System

Abstract

An object of a system according to an embodiment is to support efficient supply of commodities by predicting supply and demand of food and daily commodities and optimizing a distribution route at the time of a disaster.SOLUTION: A system according to an embodiment includes a supply and demand prediction unit, a distribution route generation unit, a delivery schedule generation unit, and a resource supply support unit. The supply and demand prediction unit performs supply and demand prediction using the generated AI. The distribution route generation unit generates an optimal distribution route based on the supply and demand prediction result obtained by the supply and demand prediction unit. The delivery schedule generation unit generates an optimal delivery schedule based on the physical distribution route generated by the physical distribution route generation unit. The commodity supply support section supports efficient commodity supply based on the delivery schedule generated by the delivery schedule generation section.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 not adequately predicted the supply and demand of food and daily necessities during disasters or optimized logistics routes, making it difficult to efficiently supply supplies.

[0005] The system according to the embodiment aims to support efficient supply of goods by predicting the supply and demand of food and daily necessities and optimizing logistics routes during disasters. [Means for solving the problem]

[0006] The system according to the embodiment includes a supply and demand forecasting unit, a logistics route generating unit, a delivery schedule generating unit, and a material supply support unit. The supply and demand forecasting unit performs supply and demand forecasting using generation AI. The logistics route generating unit generates an optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecasting unit. The delivery schedule generating unit generates an optimal delivery schedule based on the logistics route generated by the logistics route generating unit. The material supply support unit supports efficient material supply based on the delivery schedule generated by the delivery schedule generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the supply and demand of food and daily necessities during disasters and optimize logistics routes, thereby supporting the efficient supply of supplies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A supply and demand forecasting and logistics route optimization system according to an embodiment of the present invention is a system in which a generation AI performs supply and demand forecasting and generates optimal logistics routes and delivery schedules in order to optimize the logistics of food and daily necessities during disasters. As a result, the supply and demand forecasting and logistics route optimization system can efficiently optimize the logistics of food and daily necessities during disasters and realize the rapid supply of supplies to disaster-stricken areas.

[0029] A supply and demand forecasting and logistics route optimization system according to an embodiment includes a supply and demand forecasting unit, a logistics route generating unit, a delivery schedule generating unit, and a material supply support unit. The supply and demand forecasting unit performs supply and demand forecasting using a generation AI. For example, the generation AI collects and analyzes data such as past disaster data, demographics, weather information, and the status of disaster-stricken areas. The generation AI performs supply and demand forecasting based on prompts including instructions on the type and scale of the disaster, the population of the disaster-stricken areas, etc. The logistics route generating unit generates an optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecasting unit. For example, the optimal logistics route is generated based on the location of a logistics center, road conditions, traffic information, geographic information of the disaster-stricken areas, etc. The generation AI optimizes the logistics route based on prompts including instructions on the location of a logistics center and road conditions, etc. The delivery schedule generating unit generates an optimal delivery schedule based on the logistics route generated by the logistics route generating unit. For example, the delivery schedule optimizes the timing and frequency of supplying materials to disaster-stricken areas. The generation AI optimizes the delivery schedule based on prompts including instructions on the supply and demand forecast results and logistics routes, etc. The supply support unit supports efficient supply of supplies based on the delivery schedule generated by the delivery schedule generation unit. For example, it supports the prompt and efficient supply of supplies to disaster-stricken areas. The generation AI supports supply of supplies based on prompts including instructions on logistics routes, delivery schedules, etc. As a result, the supply and demand forecasting and logistics route optimization system according to the embodiment can efficiently optimize the logistics of food and daily necessities in the event of a disaster and realize the prompt supply of supplies to disaster-stricken areas.

[0030] The supply and demand forecasting unit can make demand forecasts based on past disaster data and respond to the current disaster situation. For example, the supply and demand forecasting unit collects social media posts from disaster-stricken areas in real time, and the generation AI analyzes their content to immediately reflect any sudden changes in demand. For example, it detects posts reporting shortages of food or water and reflects these in the supply and demand forecast. The supply and demand forecasting unit also uses the generation AI to analyze news articles to understand the situation in the disaster-stricken areas. For example, it extracts information from news articles about the status of evacuation shelters and shortages of supplies in the disaster-stricken areas and reflects this in the supply and demand forecast. The supply and demand forecasting unit also comprehensively analyzes social media posts and news articles from the disaster-stricken areas, and the generation AI immediately reflects any sudden changes in demand. For example, it integrates data obtained from multiple sources to improve the accuracy of the supply and demand forecast. This makes it possible to make highly accurate demand forecasts that respond to the current disaster situation by utilizing past disaster data.

[0031] The logistics route generation unit can generate optimal logistics routes based on the location of logistics centers, road conditions, traffic information, and geographic information of the disaster-stricken area. For example, the logistics route generation unit uses a drone to photograph the current state of the disaster-stricken area, and the generation AI analyzes the image data. For example, it grasps the damage to buildings in the disaster-stricken area and the congestion of evacuation centers, and reflects this in supply and demand forecasts. The logistics route generation unit also analyzes satellite images to grasp the broader situation in the disaster-stricken area. For example, it analyzes the damage situation and traffic disruptions across the entire disaster-stricken area, and reflects this in supply and demand forecasts. The logistics route generation unit also combines drone and satellite images, and the generation AI visually analyzes the current situation in the disaster-stricken area. For example, it integrates detailed local conditions with conditions across a wider area to improve the accuracy of supply and demand forecasts. This allows the generation of optimal logistics routes by taking into account the location of logistics centers, road conditions, and other factors.

[0032] The delivery schedule generation unit can optimize the timing and frequency of supply. For example, the delivery schedule generation unit analyzes social media posts by disaster victims using an emotion estimation function to understand their psychological needs. For example, it detects posts expressing stress or anxiety and reflects this in the supply and demand forecast for psychological support supplies. The delivery schedule generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and reflect this in the supply and demand forecast. For example, it prioritizes the supply of psychological support supplies to areas with few positive emotions. The delivery schedule generation unit also collects emotion data from disaster victims' social media posts, and the generation AI incorporates this data into the supply and demand forecast. For example, it predicts demand for psychological support supplies based on the emotion data and creates a supply plan. This enables efficient supply of supplies by optimizing the timing and frequency of supply.

[0033] The Supply Support Unit can support the rapid and efficient supply of supplies. For example, the Supply Support Unit uses a generation AI to analyze real-time traffic data in the disaster-stricken area and dynamically update optimal logistics routes. For example, it grasps traffic congestion and road closures and adjusts logistics routes. The Supply Support Unit also collects traffic data in real time and has a generation AI analyze it. For example, it dynamically updates optimal logistics routes based on information on traffic accidents and road construction. The Supply Support Unit also uses a generation AI to analyze traffic data in the disaster-stricken area and dynamically update logistics routes. For example, it grasps fluctuations in traffic volume in real time and proposes optimal routes. This helps ensure the rapid and efficient supply of supplies, allowing for a smooth supply of supplies to the disaster-stricken area.

