system

Abstract

An object of a system according to an embodiment is to accurately predict the demand for supplies required in the event of a disaster and to quickly provide the supplies.SOLUTION: A system includes a disaster data collection part, a resource demand prediction part, and a prediction data provision part. The disaster data collection unit collects past disaster data, weather data, and geographic information. The resource demand forecasting section forecasts resource demand based on the data collected by the disaster data collection section. The prediction data providing section provides the demand data of the resource predicted by the resource demand prediction section to the local government, the NGO, and the relief organization.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately predict demand for supplies needed in the event of a disaster and to provide them quickly.

[0005] The system according to the embodiment aims to accurately predict the demand for supplies needed in the event of a disaster and to provide them promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a disaster data collection unit, a supply demand forecasting unit, and a forecast data providing unit. The disaster data collection unit collects past disaster data, meteorological data, and geographic information. The supply demand forecasting unit forecasts demand for supplies based on the data collected by the disaster data collection unit. The forecast data providing unit provides demand data for supplies forecasted by the supply demand forecasting unit to local governments, NGOs, and relief organizations. [Effects of the Invention]

[0007] The system according to the embodiment can accurately predict the demand for supplies needed in the event of a disaster and provide them quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The disaster relief supplies prediction system according to an embodiment of the present invention is a system that predicts the supplies that will be most needed in the event of a specific disaster and provides useful information to local governments, NGOs, and relief organizations. As a result, the disaster relief supplies prediction system can improve the efficiency and effectiveness of disaster prevention measures.

[0029] A disaster response supply prediction system according to an embodiment includes a disaster data collection unit, a supply demand prediction unit, and a prediction data provision unit. The disaster data collection unit collects past disaster data, meteorological data, and geographic information. For example, the disaster data collection unit collects data on past earthquakes, floods, typhoons, and the like. The disaster data collection unit can also collect meteorological data such as temperature, precipitation, and wind speed. The disaster data collection unit can also collect topographical data and land use data as geographical information. The supply demand prediction unit predicts demand for supplies based on the data collected by the disaster data collection unit. For example, the supply demand prediction unit predicts that drinking water, food, medicine, and the like will be needed in the event of an earthquake. The supply demand prediction unit predicts that evacuation boats, waterproof sheets, water purifiers, and the like will be needed in the event of a flood. The supply demand prediction unit predicts that windbreak sheets, emergency food, and the like will be needed in the event of a typhoon. The prediction data provision unit provides the demand data for supplies predicted by the supply demand prediction unit to local governments, NGOs, and relief organizations. For example, the prediction data providing unit can prepare necessary supplies in advance based on the prediction data and respond quickly when a disaster occurs. The prediction data providing unit can also formulate efficient countermeasures based on the prediction data. The prediction data providing unit can also quickly provide necessary supplies to disaster victims based on the prediction data. As a result, the disaster response supply prediction system according to the embodiment can improve the efficiency and effectiveness of disaster response measures. For example, the prediction data providing unit can provide the prediction data through a web application or a mobile application. The prediction data providing unit can also send the prediction data by email. The prediction data providing unit can also provide the prediction data on paper.

[0030] The disaster data collection unit adds real-time information from social media or news articles, allowing the system to instantly reflect the status of a disaster. For example, the disaster data collection unit collects social media posts in real time to instantly reflect the status of a disaster. For example, it analyzes posts on Twitter or Facebook to grasp the current situation in the disaster-stricken area. The disaster data collection unit also collects news articles in real time to instantly reflect the status of a disaster. For example, it analyzes online news and newspaper articles to grasp the current situation in the disaster-stricken area. The disaster data collection unit also collects live feeds and breaking news to instantly reflect the status of a disaster. This allows the system to instantly reflect the status of a disaster.

[0031] The disaster data collection unit analyzes the current situation in the disaster-stricken area in real time using drone or satellite images, enabling more accurate prediction of demand for supplies. For example, the disaster data collection unit uses a drone to photograph the current situation in the disaster-stricken area, and AI analyzes the images. For example, the damage to buildings and disruption of roads in the disaster-stricken area are grasped in real time. The disaster data collection unit also analyzes the current situation in the disaster-stricken area using satellite images. For example, optical satellite images and radar satellite images are analyzed to grasp the current situation in the disaster-stricken area. The disaster data collection unit also analyzes the current situation in the disaster-stricken area using real-time analysis technology. For example, image analysis algorithms and data processing technology are used to grasp the current situation in the disaster-stricken area in real time. This enables more accurate prediction of demand for supplies.

