection unit, for example, can prioritize data collection under a specific situation by referring to the user's past behavior history when collecting data. For example, the collection unit can prioritize data collection in a place where the user has frequently visited in the past. The collection unit can also prioritize data collection in a place where the user has encountered a dangerous situation in the past. The collection unit can also prioritize data collection in a place where the user has evacuated in the past. Thereby, the collection unit can prioritize data collection under a specific situation by referring to the past behavior history. Part 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 the user's past behavior history data to the generation AI and cause the generation AI to execute the priority of data collection under a specific situation.

[0078] The collection unit can adjust the collection frequency in consideration of the remaining battery level of the user's device when collecting data. For example, the collection unit adjusts the collection frequency in consideration of the remaining battery level of the user's device when collecting data. For example, when the remaining battery level of the user's device is low, the collection unit can reduce the frequency of data collection to suppress battery consumption. In addition, when the remaining battery level of the user's device is sufficient, the collection unit can increase the frequency of data collection to provide detailed information. In addition, when the remaining battery level of the user's device is medium, the collection unit can set the frequency of data collection to an appropriate level and provide balanced information. As a result, the collection unit can provide appropriate information while suppressing battery consumption of the device by considering the remaining battery level. Part 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 the remaining battery level data of the user's device to the generation AI and cause the generation AI to adjust the collection frequency.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can provide a simple and highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. The analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, making it possible to provide more appropriate information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0080] The analysis unit can evaluate the reliability of data during analysis and exclude low-reliability data. The analysis unit, for example, evaluates the reliability of data during analysis and excludes low-reliability data. For example, the analysis unit can exclude data when the source of data collection is unclear. The analysis unit can also exclude data when the collection time of data is unclear. The analysis unit can also exclude data when the content of data is unclear. In this way, the analysis unit improves the accuracy of the analysis result by excluding low-reliability data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to perform a reliability evaluation of data and exclude low-reliability data.

[0081] The analysis unit can detect abnormal values ​​by comparing with past data during analysis and perform special processing on the abnormal values. The analysis unit can, for example, detect abnormal values ​​by comparing with past data during analysis and perform special processing on the abnormal values. For example, the analysis unit can detect an abnormally high water level by comparing with past data and issue a warning. The analysis unit can also detect an abnormally low temperature by comparing with past data and issue a warning. The analysis unit can also detect an abnormally high noise level by comparing with past data and issue a warning. This allows the analysis unit to quickly respond to abnormal situations by detecting abnormal values ​​and performing special processing. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can cause the generation AI to perform a process of detecting abnormal values ​​by comparing with past data, and cause the generation AI to perform special processing on the abnormal values.

[0082] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis result based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the level of detail of the analysis result based on the estimated user's emotions. For example, the analysis unit can provide a simple and highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide an analysis result that focuses on the main points when the user is in a hurry. This allows the analysis unit to adjust the level of detail of the analysis result according to the user's emotions, making it possible to provide more appropriate information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the analysis result.

[0083] The analysis unit can apply different analysis algorithms for each data collection source during analysis. The analysis unit, for example, applies different analysis algorithms for each data collection source during analysis. For example, the analysis unit can apply a vehicle-specific analysis algorithm to data collected from vehicles. The analysis unit can also apply a pedestrian-specific analysis algorithm to data collected from pedestrians. The analysis unit can also apply a resident-specific analysis algorithm to data collected from residents. In this way, the analysis unit applies an appropriate analysis algorithm for each data collection source, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input vehicle data, pedestrian data, and resident data to the generation AI and cause the generation AI to execute an analysis algorithm suitable for each data.

[0084] The analysis unit can change the analysis method based on the collection time period of the data during analysis. The analysis unit, for example, changes the analysis method based on the collection time period of the data during analysis. For example, the analysis unit can apply a daytime analysis method to data collected during the day. The analysis unit can also apply a nighttime analysis method to data collected at night. The analysis unit can also apply an early morning analysis method to data collected in the early morning. Thereby, the analysis unit applies an analysis method according to the collection time period, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collection time period data to the generation AI and cause the generation AI to execute a change in the analysis method based on the collection time period.