[0034] The supply and demand forecasting unit analyzes social media posts or news articles from disaster-stricken areas in real time, allowing sudden changes in demand to be reflected immediately. For example, the supply and demand forecasting unit collects social media posts from disaster-stricken areas in real time, and the generation AI analyzes their content to immediately reflect sudden changes in demand. For example, it detects posts reporting shortages of food or water and reflects these in the supply and demand forecast. The supply and demand forecasting unit also uses the generation AI to analyze news articles to grasp the situation in the disaster-stricken areas. For example, it extracts information from news articles about the status of evacuation centers in the disaster-stricken areas and shortages of supplies, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also comprehensively analyzes social media posts and news articles from disaster-stricken areas, allowing the generation AI to immediately reflect sudden changes in demand. For example, it integrates data obtained from multiple sources to improve the accuracy of supply and demand forecasts. This allows sudden changes in demand to be reflected immediately by analyzing social media posts and news articles from disaster-stricken areas in real time.

[0035] The supply and demand forecasting unit can visually analyze the current situation in the disaster-stricken area using drone or satellite imagery and reflect it in the demand forecast. For example, the supply and demand forecasting unit uses a drone to photograph the current situation in the disaster-stricken area, and the generation AI analyzes the image data. For example, it grasps the damage to buildings in the disaster-stricken area and the congestion status of evacuation centers, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also analyzes satellite imagery with the generation AI to grasp the broader situation in the disaster-stricken area. For example, it analyzes the damage status and traffic blockage status of the entire disaster-stricken area, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also combines drone and satellite imagery, and the generation AI visually analyzes the current situation in the disaster-stricken area. For example, it integrates detailed local conditions with the situation in a wider area, improving the accuracy of the supply and demand forecast. In this way, the current situation in the disaster-stricken area can be visually grasped and reflected in the demand forecast by using drone and satellite imagery.

[0036] The supply and demand prediction unit can analyze medical data from disaster-stricken areas and predict the supply and demand of medical supplies. For example, the generation AI analyzes data collected from medical institutions in disaster-stricken areas to predict the supply and demand of medical supplies. For example, predictions are made based on data on the number of patients in hospitals and the supplies needed for treatment. The supply and demand prediction unit also collects medical data from disaster-stricken areas in real time, which the generation AI analyzes. For example, it grasps the number of emergency transports and demand by medical department, and reflects this in the supply and demand prediction for medical supplies. The generation AI also analyzes the medical data in the supply and demand prediction unit to predict the supply and demand of medical supplies in disaster-stricken areas. For example, it makes predictions based on data on the outbreak of infectious diseases and the supplies needed to treat trauma. In this way, by analyzing medical data from disaster-stricken areas, it becomes possible to predict the supply and demand of medical supplies.

[0037] The supply and demand forecasting unit can analyze infrastructure data in the disaster-stricken areas and predict the supply and demand of restoration materials. For example, the generation AI analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, the damage status of roads and bridges is grasped and the required restoration materials are predicted. The supply and demand forecasting unit also collects infrastructure data in real time and the generation AI analyzes it. For example, the supply status of electricity and water is grasped and reflected in the supply and demand forecast of materials required for restoration. The generation AI also analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, the damage status of communication infrastructure is grasped and the required restoration materials are predicted. In this way, by analyzing infrastructure data in the disaster-stricken areas, it is possible to predict the supply and demand of restoration materials.

[0038] The logistics route generation unit can analyze real-time traffic data in the disaster-stricken area and dynamically update the logistics route. For example, the logistics route generation unit uses a generation AI to analyze real-time traffic data in the disaster-stricken area and dynamically update the optimal logistics route. For example, it grasps traffic congestion and road closures and adjusts the logistics route. The logistics route generation unit also collects traffic data in real time and the generation AI analyzes it. For example, it dynamically updates the optimal logistics route based on information on traffic accidents and road construction. The logistics route generation unit also uses a generation AI to analyze traffic data in the disaster-stricken area and dynamically update the logistics route. For example, it grasps fluctuations in traffic volume in real time and proposes the optimal route. In this way, the optimal logistics route can be dynamically updated by analyzing real-time traffic data in the disaster-stricken area.

[0039] The logistics route generation unit can analyze topographical data of the disaster-stricken area and generate the optimal logistics route according to the topography. For example, the logistics route generation unit uses a generation AI to analyze topographical data of the disaster-stricken area and generate the optimal logistics route according to the topography. For example, it grasps the conditions of mountainous areas and rivers and proposes the optimal route. The logistics route generation unit also collects topographical data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the risk of landslides and floods. The logistics route generation unit also uses a generation AI to analyze topographical data of the disaster-stricken area and optimizes the logistics route. For example, it proposes the optimal route taking into account the undulations of the terrain and the gradient of the roads. In this way, by analyzing the topographical data of the disaster-stricken area, it is possible to generate the optimal logistics route according to the topography.

[0040] The logistics route generation unit can analyze power supply data in disaster-stricken areas and prioritize routes with stable power supplies. For example, the logistics route generation unit uses a generation AI to analyze power supply data in disaster-stricken areas and prioritize routes with stable power supplies. For example, it proposes routes that pass through areas with a secured power supply. The logistics route generation unit also collects power supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with a low risk of power outages. The logistics route generation unit also uses a generation AI to analyze power supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects routes that pass through areas with a stable power supply. In this way, by analyzing power supply data in disaster-stricken areas, it is possible to prioritize routes with a stable power supply.

[0041] The logistics route generation unit can analyze communication infrastructure data in the disaster-stricken area and prioritize routes where communication is ensured. For example, the logistics route generation unit uses a generation AI to analyze communication infrastructure data in the disaster-stricken area and prioritize routes where communication is ensured. For example, it proposes a route that passes through areas where communication infrastructure is well developed. The logistics route generation unit also collects communication infrastructure data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas where the risk of communication failure is low. The logistics route generation unit also uses a generation AI to analyze communication infrastructure data in the disaster-stricken area and optimizes logistics routes. For example, it selects a route that passes through areas where communication is ensured. In this way, by analyzing communication infrastructure data in the disaster-stricken area, it is possible to prioritize routes where communication is ensured.

[0042] The logistics route generation unit can analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, the logistics route generation unit uses a generation AI to analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, it proposes a route that avoids heavy rain and strong winds. The logistics route generation unit also collects weather data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the effects of typhoons and heavy snow. The logistics route generation unit also uses a generation AI to analyze weather data for the disaster-stricken area and optimizes the logistics route. For example, it dynamically updates the route depending on weather conditions and proposes a safe route. In this way, by analyzing weather data for the disaster-stricken area, it is possible to generate the optimal logistics route depending on the weather.

[0043] The logistics route generation unit can analyze infrastructure recovery data in disaster-stricken areas and generate optimal logistics routes according to the recovery status. For example, the logistics route generation unit uses a generation AI to analyze infrastructure recovery data in disaster-stricken areas and generate optimal logistics routes according to the recovery status. For example, it proposes a route that passes through areas where recovery is progressing. The logistics route generation unit also collects infrastructure recovery data in real time and the generation AI analyzes it. For example, it grasps the recovery status of roads and bridges and generates optimal logistics routes. The logistics route generation unit also uses a generation AI to analyze infrastructure recovery data in disaster-stricken areas and optimizes logistics routes. For example, it selects a route that passes through areas where recovery has been completed. In this way, by analyzing infrastructure recovery data in disaster-stricken areas, it is possible to generate optimal logistics routes according to the recovery status.