[0032] The disaster data collection unit can integrate data from different countries or regions and make predictions from a global perspective. The disaster data collection unit, for example, collects and integrates disaster data from different countries or regions. For example, earthquake data from the United States and typhoon data from Japan are compiled into a single database. The disaster data collection unit also makes predictions from a global perspective. For example, international cooperation and data standardization are carried out to make predictions from a global perspective. The disaster data collection unit also makes predictions based on data from different countries or regions. For example, predictions are made based on data from Asia, Europe, Africa, etc. This makes it possible to make predictions from a global perspective.

[0033] The disaster data collection unit can improve the accuracy of predictions, including by incorporating feedback from medical institutions or relief organizations. The disaster data collection unit, for example, collects feedback from medical institutions, and AI analyzes the data. For example, the demand for medicines and the status of hospitals in disaster-stricken areas are grasped in real time. The disaster data collection unit also collects feedback from relief organizations, and AI analyzes the data. For example, the demand for supplies and the status of relief activities in disaster-stricken areas are grasped in real time. The disaster data collection unit also improves the accuracy of predictions based on the feedback. For example, the accuracy of predictions is improved based on questionnaire surveys and on-site reports. This can improve the accuracy of predictions.

[0034] The supply demand prediction unit can include specific supplies taking into consideration the culture or lifestyle of the disaster-stricken area. The supply demand prediction unit, for example, takes into consideration the culture and lifestyle of the disaster-stricken area and includes specific supplies in the prediction list. For example, food ingredients and daily necessities that are commonly used in a specific area are added to the list. The supply demand prediction unit also predicts specific supplies based on culture and lifestyle. For example, it predicts specific supplies taking into consideration religious background and traditional customs. The supply demand prediction unit also predicts specific supplies based on lifestyle. For example, it predicts specific supplies taking into consideration eating habits and daily routines. This makes it possible to provide supplies that take into consideration the culture and lifestyle of the disaster-stricken area.

[0035] The material demand forecasting unit can forecast material demand taking into account seasonal or weather fluctuations based on past disaster data. The material demand forecasting unit, for example, analyzes past disaster data and forecasts material demand taking into account seasonal and weather fluctuations. For example, in a winter disaster, demand for cold weather gear and heating appliances increases. The material demand forecasting unit also forecasts material demand based on seasonal and weather fluctuations. For example, in a summer disaster, demand for cooling goods and hydration supplies increases. The material demand forecasting unit also forecasts material demand based on weather fluctuations. For example, in a typhoon or heavy rain, demand for waterproof sheets and water purifiers increases. This makes it possible to forecast material demand taking into account seasonal and weather fluctuations.

[0036] The supply demand forecasting unit can simulate different disaster scenarios and generate supply lists corresponding to the multiple scenarios. The supply demand forecasting unit, for example, simulates different disaster scenarios and predicts supply demand. For example, a supply list is generated for each scenario, such as an earthquake, flood, or typhoon. The supply demand forecasting unit also analyzes multiple disaster scenarios using simulation technology. For example, the disaster scenario is analyzed using computer simulation or modeling technology. The supply demand forecasting unit also generates a supply list for each scenario. For example, a list of drinking water, food, and medicine is generated for an earthquake scenario, and a list of evacuation boats, waterproof sheets, and water purifiers is generated for a flood scenario. In this way, supply lists corresponding to multiple disaster scenarios can be generated.

[0037] The supply demand forecasting unit can include supplies that take into account the infrastructure status of the disaster-stricken area. The supply demand forecasting unit, for example, analyzes the infrastructure status of the disaster-stricken area and customizes the supply list. For example, if the transportation network is cut off, supplies that can be transported by helicopter are included in the list. The supply demand forecasting unit also generates a supply list based on the infrastructure status. For example, if the power supply is cut off, it generates a list of generators and batteries. The supply demand forecasting unit also provides supplies taking into account the infrastructure status. For example, if the communication network is cut off, it generates a list of satellite phones and walkie-talkies. This makes it possible to provide supplies that take into account the infrastructure status of the disaster-stricken area.