[0085] The prediction unit can estimate the user's emotion and adjust the display method of the risk prediction based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the display method of the risk prediction based on the estimated user's emotion. For example, the prediction unit can provide a simple and highly visible display method when the user is nervous. The prediction unit can also provide a display method including detailed information when the user is relaxed. The prediction unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the prediction unit to adjust the display method of the risk prediction according to the user's emotion, thereby making it possible to provide more appropriate information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the risk prediction.

[0086] The prediction unit can improve prediction accuracy by referring to past disaster data at the time of prediction. The prediction unit can improve prediction accuracy by referring to past disaster data at the time of prediction, for example. For example, the prediction unit can predict a current risk by referring to past flood data. The prediction unit can also predict a current risk by referring to past typhoon data. The prediction unit can also predict a current risk by referring to past earthquake data. As a result, the prediction unit improves prediction accuracy by referring to past disaster data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input past disaster data to the generation AI and cause the generation AI to improve prediction accuracy.

[0087] The prediction unit can simulate risks under different weather conditions at the time of prediction and present a plurality of scenarios. The prediction unit, for example, simulates risks under different weather conditions at the time of prediction and presents a plurality of scenarios. For example, the prediction unit can simulate risks in rainy weather and present a risk scenario. The prediction unit can also simulate risks in sunny weather and present a risk scenario. The prediction unit can also simulate risks on snowy days and present a risk scenario. Thereby, the prediction unit can present a plurality of scenarios by simulating risks under different weather conditions. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input different weather condition data to the generation AI and cause the generation AI to execute a risk simulation.

[0088] The prediction unit can estimate the user's emotions and determine the priority of risk prediction based on the estimated user's emotions. The prediction unit, for example, estimates the user's emotions and determines the priority of risk prediction based on the estimated user's emotions. For example, the prediction unit can display the most important risks preferentially when the user is nervous. The prediction unit can also provide detailed risk information when the user is relaxed. The prediction unit can also provide risk information with the gist when the user is in a hurry. This allows the prediction unit to determine the priority of risk prediction according to the user's emotions, thereby making it possible to provide more appropriate information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of risk prediction.

[0089] The prediction unit can perform risk prediction taking into account the user's current location information at the time of prediction. The prediction unit performs risk prediction taking into account the user's current location information, for example, at the time of prediction. For example, the prediction unit can predict the risk of the user's current location with priority. In addition, the prediction unit can predict the risk of the destination when the user is moving. In addition, the prediction unit can predict the risk of the location when the user is staying in a specific location. Thereby, the prediction unit can perform more accurate risk prediction by taking into account the current location information. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the user's current location information data to the generation AI and cause the generation AI to execute risk prediction.

[0090] The prediction unit can customize the risk prediction by referring to the user's past evacuation history at the time of prediction. The prediction unit, for example, customizes the risk prediction by referring to the user's past evacuation history at the time of prediction. For example, the prediction unit can predict the risk of a place where the user has evacuated in the past with priority. The prediction unit can also predict the risk of a time period when the user evacuated in the past. The prediction unit can also predict the risk of a route where the user has evacuated in the past. Thereby, the prediction unit can customize the risk prediction by referring to the past evacuation history. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the user's past evacuation history data to the generation AI and cause the generation AI to customize the risk prediction.

[0091] The instruction unit can estimate the user's emotion and adjust the expression method of the action instruction based on the estimated user's emotion. The instruction unit, for example, estimates the user's emotion and adjusts the expression method of the action instruction based on the estimated user's emotion. For example, the instruction unit can give the action instruction in a calm voice when the user is nervous. Also, the instruction unit can give the action instruction in a bright voice when the user is relaxed. Also, the instruction unit can give the action instruction in a quick and concise voice when the user is in a hurry. As a result, the instruction unit can provide more appropriate information by adjusting the expression method of the action instruction according to the user's emotion. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the action instruction.