[0044] The logistics route generation unit can analyze water supply data in disaster-stricken areas and prioritize routes with stable water supplies. For example, the logistics route generation unit uses a generation AI to analyze water supply data in disaster-stricken areas and prioritize routes with stable water supplies. For example, it proposes routes that pass through areas where water supply has been restored. The logistics route generation unit also collects water supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with a low risk of water outages. The logistics route generation unit also uses a generation AI to analyze water supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects routes that pass through areas with stable water supplies. In this way, by analyzing water supply data in disaster-stricken areas, it is possible to prioritize routes with stable water supplies.

[0045] The logistics route generation unit can analyze gas supply data in disaster-stricken areas and prioritize routes with stable gas supplies. For example, the logistics route generation unit uses a generation AI to analyze gas supply data in disaster-stricken areas and prioritize routes with stable gas supplies. For example, it proposes a route that passes through areas where gas supply has been restored. The logistics route generation unit also collects gas supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with low gas supply risks. The logistics route generation unit also uses a generation AI to analyze gas supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects a route that passes through areas with stable gas supplies. In this way, by analyzing gas supply data in disaster-stricken areas, it is possible to prioritize routes with stable gas supplies.

[0046] The delivery schedule generation unit can analyze demographic data of the disaster-stricken area and generate an optimal delivery schedule based on population density. For example, the delivery schedule generation unit uses a generation AI to analyze demographic data of the disaster-stricken area and generate an optimal delivery schedule based on population density. For example, priority is given to delivering supplies to areas with high population density. The delivery schedule generation unit also collects demographic data in real time and the generation AI analyzes it. For example, it grasps the population density of evacuation centers and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze demographic data of the disaster-stricken area and optimizes the delivery schedule. For example, it proposes a schedule that increases frequency for areas with high population density. In this way, by analyzing the demographic data of the disaster-stricken area, an optimal delivery schedule based on population density can be generated.

[0047] The delivery schedule generation unit can analyze medical facility data in the disaster-stricken area and generate a priority delivery schedule for medical supplies. For example, the delivery schedule generation unit uses a generation AI to analyze medical facility data in the disaster-stricken area and generate a priority delivery schedule for medical supplies. For example, medical supplies are delivered to hospitals and clinics on a priority basis. The delivery schedule generation unit also collects medical facility data in real time and the generation AI analyzes it. For example, it grasps the demand for medical facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze medical facility data in the disaster-stricken area and optimizes the delivery schedule for medical supplies. For example, it adjusts the delivery frequency according to the demand for medical facilities. In this way, a priority delivery schedule for medical supplies can be generated by analyzing medical facility data in the disaster-stricken area.

[0048] The delivery schedule generation unit can analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, the delivery schedule generation unit uses a generation AI to analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, educational supplies are delivered to schools and learning facilities on a priority basis. The delivery schedule generation unit also collects educational facility data in real time and the generation AI analyzes it. For example, it grasps demand at educational facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze data on educational facilities in disaster-stricken areas and optimizes the delivery schedule for educational supplies. For example, it adjusts the frequency of delivery according to demand at educational facilities. In this way, a priority delivery schedule for educational supplies can be generated by analyzing data on educational facilities in disaster-stricken areas.

[0049] The delivery schedule generation unit can analyze public facility data in the disaster-stricken area and generate a priority delivery schedule for public supplies. For example, the delivery schedule generation unit uses a generation AI to analyze public facility data in the disaster-stricken area and generate a priority delivery schedule for public supplies. For example, public supplies are delivered to evacuation centers and community centers on a priority basis. The delivery schedule generation unit also collects public facility data in real time and the generation AI analyzes it. For example, it grasps the demand for public facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze public facility data in the disaster-stricken area and optimizes the delivery schedule for public supplies. For example, it adjusts the delivery frequency according to the demand for public facilities. In this way, a priority delivery schedule for public supplies can be generated by analyzing public facility data in the disaster-stricken area.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The supply and demand prediction unit can analyze medical data from disaster-stricken areas and make supply and demand forecasts for medical supplies. For example, the generation AI analyzes data collected from medical institutions in disaster-stricken areas to make supply and demand forecasts for medical supplies. For example, predictions are made based on data on the number of patients in hospitals and the supplies needed for treatment. The supply and demand prediction unit also collects medical data from disaster-stricken areas in real time, which the generation AI analyzes. For example, it grasps the number of emergency transports and demand by medical department, and reflects this in the supply and demand forecast for medical supplies. The supply and demand prediction unit also analyzes medical data using the generation AI to make supply and demand forecasts for medical supplies in disaster-stricken areas. For example, it makes predictions based on data on the outbreak of infectious diseases and the supplies needed to treat trauma. In this way, by analyzing medical data from disaster-stricken areas, it becomes possible to make supply and demand forecasts for medical supplies.

[0052] The logistics route generation unit can analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, the generation AI analyzes weather data for the disaster-stricken area and generates the optimal logistics route depending on the weather. For example, it proposes a route that avoids heavy rain and strong winds. The logistics route generation unit also collects weather data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the effects of typhoons and heavy snow. The logistics route generation unit also analyzes weather data for the disaster-stricken area using the generation AI and optimizes the logistics route. For example, it dynamically updates the route depending on weather conditions and proposes a safe route. In this way, by analyzing weather data for the disaster-stricken area, it is possible to generate the optimal logistics route depending on the weather.

[0053] The delivery schedule generation unit can analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, the generation AI analyzes data on educational facilities in disaster-stricken areas and generates a priority delivery schedule for educational supplies. For example, educational supplies are delivered to schools and learning facilities on a priority basis. The delivery schedule generation unit also collects educational facility data in real time and the generation AI analyzes it. For example, it grasps the demand for educational facilities and generates an optimal delivery schedule. The delivery schedule generation unit also analyzes data on educational facilities in disaster-stricken areas with the generation AI and optimizes the delivery schedule for educational supplies. For example, it adjusts the frequency of delivery according to the demand for educational facilities. In this way, a priority delivery schedule for educational supplies can be generated by analyzing data on educational facilities in disaster-stricken areas.

[0054] In the Supply Support Department, the generation AI analyzes real-time traffic data from the disaster-stricken areas and dynamically updates optimal logistics routes. For example, it grasps traffic congestion and road closures and adjusts logistics routes. The Supply Support Department also collects traffic data in real time and the generation AI analyzes it. For example, it dynamically updates optimal logistics routes based on information on traffic accidents and road construction. The supply support department also analyzes traffic data from the disaster-stricken areas and dynamically updates logistics routes. For example, it grasps fluctuations in traffic volume in real time and proposes optimal routes. This helps ensure the supply of supplies is carried out quickly and efficiently, allowing for a smooth supply of supplies to the disaster-stricken areas.