[0038] The forecast data provision unit can customize the user interface and provide information tailored to the needs of each organization. The forecast data provision unit, for example, customizes the user interface and provides forecast data tailored to the needs of each organization. For example, it provides a detailed list of supplies for local governments and a concise list for NGOs. The forecast data provision unit also performs UI design and usability testing to provide information tailored to the needs of each organization. The forecast data provision unit also understands the needs of each organization based on questionnaire surveys and interviews and provides information. This makes it possible to provide information tailored to the needs of each organization.

[0039] The prediction data providing unit has a real-time update function after a disaster occurs, and can instantly update information in response to changes in the situation. The prediction data providing unit, for example, builds a system that updates prediction data in real time in response to changes in the situation after a disaster occurs. For example, the supply list is updated every time the situation in the disaster-stricken area changes. The prediction data providing unit also instantly updates information using data streaming and a real-time database. The prediction data providing unit also updates information in response to the progress of the disaster and the expansion of damage. For example, if the situation in the disaster-stricken area worsens, the list of necessary supplies is immediately updated. This allows information to be updated instantly in response to changes in the situation.

[0040] The prediction data providing unit can make prediction data accessible on different devices, improving convenience. The prediction data providing unit, for example, builds a system that makes prediction data accessible on different devices. For example, it makes it possible to access the same data on smartphones, tablets, PCs, etc. The prediction data providing unit also introduces technology to support different devices. For example, it adopts cross-platform compatibility and responsive design. The prediction data providing unit also improves usability on different devices. For example, it provides a UI optimized for each device. This improves the convenience of the prediction data.

[0041] The prediction data providing unit can link the prediction data with other disaster prevention systems to support comprehensive disaster prevention measures. The prediction data providing unit, for example, links the prediction data with an evacuation planning system to support comprehensive disaster prevention measures. For example, it adjusts the optimization of evacuation routes and the placement of evacuation shelters based on the prediction data. The prediction data providing unit also links with a medical support system to support comprehensive disaster prevention measures. For example, it adjusts the distribution of medical supplies and the placement of medical staff based on the prediction data. The prediction data providing unit also links with other disaster prevention systems to share and integrate data. For example, it standardizes data and integrates with APIs to support comprehensive disaster prevention measures. This makes it possible to support comprehensive disaster prevention measures.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The disaster data collection unit can collect data in real time using IoT devices such as seismometers and weather sensors. For example, a seismometer can be installed to instantly detect the occurrence of an earthquake. Weather sensors can also be used to measure wind speed and precipitation in real time. IoT devices can also be used to monitor changes in topography and rising water levels. This allows for a more accurate understanding of the disaster situation.

[0044] The supplies demand forecasting unit can predict demand for supplies taking into account the population density and age groups of the disaster-stricken area. For example, demand for medicines and nursing care products will increase in areas with a large elderly population. Demand for baby food and diapers will also increase in areas with a large number of children. Furthermore, it is predicted that large amounts of drinking water and food will be needed in areas with high population densities. This makes it possible to predict demand for supplies according to the population structure of the disaster-stricken area.

[0045] The Disaster Data Collection Unit monitors the infrastructure status of disaster-stricken areas in real time and can predict the demand for supplies. For example, if the transportation network is disrupted, supplies that can be transported by helicopter are included in the list. Also, if the power supply is cut off, it predicts an increase in demand for generators and batteries. Furthermore, if the communication network is disrupted, it predicts an increase in demand for satellite phones and walkie-talkies. This makes it possible to predict the demand for supplies according to the infrastructure status of the disaster-stricken area.

[0046] The forecast data provider can improve convenience by making it accessible on different devices. For example, the same data can be accessed on smartphones, tablets, PCs, etc. In addition, technology can be introduced to support different devices, and cross-platform compatibility and responsive design can be adopted. This improves the usability of the forecast data.

[0047] The supply demand forecasting unit can include specific supplies that take into account the culture and lifestyle of the affected area. For example, ingredients and daily necessities commonly used in a particular area can be added to the list. It can also predict specific supplies based on culture and lifestyle. This allows supplies to be provided that take into account the culture and lifestyle of the affected area.