[0092] The instruction unit can propose an optimal evacuation route in consideration of the user's current physical condition when instructing. The instruction unit, for example, proposes an optimal evacuation route in consideration of the user's current physical condition when instructing. For example, the instruction unit can propose the shortest route when the user is tired. In addition, the instruction unit can also propose a slightly detour route when the user is seeking healthy exercise. In addition, the instruction unit can also propose a route including a rest point when the user is in poor health. In this way, the instruction unit can propose a more appropriate evacuation route by considering the user's physical condition. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's physical condition data to the generation AI and cause the generation AI to execute the proposal of an optimal evacuation route.

[0093] The instruction unit can change the instruction method in consideration of the communication status of the user's device when giving an instruction. The instruction unit, for example, changes the instruction method in consideration of the communication status of the user's device when giving an instruction. For example, when the user's device is out of communication range, the instruction unit can provide an instruction method that can be used offline. In addition, when the user's device is in low-speed communication, the instruction unit can also provide a text-based instruction method. In addition, when the user's device is in high-speed communication, the instruction unit can also provide an instruction method including video and audio. Thereby, the instruction unit can provide more appropriate information by changing the instruction method according to the communication status. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input communication status data of the user's device to the generation AI and cause the generation AI to execute the change in the instruction method.

[0094] The instruction unit can estimate the user's emotion and determine the priority of the action instructions based on the estimated user's emotion. The instruction unit, for example, estimates the user's emotion and determines the priority of the action instructions based on the estimated user's emotion. For example, when the user is nervous, the instruction unit can provide the most important action instructions preferentially. Furthermore, when the user is relaxed, the instruction unit can provide detailed action instructions. Furthermore, when the user is in a hurry, the instruction unit can provide action instructions that focus on the main points. As a result, the instruction unit can determine the priority of the action instructions according to the user's emotion, thereby enabling more appropriate information provision. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the action instructions.

[0095] The instruction unit can propose an evacuation route by taking into consideration the location information of the user's family and friends when instructing. The instruction unit, for example, proposes an evacuation route by taking into consideration the location information of the user's family and friends when instructing. For example, the instruction unit can propose a route by which the user can evacuate together when the user's family is nearby. In addition, the instruction unit can also propose a route by which the user can evacuate together when the user's friends are nearby. In addition, the instruction unit can also propose the safest evacuation route when the user's family and friends are far away. Thereby, the instruction unit can propose a more appropriate evacuation route by taking into consideration the location information of the family and friends. Part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input location information data of the user's family and friends to the generation AI and cause the generation AI to execute the proposal of an evacuation route.

[0096] The instruction unit can select an optimal instruction method by referring to the user's past evacuation behavior at the time of instruction. The instruction unit, for example, selects an optimal instruction method by referring to the user's past evacuation behavior at the time of instruction. For example, the instruction unit can provide an optimal instruction method based on an evacuation route used by the user in the past. The instruction unit can also provide an optimal instruction method based on a time period in which the user evacuated in the past. The instruction unit can also provide an optimal instruction method based on a place in which the user evacuated in the past. Thereby, the instruction unit can select a more appropriate instruction method by referring to the past evacuation behavior. A part or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI or may be performed without using AI. For example, the instruction unit can input the user's past evacuation behavior data to the generation AI and cause the generation AI to select an optimal instruction method.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is nervous, the frequency of data collection can be increased to provide detailed information in real time. Also, if the user is relaxed, the frequency of data collection can be reduced to provide the minimum necessary information. Furthermore, if the user is in a hurry, the frequency of data collection can be set to a medium level to provide quick and appropriate information. This allows the collection unit to provide more appropriate information by adjusting the frequency of data collection according to the user's emotions.

[0099] The collection unit can dynamically change the timing of data collection according to the speed of the vehicle and the moving speed of the pedestrian. For example, when the vehicle is moving at high speed, the timing of data collection can be shortened to update the information in real time. Also, when the pedestrian is moving slowly, the timing of data collection can be lengthened to reduce battery consumption. Furthermore, when the vehicle is stopped, the timing of data collection can be set appropriately to provide necessary information. As a result, the collection unit can provide appropriate information in real time by adjusting the timing of data collection according to the moving speed.