[0055] The supply and demand forecasting unit can analyze infrastructure data in the disaster-stricken areas and predict the supply and demand of restoration materials. For example, the generation AI analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, it grasps the damage status of roads and bridges and predicts the restoration materials needed. The supply and demand forecasting unit also collects infrastructure data in real time, which the generation AI analyzes. For example, it grasps the supply status of electricity and water and reflects this in the supply and demand forecast of materials needed for restoration. The supply and demand forecasting unit also analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, it grasps the damage status of communication infrastructure and predicts the restoration materials needed. In this way, by analyzing infrastructure data in the disaster-stricken areas, it becomes possible to predict the supply and demand of restoration materials.

[0056] The logistics route generation unit analyzes communication infrastructure data in the disaster-stricken area and can prioritize routes where communication is ensured. For example, the generation AI analyzes communication infrastructure data in the disaster-stricken area and prioritizes routes where communication is ensured. For example, it proposes a route that passes through areas where communication infrastructure is well developed. The logistics route generation unit also collects communication infrastructure data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas where the risk of communication failure is low. The logistics route generation unit also analyzes communication infrastructure data in the disaster-stricken area and optimizes logistics routes. For example, it selects a route that passes through areas where communication is ensured. In this way, by analyzing communication infrastructure data in the disaster-stricken area, it is possible to prioritize routes where communication is ensured.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The supply and demand forecasting unit uses the generation AI to make supply and demand forecasts. For example, the generation AI collects and analyzes data such as past disaster data, demographics, weather information, and the situation in the affected area. The generation AI makes supply and demand forecasts based on prompts that include instructions on the type and scale of the disaster, the population of the affected area, etc. Step 2: The logistics route generation unit generates the optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecast unit. For example, the optimal logistics route is generated based on the location of the logistics center, road conditions, traffic information, geographic information of the disaster area, etc. The generation AI optimizes the logistics route based on prompts including instructions on the location of the logistics center and road conditions, etc. Step 3: The delivery schedule generation unit generates an optimal delivery schedule based on the logistics routes generated by the logistics route generation unit. For example, it optimizes the timing and frequency of supplying supplies to disaster-stricken areas. The generation AI optimizes the delivery schedule based on prompts including supply and demand forecast results and instructions on logistics routes. Step 4: The Supply Support Unit supports efficient supply of supplies based on the delivery schedule generated by the Delivery Schedule Generation Unit. For example, it helps ensure that supplies are delivered to disaster-stricken areas quickly and efficiently. The generation AI supports supply of supplies based on prompts including instructions on logistics routes and delivery schedules.

[0059] (Example 2) A supply and demand forecasting and logistics route optimization system according to an embodiment of the present invention is a system in which a generation AI performs supply and demand forecasting and generates optimal logistics routes and delivery schedules in order to optimize the logistics of food and daily necessities during disasters. As a result, the supply and demand forecasting and logistics route optimization system can efficiently optimize the logistics of food and daily necessities during disasters and realize the rapid supply of supplies to disaster-stricken areas.

[0060] A supply and demand forecasting and logistics route optimization system according to an embodiment includes a supply and demand forecasting unit, a logistics route generating unit, a delivery schedule generating unit, and a material supply support unit. The supply and demand forecasting unit performs supply and demand forecasting using a generation AI. For example, the generation AI collects and analyzes data such as past disaster data, demographics, weather information, and the status of disaster-stricken areas. The generation AI performs supply and demand forecasting based on prompts including instructions on the type and scale of the disaster, the population of the disaster-stricken areas, etc. The logistics route generating unit generates an optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecasting unit. For example, the optimal logistics route is generated based on the location of a logistics center, road conditions, traffic information, geographic information of the disaster-stricken areas, etc. The generation AI optimizes the logistics route based on prompts including instructions on the location of a logistics center and road conditions, etc. The delivery schedule generating unit generates an optimal delivery schedule based on the logistics route generated by the logistics route generating unit. For example, the delivery schedule optimizes the timing and frequency of supplying materials to disaster-stricken areas. The generation AI optimizes the delivery schedule based on prompts including instructions on the supply and demand forecast results and logistics routes, etc. The supply support unit supports efficient supply of supplies based on the delivery schedule generated by the delivery schedule generation unit. For example, it supports the prompt and efficient supply of supplies to disaster-stricken areas. The generation AI supports supply of supplies based on prompts including instructions on logistics routes, delivery schedules, etc. As a result, the supply and demand forecasting and logistics route optimization system according to the embodiment can efficiently optimize the logistics of food and daily necessities in the event of a disaster and realize the prompt supply of supplies to disaster-stricken areas.

[0061] The supply and demand forecasting unit can make demand forecasts based on past disaster data and respond to the current disaster situation. For example, the supply and demand forecasting unit collects social media posts from disaster-stricken areas in real time, and the generation AI analyzes their content to immediately reflect any sudden changes in demand. For example, it detects posts reporting shortages of food or water and reflects these in the supply and demand forecast. The supply and demand forecasting unit also uses the generation AI to analyze news articles to understand the situation in the disaster-stricken areas. For example, it extracts information from news articles about the status of evacuation shelters and shortages of supplies in the disaster-stricken areas and reflects this in the supply and demand forecast. The supply and demand forecasting unit also comprehensively analyzes social media posts and news articles from the disaster-stricken areas, and the generation AI immediately reflects any sudden changes in demand. For example, it integrates data obtained from multiple sources to improve the accuracy of the supply and demand forecast. This makes it possible to make highly accurate demand forecasts that respond to the current disaster situation by utilizing past disaster data.

[0062] The logistics route generation unit can generate optimal logistics routes based on the location of logistics centers, road conditions, traffic information, and geographic information of the disaster-stricken area. For example, the logistics route generation unit uses a drone to photograph the current state of the disaster-stricken area, and the generation AI analyzes the image data. For example, it grasps the damage to buildings in the disaster-stricken area and the congestion of evacuation centers, and reflects this in supply and demand forecasts. The logistics route generation unit also analyzes satellite images to grasp the broader situation in the disaster-stricken area. For example, it analyzes the damage situation and traffic disruptions across the entire disaster-stricken area, and reflects this in supply and demand forecasts. The logistics route generation unit also combines drone and satellite images, and the generation AI visually analyzes the current situation in the disaster-stricken area. For example, it integrates detailed local conditions with conditions across a wider area to improve the accuracy of supply and demand forecasts. This allows the generation of optimal logistics routes by taking into account the location of logistics centers, road conditions, and other factors.

[0063] The delivery schedule generation unit can optimize the timing and frequency of supply. For example, the delivery schedule generation unit analyzes social media posts by disaster victims using an emotion estimation function to understand their psychological needs. For example, it detects posts expressing stress or anxiety and reflects this in the supply and demand forecast for psychological support supplies. The delivery schedule generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and reflect this in the supply and demand forecast. For example, it prioritizes the supply of psychological support supplies to areas with few positive emotions. The delivery schedule generation unit also collects emotion data from disaster victims' social media posts, and the generation AI incorporates this data into the supply and demand forecast. For example, it predicts demand for psychological support supplies based on the emotion data and creates a supply plan. This enables efficient supply of supplies by optimizing the timing and frequency of supply.