[0048] The supply demand forecasting unit can simulate different disaster scenarios and generate supply lists corresponding to multiple scenarios. For example, a supply list is generated for each scenario, such as an earthquake, flood, or typhoon. It can also analyze multiple disaster scenarios using simulation technology and generate a supply list for each scenario. This makes it possible to generate supply lists corresponding to multiple disaster scenarios.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The disaster data collection unit collects past disaster data, meteorological data, and geographical information. For example, the disaster data collection unit collects data on past earthquakes, floods, typhoons, etc. The disaster data collection unit can also collect meteorological data such as temperature, precipitation, and wind speed. Furthermore, the disaster data collection unit can collect topographical data and land use data as geographical information. Step 2: The supplies demand forecasting unit predicts the demand for supplies based on the data collected by the disaster data collection unit. For example, in the event of an earthquake, it predicts that drinking water, food, medicine, etc. will be needed. In the event of a flood, it predicts that evacuation boats, waterproof sheets, water purifiers, etc. will be needed. In the event of a typhoon, it predicts that windbreak sheets, emergency food, etc. will be needed. Step 3: The forecast data providing unit provides local governments, NGOs, and relief organizations with the demand data for supplies predicted by the supply demand forecasting unit. For example, the forecast data providing unit can prepare necessary supplies in advance based on the forecast data, allowing for a rapid response when a disaster occurs. The forecast data providing unit can also develop efficient countermeasures based on the forecast data. Furthermore, the forecast data providing unit can quickly provide necessary supplies to disaster victims based on the forecast data. The forecast data providing unit can provide the forecast data through a web application or a mobile application. The forecast data providing unit can also send the forecast data by email. The forecast data providing unit can also provide the forecast data in paper form.

[0051] (Example 2) The disaster relief supplies prediction system according to an embodiment of the present invention is a system that predicts the supplies that will be most needed in the event of a specific disaster and provides useful information to local governments, NGOs, and relief organizations. As a result, the disaster relief supplies prediction system can improve the efficiency and effectiveness of disaster prevention measures.

[0052] A disaster response supply prediction system according to an embodiment includes a disaster data collection unit, a supply demand prediction unit, and a prediction data provision unit. The disaster data collection unit collects past disaster data, meteorological data, and geographic information. For example, the disaster data collection unit collects data on past earthquakes, floods, typhoons, and the like. The disaster data collection unit can also collect meteorological data such as temperature, precipitation, and wind speed. The disaster data collection unit can also collect topographical data and land use data as geographical information. The supply demand prediction unit predicts demand for supplies based on the data collected by the disaster data collection unit. For example, the supply demand prediction unit predicts that drinking water, food, medicine, and the like will be needed in the event of an earthquake. The supply demand prediction unit predicts that evacuation boats, waterproof sheets, water purifiers, and the like will be needed in the event of a flood. The supply demand prediction unit predicts that windbreak sheets, emergency food, and the like will be needed in the event of a typhoon. The prediction data provision unit provides the demand data for supplies predicted by the supply demand prediction unit to local governments, NGOs, and relief organizations. For example, the prediction data providing unit can prepare necessary supplies in advance based on the prediction data and respond quickly when a disaster occurs. The prediction data providing unit can also formulate efficient countermeasures based on the prediction data. The prediction data providing unit can also quickly provide necessary supplies to disaster victims based on the prediction data. As a result, the disaster response supply prediction system according to the embodiment can improve the efficiency and effectiveness of disaster response measures. For example, the prediction data providing unit can provide the prediction data through a web application or a mobile application. The prediction data providing unit can also send the prediction data by email. The prediction data providing unit can also provide the prediction data on paper.

[0053] The disaster data collection unit adds real-time information from social media or news articles, allowing the system to instantly reflect the status of a disaster. For example, the disaster data collection unit collects social media posts in real time to instantly reflect the status of a disaster. For example, it analyzes posts on Twitter or Facebook to grasp the current situation in the disaster-stricken area. The disaster data collection unit also collects news articles in real time to instantly reflect the status of a disaster. For example, it analyzes online news and newspaper articles to grasp the current situation in the disaster-stricken area. The disaster data collection unit also collects live feeds and breaking news to instantly reflect the status of a disaster. This allows the system to instantly reflect the status of a disaster.