[0100] The collection unit can simultaneously collect additional information such as surrounding environmental sounds and temperature when collecting data. For example, surrounding environmental sounds can be collected and the noise level can be analyzed when collecting data. The surrounding temperature can also be collected and changes in temperature can be analyzed. Furthermore, humidity information can be collected and changes in humidity can be analyzed. In this way, the collection unit can grasp the environmental situation in more detail by collecting the additional information.

[0101] The collection unit can estimate the user's emotions and select the type of data to be collected based on the estimated user's emotions. For example, when the user is nervous, detailed environmental information can be collected to provide a sense of security. When the user is relaxed, only basic environmental information can be collected to avoid providing excessive information. Furthermore, when the user is in a hurry, important environmental information can be collected with priority, enabling a quick response. This allows the collection unit to provide more appropriate information by selecting the type of data to be collected according to the user's emotions.

[0102] When collecting data, the collection unit can prioritize data collection under specific circumstances by referring to the user's past behavior history. For example, data collection can be prioritized in places that the user has frequently visited in the past. Data collection can also be prioritized in places where the user has encountered dangerous situations in the past. Furthermore, data collection can be prioritized in places where the user has evacuated in the past. In this way, the collection unit can prioritize data collection under specific circumstances by referring to the user's past behavior history.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0104] During analysis, the analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, if the source of data collection is unclear, the data can be excluded. Also, if the time of data collection is unclear, the data can be excluded. Furthermore, if the content of the data is unclear, the data can be excluded. In this way, the analysis unit can improve the accuracy of the analysis results by excluding unreliable data.

[0105] During analysis, the analysis unit can detect abnormal values ​​by comparing with past data and perform special processing for the abnormal values. For example, it can detect abnormally high water levels by comparing with past data and issue a warning. It can also detect abnormally low air temperatures by comparing with past data and issue a warning. It can also detect abnormally high noise levels by comparing with past data and issue a warning. In this way, the analysis unit can quickly respond to abnormal situations by detecting abnormal values ​​and performing special processing.

[0106] The prediction unit can estimate the user's emotions and adjust the display method of the risk prediction based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the prediction unit to provide more appropriate information by adjusting the display method of the risk prediction according to the user's emotions.