[0064] The Supply Support Unit can support the rapid and efficient supply of supplies. For example, the Supply Support Unit uses a generation AI to analyze real-time traffic data in the disaster-stricken area and dynamically update optimal logistics routes. For example, it grasps traffic congestion and road closures and adjusts logistics routes. The Supply Support Unit also collects traffic data in real time and has a generation AI analyze it. For example, it dynamically updates optimal logistics routes based on information on traffic accidents and road construction. The Supply Support Unit also uses a generation AI to analyze traffic data in the disaster-stricken area and dynamically update logistics routes. For example, it grasps fluctuations in traffic volume in real time and proposes optimal routes. This helps ensure the rapid and efficient supply of supplies, allowing for a smooth supply of supplies to the disaster-stricken area.

[0065] The supply and demand forecasting unit analyzes social media posts or news articles from disaster-stricken areas in real time, allowing sudden changes in demand to be reflected immediately. For example, the supply and demand forecasting unit collects social media posts from disaster-stricken areas in real time, and the generation AI analyzes their content to immediately reflect sudden changes in demand. For example, it detects posts reporting shortages of food or water and reflects these in the supply and demand forecast. The supply and demand forecasting unit also uses the generation AI to analyze news articles to grasp the situation in the disaster-stricken areas. For example, it extracts information from news articles about the status of evacuation centers in the disaster-stricken areas and shortages of supplies, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also comprehensively analyzes social media posts and news articles from disaster-stricken areas, allowing the generation AI to immediately reflect sudden changes in demand. For example, it integrates data obtained from multiple sources to improve the accuracy of supply and demand forecasts. This allows sudden changes in demand to be reflected immediately by analyzing social media posts and news articles from disaster-stricken areas in real time.

[0066] The supply and demand forecasting unit can visually analyze the current situation in the disaster-stricken area using drone or satellite imagery and reflect it in the demand forecast. For example, the supply and demand forecasting unit uses a drone to photograph the current situation in the disaster-stricken area, and the generation AI analyzes the image data. For example, it grasps the damage to buildings in the disaster-stricken area and the congestion status of evacuation centers, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also analyzes satellite imagery with the generation AI to grasp the broader situation in the disaster-stricken area. For example, it analyzes the damage status and traffic blockage status of the entire disaster-stricken area, and reflects this in the supply and demand forecast. The supply and demand forecasting unit also combines drone and satellite imagery, and the generation AI visually analyzes the current situation in the disaster-stricken area. For example, it integrates detailed local conditions with the situation in a wider area, improving the accuracy of the supply and demand forecast. In this way, the current situation in the disaster-stricken area can be visually grasped and reflected in the demand forecast by using drone and satellite imagery.

[0067] The supply and demand forecasting unit can use the emotion estimation function to analyze emotions from disaster victims' social media posts and incorporate their psychological needs into the supply and demand forecast. For example, the supply and demand forecasting unit can analyze disaster victims' social media posts using the emotion estimation function to understand their psychological needs. For example, it can detect posts expressing stress or anxiety and reflect this in the supply and demand forecast for psychological relief supplies. The supply and demand forecasting unit also uses the emotion estimation function to analyze disaster victims' emotions in real time and reflect this in the supply and demand forecast. For example, it can prioritize the supply of psychological relief supplies to areas with few positive emotions. The supply and demand forecasting unit also collects emotion data from disaster victims' social media posts, and the generation AI incorporates this data into the supply and demand forecast. For example, it can predict demand for psychological relief supplies based on the emotion data and create a supply plan. In this way, by analyzing disaster victims' emotions, it can reflect their psychological needs in the supply and demand forecast.

[0068] The supply and demand prediction unit can analyze medical data from disaster-stricken areas and predict the supply and demand of medical supplies. For example, the generation AI analyzes data collected from medical institutions in disaster-stricken areas to predict the supply and demand of medical supplies. For example, predictions are made based on data on the number of patients in hospitals and the supplies needed for treatment. The supply and demand prediction unit also collects medical data from disaster-stricken areas in real time, which the generation AI analyzes. For example, it grasps the number of emergency transports and demand by medical department, and reflects this in the supply and demand prediction for medical supplies. The generation AI also analyzes the medical data in the supply and demand prediction unit to predict the supply and demand of medical supplies in disaster-stricken areas. For example, it makes predictions based on data on the outbreak of infectious diseases and the supplies needed to treat trauma. In this way, by analyzing medical data from disaster-stricken areas, it becomes possible to predict the supply and demand of medical supplies.

[0069] The supply and demand forecasting unit can analyze infrastructure data in the disaster-stricken areas and predict the supply and demand of restoration materials. For example, the generation AI analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, the damage status of roads and bridges is grasped and the required restoration materials are predicted. The supply and demand forecasting unit also collects infrastructure data in real time and the generation AI analyzes it. For example, the supply status of electricity and water is grasped and reflected in the supply and demand forecast of materials required for restoration. The generation AI also analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, the damage status of communication infrastructure is grasped and the required restoration materials are predicted. In this way, by analyzing infrastructure data in the disaster-stricken areas, it is possible to predict the supply and demand of restoration materials.

[0070] The supply and demand prediction unit can use the emotion estimation function to analyze the emotions of disaster victims and predict the supply and demand of psychological support supplies. The supply and demand prediction unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and predicts the supply and demand of psychological support supplies. For example, it detects posts expressing stress or anxiety and reflects these in the supply and demand prediction of psychological support supplies. The supply and demand prediction unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and predicts the supply and demand of psychological support supplies. For example, it prioritizes the supply of psychological support supplies to areas with few positive emotions. The supply and demand prediction unit also collects emotional data from disaster victims, and the generation AI predicts the supply and demand of psychological support supplies. For example, it predicts the demand for psychological support supplies based on the emotion data and creates a supply plan. In this way, it is possible to predict the supply and demand of psychological support supplies by analyzing the emotions of disaster victims.

[0071] The logistics route generation unit can analyze real-time traffic data in the disaster-stricken area and dynamically update the logistics route. For example, the logistics route generation unit uses a generation AI to analyze real-time traffic data in the disaster-stricken area and dynamically update the optimal logistics route. For example, it grasps traffic congestion and road closures and adjusts the logistics route. The logistics route generation unit also collects traffic data in real time and the generation AI analyzes it. For example, it dynamically updates the optimal logistics route based on information on traffic accidents and road construction. The logistics route generation unit also uses a generation AI to analyze traffic data in the disaster-stricken area and dynamically update the logistics route. For example, it grasps fluctuations in traffic volume in real time and proposes the optimal route. In this way, the optimal logistics route can be dynamically updated by analyzing real-time traffic data in the disaster-stricken area.

[0072] The logistics route generation unit can analyze topographical data of the disaster-stricken area and generate the optimal logistics route according to the topography. For example, the logistics route generation unit uses a generation AI to analyze topographical data of the disaster-stricken area and generate the optimal logistics route according to the topography. For example, it grasps the conditions of mountainous areas and rivers and proposes the optimal route. The logistics route generation unit also collects topographical data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the risk of landslides and floods. The logistics route generation unit also uses a generation AI to analyze topographical data of the disaster-stricken area and optimizes the logistics route. For example, it proposes the optimal route taking into account the undulations of the terrain and the gradient of the roads. In this way, by analyzing the topographical data of the disaster-stricken area, it is possible to generate the optimal logistics route according to the topography.