[0054] The disaster data collection unit analyzes the current situation in the disaster-stricken area in real time using drone or satellite images, enabling more accurate prediction of demand for supplies. For example, the disaster data collection unit uses a drone to photograph the current situation in the disaster-stricken area, and AI analyzes the images. For example, the damage to buildings and disruption of roads in the disaster-stricken area are grasped in real time. The disaster data collection unit also analyzes the current situation in the disaster-stricken area using satellite images. For example, optical satellite images and radar satellite images are analyzed to grasp the current situation in the disaster-stricken area. The disaster data collection unit also analyzes the current situation in the disaster-stricken area using real-time analysis technology. For example, image analysis algorithms and data processing technology are used to grasp the current situation in the disaster-stricken area in real time. This enables more accurate prediction of demand for supplies.

[0055] The disaster data collection unit can use the emotion estimation function to analyze emotions from the social media posts of disaster victims and predict demand for psychological relief supplies. The disaster data collection unit, for example, analyzes the social media posts of disaster victims and analyzes emotions using the emotion estimation function. For example, it measures the degree of stress and anxiety from the content of the posts and predicts demand for psychological relief supplies. The disaster data collection unit also uses the emotion estimation function to analyze the emotions of disaster victims. For example, it uses natural language processing and emotion analysis algorithms to analyze the emotions of disaster victims. The disaster data collection unit also predicts demand for psychological relief supplies. For example, it predicts demand for counseling services and relaxation items. This makes it possible to predict demand for psychological relief supplies.

[0056] The disaster data collection unit can integrate data from different countries or regions and make predictions from a global perspective. The disaster data collection unit, for example, collects and integrates disaster data from different countries or regions. For example, earthquake data from the United States and typhoon data from Japan are compiled into a single database. The disaster data collection unit also makes predictions from a global perspective. For example, international cooperation and data standardization are carried out to make predictions from a global perspective. The disaster data collection unit also makes predictions based on data from different countries or regions. For example, predictions are made based on data from Asia, Europe, Africa, etc. This makes it possible to make predictions from a global perspective.

[0057] The disaster data collection unit can improve the accuracy of predictions, including by incorporating feedback from medical institutions or relief organizations. The disaster data collection unit, for example, collects feedback from medical institutions, and AI analyzes the data. For example, the demand for medicines and the status of hospitals in disaster-stricken areas are grasped in real time. The disaster data collection unit also collects feedback from relief organizations, and AI analyzes the data. For example, the demand for supplies and the status of relief activities in disaster-stricken areas are grasped in real time. The disaster data collection unit also improves the accuracy of predictions based on the feedback. For example, the accuracy of predictions is improved based on questionnaire surveys and on-site reports. This can improve the accuracy of predictions.

[0058] The disaster data collection unit can use the emotion estimation function to monitor the emotions of disaster victims in real time and identify areas where psychological support is needed. The disaster data collection unit, for example, analyzes social media posts by disaster victims in real time and monitors their emotions using the emotion estimation function. For example, it measures the level of stress and anxiety from the content of the posts. The disaster data collection unit also uses the emotion estimation function to monitor the emotions of disaster victims in real time. For example, it uses natural language processing and emotion analysis algorithms to monitor the emotions of disaster victims in real time. The disaster data collection unit also identifies areas where psychological support is needed. For example, it identifies areas with high levels of stress and anxiety and provides psychological support. This makes it possible to identify areas where psychological support is needed.

[0059] The supply demand prediction unit can include specific supplies taking into consideration the culture or lifestyle of the disaster-stricken area. The supply demand prediction unit, for example, takes into consideration the culture and lifestyle of the disaster-stricken area and includes specific supplies in the prediction list. For example, food ingredients and daily necessities that are commonly used in a specific area are added to the list. The supply demand prediction unit also predicts specific supplies based on culture and lifestyle. For example, it predicts specific supplies taking into consideration religious background and traditional customs. The supply demand prediction unit also predicts specific supplies based on lifestyle. For example, it predicts specific supplies taking into consideration eating habits and daily routines. This makes it possible to provide supplies that take into consideration the culture and lifestyle of the disaster-stricken area.

[0060] The material demand forecasting unit can forecast material demand taking into account seasonal or weather fluctuations based on past disaster data. The material demand forecasting unit, for example, analyzes past disaster data and forecasts material demand taking into account seasonal and weather fluctuations. For example, in a winter disaster, demand for cold weather gear and heating appliances increases. The material demand forecasting unit also forecasts material demand based on seasonal and weather fluctuations. For example, in a summer disaster, demand for cooling goods and hydration supplies increases. The material demand forecasting unit also forecasts material demand based on weather fluctuations. For example, in a typhoon or heavy rain, demand for waterproof sheets and water purifiers increases. This makes it possible to forecast material demand taking into account seasonal and weather fluctuations.