[0107] The prediction unit can improve prediction accuracy by referring to past disaster data when making predictions. For example, the current risk can be predicted by referring to past flood data. The current risk can also be predicted by referring to past typhoon data. Furthermore, the current risk can also be predicted by referring to past earthquake data. In this way, the prediction unit can improve prediction accuracy by referring to past disaster data.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects real-time video and sensor data from vehicles, pedestrians, and residents. For example, data is collected from cameras and sensors mounted on vehicles, sensors on pedestrians' smartphones, and home sensors installed by residents. The collection unit collects vehicle camera video, location information from pedestrians' smartphones, and water level information from residents' home sensors in real time, and sends it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit and grasps the surrounding situation. For example, it analyzes the flooding status of roads from vehicle camera footage, identifies safe evacuation routes from pedestrian location information, and predicts the progress of flooding based on water level information obtained from residents' home sensors. Step 3: The prediction unit combines the data analyzed by the analysis unit with publicly available information over a wide area to predict disaster risks locally and with a high degree of accuracy. For example, publicly available information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments can be combined with individual environmental information to make risk predictions. Step 4: The instruction unit provides behavioral instructions optimized for each target person based on the risks predicted by the prediction unit. For example, the unit calculates the optimal detour route based on collected data and public information, and instructs vehicles on the detour route through voice guidance and navigation systems, presents safe evacuation routes to pedestrians through smartphone apps, and instructs residents to evacuate through home sensors.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the collection unit can collect real-time video and sensor data from vehicles and pedestrians using the camera 42 and sensors of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect water level information from residents' home sensors, for example. The analysis unit can analyze the collected data and grasp the surrounding situation by the control unit 46A of the smart device 14. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12, and can analyze the flooding situation of roads from the camera video of the vehicle, for example. The prediction unit can integrate the analyzed data and wide-area public information by the specific processing unit 290 of the data processing device 12, and predict the disaster risk locally and with high accuracy. The instruction unit can provide action instructions optimized for each target person by the control unit 46A of the smart device 14. The instruction unit can also be realized by the specific processing unit 290 of the data processing device 12, and can instruct the vehicle to take a detour route through voice guidance or a navigation system, for example. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the collection unit can collect real-time video and sensor data from vehicles and pedestrians using the camera 42 and sensors of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect water level information from residents' home sensors, for example. The analysis unit can analyze the collected data and grasp the surrounding situation by the control unit 46A of the smart glasses 214. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12, and can analyze the flooding situation of the road from the camera image of the vehicle, for example. The prediction unit can integrate the analyzed data and wide-area public information by the specific processing unit 290 of the data processing device 12, and predict the disaster risk locally and with high accuracy. The instruction unit can provide action instructions optimized for each target person by the control unit 46A of the smart glasses 214. The instruction unit can also be realized by the specific processing unit 290 of the data processing device 12, and can instruct the vehicle to take a detour route through voice guidance or a navigation system, for example. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the collection unit can collect real-time video and sensor data from vehicles and pedestrians using the camera 42 and sensors of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect water level information from residents' home sensors, for example. The analysis unit can analyze the collected data and grasp the surrounding situation by the control unit 46A of the headset terminal 314. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12, and can analyze the flooding situation of roads from the camera video of the vehicle, for example. The prediction unit can integrate the analyzed data and wide-area public information by the specific processing unit 290 of the data processing device 12, and predict the disaster risk locally and with high accuracy. The instruction unit can provide action instructions optimized for each target person by the control unit 46A of the headset terminal 314. The instruction unit can also be realized by the specific processing unit 290 of the data processing device 12, and can instruct the vehicle to take a detour route through voice guidance or a navigation system, for example. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] For example, the collection unit can collect real-time video and sensor data from vehicles and pedestrians using the camera 42 and sensors of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect water level information from residents' home sensors, for example. The analysis unit can analyze the collected data and grasp the surrounding situation by the control unit 46A of the robot 414. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12, and can analyze the flooding situation of roads from the camera video of the vehicle, for example. The prediction unit can integrate the analyzed data and wide-area public information by the specific processing unit 290 of the data processing device 12, and predict the disaster risk locally and with high accuracy. The instruction unit can provide action instructions optimized for each target person by the control unit 46A of the robot 414. The instruction unit can also be realized by the specific processing unit 290 of the data processing device 12, and can instruct the vehicle to take a detour route through voice guidance or a navigation system, for example. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) a collection unit that collects real-time video and sensor data from vehicles, pedestrians, and residents; an analysis unit that analyzes the data collected by the collection unit and grasps the surrounding situation; A prediction unit that integrates the data analyzed by the analysis unit with wide-area public information and predicts disaster risks locally and precisely; An instruction unit that provides an individualized behavioral instruction for each subject based on the risk predicted by the prediction unit. A system characterized by: (Appendix 2) The collecting unit includes: Data will be collected from cameras or sensors mounted on vehicles, sensors on pedestrians' smartphones, and home sensors installed by residents. 2. The system of claim 1, (Appendix 3) The analysis unit is The system analyzes the flooding status of roads using vehicle camera footage, identifies safe evacuation routes using pedestrian location information, and predicts the progression of flooding based on water level information obtained from residents' home sensors. 2. The system of claim 1, (Appendix 4) The prediction unit is It integrates public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments, and combines it with individual environmental information to make risk predictions. 2. The system of claim 1, (Appendix 5) The instruction unit is Appropriate detour routes are calculated based on collected data and publicly available information, and detour routes are instructed to vehicles via voice guidance and navigation systems, safe evacuation routes are presented to pedestrians via smartphone apps, and evacuation instructions are given to residents via home sensors. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 7) The collecting unit includes: Dynamically change the timing of data collection depending on vehicle speed and pedestrian movement speed 2. The system of claim 1, (Appendix 8) The collecting unit includes: During data collection, additional information such as ambient sound and temperature is collected at the same time. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user emotions and select the type of data to collect based on the inferred user emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, look at a user's past behavior to prioritize data collection in certain situations 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, adjust the frequency of collection to take into account the remaining battery life of the user's device. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, evaluate the reliability of the data and exclude unreliable data. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, outliers are detected by comparing with past data and special processing is applied to the outliers 2. The system of claim 1, (Appendix 15) The analysis unit is Inferring user emotions and adjusting the level of detail of analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the source of the data 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, change the analysis method based on the time period during which the data was collected 2. The system of claim 1, (Appendix 18) The prediction unit is Inferring a user's emotions and adjusting the way risk predictions are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The prediction unit is When making predictions, refer to past disaster data to improve the accuracy of the predictions. 2. The system of claim 1, (Appendix 20) The prediction unit is When forecasting, it simulates risks under different weather conditions and presents multiple scenarios. 2. The system of claim 1, (Appendix 21) The prediction unit is Estimate user emotions and prioritize risk predictions based on the estimated user emotions 2. The system of claim 1, (Appendix 22) The prediction unit is When making predictions, the system takes into account the user's current location information. 2. The system of claim 1, (Appendix 23) The prediction unit is When making predictions, the system customizes risk predictions by referring to the user's past evacuation history. 2. The system of claim 1, (Appendix 24) The instruction unit is Estimating a user's emotions and adjusting the way instructions are expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The instruction unit is When instructed, the system suggests the optimal evacuation route, taking into account the user's current physical condition. 2. The system of claim 1, (Appendix 26) The instruction unit is When giving instructions, the method of giving instructions is changed taking into account the communication status of the user's device. 2. The system of claim 1, (Appendix 27) The instruction unit is Estimating a user's emotions and determining the priority of action instructions based on the estimated user's emotions 2. The system of claim 1, (Appendix 28) The instruction unit is When instructed, the system suggests evacuation routes taking into account the location information of the user's family and friends. 2. The system of claim 1, (Appendix 29) The instruction unit is When issuing instructions, the system selects the optimal instruction method by referring to the user's past evacuation actions. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects real-time video and sensor data from vehicles, pedestrians, and residents; an analysis unit that analyzes the data collected by the collection unit and grasps the surrounding situation; A prediction unit that integrates the data analyzed by the analysis unit with wide-area public information and predicts disaster risks locally and precisely; An instruction unit that provides an individualized behavioral instruction for each subject based on the risk predicted by the prediction unit. A system characterized by:

2. The collecting unit includes: Data will be collected from cameras or sensors mounted on vehicles, sensors on pedestrians' smartphones, and home sensors installed by residents.

2. The system of claim 1 .

3. The analysis unit is The system analyzes the flooding status of roads using vehicle camera footage, identifies safe evacuation routes using pedestrian location information, and predicts the progression of flooding based on water level information obtained from residents' home sensors.

2. The system of claim 1 .

4. The prediction unit is It integrates public information such as flood forecasts from the Japan Meteorological Agency and evacuation information from local governments, and combines it with individual environmental information to make risk predictions.

2. The system of claim 1 .

5. The instruction unit is Appropriate detour routes are calculated based on collected data and publicly available information, and detour routes are instructed to vehicles via voice guidance and navigation systems, safe evacuation routes are presented to pedestrians via smartphone apps, and evacuation instructions are given to residents via home sensors.

2. The system of claim 1 .

6. The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1 .

7. The collecting unit includes: Dynamically change the timing of data collection depending on vehicle speed and pedestrian movement speed 2. The system of claim 1 .

8. The collecting unit includes: During data collection, additional information such as ambient sound and temperature is collected at the same time.

2. The system of claim 1 .

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