[0073] The logistics route generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose routes that provide psychological security. The logistics route generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes routes that provide psychological security. For example, it prioritizes routes that are close to evacuation shelters and medical facilities. The logistics route generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes routes that provide psychological security. For example, it prioritizes routes that pass through areas with strong positive emotions. The logistics route generation unit also collects emotional data from disaster victims, and the generation AI proposes routes that provide psychological security. For example, it selects routes that are close to evacuation shelters and support facilities based on the emotional data. In this way, it is possible to propose routes that provide psychological security by analyzing the emotions of disaster victims.

[0074] The logistics route generation unit can analyze power supply data in disaster-stricken areas and prioritize routes with stable power supplies. For example, the logistics route generation unit uses a generation AI to analyze power supply data in disaster-stricken areas and prioritize routes with stable power supplies. For example, it proposes routes that pass through areas with a secured power supply. The logistics route generation unit also collects power supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with a low risk of power outages. The logistics route generation unit also uses a generation AI to analyze power supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects routes that pass through areas with a stable power supply. In this way, by analyzing power supply data in disaster-stricken areas, it is possible to prioritize routes with a stable power supply.

[0075] The logistics route generation unit can analyze communication infrastructure data in the disaster-stricken area and prioritize routes where communication is ensured. For example, the logistics route generation unit uses a generation AI to analyze communication infrastructure data in the disaster-stricken area and prioritize routes where communication is ensured. For example, it proposes a route that passes through areas where communication infrastructure is well developed. The logistics route generation unit also collects communication infrastructure data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas where the risk of communication failure is low. The logistics route generation unit also uses a generation AI to analyze communication infrastructure data in the disaster-stricken area and optimizes logistics routes. For example, it selects a route that passes through areas where communication is ensured. In this way, by analyzing communication infrastructure data in the disaster-stricken area, it is possible to prioritize routes where communication is ensured.

[0076] The logistics route generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose evacuation routes that provide psychological peace of mind. The logistics route generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes evacuation routes that provide psychological peace of mind. For example, it prioritizes routes that are close to evacuation shelters and medical facilities. The logistics route generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes evacuation routes that provide psychological peace of mind. For example, it prioritizes routes that pass through areas with strong positive emotions. The logistics route generation unit also collects emotional data from disaster victims, and the generation AI proposes evacuation routes that provide psychological peace of mind. For example, it selects routes that are close to evacuation shelters and support facilities based on the emotional data. In this way, by analyzing the emotions of disaster victims, it is possible to propose evacuation routes that provide psychological peace of mind.

[0077] The logistics route generation unit can analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, the logistics route generation unit uses a generation AI to analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, it proposes a route that avoids heavy rain and strong winds. The logistics route generation unit also collects weather data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the effects of typhoons and heavy snow. The logistics route generation unit also uses a generation AI to analyze weather data for the disaster-stricken area and optimizes the logistics route. For example, it dynamically updates the route depending on weather conditions and proposes a safe route. In this way, by analyzing weather data for the disaster-stricken area, it is possible to generate the optimal logistics route depending on the weather.

[0078] The logistics route generation unit can analyze infrastructure recovery data in disaster-stricken areas and generate optimal logistics routes according to the recovery status. For example, the logistics route generation unit uses a generation AI to analyze infrastructure recovery data in disaster-stricken areas and generate optimal logistics routes according to the recovery status. For example, it proposes a route that passes through areas where recovery is progressing. The logistics route generation unit also collects infrastructure recovery data in real time and the generation AI analyzes it. For example, it grasps the recovery status of roads and bridges and generates optimal logistics routes. The logistics route generation unit also uses a generation AI to analyze infrastructure recovery data in disaster-stricken areas and optimizes logistics routes. For example, it selects a route that passes through areas where recovery has been completed. In this way, by analyzing infrastructure recovery data in disaster-stricken areas, it is possible to generate optimal logistics routes according to the recovery status.

[0079] The logistics route generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose logistics routes that provide psychological security. The logistics route generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes logistics routes that provide psychological security. For example, it prioritizes routes that are close to evacuation shelters and medical facilities. The logistics route generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes logistics routes that provide psychological security. For example, it prioritizes routes that pass through areas with strong positive emotions. The logistics route generation unit also collects emotional data from disaster victims, and the generation AI proposes logistics routes that provide psychological security. For example, it selects routes that are close to evacuation shelters and support facilities based on the emotional data. In this way, by analyzing the emotions of disaster victims, it is possible to propose logistics routes that provide psychological security.

[0080] The logistics route generation unit can analyze water supply data in disaster-stricken areas and prioritize routes with stable water supplies. For example, the logistics route generation unit uses a generation AI to analyze water supply data in disaster-stricken areas and prioritize routes with stable water supplies. For example, it proposes routes that pass through areas where water supply has been restored. The logistics route generation unit also collects water supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with a low risk of water outages. The logistics route generation unit also uses a generation AI to analyze water supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects routes that pass through areas with stable water supplies. In this way, by analyzing water supply data in disaster-stricken areas, it is possible to prioritize routes with stable water supplies.

[0081] The logistics route generation unit can analyze gas supply data in disaster-stricken areas and prioritize routes with stable gas supplies. For example, the logistics route generation unit uses a generation AI to analyze gas supply data in disaster-stricken areas and prioritize routes with stable gas supplies. For example, it proposes a route that passes through areas where gas supply has been restored. The logistics route generation unit also collects gas supply data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas with low gas supply risks. The logistics route generation unit also uses a generation AI to analyze gas supply data in disaster-stricken areas and optimizes logistics routes. For example, it selects a route that passes through areas with stable gas supplies. In this way, by analyzing gas supply data in disaster-stricken areas, it is possible to prioritize routes with stable gas supplies.

[0082] The logistics route generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose evacuation routes that provide psychological peace of mind. The logistics route generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes evacuation routes that provide psychological peace of mind. For example, it prioritizes routes that are close to evacuation shelters and medical facilities. The logistics route generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes evacuation routes that provide psychological peace of mind. For example, it prioritizes routes that pass through areas with strong positive emotions. The logistics route generation unit also collects emotional data from disaster victims, and the generation AI proposes evacuation routes that provide psychological peace of mind. For example, it selects routes that are close to evacuation shelters and support facilities based on the emotional data. In this way, by analyzing the emotions of disaster victims, it is possible to propose evacuation routes that provide psychological peace of mind.

[0083] The delivery schedule generation unit can analyze demographic data of the disaster-stricken area and generate an optimal delivery schedule based on population density. For example, the delivery schedule generation unit uses a generation AI to analyze demographic data of the disaster-stricken area and generate an optimal delivery schedule based on population density. For example, priority is given to delivering supplies to areas with high population density. The delivery schedule generation unit also collects demographic data in real time and the generation AI analyzes it. For example, it grasps the population density of evacuation centers and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze demographic data of the disaster-stricken area and optimizes the delivery schedule. For example, it proposes a schedule that increases frequency for areas with high population density. In this way, by analyzing the demographic data of the disaster-stricken area, an optimal delivery schedule based on population density can be generated.