[0061] The supply demand prediction unit can use the emotion estimation function to predict demand for supplies according to the emotional state of the disaster victims. The supply demand prediction unit, for example, uses the emotion estimation function to analyze the emotional state of the disaster victims and predict demand for supplies. For example, it predicts demand for stress-relief goods and entertainment items. The supply demand prediction unit also uses the emotion estimation function to predict demand for supplies based on the emotional state of the disaster victims. For example, it measures the level of stress and anxiety and predicts demand for psychological support supplies. The supply demand prediction unit also predicts demand for supplies based on the emotional state. For example, it predicts demand for supplies that provide a sense of security and relaxation items. This makes it possible to predict demand for supplies according to the emotional state of the disaster victims.

[0062] The supply demand forecasting unit can simulate different disaster scenarios and generate supply lists corresponding to the multiple scenarios. The supply demand forecasting unit, for example, simulates different disaster scenarios and predicts supply demand. For example, a supply list is generated for each scenario, such as an earthquake, flood, or typhoon. The supply demand forecasting unit also analyzes multiple disaster scenarios using simulation technology. For example, the disaster scenario is analyzed using computer simulation or modeling technology. The supply demand forecasting unit also generates a supply list for each scenario. For example, a list of drinking water, food, and medicine is generated for an earthquake scenario, and a list of evacuation boats, waterproof sheets, and water purifiers is generated for a flood scenario. In this way, supply lists corresponding to multiple disaster scenarios can be generated.

[0063] The supply demand forecasting unit can include supplies that take into account the infrastructure status of the disaster-stricken area. The supply demand forecasting unit, for example, analyzes the infrastructure status of the disaster-stricken area and customizes the supply list. For example, if the transportation network is cut off, supplies that can be transported by helicopter are included in the list. The supply demand forecasting unit also generates a supply list based on the infrastructure status. For example, if the power supply is cut off, it generates a list of generators and batteries. The supply demand forecasting unit also provides supplies taking into account the infrastructure status. For example, if the communication network is cut off, it generates a list of satellite phones and walkie-talkies. This makes it possible to provide supplies that take into account the infrastructure status of the disaster-stricken area.

[0064] The supply demand prediction unit uses the emotion estimation function to set priority levels for supplies based on the emotions of disaster victims, allowing the most needed supplies to be provided quickly. The supply demand prediction unit, for example, uses the emotion estimation function to analyze emotion data from disaster victims and set priority levels for supplies. For example, psychological support supplies are provided preferentially in areas with a high level of negative emotions. The supply demand prediction unit also uses the emotion estimation function to set priority levels for supplies. For example, the level of stress and anxiety is measured, and the most needed supplies are provided quickly. The supply demand prediction unit also sets priority levels for supplies based on emotion data. For example, supplies that provide a sense of security and relaxation items are provided preferentially. This allows the most needed supplies to be provided quickly.

[0065] The forecast data provision unit can customize the user interface and provide information tailored to the needs of each organization. The forecast data provision unit, for example, customizes the user interface and provides forecast data tailored to the needs of each organization. For example, it provides a detailed list of supplies for local governments and a concise list for NGOs. The forecast data provision unit also performs UI design and usability testing to provide information tailored to the needs of each organization. The forecast data provision unit also understands the needs of each organization based on questionnaire surveys and interviews and provides information. This makes it possible to provide information tailored to the needs of each organization.

[0066] The prediction data providing unit has a real-time update function after a disaster occurs, and can instantly update information in response to changes in the situation. The prediction data providing unit, for example, builds a system that updates prediction data in real time in response to changes in the situation after a disaster occurs. For example, the supply list is updated every time the situation in the disaster-stricken area changes. The prediction data providing unit also instantly updates information using data streaming and a real-time database. The prediction data providing unit also updates information in response to the progress of the disaster and the expansion of damage. For example, if the situation in the disaster-stricken area worsens, the list of necessary supplies is immediately updated. This allows information to be updated instantly in response to changes in the situation.

[0067] The predicted data providing unit can use the emotion estimation function to analyze the emotional state of a user who receives the predicted data and devise an information providing method for reducing stress. The predicted data providing unit, for example, uses the emotion estimation function to analyze the emotional state of a user who receives the predicted data. For example, if the user is feeling stressed, it provides concise and easy-to-understand information. The predicted data providing unit also analyzes the user's emotional state using the emotion estimation function and devise an information providing method. For example, it provides relaxation techniques and mental health support. The predicted data providing unit also improves the information providing method based on the user's emotional state. For example, it devises an information providing method for reducing stress. This makes it possible to devise an information providing method for reducing the user's stress.

[0068] The prediction data providing unit can make prediction data accessible on different devices, improving convenience. The prediction data providing unit, for example, builds a system that makes prediction data accessible on different devices. For example, it makes it possible to access the same data on smartphones, tablets, PCs, etc. The prediction data providing unit also introduces technology to support different devices. For example, it adopts cross-platform compatibility and responsive design. The prediction data providing unit also improves usability on different devices. For example, it provides a UI optimized for each device. This improves the convenience of the prediction data.

[0069] The prediction data providing unit can link the prediction data with other disaster prevention systems to support comprehensive disaster prevention measures. The prediction data providing unit, for example, links the prediction data with an evacuation planning system to support comprehensive disaster prevention measures. For example, it adjusts the optimization of evacuation routes and the placement of evacuation shelters based on the prediction data. The prediction data providing unit also links with a medical support system to support comprehensive disaster prevention measures. For example, it adjusts the distribution of medical supplies and the placement of medical staff based on the prediction data. The prediction data providing unit also links with other disaster prevention systems to share and integrate data. For example, it standardizes data and integrates with APIs to support comprehensive disaster prevention measures. This makes it possible to support comprehensive disaster prevention measures.

[0070] The predicted data providing unit uses the emotion estimation function to collect feedback based on the emotions of users who receive predicted data, and can use the collected feedback to improve the system. The predicted data providing unit, for example, uses the emotion estimation function to collect feedback based on the emotions of users who receive predicted data. For example, if the user is feeling stressed, the information provision method is improved. The predicted data providing unit also uses the emotion estimation function to analyze the user's emotions and collect feedback. For example, the feedback is collected based on questionnaire surveys and on-site reports. The predicted data providing unit also improves the system based on the feedback. For example, the information provision method is improved based on the user's emotions. In this way, feedback based on the user's emotions can be collected and used to improve the system.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The disaster data collection unit can collect data in real time using IoT devices such as seismometers and weather sensors. For example, a seismometer can be installed to instantly detect the occurrence of an earthquake. Weather sensors can also be used to measure wind speed and precipitation in real time. IoT devices can also be used to monitor changes in topography and rising water levels. This allows for a more accurate understanding of the disaster situation.

[0073] The supplies demand forecasting unit can predict demand for supplies taking into account the population density and age groups of the disaster-stricken area. For example, demand for medicines and nursing care products will increase in areas with a large elderly population. Demand for baby food and diapers will also increase in areas with a large number of children. Furthermore, it is predicted that large amounts of drinking water and food will be needed in areas with high population densities. This makes it possible to predict demand for supplies according to the population structure of the disaster-stricken area.

[0074] The predictive data provider can use the emotion estimation function to analyze the emotions of disaster victims and identify areas where psychological support is needed. For example, it can measure the level of stress and anxiety from social media posts and identify areas where psychological support is needed. It can also use the emotion estimation function to monitor the emotions of disaster victims in real time and identify areas where psychological support is needed. This makes it possible to quickly identify areas where psychological support is needed and provide appropriate support.

[0075] The Disaster Data Collection Unit monitors the infrastructure status of disaster-stricken areas in real time and can predict the demand for supplies. For example, if the transportation network is disrupted, supplies that can be transported by helicopter are included in the list. Also, if the power supply is cut off, it predicts an increase in demand for generators and batteries. Furthermore, if the communication network is disrupted, it predicts an increase in demand for satellite phones and walkie-talkies. This makes it possible to predict the demand for supplies according to the infrastructure status of the disaster-stricken area.

[0076] The goods demand prediction unit can use the emotion estimation function to predict the demand for goods according to the emotional state of the disaster victims. For example, it can predict the demand for stress-reducing goods and entertainment goods. The emotion estimation function can also be used to predict the demand for goods based on the emotional state of the disaster victims. This makes it possible to predict the demand for goods according to the emotional state of the disaster victims.