[0084] The delivery schedule generation unit can analyze medical facility data in the disaster-stricken area and generate a priority delivery schedule for medical supplies. For example, the delivery schedule generation unit uses a generation AI to analyze medical facility data in the disaster-stricken area and generate a priority delivery schedule for medical supplies. For example, medical supplies are delivered to hospitals and clinics on a priority basis. The delivery schedule generation unit also collects medical facility data in real time and the generation AI analyzes it. For example, it grasps the demand for medical facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze medical facility data in the disaster-stricken area and optimizes the delivery schedule for medical supplies. For example, it adjusts the delivery frequency according to the demand for medical facilities. In this way, a priority delivery schedule for medical supplies can be generated by analyzing medical facility data in the disaster-stricken area.

[0085] The delivery schedule generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose a delivery schedule that will provide psychological peace of mind. The delivery schedule generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes a delivery schedule that will provide psychological peace of mind. For example, priority is given to delivering supplies to evacuation centers and medical facilities. The delivery schedule generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes a delivery schedule that will provide psychological peace of mind. For example, a schedule that increases the frequency of deliveries to areas with strong positive emotions is proposed. The delivery schedule generation unit also collects emotional data of disaster victims, and the generation AI proposes a delivery schedule that will provide psychological peace of mind. For example, priority is given to delivering supplies to evacuation centers and support facilities based on the emotional data. In this way, a delivery schedule that will provide psychological peace of mind can be proposed by analyzing the emotions of disaster victims.

[0086] The delivery schedule generation unit can analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, the delivery schedule generation unit uses a generation AI to analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, educational supplies are delivered to schools and learning facilities on a priority basis. The delivery schedule generation unit also collects educational facility data in real time and the generation AI analyzes it. For example, it grasps demand at educational facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze data on educational facilities in disaster-stricken areas and optimizes the delivery schedule for educational supplies. For example, it adjusts the frequency of delivery according to demand at educational facilities. In this way, a priority delivery schedule for educational supplies can be generated by analyzing data on educational facilities in disaster-stricken areas.

[0087] The delivery schedule generation unit can analyze public facility data in the disaster-stricken area and generate a priority delivery schedule for public supplies. For example, the delivery schedule generation unit uses a generation AI to analyze public facility data in the disaster-stricken area and generate a priority delivery schedule for public supplies. For example, public supplies are delivered to evacuation centers and community centers on a priority basis. The delivery schedule generation unit also collects public facility data in real time and the generation AI analyzes it. For example, it grasps the demand for public facilities and generates an optimal delivery schedule. The delivery schedule generation unit also uses a generation AI to analyze public facility data in the disaster-stricken area and optimizes the delivery schedule for public supplies. For example, it adjusts the delivery frequency according to the demand for public facilities. In this way, a priority delivery schedule for public supplies can be generated by analyzing public facility data in the disaster-stricken area.

[0088] The delivery schedule generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose a delivery schedule that will provide psychological peace of mind. The delivery schedule generation unit, for example, analyzes the emotions of disaster victims using the emotion estimation function and proposes a delivery schedule that will provide psychological peace of mind. For example, priority is given to delivering supplies to evacuation centers and medical facilities. The delivery schedule generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and proposes a delivery schedule that will provide psychological peace of mind. For example, a schedule that increases the frequency of deliveries to areas with strong positive emotions is proposed. The delivery schedule generation unit also collects emotional data of disaster victims, and the generation AI proposes a delivery schedule that will provide psychological peace of mind. For example, priority is given to delivering supplies to evacuation centers and support facilities based on the emotional data. In this way, a delivery schedule that will provide psychological peace of mind can be proposed by analyzing the emotions of disaster victims.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The supply and demand prediction unit can analyze medical data from disaster-stricken areas and make supply and demand forecasts for medical supplies. For example, the generation AI analyzes data collected from medical institutions in disaster-stricken areas to make supply and demand forecasts for medical supplies. For example, predictions are made based on data on the number of patients in hospitals and the supplies needed for treatment. The supply and demand prediction unit also collects medical data from disaster-stricken areas in real time, which the generation AI analyzes. For example, it grasps the number of emergency transports and demand by medical department, and reflects this in the supply and demand forecast for medical supplies. The supply and demand prediction unit also analyzes medical data using the generation AI to make supply and demand forecasts for medical supplies in disaster-stricken areas. For example, it makes predictions based on data on the outbreak of infectious diseases and the supplies needed to treat trauma. In this way, by analyzing medical data from disaster-stricken areas, it becomes possible to make supply and demand forecasts for medical supplies.

[0091] The logistics route generation unit can analyze weather data for the disaster-stricken area and generate the optimal logistics route depending on the weather. For example, the generation AI analyzes weather data for the disaster-stricken area and generates the optimal logistics route depending on the weather. For example, it proposes a route that avoids heavy rain and strong winds. The logistics route generation unit also collects weather data in real time and the generation AI analyzes it. For example, it generates the optimal logistics route taking into account the effects of typhoons and heavy snow. The logistics route generation unit also analyzes weather data for the disaster-stricken area using the generation AI and optimizes the logistics route. For example, it dynamically updates the route depending on weather conditions and proposes a safe route. In this way, by analyzing weather data for the disaster-stricken area, it is possible to generate the optimal logistics route depending on the weather.

[0092] The delivery schedule generation unit can analyze data on educational facilities in disaster-stricken areas and generate a priority delivery schedule for educational supplies. For example, the generation AI analyzes data on educational facilities in disaster-stricken areas and generates a priority delivery schedule for educational supplies. For example, educational supplies are delivered to schools and learning facilities on a priority basis. The delivery schedule generation unit also collects educational facility data in real time and the generation AI analyzes it. For example, it grasps the demand for educational facilities and generates an optimal delivery schedule. The delivery schedule generation unit also analyzes data on educational facilities in disaster-stricken areas with the generation AI and optimizes the delivery schedule for educational supplies. For example, it adjusts the frequency of delivery according to the demand for educational facilities. In this way, a priority delivery schedule for educational supplies can be generated by analyzing data on educational facilities in disaster-stricken areas.

[0093] In the Supply Support Department, the generation AI analyzes real-time traffic data from the disaster-stricken areas and dynamically updates optimal logistics routes. For example, it grasps traffic congestion and road closures and adjusts logistics routes. The Supply Support Department also collects traffic data in real time and the generation AI analyzes it. For example, it dynamically updates optimal logistics routes based on information on traffic accidents and road construction. The supply support department also analyzes traffic data from the disaster-stricken areas and dynamically updates logistics routes. For example, it grasps fluctuations in traffic volume in real time and proposes optimal routes. This helps ensure the supply of supplies is carried out quickly and efficiently, allowing for a smooth supply of supplies to the disaster-stricken areas.

[0094] The supply and demand forecasting unit can analyze infrastructure data in the disaster-stricken areas and predict the supply and demand of restoration materials. For example, the generation AI analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, it grasps the damage status of roads and bridges and predicts the restoration materials needed. The supply and demand forecasting unit also collects infrastructure data in real time, which the generation AI analyzes. For example, it grasps the supply status of electricity and water and reflects this in the supply and demand forecast of materials needed for restoration. The supply and demand forecasting unit also analyzes infrastructure data in the disaster-stricken areas and predicts the supply and demand of restoration materials. For example, it grasps the damage status of communication infrastructure and predicts the restoration materials needed. In this way, by analyzing infrastructure data in the disaster-stricken areas, it becomes possible to predict the supply and demand of restoration materials.

[0095] The supply and demand prediction unit can use the emotion estimation function to analyze the emotions of disaster victims and predict the supply and demand of psychological relief supplies. For example, the emotion estimation function can be used to analyze the emotions of disaster victims and predict the supply and demand of psychological relief supplies. For example, posts expressing stress or anxiety can be detected and reflected in the supply and demand prediction of psychological relief supplies. The supply and demand prediction unit can also use the emotion estimation function to analyze the emotions of disaster victims in real time and predict the supply and demand of psychological relief supplies. For example, it can prioritize the supply of psychological relief supplies to areas with few positive emotions. The supply and demand prediction unit also collects emotional data from disaster victims, and the generation AI predicts the supply and demand of psychological relief supplies. For example, it can predict the demand for psychological relief supplies based on the emotional data and create a supply plan. In this way, it is possible to predict the supply and demand of psychological relief supplies by analyzing the emotions of disaster victims.

[0096] The logistics route generation unit uses the emotion estimation function to analyze the emotions of disaster victims and propose routes that provide psychological security. For example, the emotion estimation function can analyze the emotions of disaster victims and propose routes that provide psychological security. For example, routes close to evacuation shelters and medical facilities can be prioritized. The logistics route generation unit also uses the emotion estimation function to analyze the emotions of disaster victims in real time and propose routes that provide psychological security. For example, routes that pass through areas with strong positive emotions can be prioritized. The logistics route generation unit also collects emotional data from disaster victims, and the generation AI proposes routes that provide psychological security. For example, routes close to evacuation shelters and support facilities can be selected based on the emotional data. In this way, by analyzing the emotions of disaster victims, it is possible to propose routes that provide psychological security.

[0097] The delivery schedule generation unit can use the emotion estimation function to analyze the emotions of disaster victims and propose a delivery schedule that will give them psychological peace of mind. For example, the emotion estimation function can be used to analyze the emotions of disaster victims and propose a delivery schedule that will give them psychological peace of mind. For example, priority can be given to delivering supplies to evacuation centers and medical facilities. The delivery schedule generation unit can also use the emotion estimation function to analyze the emotions of disaster victims in real time and propose a delivery schedule that will give them psychological peace of mind. For example, a schedule that increases the frequency of deliveries to areas with strong positive emotions can be proposed. The delivery schedule generation unit can also collect emotional data from disaster victims and the generation AI can propose a delivery schedule that will give them psychological peace of mind. For example, priority can be given to delivering supplies to evacuation centers and support facilities based on the emotional data. In this way, a delivery schedule that will give them psychological peace of mind can be proposed by analyzing the emotions of disaster victims.

[0098] The supply and demand forecasting unit uses the emotion estimation function to analyze emotions from victims' social media posts and incorporate their psychological needs into the supply and demand forecast. For example, the emotion estimation function can analyze victims' social media posts to understand their psychological needs. For example, posts expressing stress or anxiety can be detected and reflected in the supply and demand forecast for psychological relief supplies. The supply and demand forecasting unit also uses the emotion estimation function to analyze victims' emotions in real time and reflect this in the supply and demand forecast. For example, it can prioritize the supply of psychological relief supplies to areas with fewer positive emotions. The supply and demand forecasting unit also collects emotion data from victims' social media posts, and the generation AI incorporates this data into the supply and demand forecast. For example, it can predict demand for psychological relief supplies based on the emotion data and create a supply plan. In this way, by analyzing victims' emotions, psychological needs can be reflected in the supply and demand forecast.

[0099] The logistics route generation unit analyzes communication infrastructure data in the disaster-stricken area and can prioritize routes where communication is ensured. For example, the generation AI analyzes communication infrastructure data in the disaster-stricken area and prioritizes routes where communication is ensured. For example, it proposes a route that passes through areas where communication infrastructure is well developed. The logistics route generation unit also collects communication infrastructure data in real time and the generation AI analyzes it. For example, it prioritizes routes that pass through areas where the risk of communication failure is low. The logistics route generation unit also analyzes communication infrastructure data in the disaster-stricken area and optimizes logistics routes. For example, it selects a route that passes through areas where communication is ensured. In this way, by analyzing communication infrastructure data in the disaster-stricken area, it is possible to prioritize routes where communication is ensured.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The supply and demand forecasting unit uses the generation AI to make supply and demand forecasts. For example, the generation AI collects and analyzes data such as past disaster data, demographics, weather information, and the situation in the affected area. The generation AI makes supply and demand forecasts based on prompts that include instructions on the type and scale of the disaster, the population of the affected area, etc. Step 2: The logistics route generation unit generates the optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecast unit. For example, the optimal logistics route is generated based on the location of the logistics center, road conditions, traffic information, geographic information of the disaster area, etc. The generation AI optimizes the logistics route based on prompts including instructions on the location of the logistics center and road conditions, etc. Step 3: The delivery schedule generation unit generates an optimal delivery schedule based on the logistics routes generated by the logistics route generation unit. For example, it optimizes the timing and frequency of supplying supplies to disaster-stricken areas. The generation AI optimizes the delivery schedule based on prompts including supply and demand forecast results and instructions on logistics routes. Step 4: The Supply Support Unit supports efficient supply of supplies based on the delivery schedule generated by the Delivery Schedule Generation Unit. For example, it helps ensure that supplies are delivered to disaster-stricken areas quickly and efficiently. The generation AI supports supply of supplies based on prompts including instructions on logistics routes and delivery schedules.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 supply and demand forecasting unit that uses generation AI to make supply and demand forecasts; a logistics route generation unit that generates an optimal logistics route based on the supply and demand forecast results obtained by the supply and demand forecast unit; a delivery schedule generation unit that generates an optimal delivery schedule based on the logistics route generated by the logistics route generation unit; a material supply support unit that supports efficient material supply based on the delivery schedule generated by the delivery schedule generation unit. A system characterized by:

2. The supply and demand prediction unit Demand forecasts based on past disaster data according to the current disaster situation 2. The system of claim 1.

3. The logistics route generation unit Generate optimal logistics routes based on the location of logistics centers, road conditions, traffic information, and geographic information of disaster-stricken areas 2. The system of claim 1.

4. The delivery schedule generation unit Optimizing the timing and frequency of said supplies 2. The system of claim 1.

5. The material supply support department Support the prompt and efficient supply of said goods.

2. The system of claim 1.

6. The supply and demand prediction unit Analyze social media posts or news articles from affected areas in real time to immediately reflect sudden changes in demand 2. The system of claim 1.

7. The supply and demand prediction unit Use drone or satellite images to visually analyze the current situation in the affected areas and reflect this in demand forecasts.

2. The system of claim 1.

8. The supply and demand prediction unit Analyzing the emotions of disaster victims from their social media posts and incorporating their psychological needs into the supply and demand forecast.

2. The system of claim 1.

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