[0077] The forecast data provider can improve convenience by making it accessible on different devices. For example, the same data can be accessed on smartphones, tablets, PCs, etc. In addition, technology can be introduced to support different devices, and cross-platform compatibility and responsive design can be adopted. This improves the usability of the forecast data.

[0078] The disaster data collection unit can use the emotion estimation function to monitor the emotions of disaster victims in real time and identify areas where psychological support is needed. For example, it can measure the level of stress and anxiety from social media posts and identify areas where psychological support is needed. The emotion estimation function can also be used to monitor the emotions of disaster victims in real time and identify areas where psychological support is needed. This makes it possible to quickly identify areas where psychological support is needed and provide appropriate support.

[0079] The supply demand forecasting unit can include specific supplies that take into account the culture and lifestyle of the affected area. For example, ingredients and daily necessities commonly used in a particular area can be added to the list. It can also predict specific supplies based on culture and lifestyle. This allows supplies to be provided that take into account the culture and lifestyle of the affected area.

[0080] The prediction data providing unit can use the emotion estimation function to analyze the emotional state of a user who receives prediction data and devise an information provision method to reduce stress. For example, if the user is feeling stressed, concise and easy-to-understand information can be provided. The emotion estimation function can also be used to analyze the user's emotional state and provide relaxation techniques and mental health support. This makes it possible to devise an information provision method to reduce the user's stress.

[0081] The supply demand forecasting unit can simulate different disaster scenarios and generate supply lists corresponding to multiple scenarios. For example, a supply list is generated for each scenario, such as an earthquake, flood, or typhoon. It can also analyze multiple disaster scenarios using simulation technology and generate a supply list for each scenario. This makes it possible to generate supply lists corresponding to multiple disaster scenarios.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The disaster data collection unit collects past disaster data, meteorological data, and geographical information. For example, the disaster data collection unit collects data on past earthquakes, floods, typhoons, etc. The disaster data collection unit can also collect meteorological data such as temperature, precipitation, and wind speed. Furthermore, the disaster data collection unit can collect topographical data and land use data as geographical information. Step 2: The supplies demand forecasting unit predicts the demand for supplies based on the data collected by the disaster data collection unit. For example, in the event of an earthquake, it predicts that drinking water, food, medicine, etc. will be needed. In the event of a flood, it predicts that evacuation boats, waterproof sheets, water purifiers, etc. will be needed. In the event of a typhoon, it predicts that windbreak sheets, emergency food, etc. will be needed. Step 3: The forecast data providing unit provides local governments, NGOs, and relief organizations with the demand data for supplies predicted by the supply demand forecasting unit. For example, the forecast data providing unit can prepare necessary supplies in advance based on the forecast data, allowing for a rapid response when a disaster occurs. The forecast data providing unit can also develop efficient countermeasures based on the forecast data. Furthermore, the forecast data providing unit can quickly provide necessary supplies to disaster victims based on the forecast data. The forecast data providing unit can provide the forecast data through a web application or a mobile application. The forecast data providing unit can also send the forecast data by email. The forecast data providing unit can also provide the forecast data in paper form.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] 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.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] 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.

[0126] 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.

[0127] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] 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.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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 disaster data collection unit that collects past disaster data, meteorological data, and geographical information; a supplies demand forecasting unit that forecasts demand for supplies based on the data collected by the disaster data collecting unit; a forecast data providing unit that provides the demand data for supplies predicted by the supply demand forecasting unit to local governments, NGOs, and relief organizations. A system characterized by:

2. The disaster data collection unit Add real-time information from social media or news articles to instantly reflect the situation as it unfolds 2. The system of claim 1.

3. The disaster data collection unit Integrate data from different countries or regions to generate forecasts from a global perspective 2. The system of claim 1.

4. The material demand forecasting unit Include supplies specific to the culture or customs of the affected area.

2. The system of claim 1.

5. The prediction data providing unit Customize the user interface to provide information tailored to each organization's needs 2. The system of claim 1.

6. The disaster data collection unit Using emotion estimation to analyze emotions from disaster victims' social media posts and predict the demand for psychological relief supplies 2. The system of claim 1.

7. The material demand forecasting unit Using emotion estimation to predict demand for supplies based on the emotional state of disaster victims 2. The system of claim 1.

8. The prediction data providing unit Using emotion estimation functionality, we will analyze the emotional state of users who receive predicted data and devise ways to provide information to reduce stress.

2. The system of claim 1.

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